<p class='jove_content'>The authors would like to acknowledge the "Ligue Nationale contre le Cancer" (Comit&#233; du Septentrion), the SIRIC ONCOLille (Grant INCa-DGOS-INSERM 6041) and the Institut pour la Recherche sur le Cancer de Lille (IRCL) for supporting this work. They would like to thank Delphine Taillieu and the animal facility staff for housing the mice and maintaining their welfare. We also thank Rapha&#235;lle Caillerez and Nathalie Jouy for their respective help in microscopy and flow cytometry.</p>

<ol><li>Forthun, R. B., Hinrichs, C., Dowling, T. H., Bruserud, Ø, Selheim, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+past%2C+present+and+future+subclassification+of+patients+with+acute+myeloid+leukemia%5BTitle%5D)+AND+%22Curr.+Pharm.+Biotechnol%22%5BJournal%5D)">The past, present and future subclassification of patients with acute myeloid leukemia.</a> <em>Curr. Pharm. Biotechnol</em>. <strong>17</strong> (1), 6-19 (2016).</li>
<li>Goldie, H., Butler, C. H., Anderson, M. M., Maxwell, M. C., Hahn, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Growth+characteristics+of+free+C1498+%28granulocytic+leukemia%29+tumor+cells+in+the+peritoneal+fluid+and+the+blood+of+C57+mice%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Growth characteristics of free C1498 (granulocytic leukemia) tumor cells in the peritoneal fluid and the blood of C57 mice.</a> <em>Cancer Res</em>. <strong>13</strong> (2), 125-129 (1953).</li>
<li>Tanaka, K. K., Roberts, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biological+studies+of+E.L.4+lymphoma+and+C+Leukemia+in+susceptible+%28C57BL%29+and+resistant+%28B10.D2%29+mice%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Biological studies of E.L.4 lymphoma and C Leukemia in susceptible (C57BL) and resistant (B10.D2) mice.</a> <em>Cancer Res</em>. <strong>24</strong> (1498), 1785-1797 (1964).</li>
<li>Law, L. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterization+of+an+influence+affecting+growth+of+transplantable+leukemias+in+mice%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Characterization of an influence affecting growth of transplantable leukemias in mice.</a> <em>Cancer Res</em>. <strong>4</strong>, 257-260 (1944).</li>
<li>Graham, J. D., McMahon Welch, C., Patchen, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Studies+of+an+implanted+murine+myelogenous+leukemia+C1498%5BTitle%5D)+AND+%22Ohio+J.+Sci%22%5BJournal%5D)">Studies of an implanted murine myelogenous leukemia C1498.</a> <em>Ohio J. Sci</em>. <strong>75</strong> (4), 202-208 (1975).</li>
<li>Boyer, M. W., Orchard, P. J., Gorden, K. B., Anderson, P. M., Mclvor, R. S., Blazar, B. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Dependency+on+intercellular+adhesion+molecule+recognition+and+local+interleukin-2+provision+in+generation+of+an+in+vivo+CD8%2B+T-cell+immune+response+to+murine+myeloid+leukemia%5BTitle%5D)+AND+%22Blood%22%5BJournal%5D)">Dependency on intercellular adhesion molecule recognition and local interleukin-2 provision in generation of an in vivo CD8+ T-cell immune response to murine myeloid leukemia.</a> <em>Blood</em>. <strong>85</strong> (9), 2498-2506 (1995).</li>
<li>Labelle, J. L., Truitt, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterization+of+a+murine+NKT+cell+tumor+previously+described+as+an+acute+myelogenous+leukemia%5BTitle%5D)+AND+%22Leuk.+Lymphoma%22%5BJournal%5D)">Characterization of a murine NKT cell tumor previously described as an acute myelogenous leukemia.</a> <em>Leuk. Lymphoma</em>. <strong>43</strong> (8), 1637-1644 (2002).</li>
<li>Bradner, W. T., Pindell, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Myeloid+leukemia+as+a+screen+for+cancer+chemotherapeutic+agents%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Myeloid leukemia as a screen for cancer chemotherapeutic agents.</a> <em>Cancer Res</em>. <strong>26</strong> (4), Pt 2 375-390 (1966).</li>
<li>Lin, J. M., Li, B., Rimmer, E., VanRoey, M., Jooss, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhancement+of+the+anti-tumor+efficacy+of+a+GM-CSF-secreting+tumor+cell+immunotherapy+in+preclinical+models+by+cytosine+arabinoside%5BTitle%5D)+AND+%22Exp.+Hematol%22%5BJournal%5D)">Enhancement of the anti-tumor efficacy of a GM-CSF-secreting tumor cell immunotherapy in preclinical models by cytosine arabinoside.</a> <em>Exp. Hematol</em>. <strong>36</strong> (3), 319-329 (2008).</li>
<li>Robinson, J. P. <em><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aRobinson%2C+JP+%22Handbook+of+Flow+Cytometry+Methods%22">Handbook of Flow Cytometry Methods</a></em>. , Wiley-Liss, Inc. New York, NY. (1993).</li>
<li>Xu, Y., McKenna, R. W., Wilson, K. S., Karandikar, N. J., Schultz, R. A., Kroft, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Immunophenotypic+identification+of+acute+myeloid+leukemia+with+monocytic+differentiation%5BTitle%5D)+AND+%22Leukemia%22%5BJournal%5D)">Immunophenotypic identification of acute myeloid leukemia with monocytic differentiation.</a> <em>Leukemia</em>. <strong>20</strong> (7), 1321-1324 (2006).</li>
<li>Hassan, K., Qureshi, M., Shafi, S., Ikram, N., Akhtar, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Acute+myeloid+leukemia-FAB+classification+and+its+correlation+with+clinico-haematological+features%5BTitle%5D)+AND+%22J.+Pak.+Med.+Assoc%22%5BJournal%5D)">Acute myeloid leukemia-FAB classification and its correlation with clinico-haematological features.</a> <em>J. Pak. Med. Assoc</em>. <strong>43</strong> (10), 200-203 (1993).</li>
<li>Quesenberry, P. J., Rappeport, J. M., Fountebouni, A., Sullivan, R., Zuckerman, K., Ryan, M. Inhibition of normal murine hematopoiesis by leukemic cells. <em><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aQuesenberry%2C+PJ+%22N.Engl.J.+Med%22">N.Engl.J. Med</a></em>. <strong>299</strong> (2), 71-75 (1978).</li>
<li>Youn, J. I., Nagaraj, S., Collazo, M., Gabrilovich, D. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Subsets+of+myeloid-derived+suppressor+cells+in+tumor-bearing+mice%5BTitle%5D)+AND+%22J.+Immunol%22%5BJournal%5D)">Subsets of myeloid-derived suppressor cells in tumor-bearing mice.</a> <em>J. Immunol</em>. <strong>181</strong> (8), 5791-5802 (2008).</li>
<li>Loder, F., <em>et al.</em> <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((B+cell+development+in+the+spleen+takes+place+in+discrete+steps+and+is+determined+by+the+quality+of+B+cell+receptor-derived+signals%5BTitle%5D)+AND+%22J.+Exp.+Med%22%5BJournal%5D)">B cell development in the spleen takes place in discrete steps and is determined by the quality of B cell receptor-derived signals.</a> <em>J. Exp. Med</em>. <strong>190</strong> (1), 75-89 (1999).</li>
<li>Robbins, C. S., <em>et al.</em> <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Extramedullary+hematopoiesis+generates+Ly-6C%28high%29+monocytes+that+infiltrate+atherosclerotic+lesions%5BTitle%5D)+AND+%22Circulation%22%5BJournal%5D)">Extramedullary hematopoiesis generates Ly-6C(high) monocytes that infiltrate atherosclerotic lesions.</a> <em>Circulation</em>. <strong>125</strong> (2), 364-374 (2012).</li>
</ol>

<p class='jove_content'>The authors declare that they have no competing financial interests.</p>

<p class="jove_content">In previous studies, the C1498 cell line was described as an inducer of acute granulocytic<sup>5</sup>, myelomonocytic<sup>6</sup> or NKT<sup>7</sup> cell leukemia. However, demonstrative data in the literature were either absent or incomplete. The protocol presented here uses different techniques, such as flow cytometry, immunofluorescence, MGG staining and cytochemical assays, to characterize cultured C1498 cells and to determine the nature of the leukemia that is induced in mice after they are injected.</p>
<p class="jove_content">When we phenotyped <em>in vitro</em> cultured C1498 cells after immunostaining and flow cytometry analyses were performed, we observed some limitations because these cells expressed few cell surface hematopoietic markers that have been previously described in the literature<sup>6,7</sup>. In agreement with our results, Labelle <em>et al.</em> did not observe the cell surface expression of mature TCR on C1498 cells using flow cytometry staining. However, they considered them to be a NKT cell line after they detected CD3&#949; and TCRV&#946;8.2 mRNAs<sup>7</sup>. We also observed the intracellular expression of TCRV&#946; chains and CD3&#949; molecules in most of the cells (&#62; 70%), but their hematopoietic lineages could not be determined because there was also concomitant intracellular expression of the Mac-3 molecule.</p>
<p class="jove_content">Myeloperoxidase, MGG staining and assessments to analyze functional esterases using cytochemistry demonstrated that the C1498 cell line had a myeloid origin and was composed of monoblasts and myeloblasts. These results were concordant with the percentage of Mac-3<sup>+</sup> cells that were obtained in flow cytometry staining analysis. Although not quantitative, these steps represent key experiments to be performed. Indeed, they remain, so far, the best existing methods for characterizing the lineage and differentiation stage of hematopoietic cells that express no or few specific phenotypic markers.</p>
<p class="jove_content">Flow cytometry staining was helpful for demonstrating the development of acute leukemia in congenic mice after C1498 cells were intravenously injected. The CD45.2<sup>+</sup> C1498 cells that infiltrated into the peripheral blood and various organs were isolated, and their frequencies were determined. Quantification was also performed to analyze inherent medullary and splenic cells after immunophenotyping. Limitations were encountered when the C1498 cell phenotype was examined in organs as they expressed few hematopoietic markers (only a few of them were B220<sup>+</sup>). To define the nature of the observed acute leukemia, May-Gr&#252;nwald Giemsa staining and an analysis of the activities of monocytic and granulocytic esterases were performed using bone marrow. The results showed that C1498 cells preserved their myeloblastic and monoblastic morphology and function, revealing the onset of myelomonocytic leukemia.</p>
<p class="jove_content">In consideration for the critical steps described in this protocol, particular attention should be given to pH when performing cytochemical reactions and MGG staining because errors in pH can lead to incorrect interpretations of results. For instance, &#945;-naphthyl butyrate esterase activity is specific to monocytic cells only at a pH of 6.0 because granulocytes and lymphocytes can also stain positive for this test at higher pH values. Fixating the cells is not recommended before performing MGG staining, and we showed that only CAF fixation provided satisfactory results when performing esterases cytochemical reactions using C1498 cells. To preserve the expression of the CD115 molecule and its detection by flow cytometry, all of the samples (<em>e.g.</em>, blood, bone marrow, and spleen cells) should be kept on ice during the procedure. If no staining is observed in flow cytometry or/and immunofluorescence experiments, the reference of the antibodies, their storage recommendations and their dilutions should be checked. The references specified in the materials/equipment table have been selected for flow cytometry or immunofluorescence applications. The primary/secondary antibodies or their conjugated fluorophores might have lost their activity due to inappropriate storage (<em>e.g.</em>, exposure to light or heat), improper dilution, extensive freezing/thawing or the use of contaminated buffers. Run positive controls to ensure that they are working properly. Use mouse bone marrow or spleen-derived cells that are known to express the proteins of interest. To avoid high background and non-specific staining, make sure that the cells are washed properly and kept at high humidity (for immunofluorescence) and that the antibodies are diluted as instructed. Use the same concentration and dilution for the isotype control antibody and the primary antibody to accurately determine the background level in the sample. For esterase cytochemistry experiments, the reagents can be tested by using positive and negative control slides containing purified mouse splenic granulocytic (Ly6G<sup>+</sup>) and monocytic (CD115<sup>+</sup>) cells.</p>
<p class="jove_content">The procedure described in this study showed that many of the leukemic features observed in mice after the injection of C1498 cells shared common hallmarks with human acute myelomonocytic leukemia<sup>11,12</sup>. The invaded leukemic cells resulted in a reduction of mature and immature (progenitors and precursors) medullary hematopoietic cells. C1498 cells are present at high frequency (&#62; 20%) in the peripheral blood, as are monocytic cells. Hepatomegaly and splenomegaly were observed to result from the infiltration of leukemic cells, and significant increases in B lymphocytes and myeloid cells were also observed to accompany splenomegaly. Thrombopenia was also observed when blood platelets numbers were estimated using a hematology analyzer.</p>
<p class="jove_content">It was shown, using <em>in vitro</em> experiments, that C1498 cells inhibit normal murine hematopoiesis by secreting soluble factors<sup>13</sup>. In several tumor mouse models, immature myeloid cells (including monocytic and granulocytic cells) have also been shown to migrate from the bone marrow to the spleen, where they inhibit anti-tumor specific T cell activation and proliferation<sup>14</sup>. Thus, the reduction in hematopoietic cells that was observed in the bone marrow could have resulted from either a deficiency in hematopoiesis and/or from their emigration. This latter mechanism could explain the presence of monocytosis in the peripheral blood or the observation of enlarged myeloid fractions in the spleen. It is also conceivable that these cells could have been derived from improved splenic hematopoiesis. Indeed, under steady-state conditions, some subsets of splenic B cells were identified as precursors of mature B lymphocytes<sup>15</sup>. Moreover, under inflammatory conditions, medullary stem and progenitors cells have been shown to relocate to the spleen to induce the production of mature monocytes<sup>16</sup>. This protocol does not allow us to draw conclusions regarding the mechanisms that are involved in the development of leukemia, and additional functional as well as molecular assays should be employed to do so. However, these data include detailed information about the clinical features of acute myelomonocytic leukemia and will help researchers to evaluate and understand the effects of new therapeutic agents.</p>

<p class="jove_content">Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of hematopoietic myeloid cells that are blocked at different stages of maturation. This dysregulation can affect the granulocytic, monocytic, erythrocytic or megaryocytic differentiation pathways<sup>1</sup>. AML cells accumulate in the bone marrow, leading to impaired hematopoiesis, which results in thrombopenia, lymphopenia and anemia. The leukemic cells also invade the blood and non-lymphoid organs.</p>
<p class="jove_content">The C1498 mouse model has been used for decades as a model for acute leukemia since cancer cells were isolated from a leukemic 10-month-old C57BL/6 (H-2<sup>b</sup>) female mouse in 1941. The literature describes the invasion into the blood, hematopoietic organs (<em>e.g.</em>, the spleen and lymph nodes) and non-hematopoietic organs (<em>e.g.</em>, the liver, lungs, ovaries, and kidneys) by highly proliferative C1498 cells after they were injected via an intravenous, subcutaneous or intra-peritoneal route into susceptible mice<sup>2-4</sup>. However, this mouse model was reported to induce either granulocytic<sup>2,5 </sup>or myelomonocytic<sup>6</sup> leukemia. More recently, a study published in 2002 described this type of cancer as murine NKT cell leukemia<sup>7</sup>. Thus, the literature differs concerning the nature of this C1498 cell line and the associated leukemia it induces in mice. These discrepancies are mainly due to a lack of detailed and updated published information about the cells and the leukemic disease in general because many studies were performed in the 1950 - 70&#39;s.</p>
<p class="jove_content">Here we provide a detailed protocol to describe how to characterize C1498 cells and analyze the nature of the leukemic disease that is induced by their intravenous injection into mice. The first section of this protocol is dedicated to a description of C1498 cells that have been cultured <em>in vitro</em>. Fluorescent antibodies directed against surface and intracellular hematopoietic markers were used to determine their phenotype using flow cytometry. The presence of myeloperoxidase was assessed using immunofluorescence microscopy, their hematopoietic lineage and differentiation stage were evaluated using cytochemistry to assess the activity of esterases, and May-Gr&#252;nwald Giemsa staining was performed. The C1498 cells were then injected into mice, and the acute leukemia disease that was induced is described in the second section of this manuscript. The same techniques were used to determine the frequencies and the phenotypes of leukemic and inherent cells in the bone marrow, peripheral blood, spleen and non-hematopoietic organs (the liver and lungs).</p>
<p class="jove_content">This protocol is highly reproducible, and the data presented here will help researchers to assess the effects of new therapeutic strategies. This leukemia mouse model has already been used to test immunotherapy approaches and different cancer chemotherapeutic drugs<sup>8,9</sup>. Their efficacy was evaluated by determining the evolution of tumor burden and survival rates. This protocol can be used to provide additional information about the distribution and subsistence of leukemic and other hematopoietic cell populations during treatment.</p>

