The development of this project method was funded by the Department of Pathology and Laboratory Medicine of Weill Cornell Medical College. We acknowledge the Tri-Institutional Training Program in Computational Biology and Medicine for partial funding. We would like to thank the scientists who shared their time and knowledge with us, especially Maryke Appel; Dan Burgess; Iwanka Kozarewa; Chad Locklear; and everyone from the Weill Cornell Medical College Genomics Core Facility, including Jenny Zhang, Xiaobo (Shawn) Liang, Dong Xu, Wei Zhang, Huimin Shang, Tatiana Batson, and Tuo Zhang.

<ol><li>Abrams, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((National+Cancer+Institute%27s+Precision+Medicine+Initiatives+for+the+new+National+Clinical+Trials+Network%5BTitle%5D)+AND+%22American+Society+of+Clinical+Oncology+educational+book+%2F+ASCO.+American+Society+of+Clinical+Oncology.+Meeting%22%5BJournal%5D)">National Cancer Institute's Precision Medicine Initiatives for the new National Clinical Trials Network.</a> American Society of Clinical Oncology educational book / ASCO. American Society of Clinical Oncology. Meeting. , 71-76 (2014).</li>
<li>Gagan, J., Van Allen, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Next-generation+sequencing+to+guide+cancer+therapy%5BTitle%5D)+AND+%22Genome+medicine%22%5BJournal%5D)">Next-generation sequencing to guide cancer therapy.</a> Genome medicine. 7, 80(2015).</li>
<li>Matsuki, E., Younes, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lymphomagenesis+in+Hodgkin+lymphoma%5BTitle%5D)+AND+%22Seminars+in+cancer+biology%22%5BJournal%5D)">Lymphomagenesis in Hodgkin lymphoma.</a> Seminars in cancer biology. 34, 14-21 (2015).</li>
<li>Fromm, J. R., Kussick, S. J., Wood, B. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identification+and+purification+of+classical+Hodgkin+cells+from+lymph+nodes+by+flow+cytometry+and+flow+cytometric+cell+sorting%5BTitle%5D)+AND+%22Am+J+Clin+Pathol%22%5BJournal%5D)">Identification and purification of classical Hodgkin cells from lymph nodes by flow cytometry and flow cytometric cell sorting.</a> Am J Clin Pathol. 126 (5), 764-780 (2006).</li>
<li>Fromm, J. R., Thomas, A., Wood, B. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Flow+cytometry+can+diagnose+classical+hodgkin+lymphoma+in+lymph+nodes+with+high+sensitivity+and+specificity%5BTitle%5D)+AND+%22Am+J+Clin+Pathol%22%5BJournal%5D)">Flow cytometry can diagnose classical hodgkin lymphoma in lymph nodes with high sensitivity and specificity.</a> Am J Clin Pathol. 131 (3), 322-332 (2009).</li>
<li>Roshal, M., Wood, B. L., Fromm, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Flow+cytometric+detection+of+the+classical+hodgkin+lymphoma%3A+clinical+and+research+applications%5BTitle%5D)+AND+%22Advances+in+hematology%22%5BJournal%5D)">Flow cytometric detection of the classical hodgkin lymphoma: clinical and research applications.</a> Advances in hematology. 2011, 387034(2011).</li>
<li>Reichel, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Flow+sorting+and+exome+sequencing+reveal+the+oncogenome+of+primary+Hodgkin+and+Reed-Sternberg+cells%5BTitle%5D)+AND+%22Blood%22%5BJournal%5D)">Flow sorting and exome sequencing reveal the oncogenome of primary Hodgkin and Reed-Sternberg cells.</a> Blood. 125 (7), 1061-1072 (2015).</li>
<li>Kozarewa, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Modified+Method+for+Whole+Exome+Resequencing+from+Minimal+Amounts+of+Starting+DNA%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">A Modified Method for Whole Exome Resequencing from Minimal Amounts of Starting DNA.</a> PLoS ONE. 7 (3), e32617(2012).</li>
<li>Brunicardi, F. C. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aBrunicardi%2C+FC+%22Schwartz%27s+Principles+of+Surgery%22">Schwartz's Principles of Surgery</a>. , 10th ed, McGraw-Hill Education / Medical. (2014).</li>
<li>Kantor, A. B., Roederer, M. FACS analysis of leukocytes. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aKantor%2C+AB+%22Handbook+of+Experimental+Immunology%22">Handbook of Experimental Immunology</a>. 2, Blackwell Scientific. (1996).</li>