<table><tbody><tr><td>C1498 cell line</td><td>ATCC</td><td>TIB 49</td><td></td></tr><tr><td>C57BL/6J-Ly5.1</td><td>Charles River</td><td>B6.SJL-Ptprc a Pep3 b/BoyCrl</td><td></td></tr><tr><td>&lt;strong&gt;Cells culture reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>2-Mercaptoethanol, Gibco</td><td>ThermoFisher Scientific</td><td>21985</td><td></td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>ThermoFisher Scientific</td><td>10270</td><td></td></tr><tr><td>HEPES, Gibco (1 M)</td><td>ThermoFisher Scientific</td><td>15630</td><td></td></tr><tr><td>Non-Essential Amino Acids Solution, Gibco&amp;nbsp;&amp;nbsp;</td><td>ThermoFisher Scientific</td><td>11140</td><td></td></tr><tr><td>Penicillin-Streptomycin, Gibco&amp;nbsp;</td><td>ThermoFisher Scientific</td><td>15140</td><td></td></tr><tr><td>RPMI 1640 Medium (Gibco, GlutaMAX Supplement)</td><td>ThermoFisher Scientific</td><td>61870</td><td></td></tr><tr><td>Sodium Pyruvate, Gibco (100 mM)</td><td>ThermoFisher Scientific</td><td>11360</td><td></td></tr><tr><td>&lt;strong&gt;Flow cytometry staining reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>anti-mouse B220 APC (1)</td><td>ebiosciences</td><td>17-0452</td><td>clone RA3-6B2, final dilution 1/100</td></tr><tr><td>anti-mouse B220 biotin (2)</td><td>ebiosciences</td><td>13-0452</td><td>clone RA3-6B2, 1/400</td></tr><tr><td>anti-mouse CD3 eFluor450 (1)</td><td>ebiosciences</td><td>48-0032</td><td>clone 17A2, 1/100</td></tr><tr><td>anti-mouse CD3 PE (2)</td><td>BD biosciences</td><td>555275</td><td>clone 17A2, 1/100</td></tr><tr><td>anti-mouse CD3 PE-Cy5 (3)</td><td>ebiosciences</td><td>15-0031</td><td>clone 145-2C11, 1/100</td></tr><tr><td>anti-mouse CD4 APC (1)</td><td>ebiosciences</td><td>17-0041</td><td>clone GK1.5, 1/500</td></tr><tr><td>anti-mouse CD4 PE (2)</td><td>ebiosciences</td><td>12-0041</td><td>clone GK1.5, 1/200</td></tr><tr><td>anti-mouse CD8 biotin (1)</td><td>ebiosciences</td><td>13-0081</td><td>clone 53-6.7, 1/100</td></tr><tr><td>anti-mouse CD8 eFluor450 (2)</td><td>ebiosciences</td><td>48-0081</td><td>clone 53-6.7, 1/500</td></tr><tr><td>anti-mouse CD11a biotin</td><td>ebiosciences</td><td>13-0111</td><td>clone M17/4, 1/100</td></tr><tr><td>anti-mouse CD11b PE&amp;nbsp;</td><td>ebiosciences</td><td>12-0112</td><td>clone M1/70, 1/200</td></tr><tr><td>anti-mouse CD16/32 biotin</td><td>ebiosciences</td><td>13-0161</td><td>clone 93, 1/400</td></tr><tr><td>purified anti-mouse CD16/32 (FcR blocking)&amp;nbsp;</td><td>BD biosciences</td><td>553141</td><td>clone 2.4G2</td></tr><tr><td>anti-mouse CD18 biotin (1)</td><td>ebiosciences</td><td>13-0181</td><td>clone M18/2, 1/100</td></tr><tr><td>anti-mouse CD18 FITC (2)</td><td>ebiosciences</td><td>11-0181</td><td>clone M18/2, 1/50</td></tr><tr><td>anti-mouse CD19 PE</td><td>ebiosciences</td><td>12-0193</td><td>clone 1D3, 1/200</td></tr><tr><td>anti-mouse CD21/35 PE</td><td>ebiosciences</td><td>12-0211</td><td>clone 8D9, 1/50</td></tr><tr><td>anti-mouse CD31 PE</td><td>ebiosciences</td><td>12-0311</td><td>clone 390, 1/100</td></tr><tr><td>anti-mouse CD34 eFluor660</td><td>ebiosciences</td><td>50-0341</td><td>clone RAM34, 1/20</td></tr><tr><td>anti-mouse CD44 PE</td><td>BD biosciences</td><td>553134</td><td>clone IM7, 1/50</td></tr><tr><td>anti-mouse CD45.2 FITC&amp;nbsp;</td><td>ebiosciences</td><td>11-0454</td><td>clone 104, 1/100</td></tr><tr><td>anti-mouse CD49b/Pan NK PE</td><td>&amp;nbsp;BD biosciences</td><td>553858</td><td>clone DX5, 1/50</td></tr><tr><td>anti-mouse CD80 biotin</td><td>ebiosciences</td><td>13-0801</td><td>clone 16-10A1, 1/200</td></tr><tr><td>anti-mouse CD86 biotin</td><td>ebiosciences</td><td>13-0862</td><td>clone GL1, 1/200</td></tr><tr><td>anti-mouse CD107b (Mac-3) PE</td><td>ebiosciences</td><td>12-5989</td><td>clone M3/84, 1/40</td></tr><tr><td>anti-mouse CD115 PE</td><td>ebiosciences</td><td>12-1152</td><td>clone AFS98, 1/100</td></tr><tr><td>anti-mouse CD117 eFluor450</td><td>ebiosciences</td><td>48-1171</td><td>clone 2B8, 1/100</td></tr><tr><td>anti-mouse CD127 PE</td><td>ebiosciences</td><td>12-1271</td><td>clone A7R34, 1/100</td></tr><tr><td>anti-mouse CD150 APC</td><td>ebiosciences</td><td>17-1501</td><td>clone 9D1, 1/20</td></tr><tr><td>anti-mouse CD274 (PD-L1) biotin</td><td>ebiosciences</td><td>13-5982</td><td>clone MIH5, 1/200</td></tr><tr><td>anti-mouse Ly-6A/E (Sca-1) PE</td><td>ebiosciences</td><td>12-5981</td><td>clone D7, 1/100</td></tr><tr><td>anti-mouse Ly-6C APC</td><td>ebiosciences</td><td>17-5932</td><td>clone HK1.4, 1/200</td></tr><tr><td>anti-mouse Ly-6G&amp;nbsp; (Gr-1) biotin (1)</td><td>ebiosciences</td><td>13-5931</td><td>clone RB6-8C5, 1/400</td></tr><tr><td>anti-mouse Ly-6G FITC (2)</td><td>ebiosciences</td><td>11-9668</td><td>clone 1A8, 1/50</td></tr><tr><td>anti-mouse MHC class I (H-2Db) biotin</td><td>ebiosciences</td><td>13-5999</td><td>clone 28-14-8, 1/50</td></tr><tr><td>anti-mouse MHC class II (I-A/I-E) PE-Cy5</td><td>ebiosciences</td><td>15-5321</td><td>clone M5/114.15.2, 1/1000</td></tr><tr><td>anti-mouse NK1.1 PE</td><td>BD biosciences</td><td>557391</td><td>clone PK136, 1/50</td></tr><tr><td>anti-mouse TCRVb FITC</td><td>BD biosciences</td><td>553170</td><td>clone H57-597, 1/50</td></tr><tr><td>streptavidin PE-Cy5</td><td>ebiosciences</td><td>15-4317</td><td>&amp;nbsp;&amp;nbsp; 1/200</td></tr><tr><td>Immunofluorescence staining reagents</td><td></td><td></td><td></td></tr><tr><td>Anti-mouse myeloperoxidase (heavy chain antibody)&amp;nbsp;</td><td>Santa Cruz Biotechnology</td><td>sc-16129</td><td>1/10 (20 &amp;micro;g/ml)</td></tr><tr><td>&amp;nbsp;anti-goat IgG (Texas Red coupled antibody)</td><td>Jackson Immunoresearch</td><td>705-076-147</td><td>1/250</td></tr><tr><td>Normal donkey serum</td><td>Jackson Immunoresearch</td><td>017-000-001</td><td></td></tr><tr><td>Hoechst solution</td><td>BD Biosciences</td><td>561908</td><td>1/1000</td></tr><tr><td>Mounting medium 1 (Fluoromount)&amp;nbsp;</td><td>Sigma-Aldrich</td><td>F4680</td><td></td></tr><tr><td>Acetone</td><td>VWR&amp;nbsp;</td><td>20066</td><td></td></tr><tr><td>Methanol</td><td>Merck Millipore</td><td>106009</td><td></td></tr><tr><td>&lt;strong&gt;Esterase cytochemical staining reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Naphtol AS-D chloroacetate solution</td><td>Sigma-Aldrich</td><td>911</td><td></td></tr><tr><td>Dye solution (Fast Red Violet LB Base solution)</td><td>Sigma-Aldrich</td><td>912</td><td></td></tr><tr><td>pH6.3 buffer concentrate (TRIZMAL)</td><td>Sigma-Aldrich</td><td>913</td><td></td></tr><tr><td>Sodium nitrite solution</td><td>Sigma-Aldrich</td><td>914</td><td></td></tr><tr><td>Citrate solution</td><td>Sigma-Aldrich</td><td>915</td><td></td></tr><tr><td>Hematoxylin solution</td><td>Sigma-Aldrich</td><td>GHS3</td><td></td></tr><tr><td>Acetone</td><td>VWR&amp;nbsp;</td><td>20066</td><td></td></tr><tr><td>Formaldehyde solution, 37%</td><td>Sigma-Aldrich</td><td>F1635</td><td></td></tr><tr><td>&amp;alpha;-naphthyl butyrate solution</td><td>Sigma-Aldrich</td><td>1801</td><td></td></tr><tr><td>Phosphate buffer solution</td><td>Sigma-Aldrich</td><td>1805</td><td></td></tr><tr><td>Sodium nitrite Tablet</td><td>Sigma-Aldrich</td><td>1809</td><td></td></tr><tr><td>Pararosaniline solution</td><td>Sigma-Aldrich</td><td>1804</td><td></td></tr><tr><td>Methylene blue solution</td><td>Sigma-Aldrich</td><td>1808</td><td>1/10</td></tr><tr><td>mounting medium 2 (Clearmount)</td><td>Invitrogen</td><td>00-8010</td><td></td></tr><tr><td>&lt;strong&gt;May-Gr&amp;uuml;nwald Giemsa staining reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>May-Gr&amp;uuml;nwald solution</td><td>RAL Diagnostics</td><td>320070</td><td></td></tr><tr><td>Giemsa R solution&amp;nbsp;</td><td>RAL Diagnostics</td><td>320310</td><td>&amp;nbsp;&amp;nbsp; 1/20</td></tr><tr><td>pH 6.8 buffer solution</td><td>RAL Diagnostics</td><td>330368</td><td></td></tr><tr><td>&lt;strong&gt;Others materials, reagents and equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Ketamine 1000 (100 mg/ml)</td><td>VIRBAC</td><td>3597132111010</td><td></td></tr><tr><td>Xylazine SEDAXYLAN (20 mg/ml)</td><td>CEVA</td><td></td><td></td></tr><tr><td>Bovin albumin serum (BSA) powder</td><td>ThermoFisher Scientific</td><td>BP671</td><td></td></tr><tr><td>PBS solution (1x concentrate)</td><td>ThermoFisher Scientific</td><td>14190</td><td></td></tr><tr><td>Paraformaldehyde (PFA)&amp;nbsp;</td><td>Sigma-Aldrich</td><td>158127</td><td></td></tr><tr><td>Saponin&amp;nbsp;</td><td>Sigma-Aldrich</td><td>47036</td><td></td></tr><tr><td>Ultra Pure 0.5 M EDTA solution, pH 8.0</td><td>ThermoFisher Scientific</td><td>15575</td><td></td></tr><tr><td>Separating solution (Pancoll Mouse)</td><td>PANBIOTECH</td><td>P04-64100</td><td></td></tr><tr><td>Lysis buffer (Red Blood Cells Lysing Buffer) (10x)</td><td>BD Biosciences</td><td>555899</td><td>1/10</td></tr><tr><td>NaOH 10 N</td><td>ThermoFisher Scientific</td><td>SS267</td><td></td></tr><tr><td>Trypan blue solution (0.4 %)</td><td>ThermoFisher Scientific</td><td>15250-061</td><td>1/2</td></tr><tr><td>50 ml tube (Falcon)</td><td>Fisher Scientific</td><td>14-432-22</td><td></td></tr><tr><td>70 &amp;micro;m Cell Strainer (Falcon)&amp;nbsp;</td><td>Corning Life Sciences</td><td>352350</td><td></td></tr><tr><td>&amp;nbsp;Chamber &amp;amp; filter card (EZ Cytofunnel Shandon)</td><td>Thermo Scientific</td><td>A78710003</td><td></td></tr><tr><td>Microscope Cover Glasses, 24 x 24mm</td><td>Knittel Glass</td><td>VD1 2424 Y100</td><td></td></tr><tr><td>Slides (Starfrost - ground edges 90)</td><td>Knittel Glass</td><td>VS1137# 077FKB</td><td></td></tr><tr><td>EDTA Tube</td><td>Greiner Bio-One</td><td>454034</td><td></td></tr><tr><td>Pasteur Pipette 150 mm (capillary tube)</td><td>Fisherbrand</td><td>1154-6963</td><td></td></tr><tr><td>26 G needle</td><td>Terumo</td><td>NN-2613R</td><td></td></tr><tr><td>&amp;nbsp;insulin syringe and needle 29 G</td><td>Terumo</td><td>BS05M2913</td><td></td></tr><tr><td>30 G needle&amp;nbsp;</td><td>Becton &amp;amp; Dickinson</td><td>304000</td><td></td></tr><tr><td>Flow cytometry tubes (blue)</td><td>Beckman Coulter</td><td>2523749</td><td></td></tr><tr><td>Water-repellent pen (Dakopen)</td><td>Dako</td><td>S200230</td><td></td></tr><tr><td>Sharp sterile scissors&amp;nbsp;</td><td>Nessi-care</td><td>SCI-01</td><td></td></tr><tr><td>Sterile forceps</td><td>Dominique Dutscher</td><td>956506</td><td></td></tr><tr><td>Thoma cell counting chamber</td><td>VWR</td><td>631-0397</td><td></td></tr><tr><td>Petri Dishes (Fisherbrand Plastic)</td><td>ThermoFisher Scientific</td><td>S33580A</td><td></td></tr><tr><td>Microcentrifuge tube (1.5 ml)</td><td>ThermoFisher Scientific</td><td>05-408-129</td><td></td></tr><tr><td>Cylindrical Restrainer 15 - 30 gm</td><td>Stoelting</td><td>51338</td><td></td></tr><tr><td>Shandon Cytospin 3 Cytocentrifuge</td><td>ThermoFisher Scientific</td><td></td><td></td></tr><tr><td>10 ml syringe</td><td>Terumo</td><td>SS-10L</td><td></td></tr><tr><td>1 ml syringe</td><td>Terumo</td><td>SS-01T</td><td></td></tr><tr><td>Ethanol</td><td>Merck</td><td>1.08543</td><td></td></tr><tr><td>Sterile gauze sponges</td><td>URGO</td><td>501580</td><td></td></tr></tbody></table>

a detailed protocol for characterizing the murine c1498 cell line and its associated leukemia mouse model

<p class="jove_content">The intravenous injection of C1498 cells into syngeneic or congenic mice has been performed since 1941. These injections result in the development of acute leukemia. However, the nature of this disease has not been well documented in the literature. Here, we provide a technical protocol for characterizing C1498 cells <em>in vitro</em> and for determining the nature of the induced leukemia <em>in vivo</em>. The first part of this procedure is focused on determining the hematopoietic lineage and the stage of differentiation of cultured C1498 cells. To achieve this, multi-parametric flow cytometric staining is used to detect hematopoietic cell markers. Immunofluorescence microscopy, cytochemistry and a May-Gr&#252;nwald Giemsa staining are then performed to assess the expression of myeloperoxidase, the activity of esterases and cellular morphology, respectively. The second part of this protocol is dedicated to describing the leukemia disease that is induced <em>in vivo</em>. The latter can be achieved by determining the frequencies of leukemic and inherent cells in the blood, hematopoietic organs (<em>e.g.</em>, bone marrow and spleen) and non-lymphoid tissues (<em>e.g.</em>, the liver and lungs) using specific staining and flow cytometry analyses. The nature of the leukemia is then confirmed using May-Gr&#252;nwald Giemsa staining and staining for specific esterases in the bone marrow. Here, we present the results that were obtained using this protocol in age-matched C1498- and PBS-injected mice.</p>

<p class="jove_content">To characterize the C1498 mouse model, we proceeded with two major steps. First, the C1498 cells were characterized to determine their hematopoietic lineage and maturation stage <em>in vitro</em> (<strong>Figure 1</strong>). These cells were then injected into congenic mice, and the nature of the induced leukemic disease was assessed to determine different features: leukemic cell infiltration, their phenotype, a quantification of the hematopoietic cells (mature and progenitors/precursors) in bone marrow, the frequencies of C1498 cells and mature hematopoietic cells in the blood and an evaluation of organ swelling (in the spleen, liver, and lungs) and cellular composition.</p>
<p class="jove_content">To characterize the C1498 cell phenotypes<em> in vitro</em>, the cells were labeled with antibodies directed against molecules that are expressed by hematopoietic precursors and mature cells (<strong>Table 1</strong>), and the results were analyzed using flow cytometry. The C1498 cells were positive for cell surface expression of Mac-1 (CD11b/CD18) (&#8764; 7%), B220 (&#62; 25%), and they displayed intracellular expression of CD3&#949;, T-Cell Receptor (TCR) V&#946; chains and Mac-3 (<strong>Figures 2A and B</strong>). The cells were negative for the cell surface markers Ly6G, Ly6C, CD115, CD21/CD35, CD19, CD3, CD4, CD8, NK1.1, and pan-NK molecules and for the intracellular expression of CD4 and CD8 (data not shown). They were then examined for markers of hematopoietic stem cells and progenitors (<strong>Table 1</strong>). They were also negative for cell surface expression of CD117, CD34, Sca-1, CD150 and CD16/32 (data not shown). These leukemic cells were then tested to determine the expression of adhesion, antigen presentation and co-stimulatory molecules. The cells expressed the surface markers LFA-1 (CD11a/CD18), CD44, CD31 (PECAM-1), and H-2D<sup>b </sup>and were negative for MHC class II, CD80, CD86 and CD274 (data not shown). C1498 cells therefore expressed both myeloid (Mac-1, Mac-3) and lymphoid markers (B220, CD3, TCR).</p>
<p class="jove_content">To better characterize their hematopoietic lineage, myeloperoxidase expression was assessed using immunofluorescence microscopy. All of the cells were positive for the myeloperoxidase, which verified their myeloid origin (<strong>Figure 3A</strong>). A majority of the cells also stained positive for &#945;-naphthyl butyrate esterases (<strong>Figure 3B, left panel</strong>), and some of them stained for the naphthol AS-D chloroacetate esterases (black arrows) (<strong>Figure 3B, right panel</strong>). The results indicate that the cells contained mixtures of monocytic and granulocytic cells. After May-Gr&#252;nwald Giemsa staining was performed, the C1498 cells were observed to display a blast-like morphology with a high nucleo-cytoplasmic ratio, 3 to 5 nucleoli in the nucleus, a perinuclear halo, numerous vacuoles and a basophilic cytoplasm (<strong>Figure 3C</strong>). Thus, the C1498 cell line is composed of monoblasts and myeloblasts.</p>
<p class="jove_content">The C1498 cells (CD45.2<sup>+</sup>) were then intravenously injected into CD45.1<sup>+ </sup>mice. The mice succumbed 17 to 19 days after the cells were injected. These mice were sacrificed so that their leukemia type could be analyzed before they died from the disease. The control mice, which were injected with PBS, were analyzed at the same time points for comparison. The C1498 cell-injected mice displayed massive infiltration of C1498 cells into their bone marrow, as demonstrated by the blast-like appearance of the cells after May-Gr&#252;nwald Giemsa staining was performed (<strong>Figure 4A</strong>). They also preserved their monocytic and granulocytic phenotypes (<strong>Figure 4B and C</strong>), demonstrating an accumulation of monoblastic and myeloblastic cells that is characteristic of acute myelomonocytic leukemia.</p>
<p class="jove_content" fo:keep-together.within-page="1">To determine whether medullary hematopoietic cells numbers were lower following leukemic cells invasion, CD45.2<sup>+ </sup>C1498 cells, B lymphocytic, monocytic and granulocytic populations (including progenitors, precursors and mature cells), were quantified using immunofluorescent staining and multi-parametric flow cytometry analysis. Leukemic cells represented 16 to 36% of the hematopoietic cells (data not shown). The other cell types were all present in significantly lower numbers in the C1498-injected mice than in the PBS-injected mice (by 5-fold on average for B cell subsets, 4-fold on average for granulocytic cells and 3-fold on average for monocytic subsets) (<strong>Figure 5A to C</strong>).</p>
<p class="jove_content" fo:keep-together.within-page="1">An investigation of the frequencies of mononuclear cells in leukemic and control mouse blood samples showed that they contained a comparable percentage of lymphocytes (<strong>Figure 6A</strong>) but a higher frequency of monocytic and leukemic cells. These characteristics are representative of acute myelomonocytic leukemia<sup>11</sup> (<strong>Figure 6B</strong>).</p>
<p class="jove_content" fo:keep-together.within-page="1">Among the other features of acute myelomonocytic leukemia<sup>12</sup>, the C1498-injected mice presented with swollen livers (hepatomegaly), lungs and spleens (splenomegaly) (<strong>Figure 7A</strong>). Various frequencies of CD45.2<sup>+ </sup>C1498 cells were detected in these organs using immunofluorescent staining and flow cytometry analysis (<strong>Figure 7B</strong>). As splenomegaly can result from high numbers of infiltrated monocytes, we also estimated the proportions of splenic populations. The numbers of cells in the B lymphocytic, monocytic and granulocytic cell fractions were significantly larger, by an average of 2-fold, 2.5-fold and 3-fold, respectively, in leukemic spleens than in control spleens (<strong>Figure 7C</strong>).</p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 1" src="/files/ftp_upload/54270/54270fig1.jpg" /><br />
<strong>Figure 1. Schematic Representation of the Protocol Set Up for Characterizing <em>in vitro</em> Cultured C1498 Cell Lines and <em>in vivo </em>Descriptions of Acute Leukemia. </strong>The hematopoietic lineage and the differentiation stage of tissue-cultured C1498 cells were first determined. C1498 cells were then injected into congenic mice to induce the development of acute leukemia. The isolation of bone marrow, peripheral blood, spleen, liver and lung tissues was performed to determine the frequencies, phenotypes and morphological changes after the C1498 cells infiltration. IV: Intravenous MGG: May-Gr&#252;nwald Giemsa<strong>.</strong> <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 2" src="/files/ftp_upload/54270/54270fig2.jpg" /><br />
<strong>Figure 2. Phenotypic Analysis of C1498 Cells after <em>in vitro</em> Culture. </strong>Representative flow cytometry dot plots and histograms of cell surface <strong>(A)</strong> and intracellular (<strong>B)</strong> C1498-expressed molecules that were associated with hematopoietic mature cell differentiation are shown. C1498 cells were harvested from cultures, washed and labeled using fluorescent antibodies that were specific for the cell surface CD11b, CD18 and B220 markers or their isotype controls. For intracellular staining, the cells were fixed, permeabilized and labeled using antibodies directed against Mac-3, CD3&#949;, and a common epitope of the TCR (T-Cell Receptor) V&#946; chain or their isotype controls. Analyses were performed using gating with live cells. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 3" src="/files/ftp_upload/54270/54270fig3.jpg" /><br />
<strong>Figure 3. Functional and Morphological Characterization of Cultured C1498 Cells</strong>. C1498 cells were harvested from cultures and centrifuged on slides for microscopy.<strong> (A)</strong> Staining for myeloperoxidase expression was performed using immunofluorescence. <strong>(B)</strong> Cytochemical reactions were used to analyze the &#945;-naphthyl butyrate esterase (NBE) and naphthol AS-D chloroacetate esterase (CAE) activities in C1498 cells. Cells were considered to be positive for each label when brown and red-purple, large cytoplasmic granules, respectively, were observed. <strong>(C)</strong> May-Gr&#252;nwald Giemsa (MGG) staining of C1498 cells. For each staining experiment, the microscopy objective magnification is indicated. Each image is representative of three separate experiments. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 4" src="/files/ftp_upload/54270/54270fig4.jpg" /><br />
<strong>Figure 4. Bone Marrow Morphologies in PBS- and C1498-injected Mice.</strong> Bone marrow cells were isolated from PBS- and C1498 cell-injected mice and centrifuged onto slides for microscopy. <strong>(A)</strong> May-Gr&#252;nwald Giemsa (MGG) staining. <strong>(B)</strong> &#945;-naphthyl butyrate esterase (NBE) and <strong>(C)</strong> naphthol AS-D chloroacetate esterase (CAE) functions were evaluated using cytochemistry. In panel A, the band (immature) or segmented (mature) neutrophils are less visible in the bone marrow of the C1498-injected mice than the PBS-injected mice. Panel B and C indicate that there was an accumulation of monocytic and granulocytic cells in the leukemic bone marrow compared to the numbers observed in the control bone marrow. All microscopic analyses were performed using a 100X magnification objective. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 5" src="/files/ftp_upload/54270/54270fig5.jpg" /><br />
<strong>Figure 5. Quantitative Analysis of Medullary Populations in PBS- and C1498-injected Mice.</strong> Bone marrow cells were isolated from PBS- and C1498 cell-injected mice and estimated after cell counting was performed. The frequencies of the different cell populations were determined after immunostaining and live cell gated flow cytometry analysis. <strong>(A)</strong> The B cell subsets included CD19<sup>+</sup>B220<sup>+</sup>cells in stages from pro-B cells to mature B lymphocytes <strong>(B)</strong> granulocytic cells in the CD3<sup>-</sup> and CD11b<sup>+</sup>Ly6G<sup>+</sup> lineages, which included precursors and immature and mature granulocytes.<strong> (C)</strong> The monocytic subsets were defined as CD3<sup>-</sup>CD115<sup>+</sup> and included cells in the progenitor to mature monocyte stages. n = 7 mice/group, and the data are presented as histograms showing the means &#177; SEM. ***, p&#60; 0.0001 and **, p&#60; 0.01, unpaired Student&#39;s <em>t-</em>test comparing PBS- and C1498-injected mice. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 6" src="/files/ftp_upload/54270/54270fig6.jpg" /><br />
<strong>Figure 6. Blood Analysis of Mononuclear Cell Subsets in PBS- and C1498-injected mice</strong>. Representative flow cytometry dot plots of <strong>(A)</strong> T and B lymphocyte percentages, which were respectively defined as CD3<sup>+</sup> and B220<sup>+</sup> cells in PBS- and C1498 cell-injected mice.<strong> (B)</strong> Monocytic cell frequencies in C1498 leukemic and control (PBS) mice were determined by analyzing CD115<sup>+</sup>Ly6C<sup>- </sup>and CD115<sup>+</sup>Ly6C<sup>high</sup> cells. The analysis was performed by gating live cells. To compare leukemic and control mice, CD45.2<sup>+</sup> C1498 cells were excluded. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<p class="jove_content" fo:keep-together.within-page="1"><img alt="Figure 7" src="/files/ftp_upload/54270/54270fig7.jpg" /><br />
<strong>Figure 7. Estimation of Splenic Populations in Leukemic and Control Mice. (A) </strong>Representative photographs of liver, lung and spleen swelling in leukemic mice compared to control mice. Spleens were collected and weighed, and splenocytes were counted following tissue disruption. <strong>(B)</strong> Histogram representing leukemic cell frequencies in different organs after immunostaining was performed for CD45.2<sup>+</sup> cells and the results were analyzed using flow cytometry. <strong>(C) </strong>Estimations of splenic B, granulocytic and monocytic cell numbers after immunostaining and flow cytometry analysis gating were performed to identify live CD19<sup>+</sup>B220<sup>+</sup>, CD3<sup>-</sup>CD11b<sup>+</sup>Ly6G<sup>+</sup>, CD3<sup>-</sup>CD11b<sup>+</sup>Ly6C<sup>-</sup> and CD3<sup>-</sup>CD11b<sup>+</sup>Ly6C<sup>high </sup>cells. The scale bars shown for the lungs, spleens and livers indicate 1 cm. n = 5 - 8 mice/group, and the data are represented in histograms as the means&#160;&#177; SEM. <em>*, p&#60; 0.05</em>; <em>**, </em>p= 0.0033, unpaired Student&#39;s <em>t-</em>test comparing PBS- and C1498-injected mice. <a href="http://cloudflare.jove.com/files/ftp_upload/54270/54270fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></p>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td><strong>Cell Type</strong></td>
			<td><strong>Membrane or Intracellular Molecules&#160;</strong></td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td><strong>Precursors and Mature cells</strong></td>
			<td></td>
		</tr>
		<tr>
			<td>NK cells</td>
			<td>NK1.1+, pan-NK+</td>
		</tr>
		<tr>
			<td>NKT cells</td>
			<td>NK1.1+, pan-NK+, TCR Vbeta+(8.2), CD3+</td>
		</tr>
		<tr>
			<td>T lymphocytes</td>
			<td>TCR Vbeta+, CD3+, CD4+, CD8+</td>
		</tr>
		<tr>
			<td>B cells precursors and B lymphocytes</td>
			<td>B220+, CD19+, CD21/35+&#160;</td>
		</tr>
		<tr>
			<td>granulocytic precursors and granulocytes</td>
			<td>Ly6G+, Mac-1+, CD11b+</td>
		</tr>
		<tr>
			<td>monocytic precursors and monocytes/macrophages</td>
			<td>CD11b+, Mac-1+, Mac-3+, CD21/35+, CD115+, Ly6Chi</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td><strong>Progenitors</strong></td>
			<td></td>
		</tr>
		<tr>
			<td>multipotent progenitors</td>
			<td>CD117+ Sca-1+ CD34+ (Lin- CD150-)</td>
		</tr>
		<tr>
			<td>lymphoid-primed multipotent progenitors&#160;</td>
			<td>CD117hi Sca-1hi CD127+ (Lin- )</td>
		</tr>
		<tr>
			<td>common lymphoid progenitors&#160;</td>
			<td>CD117lo Sca-1lo CD127+ (Lin- )</td>
		</tr>
		<tr>
			<td>common myeloid progenitors&#160;</td>
			<td>CD16/32lo CD117+ CD34int (Lin- Sca-1-)</td>
		</tr>
		<tr>
			<td>granulocyte-macrophage progenitors&#160;</td>
			<td>CD16/32hi CD117+ CD34hi (Lin- Sca-1-)</td>
		</tr>
		<tr>
			<td>megakaryocyte-erythroid progenitors&#160;</td>
			<td>CD16/32lo CD117+ CD34lo (Lin- Sca-1-)</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td><strong>Hematopoietic stem cells&#160;</strong></td>
			<td>CD117+ Sca-1+ CD150+ (Lin- CD34-)</td>
		</tr>
	</tbody>
</table>
<p class="jove_content" fo:keep-together.within-page="1"><strong>Table 1. Markers of Hematopoietic Cell Lineages and Differentiation.</strong><br />
CD: cluster of differentiation; Lin: markers of mature cells; lo: low expression; hi: high expression; int: intermediate expression; NK: natural killer cells; TCR: T-cell receptor.</p>

Watch this Scientific Journal Video about A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model at JoVE.com

A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

<p class="jove_content">This manuscript provides a technical procedure that can be used to characterize C1498 cell cultures <em>in vitro</em> and the acute leukemia induced in mice after their injection. Phenotypic and functional analyses are performed using flow cytometry, immunofluorescence microscopy, cytochemistry and May-Gr&#252;nwald Giemsa staining.</p>

The intravenous injection of C1498 cells into syngeneic or congenic mice has been performed since 1941. These injections result in the development of ...

a-detailed-protocol-for-characterizing-murine-c1498-cell-line-its

Universidad Científica del Sur

Research

JoVE Journal

Cancer Research

20.0K Views.  INSERM, UMR-S-1172, Centre de Recherche Jean-Pierre Aubert (JPARC). This manuscript provides a technical procedure that can be used to characterize C1498 cell cultures in vitro and the acute leukemia induced in mice after their injection. Phenotypic and functional analyses are performed using flow cytometry, immunofluorescence microscopy, cytochemistry and May-Grünwald Giemsa staining.

Video: A Detailed Protocol for Characterizing the Murine C1498 Cell Line and its Associated Leukemia Mouse Model

Evolutionary Relationships

<p>Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4<sup>th</sup> century BC. Aristotle&#8217;s system was improved upon during the Renaissance and then, subsequently, by Carolus Linnaeus in the mid 1700&#8217;s. These more formal classification and organization systems grouped species by their physical similarity to one another. For example, all vertebrates have a backbone, but invertebrates do not. Traits like the backbone are called synapomorphies, which are traits that are shared by a group of organisms, presumably because they were derived from a common ancestor. As we will explore, this method has been shown to have limitations and has more recently been amended to include genetic analysis. Still, scientists construct trees called dendrograms to create a visual representation of how species are related to one another and share common ancestors. These dendrograms can aid in our understanding of the evolutionary processes that drive these relationships. Genetic comparisons have added an important tool guiding the analysis of evolutionary relationships.</p>
<h4>Types of Dendrogram</h4>
<p>A type of dendrogram, called a cladogram, depicts the hypothetical genealogical relationships between species with the tips (or leaves) of the tree representing a species and the branches showing how species are related to each other. A slightly more complicated type of tree, called a phylogram, differs from a cladogram in that the branches leading to the species are of different lengths. The length of a branch in this type of tree represents the degree of change between species: the longer the branch, the more time since the species have diverged from a common ancestor. In both types of tree, the common ancestor of a group of species is indicated by a node, which is the point where a series of branches meet. Species that are more closely related to each other (most recently shared a common ancestor) will be located closest to the node. The two species that share a node are called a sister group<sup>1</sup>.</p>
<h4>Understanding Evolutionary Relationships using Genetic Data</h4>
<p>Historically, cladograms were constructed by comparing the morphology (physical structure) of organisms. This method is still practiced but the techniques have been modernized to include comparison of DNA (deoxyribonucleic acid) sequences between species. Using DNA for building trees has several advantages over relying solely on morphology, including being able to calculate an estimate of how long ago different species shared a common ancestor<sup>1</sup>. However, using DNA is not always feasible, especially when trees include extinct organisms. DNA is best found in soft tissues, which are not preserved during the fossilization process, and therefore it is uncommon for a DNA sample of an extinct species to be available.</p>
<p>DNA is passed on from parents to their offspring in hereditary units called genes. The nucleotide (A, G, C, and T) sequence of genes found in different species are frequently quite similar, likely due to their having come from a common ancestor. This fact allows researchers to align sequences from different species with one another to build the trees described above. Species with more similarity between their nucleotide sequences will be placed next to each other in a tree, and species with less sequence similarity will be placed further apart from each other.</p>
<p>Bioinformatics are the tools used by biologists to analyze large datasets using a combination of computer science, mathematical modeling, and statistics. One such tool is called BLAST (Basic Local Alignment Search Tool), which can be used to quickly search the entire genome of any species that is available in the NCBI (National Center for Biotechnology Information) database<sup>2</sup>. The NCBI database combines several different databases that hold different types of DNA sequence information. The process of a BLAST search includes complex computer algorithms, but basically, BLAST aligns sequences of each nucleotide base from a submitted DNA sequence (known as the <em>query</em> sequence) with sequences in the data base that most closely match it. The DNA sequences that are found will be listed in order of similarity to the sequence in question, and will therefore be from species closely related to the species containing the query gene. This comparison may or may not depict the actual evolutionary relationship between species because genes evolve at different rates. Additionally, genomes sometimes contain more than one instance of a similar sequence.</p>
<p>Comparison of DNA sequences of genes is valuable beyond consideration of evolutionary relationships. Frequently, genes are identified in model organisms, such as the fruit fly, <em>Drosophila melanogaster</em>, or the mouse<sup>3</sup>. Integral to studying a gene, the function of its product is commonly identified and analyzed. If a researcher is interested in studying that function in a different organism (humans for example), BLAST or other bioinformatic tools can be used to find candidate genes based on their similarities to the genes of known function from model organisms.</p>
<p>Human genes can also be used as the starting point to find homologs in model organisms. In fact, human disease research depends heavily on this. Once a human gene of interest is identified, mice can be genetically manipulated to have the homologous gene disrupted, or &#8220;knocked out,&#8221; creating a model of the human disease that can be studied in order to understand and treat the disease. There are many of these mouse strains currently available. For example, there is a mouse model for human Cystic Fibrosis (CF) called the <em>Cftr</em> knockout mouse and another modeling atherosclerosis, called the <em>Apoe</em> knockout<sup>3</sup>.<br />
 </p>
<h4>References:</h4>
<ol class="references">
	<li><strong>Annenberg Learner.</strong> Online Textbook: Unit 3 Evolution and Phylogenetics. <em>Rediscovering Biology. </em>[Online] 2017. [Cited: August 23, 2018.] https://www.learner.org/courses/biology/textbook/compev/compev_3.html.</li>
	<li><strong>National Center for Biotechnology Information, U.S. National Library of Medicine.</strong> BLAST: Basic Local Alignment Search Tool. [Online] https://blast.ncbi.nlm.nih.gov/Blast.cgi.</li>
	<li><strong>National Human Genome Research Institute.</strong> Background on Mouse as a Model Organism. <em>National Human Genome Research Institute. </em>[Online] December 2002. [Cited: August 23, 2018.] <a href="https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/" target="__blank">https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/</a>.</li>
</ol>

Evolutionary Relationships: Using BLAST to Test Evolutionary Hypotheses - Concept

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system ...

Education

JoVE Lab Manual

Biology

Evolution

Concentration Dependence

<p>Source: Smaa Koraym at Johns Hopkins University, MD, USA</p>
<ol class="note-items">
	<li data-note-item="1" data-parent-collapse="">Concentration Dependence<button class="expand">Expand</button>
	<p>In this experiment, you will combine varying concentrations of aqueous HCl and sodium thiosulfate to make solid sulfur, which rapidly gathers into opaque yellow particles at a certain sulfur concentration. Since the reaction solution starts clear and colorless, you can easily tell when it reaches that concentration.</p>
	<p>You&#39;ll use the same test tube and total volume each time, so it will take the same amount of sulfur to make each solution completely opaque. Thus, you&#39;ll measure the reaction progress by timing how long that takes. You&#39;ll then use that data to estimate the reaction orders of the individual reactants and of the overall reaction.</p>
	<p>Before starting the lab, make a table listing the reactant concentrations, the time to solution opacity, and the solution temperature for the trials.</p>
	<h4><strong>Table 1: Reaction progress</strong></h4>
	<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
		<tbody>
			<tr>
				<td><strong>Trial</strong></td>
				<td><strong>[Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>] (M)</strong></td>
				<td><strong>[HCl] (M)</strong></td>
				<td><strong>Time (s)</strong></td>
				<td><strong>Temp (&#176;C)</strong></td>
			</tr>
			<tr>
				<td style="text-align: center;">1</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">2</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">3</td>
				<td style="text-align: center;">0.2 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">4</td>
				<td style="text-align: center;">0.15 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">5</td>
				<td style="text-align: center;">0.05 M</td>
				<td style="text-align: center;">3.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">6</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">6.0 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">7</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">4.5 M</td>
				<td></td>
				<td></td>
			</tr>
			<tr>
				<td style="text-align: center;">8</td>
				<td style="text-align: center;">0.1 M</td>
				<td style="text-align: center;">1.5 M</td>
				<td></td>
				<td></td>
			</tr>
		</tbody>
	</table>
	<p><a href="https://www.jove.com/CDNSource/lm/labs/50/Table_1._Reaction_progress.xlsx" target="_blank">Click Here to download Table 1</a></p>
	<p>We want to make sure that the reactions are all taking place at room temperature. You&#39;ll perform two benchmark trials, three trials with different sodium thiosulfate concentrations, and three trials with different HCl concentrations. You will vary the concentrations by diluting stock solutions of sodium thiosulfate and HCl, as shown in the following tables.</p>
	<h4><strong>Table 2: Sodium thiosulfate dilutions</strong></h4>
	<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
		<tbody>
			<tr>
				<td style="text-align: center;"><strong>Target Concentration</strong></td>
				<td style="text-align: center;"><strong>Volume of 0.2 M sodium thiosulfate</strong></td>
				<td style="text-align: center;"><strong>Volume of DI water</strong></td>
			</tr>
			<tr>
				<td style="text-align: center;">0.05 M</td>
				<td style="text-align: center;">5 mL</td>
				<td style="text-align: center;">15 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">0.10 M</td>
				<td style="text-align: center;">10 mL</td>
				<td style="text-align: center;">10 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">0.15 M</td>
				<td style="text-align: center;">15 mL</td>
				<td style="text-align: center;">5 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">0.20 M</td>
				<td style="text-align: center;">20 mL</td>
				<td style="text-align: center;">0 mL</td>
			</tr>
		</tbody>
	</table>
	<p><a href="https://www.jove.com/CDNSource/lm/labs/50/Table_2._Sodium_thiosulfate_dilutions.xlsx" target="_blank">Click Here to download Table 2</a></p>
	<h4><strong>Table 3: HCl Dilutions</strong></h4>
	<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
		<tbody>
			<tr>
				<td style="text-align: center;"><strong>Target Concentration</strong></td>
				<td style="text-align: center;"><strong>Volume of 6.0 M HCl</strong></td>
				<td style="text-align: center;"><strong>Volume of DI water</strong></td>
			</tr>
			<tr>
				<td style="text-align: center;">1.5 M</td>
				<td style="text-align: center;">2.5 mL</td>
				<td style="text-align: center;">7.5 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">3.0 M</td>
				<td style="text-align: center;">5.0 mL</td>
				<td style="text-align: center;">5.0 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">4.5 M</td>
				<td style="text-align: center;">7.5 mL</td>
				<td style="text-align: center;">2.5 mL</td>
			</tr>
			<tr>
				<td style="text-align: center;">6.0 M</td>
				<td style="text-align: center;">10 mL</td>
				<td style="text-align: center;">0 mL</td>
			</tr>
		</tbody>
	</table>
	<p><a href="https://www.jove.com/CDNSource/lm/labs/50/Table_3._HCl_Dilutions.xlsx" target="_blank">Click Here to download Table 3</a></p>
	<p>Remember to use caution when handling HCl, which is toxic and highly corrosive. Sulfur dioxide, which is a gaseous product of this reaction, is also toxic. You will leave the reaction waste in the fume hood overnight to let the sulfur dioxide escape harmlessly.</p>
	<ul class="lab-items">
		<li data-sub-note-item="1-6">
		<div class="lab-step">First, put on a lab coat, splash-proof safety glasses, and nitrile gloves.</div>
		</li>
		<li data-sub-note-item="1-7">
		<div class="lab-step">Fill a plastic wash bottle with deionized water and get a test tube marked with an X.</div>
		</li>
		<li data-sub-note-item="1-8">
		<div class="lab-step">Clamp the test tube over the center of a small stir plate. The label must be on the opposite side from you. Make sure that you can clearly see the X. Place a small magnetic stir bar in the test tube.</div>
		</li>
		<li data-sub-note-item="1-9">
		<div class="lab-step">Use a thermometer clamp to secure a glass thermometer in the lower third of the tube, keeping it clear of the X and the stir bar.</div>
		</li>
		<li data-sub-note-item="1-10">
		<div class="lab-step">Cut a sheet of about 20 squares of plastic paraffin film from a roll and bring the sheet back to your hood.</div>
		</li>
		<li data-sub-note-item="1-11">
		<div class="lab-step">Lay paper towels in the fume hood as a clean surface for labware to be reused.</div>
		</li>
		<li data-sub-note-item="1-12">
		<div class="lab-step">Label a 600-mL beaker as &#8216;waste&#8217; for the reaction, a 100-mL beaker as &#8216;0.2 M sodium thiosulfate&#8217;, and a 50-mL beaker as &#8216;6 M HCl&#8217;.</div>
		</li>
		<li data-sub-note-item="1-13">
		<div class="lab-step">Fill the 100-mL beaker with 0.2 M sodium thiosulfate from the stock bottle in the dispensing hood.</div>
		</li>
		<li data-sub-note-item="1-14">
		<div class="lab-step">After that, bring the 50-mL beaker and a watch glass to the acid-dispensing hood to obtain 50 mL of 6 M HCl. Cover the filled beaker with the watch glass and bring your portion of HCl back to your hood.</div>
		</li>
		<li data-sub-note-item="1-15">
		<div class="lab-step">Prepare 20 mL of 0.1 M sodium thiosulfate for the first benchmark trial. Use a 10-mL volumetric pipette to measure 10 mL of 0.2 M sodium thiosulfate and dispense it into a 20-mL volumetric flask. Fill the 20-mL volumetric flask with deionized water to the mark.</div>
		</li>
		<li data-sub-note-item="1-16">
		<div class="lab-step">Then, cut a square of plastic paraffin film, seal the flask, and invert it several times to thoroughly mix the solution.</div>
		</li>
		<li data-sub-note-item="1-17">
		<div class="lab-step">Remove the film and pour the solution into the test tube.</div>
		</li>
		<li data-sub-note-item="1-18">
		<div class="lab-step">Prepare 10 mL of 3 M HCl. Pipette 5 mL of 6 M HCl into a 10-mL graduated cylinder. Add deionized water to fill the graduated cylinder to the 10-mL line.</div>
		</li>
		<li data-sub-note-item="1-19">
		<div class="lab-step">Seal the top of the graduated cylinder with plastic paraffin film and thoroughly mix the HCl solution by inverting it several times.</div>
		</li>
		<li data-sub-note-item="1-20">
		<div class="lab-step">Now, start the stir motor and get a reset stopwatch. Confirm that you can see the X through the solution in the test tube.</div>
		</li>
		<li data-sub-note-item="1-21">
		<div class="lab-step">Simultaneously start the stopwatch and pour the 3 M HCl into the test tube.</div>
		</li>
		<li data-sub-note-item="1-22">
		<div class="lab-step">Watch the X through the solution, which will start becoming cloudy when enough sulfur has been produced to saturate it. Stop the stopwatch exactly when the solution becomes so opaque that the X disappears.</div>
		</li>
		<li data-sub-note-item="1-23">
		<div class="lab-step">Record the time in your lab notebook for benchmark trial 1 and then reset the stopwatch. Also, record the temperature of the solution.</div>
		</li>
		<li data-sub-note-item="1-24">
		<div class="lab-step">Now, turn off the stir motor, remove the thermometer, and rinse it with deionized water into the waste beaker.</div>
		</li>
		<li data-sub-note-item="1-25">
		<div class="lab-step">Then, empty the test tube into the waste beaker, retrieve the stir bar with long forceps, and rinse the test tube and stir bar with deionized water.</div>
		</li>
		<li data-sub-note-item="1-26">
		<div class="lab-step">Thoroughly clean the test tube with a test tube brush. Briefly rinse the volumetric flask and graduated cylinder with deionized water as well. Dry everything with paper towels.</div>
		</li>
		<li data-sub-note-item="1-27">
		<div class="lab-step">Repeat the trial in the exact same way for the second benchmark time. If the time is not within 3 - 5 s of the first trial, repeat the benchmark trial until you have two consistent trials.</div>
		</li>
		<li data-sub-note-item="1-28">
		<div class="lab-step">Follow the same overall procedure for each subsequent trial, changing the dilutions as indicated in your table and cleaning your glassware between each trial. Use a 5-mL volumetric pipette as needed when measuring the sodium thiosulfate stock solution in trials four and five.</div>
		</li>
		<li data-sub-note-item="1-29">
		<div class="lab-step">Once you have performed all eight trials, rinse your glassware and tools one last time and neutralize the reaction waste with baking soda. Use caution because sulfur dioxide is one of the reaction products.</div>
		</li>
		<li data-sub-note-item="1-30">
		<div class="lab-step">Transfer the neutralized reaction waste to the designated waste container. Then, pour any remaining sodium thiosulfate solution into its labeled waste container and rinse the beaker with deionized water.</div>
		</li>
		<li data-sub-note-item="1-31">
		<div class="lab-step">Neutralize any remaining HCl with baking soda before flushing it down the drain with tap water.</div>
		</li>
		<li data-sub-note-item="1-32">
		<div class="lab-step">Finally, clean and put away your labware according to your standard practices and throw out any trash remaining in your hood. Your instructor will dispose of the reaction waste later.</div>
		</li>
	</ul>
	</li>
	<li data-note-item="2" data-parent-collapse="">Results<button class="expand">Expand</button>
	<ul class="lab-items">
		<li data-sub-note-item="2-1">
		<div class="lab-step">Now, estimate the reaction order of sodium thiosulfate and HCl. Calculate the starting concentration of sodium thiosulfate in the reaction mixture for trials 1 through 5. Looking at the concentration of sodium thiosulfate and the reaction times, we see that as the concentration doubles, the time to opacity is halved.
		<h4><strong>Table 4: Estimating reaction order sodium thiosulfate</strong></h4>
		<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
			<tbody>
				<tr>
					<td style="text-align: center;"><strong>Trial</strong></td>
					<td style="text-align: center;"><strong>[Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>] added</strong></td>
					<td style="text-align: center;"><strong>Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub> volume (mL)</strong></td>
					<td style="text-align: center;"><strong>Total volume (mL)</strong></td>
					<td style="text-align: center;"><strong>[Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub>] in mixture </strong></td>
					<td style="text-align: center;"><strong>Time (s)</strong></td>
					<td style="text-align: center;"><strong>Inverse time (s<sup>-1</sup>)</strong></td>
				</tr>
				<tr>
					<td style="text-align: center;">1, 2</td>
					<td style="text-align: center;">0.1</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">3</td>
					<td style="text-align: center;">0.2</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">4</td>
					<td style="text-align: center;">0.15</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">5</td>
					<td style="text-align: center;">0.05</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
			</tbody>
		</table>
		<a href="https://www.jove.com/CDNSource/lm/labs/50/Table_4._Estimating_reaction_order_sodium_thiosulfate.xlsx" target="_blank">Click Here to download Table 4</a></div>
		</li>
		<li data-sub-note-item="2-2">
		<div class="lab-step">Remember that each trial used the same volume, so it took the same amount of sulfur to make each solution opaque. Thus, the reaction is twice as fast when the sodium thiosulfate concentration is doubled.</div>
		</li>
		<li data-sub-note-item="2-3">
		<div class="lab-step">When the concentration increases by a factor of 4, the reaction rate also increases by a factor of 4. This one-to-one relationship suggests that the reaction was first order for sodium thiosulfate. If so, we would expect a plot of rate versus sodium thiosulfate concentration to be linear.</div>
		</li>
		<li data-sub-note-item="2-4">
		<div class="lab-step">We didn&#39;t measure how much sulfur it took to turn the solution opaque, but we assume that it was the same for each trial. This lets us use the inverse of time to opacity as a stand-in for the rate. The slope won&#39;t be equal to k, but that&#39;s OK for this lab.</div>
		</li>
		<li data-sub-note-item="2-5">
		<div class="lab-step">Plot inverse reaction time as a function of sodium thiosulfate concentration. This confirms the linear relationship between sulfur production and thiosulfate concentration. Thus, the reaction seems to be first order with respect to thiosulfate.</div>
		</li>
		<li data-sub-note-item="2-6">
		<div class="lab-step">Use the same method to estimate the reaction order of HCl. First, calculate the starting concentration of HCl in the benchmark trials and in trials 6 through 8.
		<h4><strong>Table 5: Estimating reaction order HCl</strong></h4>
		<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
			<tbody>
				<tr>
					<td style="text-align: center;"><strong>Trial</strong></td>
					<td style="text-align: center;"><strong>[HCl] added</strong></td>
					<td style="text-align: center;"><strong>HCl volume (mL) </strong></td>
					<td style="text-align: center;"><strong>Total volume (mL) </strong></td>
					<td style="text-align: center;"><strong>[HCl] in mixture </strong></td>
					<td style="text-align: center;"><strong>Time (s)</strong></td>
				</tr>
				<tr>
					<td style="text-align: center;">1, 2</td>
					<td style="text-align: center;">0.1</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">3</td>
					<td style="text-align: center;">0.2</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">4</td>
					<td style="text-align: center;">0.15</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
				<tr>
					<td style="text-align: center;">5</td>
					<td style="text-align: center;">0.05</td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
					<td style="text-align: center;"></td>
				</tr>
			</tbody>
		</table>
		<a href="https://www.jove.com/CDNSource/lm/labs/50/Table_5._Estimating_reaction_order_HCl.xlsx" target="_blank">Click Here to download Table 5</a></div>
		</li>
		<li data-sub-note-item="2-7">
		<div class="lab-step">Then, look at the concentration of HCl and the reaction times for these trials. The reaction times are nearly identical, so the HCl concentration has no effect on the reaction rate. This means that HCl seems to be zeroth order for this reaction.</div>
		</li>
		<li data-sub-note-item="2-8">
		<div class="lab-step">Lastly, add up the reaction orders of the reactants to get the overall reaction order.</div>
		</li>
	</ul>
	</li>
</ol>

Concentration Dependence and Reaction Order - Procedure

Source: Smaa Koraym at Johns Hopkins University, MD, USA

	Concentration DependenceExpand
	In this experiment, you will combine varying concentrations of ...

Chemistry

Lab Manual Sub Articles

Bacterial Transformation

<ol class="note-items"><li data-note-item="1" data-parent-collapse=""><strong>Bacterial Transformation</strong>
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">IMPORTANT: In addition to wearing the appropriate personal protective equipment, be sure to take care to keep your face away from suspension cultures, and to avoid inhaling reagents. Do not touch your face while performing the experiment. Always wash your hands before and after every experiment.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">When you are ready to safely begin, check to make sure you have a tube or tubes of competent <em>E. coli</em> cells in your ice bucket.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Then, transfer 50 &#181;L from one of these tubes to each of two new micro-centrifuge tubes, one labeled '&#8211;', and one labeled '+'.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Next, pipet 2 &#181;L of p-GREEN plasmid into the plus tube and flick the tube gently to incorporate the plasmid throughout the bacterial solution. HYPOTHESES: The experimental hypothesis is that on plates containing no antibiotic, bacteria with or without the plasmid can survive, and so both will grow lawns of bacteria. Conversely, on the plates containing the ampicillin antibiotic, only the bacteria successfully transformed with the resistance plasmid will grow, whereas the minus plasmid plates will contain no bacteria. The null hypothesis of this experiment is that there will be no difference in bacterial growth between the plates.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Incubate both tubes on ice.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">After 30 minutes submerge both tubes 3/4 of the way into the water of a 42 &#176;C water bath for 30 seconds.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">At the end of the water bath incubation, place the tubes back on ice for 5minutes, then add 950 &#956;L of room temperature SOC media to each tube.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Incubate the tubes in a shaker at 37 &#176;C for 60 minutes at 250 rpm.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">While the tubes are incubating, label the bottoms of one LB and one LB/ampicillin, or AMP plate, &#8220;+ plasmid&#8221;, and the bottoms of one LB plate and one LB/AMP plate &#8220;- plasmid&#8221;.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">At the end of the incubation, pipet 50 &#956;L from the &#8220;+ plasmid&#8221; cell tube onto both the LB/AMP plus plate and the LB plus plate.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">Then, with a fresh tip, add 50 &#956;L of the bacteria that were not exposed to the plasmid to each of the minus plates.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, spread each plate using a fresh pipette tip.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Cover the plates with their lids, and then seal them with laboratory film.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Then store the plates upside down in a 37 &#176;C incubator for 24 hours.
</div></li></ul></li><li data-note-item="2" data-parent-collapse=""><strong>Results</strong>
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">The day after the transformation, observe which plates have bacteria, and how the bacteria are distributed over each plate.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Record the number of colonies and the color of the colonies for different conditions. <a href="https://www.jove.com/CDNSource/lm/labs/11/Table_1_Bacterial_Transformation.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table 1</a>
</div></li><li data-sub-note-item="2-3"><div class="lab-step">To determine the initial mass, IM, of the plasmid, first multiply the plasmid concentration from the stock tube of p-GREEN by the volume transferred into the experimental tube.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">To calculate how much DNA is spread onto each plate, divide the volume of bacterial suspension spread onto each plate by the volume of the suspension in the tube, and multiply this fraction by the initial mass of plasmid used.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">To determine the transformation efficiency, count all of the visibly distinct colonies on the plus plasmid LB/AMP plate, and divide this number by the mass of plasmid spread. NOTE: The unit for transformation efficiencies is cfu/&#956;g of plasmid DNA.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">Record all of the results for each plate in the table and analyze them.
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Observe any differences between the colonies and make hypotheses to explain this observation. Consider the factors that may affect the transformation efficiency of competent bacteria.
</div></li><li data-sub-note-item="2-8"><div class="lab-step">NOTE: You will have also noticed that each plate had a different outcome. The minus plasmid and plus plasmid LB plates should have grown a lot of bacteria across their plates. The minus plasmid LB/AMP plate should have no bacterial growth. And the plus plasmid LB/AMP plate should have grown bacterial colonies that fluoresced green.
</div></li></ul></li></ol>

Bacterial Transformation using Plasmids - Procedure

Bacterial Transformation
ExpandIMPORTANT: In addition to wearing the appropriate personal protective equipment, be sure to take care to keep your face ...

Genetics

Animal Behavior

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Making Choice Chambers
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">Before beginning the experiments, use one of the Sharpies from the lab table to trace a line around the circumference of the bottom of both bottles. One line should be 1 cm from the base of the bottle and the other should be 1.5 cm from the base of the bottle.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Use a razor blade to make a small cut in each bottle at the mark. 
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Then, insert the scissors and carefully cut along the line until the bottoms of the bottles are removed completely.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Fit one bottle bottom into the other.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Tape the bottles together to make a secure chamber.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Use the razor blade and scissors to poke a hole into the center of the chamber. 
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Take a piece of tape and fold the edge onto itself to make a tab and place the tape over the hole.  Note: The tab helps with easy removal and reattachment of the tape to the chamber.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Use the Sharpie to label one side of the chamber A and the other side B.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Upon receiving the colony of <em>Drosophila</em>, perform a count to make sure there are 40 living individuals in the vial.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Geotaxis
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">To perform a geotaxis assay, first attach a clean, empty bottle cap to each end of the choice chamber and turn the fly vial upside down onto its lid.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Open the tape flap in the center of the choice chamber.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Insert the funnel into the hole and tap all of the flies into the chamber. HYPOTHESES: The alternate hypothesis is that more flies will be moving against gravity, or up, regardless of the orientation of the choice chamber. Alternatively, if the flies are positively geotactic, then more flies will move down, or with gravity. The null hypothesis is that the flies will have no preference with regard to gravity and will move randomly within the chamber. 
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Once the <em>Drosophila</em> are in the chamber, close the tape flap, rotate the chamber so that it stands vertically with side A on the bottom, and start the timer.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Immediately, count the number of <em>Drosophila</em> that are moving up and the number that are moving down. Record these data for time point 0 in the table. 
</div></li><li data-sub-note-item="2-6"><div class="lab-step">After 30 seconds, rotate the chamber so that side B is on the bottom and count the number of <em>Drosophila</em> that are moving up or down. Record these data for time point 30 in the table.
</div></li><li data-sub-note-item="2-7"><div class="lab-step">After 60 seconds, rotate the chamber so that side A is on the bottom again and record the number of moving flies for time point 60.  <a href="https://www.jove.com/CDNSource/lm/labs/15/Table_1_Animal_behavior.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table 1</a>
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Hold the chamber over the fly vial and carefully open the cap to return all of the <em>Drosophila</em> back to their empty vial. 
</div></li></ul></li><li data-note-item="3" data-parent-collapse="">Phototaxis
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="3-1"><div class="lab-step">To perform a phototaxis analysis, first cover side A of the choice chamber with tin foil, taking care that the foil reaches to the exact center between the two sides. 
</div></li><li data-sub-note-item="3-2"><div class="lab-step">Ensure the caps are secured on both ends of the chamber.  HYPOTHESES: In this exercise, the alternative hypothesis is that more flies will be found in the light side of the choice chamber, if the flues are attracted to light, or on the dark side if they are repelled by light. The null hypothesis is that <em>Drosophila</em> will have no preference with regard to either stimulus. Therefore, equal numbers of flies will be found on either side of the choice chamber.
</div></li><li data-sub-note-item="3-3"><div class="lab-step">Open the tape flap in the center of the choice chamber, insert the funnel into the hole, and tap all 40 flies into the chamber as previously described.
</div></li><li data-sub-note-item="3-4"><div class="lab-step">Set the timer to sound an alarm at 10 minutes.
</div></li><li data-sub-note-item="3-5"><div class="lab-step">When the alarm sounds, stop the timer, and count the number of flies in the open side B of the chamber.
</div></li><li data-sub-note-item="3-6"><div class="lab-step">Subtract that number from the total number of flies added to the choice chamber to calculate the number of flies in the covered side A.
</div></li><li data-sub-note-item="3-7"><div class="lab-step">Record these data in the table for time point 10.  <a href="https://www.jove.com/CDNSource/lm/labs/15/Table_2_Animal_behavior.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table 2</a>
</div></li><li data-sub-note-item="3-8"><div class="lab-step">Carefully, return all of the flies back to their empty vial.
</div></li></ul></li><li data-note-item="4" data-parent-collapse="">Chemotaxis
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="4-1"><div class="lab-step">HYPOTHESES: In this exercise, the alternative hypothesis is that more flies will be found on the side of the chamber with the apple cider vinegar if the flies are attracted to the odor. Or, if the flies are repelled by the vinegar, then more of them will be found on the side of the chamber presenting the odorless water. The null hypothesis is that <em>Drosophila</em> will have no preference with regard to either stimulus and will move randomly in the chamber.  Use an individual disposable plastic pipette to dispense two drops of the apple cider vinegar onto one cotton ball for side A of the chamber. 
</div></li><li data-sub-note-item="4-2"><div class="lab-step">Use an individual disposable plastic pipette to dispense two drops of the distilled water onto a cotton ball for side B of the chamber. 
</div></li><li data-sub-note-item="4-3"><div class="lab-step">Fold two pieces of tape over themselves to create individual pieces of double-sided tape and use the tape to attach each cotton ball to the inside of the single bottle cap.
</div></li><li data-sub-note-item="4-4"><div class="lab-step">Screw the bottle caps with the cotton balls onto their corresponding sides of the chamber.
</div></li><li data-sub-note-item="4-5"><div class="lab-step">Transfer all of the flies into the chamber and set the timer to sound an alarm after 10 minutes. 
</div></li><li data-sub-note-item="4-6"><div class="lab-step">When the alarm sounds, stop the timer and record the number of flies in each side of the chamber. <a href="https://www.jove.com/CDNSource/lm/labs/15/Table_3_Animal_behavior.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table 3</a>
</div></li><li data-sub-note-item="4-7"><div class="lab-step">At the end of the final experiment, funnel the flies into a morgue tube containing 70% ethanol. 
</div></li><li data-sub-note-item="4-8"><div class="lab-step">Dismantle the choice chambers for recycling.  NOTE: Since all of the chemicals used in these experiments are lab safe, the liquids can be disposed of down the sink, the chambers rinsed, and the solids can be thrown away in the trash. 
</div></li></ul></li><li data-note-item="5" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="5-1"><div class="lab-step">Gather all of the data collected for the geotaxis, phototaxis, and chemotaxis experiments.
</div></li><li data-sub-note-item="5-2"><div class="lab-step">Plot these values as bar graphs for different conditions and different time points.
</div></li><li data-sub-note-item="5-3"><div class="lab-step">Use these data to draw inferences about the behavior of <em>drosophila melanogaster</em>. 
</div></li></ul></li></ol>

Animal Behavior: Studying Taxis Behavior of Drosophila - Procedure

Making Choice Chambers
ExpandBefore beginning the experiments, use one of the Sharpies from the lab table to trace a line around the circumference of the ...

Ecology

Cell Structure

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Visualizing Onion and Human Cells
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">In this experiment you will prepare different types of cells from a plant and animal, onion and human respectively, and then visualize them under a microscope. HYPOTHESES: The experimental hypothesis is that the cells will appear different in overall shape and membrane structure, but there will be some shared similar structures, like nuclei, in both cells. The null hypothesis of the experiment is that there will be no difference in structure between the two cell types.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">First, to prepare an animal cell slide, start by pouring 30 mL of distilled water into a beaker.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Then, use a glass dropper to place 2 - 3 drops of the water onto the center of a clean microscope slide.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Next, take a clean toothpick and scrape the inside of your cheek to gather cells.  CAUTION: Do not scrape your cheeks too aggressively. The scraping should not cause discomfort or bleeding. 
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Put the end of the toothpick with the cheek cells onto the wetted area of the slide and mix the water and cheek cells together.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Next, add two drops of methylene blue staining solution to the wetted area of the slide and mix it in with a clean toothpick.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Let the mixture sit at room temperature for 3 minutes.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">After 3 minutes have passed, use a glass dropper to add a drop of glycerol to the mixture.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Carefully, place a cover slip over the mix on the slide by first standing one end of the cover slip vertically and aligning it along to one side of the mixture. Then, carefully lower the other end of the cover slip down until the mixture is completely covered.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Finally, remove any excess liquid with blotting paper.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">Next, prepare a slide of plant cells by placing five drops of distilled water into a clean watch glass.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, with forceps, take a thin strip of onion and place it into the water.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Apply 5 drops of safranin solution to another watch glass.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Now using a pair of forceps transfer the piece of onion from the distilled water into the safranin.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">Allow the onion to soak for 30 seconds.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Then, again with forceps, move the onion back into the distilled water.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Now, place 3 drops of glycerol onto the center of a glass microscope slide.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Using the forceps again, transfer the onion into the glycerol on the slide.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">Gently place a cover slip over the onion on the slide and blot away any excess liquid with a piece of blotting paper.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">Next, you will visualize the slides you have prepared using a compound microscope. First, turn on the scope and set the objective lens to the lowest level of magnification.  NOTE: A compound microscope typically has two levels of magnification.
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Place the cheek tissue slide onto the stage of the microscope and bring the cells into focus by adjusting the stage and using the coarse adjustment knob.
</div></li><li data-sub-note-item="1-22"><div class="lab-step">When the cells are in focus, observe the individual cells and draw what you see. Repeat these observations at medium and high magnifications adjusting the focus with the fine adjustment knob. IMPORTANT: Do NOT use the coarse adjustment knob at high magnifications. 
</div></li><li data-sub-note-item="1-23"><div class="lab-step">Next, rotate the nose piece so that the oil lens is almost in place.
</div></li><li data-sub-note-item="1-24"><div class="lab-step">Place a drop of immersion oil on top of the cover slip of the cheek cell slide.
</div></li><li data-sub-note-item="1-25"><div class="lab-step">Use the oil lens to view the cheek cells and record your observations.
</div></li><li data-sub-note-item="1-26"><div class="lab-step">Repeat these steps to view the onion plant cell, moving again from low to medium, high, and oil lenses (steps 20 &#8211; 24).
</div></li><li data-sub-note-item="1-27"><div class="lab-step">Record all of your observations and note the differences you see between animal and plant cells.
</div></li><li data-sub-note-item="1-28"><div class="lab-step">When you are finished, place the cheek cell slide and toothpicks into a diluted bleach solution. Dispose of the cover slips into a glass waste container.
</div></li><li data-sub-note-item="1-29"><div class="lab-step">Finally, turn off the microscope and clean the lenses with lens cleaning paper.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">Calculate the total magnification for each of the viewing levels. Remember to account for both the eyepiece magnification and the objective lens magnification. NOTE: Eye piece: 10X magnification, Objective lens: 4X, 10X, and 40X magnification
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Then, identify the structural differences between plant and animal cells using the observations made under the microscope.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Label the nuclei in both cell types and then highlight or note any differences that you see between them.
</div></li></ul></li></ol>

Cell Structure: Visualizing Onion and Human Cells - Procedure

Visualizing Onion and Human Cells
ExpandIn this experiment you will prepare different types of cells from a plant and animal, onion and human ...

Fundamentals

Population Growth

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Exponential Population Growth Model
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">NOTE: In these experiments you will use computer software to simulate different types of growth models. HYPOTHESES: The experimental hypothesis might be that if we increase either R, the reproductive rate or the initial population size, NT, that this will increase the population size after 10 generations in an exponential growth model compared to unmanipulated control populations. The null hypothesis may be that populations of different R and initial population size will have the same population size as control groups after 10 generations.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">To build an exponential population growth model, first open a new workbook in Excel.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Save the file to a secure location and name it &#8220;exponential growth&#8221;.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Label cell A1 &#8220;generation&#8221;, and cell B1 &#8220;population size&#8221;.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Type the number zero into cell A2. This row will represent the initial generation.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">In cell A3 type this formula: = 1 + A2
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Now hit enter.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Click on cell A3 and drag the small box in the bottom right corner to cell A12 for 10 generations.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Type the number two in cell B2. This will represent the initial population size in the model.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Label cell E1 &#8220;growth rate&#8221;, and in cell E2 type the number 0.05. This value represents the proportion of new offspring produced by each individual for each generation, or R in the exponential growth model.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">NOTE: In the classic exponential growth equation, T is the generation, and NT is the number of organisms in the current generation. NT+1 will be is the number of individuals in the next generation.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Enter the exponential growth equation in cell B3 as the formula shown here: = B2 + (1 + E2) * B2
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Now, hit enter.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Click cell B3 and drag the small square in the bottom right corner down to cell B12 to simulate 10 generations of exponential growth.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">To observe the results, select all data, along with the headers, in columns A and B.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Click &#8220;insert&#8221; and select &#8220;scatter plot with smooth lines and markers&#8221; from the toolbar. A scatter plot should appear in the sheet. TIP: If it does not, check to see if a new sheet was created in the workbook.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Label the Y axis &#8220;population size&#8221; and the X axis &#8220;generation&#8221;. Title the chart &#8220;exponential growth of a population&#8221;.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Observe how the population increases in a classic exponential growth pattern.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">Next, create Table 1 labeling the &#8220;growth rate R&#8221; as the header of column A, and &#8220;population size after 10 generations&#8221; as the second column.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">Record the population size at generation 10 for the growth rate of 0.05, the rate we just simulated.
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Now, set the growth rate in cell E2 to 0.1, and repeat the steps to generate a new scatter plot.
</div></li><li data-sub-note-item="1-22"><div class="lab-step">Record the population size at generation 10.
</div></li><li data-sub-note-item="1-23"><div class="lab-step">Continue to alter the growth rate using the values in table one, noticing how the Y axis changes to accommodate the increasing values.
</div></li><li data-sub-note-item="1-24"><div class="lab-step">After completing Table 1, set the growth rate back to 0.05 and change the initial population size in cell B2 to 100 individuals.
</div></li><li data-sub-note-item="1-25"><div class="lab-step">Record the population size at generation 10 in a new table, Table 2.
</div></li><li data-sub-note-item="1-26"><div class="lab-step">Proceed to change the initial population size using the values in Table 2. Once the table is complete, save the Excel sheet and close it.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Logistic Population Growth Model
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">HYPOTHESES: In a logistic growth model, the experimental hypothesis could be that populations will approach and meet the carrying capacity, and any populations with sizes greater than the carrying capacity will decrease in the subsequent generation. The null hypothesis might be that population growth will not follow a logistic S-shaped curve in the logistic models, and that generations with population sizes greater than the carrying capacity will continue to increase or stay the same in the next generation.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">To build a logistic population growth model, open a new spreadsheet in Excel.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Save the file as &#8220;logistic growth&#8221; in the same folder as before.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Then label cell A1 &#8220;generation&#8221; and B1 &#8220;population size&#8221;.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Type the number zero in cell A2. This row will represent the initial generation.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">In cell A3 type the formula: = 1 + A2
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Now, hit enter.
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Now, click on cell A3 and drag the small box in the bottom right corner to cell A12 to number up to the 10th generation.
</div></li><li data-sub-note-item="2-9"><div class="lab-step">Type the number two in cell B2 to represent the initial population size.
</div></li><li data-sub-note-item="2-10"><div class="lab-step">Label cell E1 as &#8220;max growth rate&#8221; and cell F1 as &#8220;carrying capacity&#8221;.
</div></li><li data-sub-note-item="2-11"><div class="lab-step">Finally, set the max growth rate in cell E2 to 0.05. This value will represent the maximum growth rate the population may achieve &#8211; &#8220;R max&#8221; in the discrete logistic equation. NOTE: In the classic logistic growth equation the term K represents carrying capacity. As the population size of the current generation or NT, approaches the carrying capacity, the growth of the population begins to slow.
</div></li><li data-sub-note-item="2-12"><div class="lab-step">Now set the carrying capacity in cell F2 to 50.
</div></li><li data-sub-note-item="2-13"><div class="lab-step">Enter the logistic growth equation in cell B3 as the formula below: = B2 + ((1 + E$2) * B2) * ((F$2 - B2) /F$2)
</div></li><li data-sub-note-item="2-14"><div class="lab-step">Now hit enter.
</div></li><li data-sub-note-item="2-15"><div class="lab-step">Drag the formula down to cell B12 to add it to all 10 generations.
</div></li><li data-sub-note-item="2-16"><div class="lab-step">To observe the results of this exercise, highlight all data and headers in columns A and B, then click &#8220;insert&#8221; and select &#8220;scatter plot with smooth lines and markers&#8221;.
</div></li><li data-sub-note-item="2-17"><div class="lab-step">Label the Y axis population size and the X axis generation.
</div></li><li data-sub-note-item="2-18"><div class="lab-step">Title the chart as logistic growth of a population.
</div></li><li data-sub-note-item="2-19"><div class="lab-step">Observe the growth of the population as it nears carrying capacity and record the generation that the population meets or exceeds carrying capacity in a new table.
</div></li><li data-sub-note-item="2-20"><div class="lab-step">Now set the max growth rate in cell E2 to 1.
</div></li><li data-sub-note-item="2-21"><div class="lab-step">Notice how the population responds when it rises over carrying capacity.
</div></li><li data-sub-note-item="2-22"><div class="lab-step">Continue to set the max growth rate to the values in Table 3 and record the generation at which the population rises two or above carrying capacity.
</div></li><li data-sub-note-item="2-23"><div class="lab-step">Now set the max growth rate back to 0.05 and set the carrying capacity to 1,000.
</div></li><li data-sub-note-item="2-24"><div class="lab-step">Notice how the graph changes as the growth approaches the carrying capacity.
</div></li><li data-sub-note-item="2-25"><div class="lab-step">Drag the formulas in A12 and B12 down to A14 and B14.
</div></li><li data-sub-note-item="2-26"><div class="lab-step">Adjust the plot to include these new data points to show when the population reaches carrying capacity.
</div></li><li data-sub-note-item="2-27"><div class="lab-step">Save the Excel sheet and close it.
</div></li></ul></li><li data-note-item="3" data-parent-collapse="">Predator-Prey Model
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="3-1"><div class="lab-step">Hypotheses: In our third simulation of predator-prey logistics, the experimental hypothesis could be that predator populations will increase as prey populations increase and that the two populations will experience growth and decline dependent on the population of the other species. The null hypothesis may be that the two populations will show patterns of growth unrelated to the other species&#39; population size.
</div></li><li data-sub-note-item="3-2"><div class="lab-step">To build a predator-prey model, open a new spreadsheet in Excel.
</div></li><li data-sub-note-item="3-3"><div class="lab-step">Save the file in the same folder as the other two models and name it &#8220;predator-prey model&#8221;.
</div></li><li data-sub-note-item="3-4"><div class="lab-step">Label cell A1 &#8220;time&#8221;, cell B1 &#8220;prey&#8221; and cell C1 &#8220;predator&#8221;.
</div></li><li data-sub-note-item="3-5"><div class="lab-step">In cell A2 type zero and hit enter.
</div></li><li data-sub-note-item="3-6"><div class="lab-step">In cell A3 type the formula below: = 1 + A2
</div></li><li data-sub-note-item="3-7"><div class="lab-step">Now hit enter.
</div></li><li data-sub-note-item="3-8"><div class="lab-step">Drag the formula down to cell A802 as the model will require many time points to visualize changes of both populations.
</div></li><li data-sub-note-item="3-9"><div class="lab-step">Now type 100 into cell B2 to represent the initial prey population and type 25 into cell C2 as the initial predator population.
</div></li><li data-sub-note-item="3-10"><div class="lab-step">Label cell E1 &#8220;prey growth rate&#8221;, cell F1 &#8220;consumption of prey by predator&#8221;, cell G1 &#8220;conversion efficiency&#8221; and cell H1 &#8220;starvation of predator&#8221;.
</div></li><li data-sub-note-item="3-11"><div class="lab-step">Type the number 0.02 in cell E2. This value represents the growth rate of the prey species or R in the prey population model. NOTE: Here the prey population is represented as VT and the predator population as CT. The value A is the per capita attack rate of the predator species on the prey. In the predator population model, variables are introduced for predators produced per prey consumed, F or conversion efficiency as well as for the starvation rate of the predators noted as Q.
</div></li><li data-sub-note-item="3-12"><div class="lab-step">Type 0.0005 in cell F2 for the attack rate in both the prey population and the predator population model.
</div></li><li data-sub-note-item="3-13"><div class="lab-step">Then type 0.8 in cell G2 to represent the conversion efficiency, and 0.05 in cell H2 for the starvation rate in the predator population equation.
</div></li><li data-sub-note-item="3-14"><div class="lab-step">Now type the prey growth equation in cell B3 as the formula below: =B2+(E$2*B2)-(F$2*C2*B2)
</div></li><li data-sub-note-item="3-15"><div class="lab-step">Hit enter.
</div></li><li data-sub-note-item="3-16"><div class="lab-step">Then type the prey growth equation in cell C3 as the displayed formula: =C2 + (F$2*G$2*B2*C2)-(H$2*C2)
</div></li><li data-sub-note-item="3-17"><div class="lab-step">Hit enter.
</div></li><li data-sub-note-item="3-18"><div class="lab-step">Hold down the shift keys and click to select both cells B3 and C3.
</div></li><li data-sub-note-item="3-19"><div class="lab-step">Click the small box at the bottom right corner of cell B3 and drag the two formulas down to row 802 to simulate the predator and prey populations over 800 generations.
</div></li><li data-sub-note-item="3-20"><div class="lab-step">To visualize the data, select all of the data and headers in columns A, B and C.
</div></li><li data-sub-note-item="3-21"><div class="lab-step">Click &#8220;insert&#8221; and select &#8220;scatter plot with smoothed lines&#8221;.
</div></li><li data-sub-note-item="3-22"><div class="lab-step">A chart showing the population change of the predator and the prey populations should appear.
</div></li><li data-sub-note-item="3-23"><div class="lab-step">Label the Y axis of the plot population size and the X axis time.
</div></li><li data-sub-note-item="3-24"><div class="lab-step">Title the plot &#8220;predator-prey interactions&#8221;.
</div></li><li data-sub-note-item="3-25"><div class="lab-step">Observe the interactions between the predator and prey populations.
</div></li><li data-sub-note-item="3-26"><div class="lab-step">Set the initial predator population to zero and observe what happens in the plot.
</div></li><li data-sub-note-item="3-27"><div class="lab-step">Now set the initial predator population to 120 and observe what happens to the predator population when there are more predators than prey.
</div></li><li data-sub-note-item="3-28"><div class="lab-step">Finally set the initial prey population to zero and observe what happens to the population of the predators.
</div></li><li data-sub-note-item="3-29"><div class="lab-step">Experiment with other values to see what other interactions can be created.
</div></li><li data-sub-note-item="3-30"><div class="lab-step">When finished, save the workbook and close it.
</div></li></ul></li><li data-note-item="4" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="4-1"><div class="lab-step">In the exponential growth model built in the first activity, observe the shape of the resulting population growth curve, and consider whether you think this type of growth could be sustained in nature.
</div></li><li data-sub-note-item="4-2"><div class="lab-step">Note how the growth rate R affected the population size after 10 generations in the exponential growth model.
</div></li><li data-sub-note-item="4-3"><div class="lab-step">Also consider how the initial population size affected the population size after 10 generations in the exponential growth model.
</div></li><li data-sub-note-item="4-4"><div class="lab-step">Now, consider the logistic growth model, and observe the shape of this graph, noting how it differs from the exponential growth model.
</div></li><li data-sub-note-item="4-5"><div class="lab-step">Then, note how the populations behave as they approach the carrying capacity in your logistic growth model.
</div></li><li data-sub-note-item="4-6"><div class="lab-step">Decide if you think that the maximum per capita growth rate, R max, influenced how quickly populations reached carrying capacity in the logistic growth model.
</div></li><li data-sub-note-item="4-7"><div class="lab-step">Also, consider what happens to population size when populations are over carrying capacity in the logistic growth model, or what might happen if the carrying capacity is lowered.
</div></li><li data-sub-note-item="4-8"><div class="lab-step">In the predator-prey model, note whether the prey population responded to increasing predator population. Conversely, consider how the predators then respond to decreasing prey populations.
</div></li></ul></li></ol>

Population Growth: Exponential and Logistic Growth and Predator-Prey Model - Procedure

Exponential Population Growth Model
ExpandNOTE: In these experiments you will use computer software to simulate different types of growth models. ...

Genetics of Organisms

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">REF and WEF Fly Genetics
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">NOTE: At your station, you should have four vials of flies. They will be labeled as male or female. They will also be labeled with the notation REF for red-eyed flies or WEF for white-eyed flies. The female vial should also be labeled virgin as these flies should be unmated for the purpose of this experiment.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Make sure you have around five flies in each vial. NOTE: You will perform crosses using these flies to determine if red or white eyes is dominant and if the trait is sex-linked. You should also have two fly anesthesia wands, a paint brush, magnifying glass, fly anesthetic, some empty fly vials, and a sheet of white paper.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Draw a line in the middle of the paper to make a male side and a female side.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Dip the fly anesthesia wand into the solution and insert it into the red-eyed female vial until the flies stop moving.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Then, dip the same wand into the white-eyed male vial until these flies are also anesthetized.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Tap the flies from the two vials onto the sheet - Females on one side and males on the other.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Check to make sure that all the flies from the female vial are female and likewise, confirm that the male flies are male.  NOTE: Males are slightly smaller and have a darker solid pigment on the end of their abdomen. Females are larger and have stripes rather than a solid spot.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">If there are any male flies in the female vial, obtain a new vial as these females likely have sperm from males from their own strain and are unusable.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Now, move the five red-eyed females and five white-eyed males into a new vial using the paintbrush.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Label this vial to indicate that this is a cross between red-eyed females and white-eyed males. HYPOTHESES: Use Punnett squares to formulate hypotheses for this experiment. Consider what you will see in a few days if the white allele is dominant and there is no effect of parental sex. What if red is dominant? Under the potential offspring genotype in each box, write the expected phenotype.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">To set up the reverse cross, again dip the fly anesthesia wand into the solution and insert it this time into the white-eyed female vial and then the red-eyed male vial until all the flies have stopped moving.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Tap the flies onto a sheet of white paper and check to make sure that all the flies from the female vial are female and likewise with the male flies.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Now, move the five white-eyed females and five red-eyed males into a new vial using the paintbrush.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Label this vial to indicate that this is a cross between white-eyed females and red-eyed males.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">Finally, place the two tubes containing the new crosses into a rack for incubation at room temperature. NOTE: After about three days, larvae should be moving around in the vial.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Remove the 10 adult flies and dispose of them into the morgue tube containing 70% ethanol.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Now, put the tubes back in the rack and let the larvae mature until they reach the adult stage in up to two weeks.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Once all of the larvae from these crosses have matured to adulthood, anesthetize the flies in one of the tubes using the wand.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">Then, on a piece of white paper, sort the flies by sex and phenotype and then record these data.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">Now, repeat this process and tally the sexes and phenotypes for the second cross (steps 1 &#8211; 18).
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Finally, once you have counted all of your flies, dispose of them in the morgue vial.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">To begin investigating your results, first compare the data you collected with the hypothesis you made using the Punnett squares.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Did your data match the simple Punnett square hypothesis where red or white is dominant and there is no effect of sex? Compare your data with other lab groups.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">If your data does not match what your Punnett squares predicted, does this indicate that the gene may be sex-linked?
</div></li><li data-sub-note-item="2-4"><div class="lab-step">If the red gene were dominant and sex-linked, when we cross red-eyed females with white-eyed males, all of the offspring would have red eyes. Assuming all of the females were homozygous for the color allele, does this match your data for the first cross?
</div></li><li data-sub-note-item="2-5"><div class="lab-step">If white were dominant and the gene were sex-linked, we would see the exact opposite. So all of the flies would have white eyes. From your data, can you conclude from your first cross whether red or white is dominant?
</div></li><li data-sub-note-item="2-6"><div class="lab-step">When the reciprocal cross of white-eyed females and red-eyed males is performed, if the red phenotype is dominant, all of the females would be red and all of the males would be white. This would also confirm that the gene is specifically x-linked because white-eyed females could then only be produced in crosses with white-eyed males. Did your second cross produce any white-eyed females?
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Finally, if the gene were x-linked and white was dominant, crossing white-eyed females with red-eyed males would result in all of the offspring having white eyes. Does this hypothesis fit with your second cross?
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Look over your data and conclude which of the hypotheses best fits the patterns you found in your crosses. If your data does not fit with any of these, hypothesize why this might be the case.
</div></li></ul></li></ol>

Genetics of Organisms: REF and WEF Fly Genetics - Procedure

REF and WEF Fly Genetics
ExpandNOTE: At your station, you should have four vials of flies. They will be labeled as male or female. They will also be ...

Hardy-Weinberg and Genetic Drift

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Class Simulation
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">Begin by opening a new spreadsheet file. Following the Hardy-Weinberg equation where p is the frequency of a dominant allele A in a population, and q is defined as the frequency of a recessive allele B, input frequency p of allele A into cell B2, and frequency q of allele B into cell B3.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Assign the value 0.5 to cell C2.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Following the 1 &#8211; p = q equation, enter the formula &#8220;= 1 &#8211; C2&#8221; into cell C3 to calculate the frequency q of allele B. NOTE: Cells C2 and C3 will represent the gene pool used in the next few steps.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Label cells E2 and F2 &#8220;Allele 1&#8221; and &#8220;Allele 2&#8221; respectively, then enter the RAND code for random formula into cell E3. =IF (RAND()</div></li><li data-sub-note-item="1-5"><div class="lab-step">Select cell E3 and drag the bottom right corner of the cell down to E27 to duplicate the formula into 25 cells, creating an allele for the other offspring, and enter the same formula that was used in cell E3 into cell F3.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">Add a third column of data starting with the description &#8220;Genotype&#8221; in cell G2, and add the CONCATENATE function to cell G3 to combine the two randomly generated alleles and to create a genotype.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Drag this formula down for 25 cells and label cells H2, I2, and J2 AA, AB, and BB respectively.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">Then, input one IF function as shown here into cells H3, I3, and J3. Drag the formulas down for 25 rows per column. The formulas will return a 1 if the genotype in this row matches the genotype in the header row, and a 0 if the genotype in this row is one of the other two genotypes. H3: = IF (G3=&#8221;AA&#8221;,1,0) I3: =IF(OR(G3=&#8221;AB,G3=&#8221;BA&#8221;),1,0) J3: =IF(G3=&#8221;BB&#8221;,1,0)
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Then, label cell D28 as &#8220;SUM&#8221;, add the formula to cell H28, and drag the formula to cells I28 and J28 to get the total number of each genotype.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Next, label cells H30 and J30 &#8220;A&#8221; and &#8220;B&#8221; respectively and label cell D31 &#8220;Number of alleles&#8221;.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">Add code to cells H31 and J31 to obtain the total number of alleles in the simulated generation, which is two times the frequency of the respective homozygous genotype, plus the frequency of the heterozygous genotype.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, label cell E32 &#8220;Next gen allele frequency&#8221; and add code to cells H32 and J32 to obtain the ratio of alleles in the next generation.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Using the gene frequencies generated in the mathematical model you just built, adjust the gene pool values in cells C2 and C3 to those from H32 and J32, and run 10 more generations, updating the gene pool with the resulting allele frequency each time.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Record the ratio of the two variables at each time point in the spreadsheet on the table, and create a line graph to see how a small population changes over time. Hypotheses: The experimental hypothesis in this study is that smaller populations will deviate more from the Hardy-Weinberg expected frequencies of the alleles than will larger populations, because smaller populations are more susceptible to genetic drift. The null hypothesis is that the allele frequencies within the populations will not differ from the Hardy-Weinberg equilibrium equation expected frequencies, meaning that the allele frequencies will be the same from generation to generation.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">Set the frequency of gene A to 0.3 and gene B to 0.7, and run the model 10 more times, recording the ratio of the genes in the next generation in the table after each run.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Then, drag the formulas for the cells further down to alter the model to include 100 zygotes and run the model another 10 times.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">After the last run, save all of the working data to a personal storage device and exit the spreadsheet program.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Simulation for Hardy-Weinberg and Genetic Drift
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">To begin, find a partner and then collect one bag from the instructor.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Verify that the bag contains beads of two different colors and an even number of each color. 
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Designate one color as allele capital A and the other as small a. Hypotheses: The experimental hypothesis for this simulation is that allele frequencies will not change over the 10 generations of the experiment, and Hardy-Weinberg equilibrium will be observed. The null hypothesis is that equilibrium will not be seen in the population.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Note the initial frequency in Table 1 next to generation zero. Using the Hardy-Weinberg equation, calculate the initial genotype frequencies of the population and note this in Table 1. <a href="https://www.jove.com/CDNSource/lm/labs/4/Lab_4_Table_A.xlsx" target="__blank" style="display:block;margin:20px;">Click Here to download Table A</a>
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Then, discuss with your partner about the expected final allele and genotype frequencies after 10 generations of Hardy-Weinberg equilibrium. Note this next to prediction in Table 1.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">Draw a pair of beads from the bag. This represents the genotype of one individual in the next generation.
</div></li><li data-sub-note-item="2-7"><div class="lab-step">Place a tally mark in the appropriate column next to Generation 1.
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Now, keeping each pair of beads out of the bag every time, repeat the drawing 20 times to fill out the generation one row of Table 1.
</div></li><li data-sub-note-item="2-9"><div class="lab-step">Using the tallies, calculate the allele frequency for that generation, remembering to count both alleles in each genotype. Make a note of the new allele frequency, and then adjust the 100 beads to reflect the population after Generation 1 by adding and removing beads as necessary.
</div></li><li data-sub-note-item="2-10"><div class="lab-step">Repeat the drawing of pairs from this adjusted population for another generation of 20 picks, recording all of the observations in Table 1.
</div></li><li data-sub-note-item="2-11"><div class="lab-step">After calculating the allele frequency for this generation, adjust the population again to set up a third generation draw.
</div></li><li data-sub-note-item="2-12"><div class="lab-step">Keep drawing, calculating allele frequencies, and adjusting the starting population until 10 generations have been recorded in total.
</div></li></ul></li><li data-note-item="3" data-parent-collapse="">Violations of Hardy-Weinberg 
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="3-1"><div class="lab-step">To test the effect of different physical and ecological scenarios on the Hardy-Weinberg equilibrium, first draw a slip of paper to determine which Hardy-Weinberg assumption you will be testing.
</div></li><li data-sub-note-item="3-2"><div class="lab-step">Make a prediction of what you think might happen in your selected scenario before beginning the experiment, and record this on the board.
</div></li><li data-sub-note-item="3-3"><div class="lab-step">If you selected the mutation scenario, select 5 alleles at random from the set and replace them with 5 of a third color, allele capital B.
</div></li><li data-sub-note-item="3-4"><div class="lab-step">As before, perform the simulation by pulling pairs of beads from the bag for a generation of 20 picks, recording the results.  <a href="https://www.jove.com/CDNSource/lm/labs/4/Lab_4_Table_B.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table B</a>
</div></li><li data-sub-note-item="3-5"><div class="lab-step">After each generation, make adjustments to have the starting population match the new allele frequency as before.
</div></li><li data-sub-note-item="3-6"><div class="lab-step">Then, replace 5 random alleles again with the new color, and simulate the new generation for 20 picks.
</div></li><li data-sub-note-item="3-7"><div class="lab-step">If you picked the non-random mating test, draw 1 allele at a time instead of 2 and then pair it with another of the same color pulled from the bag.
</div></li><li data-sub-note-item="3-8"><div class="lab-step">As with the previous simulation, adjust the population to match the new allele frequencies at the end of the previous generation, and carry out the simulation for 10 generations in total.
</div></li><li data-sub-note-item="3-9"><div class="lab-step">For the gene flow condition, begin by removing 10 alleles from the population and replacing them with 10 randomly selected alleles from another set of 100 beads.
</div></li><li data-sub-note-item="3-10"><div class="lab-step">Adjust the allele frequency and repeat the gene flow of removing 10 beads and adding 10 beads from another bag at the beginning of each generation.
</div></li><li data-sub-note-item="3-11"><div class="lab-step">If you drew small population size, simply start each generation with 60 alleles instead of 100.
</div></li><li data-sub-note-item="3-12"><div class="lab-step">As usual, adjust allele frequencies each generation and record 10 generations. In the selection condition, draw from the original bag of beads but do not count any homozygous recessive pairs that are drawn when calculating the allele frequency at the end of each generation.
</div></li></ul></li><li data-note-item="4" data-parent-collapse="">Simulation of Genetic Drift
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="4-1"><div class="lab-step">Make your predictions before running the simulations and record them on the board.
</div></li><li data-sub-note-item="4-2"><div class="lab-step">If you have the small population size test, run the standard Hardy-Weinberg simulation as before, but instead start each generation with 30 alleles and not 100.
</div></li><li data-sub-note-item="4-3"><div class="lab-step">If you drew the founder effect with two alleles test, start generation 1 by drawing only 5 pairs of beads from the bag.
</div></li><li data-sub-note-item="4-4"><div class="lab-step">Then, creating the gene pool for generation 2, include only 50 beads at the allele frequency determined in generation 1. When you create the gene pool for generation 3 and all subsequent generations, include 100 beads.
</div></li><li data-sub-note-item="4-5"><div class="lab-step">For a founder effect with 10 alleles, you will need to collect a bag containing random amounts of 10 different colored beads.
</div></li><li data-sub-note-item="4-6"><div class="lab-step">Designate an allele name for each of the new alleles, and then calculate their initial allele frequency.
</div></li><li data-sub-note-item="4-7"><div class="lab-step">Now, run the experiment in the same way as the founder effect with two alleles, by drawing just 5 pairs randomly for generation 1 and then creating the second generation with 50 alleles, and third and subsequent generations with 100. <a href="https://www.jove.com/CDNSource/lm/labs/4/Lab_4_Table_C.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table C</a>
</div></li><li data-sub-note-item="4-8"><div class="lab-step">If you have the natural disaster with 2 alleles, you will need to collect a paper plate from your instructor.
</div></li><li data-sub-note-item="4-9"><div class="lab-step">Draw a line down the middle of the plate. Before generation 1, pour out all of the beads from a standard 100 bead setup onto the plate, and mix them randomly, spreading them evenly.
</div></li><li data-sub-note-item="4-10"><div class="lab-step">Choose one side of the plate and take all of the beads from that side. You should draw from these to create generation 1.
</div></li><li data-sub-note-item="4-11"><div class="lab-step">For generation 2 and beyond, adjust the population and use a total of 100 beads.
</div></li><li data-sub-note-item="4-12"><div class="lab-step">Finally, if you selected the natural disaster with 10 alleles test, you will need to collect a bag containing 10 different colored beads.
</div></li><li data-sub-note-item="4-13"><div class="lab-step">Assign a name to each new allele, and then place them on the plate and select half. Draw from these 50 beads for generation 1, and then calculate the allele frequencies to make a population of 100 in each subsequent round.
</div></li></ul></li><li data-note-item="5" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="5-1"><div class="lab-step">Using the entered functions, calculate the number of each allele by multiplying the number of the homozygous genotypes by 2 and adding the number of the heterozygous genotypes.
</div></li><li data-sub-note-item="5-2"><div class="lab-step">The frequency can then be calculated by dividing the number of the allele in question by the total number of alleles. Enter the results into the table.
</div></li><li data-sub-note-item="5-3"><div class="lab-step">Next, use the Hardy-Weinberg equation to calculate the expected genotype frequency of the next generation, and compare the results of the simulation with the expected frequency, using the table as a guide.
</div></li><li data-sub-note-item="5-4"><div class="lab-step">Plot the changes of the population gene frequencies over time that you recorded in the table. Note any changes in allele frequencies over time and if any alleles decrease in frequency to the point that they disappear from the population. 
</div></li><li data-sub-note-item="5-5"><div class="lab-step">Plot the changes in the populations of the 25 and 100 zygotes with initial gene frequencies of 0.5 for each allele. Compare the results of the smaller population and the larger populations.  NOTE: The observed allele frequencies are expected to differ slightly from the Hardy-Weinberg expected values as a result of chance from the RAND function.
</div></li><li data-sub-note-item="5-6"><div class="lab-step">Using separate lines for each allele, graph the allele frequencies found for the original Hardy-Weinberg experiment over 10 generations.
</div></li><li data-sub-note-item="5-7"><div class="lab-step">Compare the results across the class, and see if your results agree.
</div></li><li data-sub-note-item="5-8"><div class="lab-step">Next, again using one line per allele, graph the results of your second experiment testing violations of the Hardy-Weinberg equilibrium, and share your finding with the class. Compare your actual results with your prediction. 
</div></li><li data-sub-note-item="5-9"><div class="lab-step">Finally, graph the results of your genetic drift simulation scenario in the same manner.
</div></li><li data-sub-note-item="5-10"><div class="lab-step">Again, present your results to the group and hypothesize why your results did or did not match your prediction. Compare your results to those of your classmates&#8217;. 
</div></li></ul></li></ol>

Hardy-Weinberg Equilibrium Principle and Genetic Drift - Procedure

Class Simulation
ExpandBegin by opening a new spreadsheet file. Following the Hardy-Weinberg equation where p is the frequency of a dominant allele A in a ...

Artificial Selection

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Observing Artificial Selection in Plants 
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">To begin the experiment, collect five two-week old grown plants from the generation one population that appear healthy. Note: Plants should appear green, not wilted, and not have damaged leaves. Experimental Hypothesis: The experimental hypothesis for this lab might be that the number of lead trichomes varies naturally, but the average number will increase over generations if only plants with the highest number of trichomes are interbred or decrease if those with the least are interbred. Null Hypothesis: The null hypothesis might be that the average number of trichomes will not change over subsequent generations after artificial selection.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Label each plant with a letter from A to E and with both partners&#8217; names.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Record any natural differences you see between your plant samples. Note: All of the plants are the same age, so any observed variation is due to expressed phenotype, not development.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Use a hand lens to count the number of trichomes found on the petiole of the lowest leaf, not including the round, first-grown cotyledons for each plant. Note: It can be helpful to shine a light onto the plant and to hold it against a dark background, such as a lab bench, during the trichome analysis. 
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Both partners will conduct counts on each plant independently and compare answers. If the counts are different, enlist a third counter or take the average of the two results.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">After this, determine the median number of trichomes for the entire class.  <a target="__blank" style="display:block;padding:20px;" href="https://www.jove.com/CDNSource/lm/labs/2/Lab_2_Table_A.xlsx">Click Here to download Table A</a>
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Determine the top and bottom 10% of the plants from the class trichome count. These groups will make up the two artificially selected lines: high hairiness and low hairiness, respectively.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">If more than 10% of the plants have no trichomes, select 10% of those plants randomly for the low hairiness plant line.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">Separate the two groups of selected plants and label them clearly.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Move the plants to a well-lit area and keep them well watered until they develop flowers. Note: Flowers typically develop around 14 days.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">When the flowers are fully developed, use a pollination wand or paintbrush to rub the stigma and anther of one plant&#8217;s flowers for a few seconds.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Using the same wand or brush, rub the inside of each flower of every other plant in the same selection line.
</div></li><li data-sub-note-item="1-13"><div class="lab-step">Using a different wand or brush, repeat steps 11 through 12 on the other line. Note: Take care not to cross the lines. Spread the pollen only among the appropriately selected plants.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Return the generation one plants to the growing areas. When the plants reach approximately 1 month old, they should produce mature seed pods.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">Allow the plants to dry out once the seed pods turn yellow.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Break the seed pods open and collect the seeds.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">Obtain new pots for the generation two seeds.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Fill the pots with moist potting soil about halfway and add a fertilizer pellet to each pot.
</div></li><li data-sub-note-item="1-19"><div class="lab-step">Fill the pots with more soil to fill just below the lip.
</div></li><li data-sub-note-item="1-20"><div class="lab-step">Make a shallow depression in the soil of each pot with a probe.
</div></li><li data-sub-note-item="1-21"><div class="lab-step">Touch a clean toothpick to a gluestick and use the toothpick to transplant the seeds into the soil one by one.
</div></li><li data-sub-note-item="1-22"><div class="lab-step">Cover the seeds with a thin layer of soil.
</div></li><li data-sub-note-item="1-23"><div class="lab-step">Label the pots with their hairiness line and place them in the designated growing area.
</div></li><li data-sub-note-item="1-24"><div class="lab-step">Use a plastic dropper to water the plants as needed to ensure they do not dry out.
</div></li><li data-sub-note-item="1-25"><div class="lab-step">When the plants are two weeks old, collect five plants from each line and label them A to E.
</div></li><li data-sub-note-item="1-26"><div class="lab-step">Then, count the trichomes on these plants as previously described (steps 4- 5).
<a target="__blank" style="display:block;padding:20px;" href="https://www.jove.com/CDNSource/lm/labs/2/Lab_2_Table_B.xlsx">Click Here to download Table B</a>
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Data Analysis
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">Create a frequency graph based on the generation one class data with the trichome count on the x axis and the number of plants on the y axis. Label the mean. 
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Then, create frequency graphs for both of the selected hairiness lines.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Compare the frequency graphs for generation one and both hairiness lines.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Take note of phenotypic shifts observed in the selected hairiness lines.
</div></li></ul></li></ol>

Artificial Selection: Visualization in Plants and Data Analysis - Procedure

Observing Artificial Selection in Plants 
ExpandTo begin the experiment, collect five two-week old grown plants from the generation one population that ...

Macromolecules

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Identifying Macromolecules with Indicator Solutions
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">HYPOTHESES: In this activity, the experimental hypothesis might be that the addition of an indicator dye to each test solution will result in a color change if the test solution contains the macromolecule of interest. The null hypothesis might be that the addition of an indicator dye will not result in a color change in any of the test solutions.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">Place 16 clean test tubes in a tube rack in four rows of four tubes and label each row A through D and each column 1 - 4.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Label each test tube with its corresponding position in the test tube rack.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">After this, stir solution A thoroughly and use a pipette to add 4 mL of solution A to each tube in Row A.
</div></li><li data-sub-note-item="1-5"><div class="lab-step">Next, stir solution B until it is well mixed and use a new pipette to add 4 mL of solution B to each tube in Row B.
</div></li><li data-sub-note-item="1-6"><div class="lab-step">After stirring solution C thoroughly, add 4 mL of solution C to each tube in Row C.
</div></li><li data-sub-note-item="1-7"><div class="lab-step">Then add 4 mL of soybean-derived vegetable oil to each tube in Row D.
</div></li><li data-sub-note-item="1-8"><div class="lab-step">When all of the tubes have been filled with their respective test solutions, add 200 mL of water to a 400 mL beaker on a hotplate and set the hotplate to high.
</div></li><li data-sub-note-item="1-9"><div class="lab-step">To perform the Benedict&#39;s Test, add 2 mL of Benedict&#39;s reagent to test tubes A1, B1, C1, and D1 while the water is warming.
</div></li><li data-sub-note-item="1-10"><div class="lab-step">Observe the initial color of the tubes.
</div></li><li data-sub-note-item="1-11"><div class="lab-step">When the water begins to boil, transfer each test tube to the beaker and heat the tubes in the boiling water bath for 3 - 5 minutes.
</div></li><li data-sub-note-item="1-12"><div class="lab-step">Then, record the final color of each of the tubes in the appropriate cells of the table and turn off the hotplate. <a href="https://www.jove.com/CDNSource/lm/labs/22/Table_1_Macromolecules.xlsx" style="display:block;margin:20px;" target="__blank">Click Here to download Table 1</a>
</div></li><li data-sub-note-item="1-13"><div class="lab-step">To perform an Iodine-Potassium-Iodide Test, first observe the color of the solutions in the test tubes in column 2.
</div></li><li data-sub-note-item="1-14"><div class="lab-step">Then, add 1 mL of iodine potassium iodide reagent to tubes A2 to D2 and record the final color change in each of the tubes.
</div></li><li data-sub-note-item="1-15"><div class="lab-step">For the Sudan IV Test, observe and record the color of the solutions in the test tubes in column 3.
</div></li><li data-sub-note-item="1-16"><div class="lab-step">Then, add 2 - 3 drops of Sudan IV reagent to test tubes A3 through D3 and record the color change, if any, in each tube.
</div></li><li data-sub-note-item="1-17"><div class="lab-step">For the Biuret Test, observe the color of the solutions in the test tubes in column 4.
</div></li><li data-sub-note-item="1-18"><div class="lab-step">Then, add 1 mL of Biuret reagent to test tubes A4 through D4 and record the final color in each of the tubes.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Results
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">Using the results from the indicator tests, each of the four original solutions can be identified as a sugar, starch, lipid, or protein. Refer to the Concepts video to help you determine which tubes contain which macromolecules.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">First, look at the results from the Benedict&#39;s Test. Determine which of the test solutions contain sugar based on your results.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Next, create a bar graph with your five plant species listed on the x-axis and the surface area in cm indicated on the y-axis.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Next, look at the results from the Iodine-Potassium-Iodide Test. Determine which of the test solutions contain starch.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Now, review the results from the Sudan IV Test. Using these observations, determine if any of the test solutions contain lipids.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">Finally, look at the results from the Biuret Test. Determine if any of the test solutions contain proteins.
</div></li></ul></li></ol>

Macromolecules: Identification Using Indicator Solutions - Procedure

Identifying Macromolecules with Indicator Solutions
ExpandHYPOTHESES: In this activity, the experimental hypothesis might be that the addition of an ...