<li>Sanders, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+pathways+of+adhesion+in+spontaneous+rosetting+of+T-lymphocytes+to+the+Hodgkin%27s+cell+line+L428%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Molecular pathways of adhesion in spontaneous rosetting of T-lymphocytes to the Hodgkin's cell line L428.</a> Cancer Res. 48 (1), 37-40 (1988).</li>
<li>Biosciences. FACSDiva Software v6.0. , Available from:
 <a target="_blank" href="http://www.bdbiosciences.com/in/instruments/software/facsdiva/resources/overview.jsp">http://www.bdbiosciences.com/in/instruments/software/facsdiva/resources/overview.jsp</a> (2007).</li>
<li>Biosciences. <a target="_blank" href="https://www.bdbiosciences.com/documents/BD_FACSAria_II_User_Guide.pdf">FACSAria II User's Guide. Part No. 644832, Revision A</a>. , (2009).</li>
<li>Life Technologies. Qubit 3.0 Fluorometer, Catalog Number Q33216. , Available from:
 <a target="_blank" href="https://www.thermofisher.com/order/catalog/product/Q33216">https://www.thermofisher.com/order/catalog/product/Q33216</a> (2017).</li>
<li>Roche Nimblegen. <a target="_blank" href="https://shop.roche.com/wcsstore/RASCatalogAssetStore/Articles/SeqCapEZLibrarySR_UGuide_v4p1.pdf">SeqCap EZ Library SR User's Guide ver. 4.1</a>. , (2013).</li>
<li>Illumina. HiSeq High-Throughput Sequencing System. , Available from:
 <a target="_blank" href="http://www.illumina.com/systems/hiseq_2500_1500.html">http://www.illumina.com/systems/hiseq_2500_1500.html</a> (2016).</li>
<li>Li, H., Durbin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fast+and+accurate+short+read+alignment+with+Burrows-Wheeler+transform%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Fast and accurate short read alignment with Burrows-Wheeler transform.</a> Bioinformatics. 25 (14), 1754-1760 (2009).</li>
<li>Li, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Sequence+Alignment%2FMap+format+and+SAMtools%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25 (16), 2078-2079 (2009).</li>
<li>The Broad Institute. Picard Tools. , Available from:
 <a target="_blank" href="http://broadinstitute.github.io/picard/">http://broadinstitute.github.io/picard/</a> (2016).</li>
<li>Saunders, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Strelka%3A+accurate+somatic+small-variant+calling+from+sequenced+tumor-normal+sample+pairs%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs.</a> Bioinformatics. 28 (14), 1811-1817 (2012).</li>
<li>Cingolani, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+program+for+annotating+and+predicting+the+effects+of+single+nucleotide+polymorphisms%2C+SnpEff%3A+SNPs+in+the+genome+of+Drosophila+melanogaster+strain+w1118%3B+iso-2%3B+iso-3%5BTitle%5D)+AND+%22Fly+%28Austin%29%22%5BJournal%5D)">A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.</a> Fly (Austin). 6 (2), 80-92 (2012).</li>
<li>Dobin, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((STAR%3A+ultrafast+universal+RNA-seq+aligner%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">STAR: ultrafast universal RNA-seq aligner.</a> Bioinformatics. 29 (1), 15-21 (2013).</li>
<li>Thorvaldsdottir, H., Robinson, J. T., Mesirov, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrative+Genomics+Viewer+%28IGV%29%3A+high-performance+genomics+data+visualization+and+exploration%5BTitle%5D)+AND+%22Briefings+in+bioinformatics%22%5BJournal%5D)">Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.</a> Briefings in bioinformatics. 14 (2), 178-192 (2013).</li>
<li>DNA copy number data analysis. v.R package version 1.34.0. , Available from:
 <a target="_blank" href="https://omictools.com/dnacopy-tool">https://omictools.com/dnacopy-tool</a> (2013).</li>
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The authors have nothing to disclose.

Future applications or directions after mastering this technique
This work allows for exome sequencing from samples containing at least 10 ng of DNA. In the clinical context, this limit excludes most fine-needle aspiration samples due to insufficient material, but it includes adequate core biopsies and excisional biopsy samples. This will enable the acquisition of data from a larger set of possible samples.
Critical steps within the protocol...

Advancements in cancer genomics as a result of next-generation sequencing have led to significant breakthroughs in the identification of therapeutic targets and in prognostication for many hematologic and non-hematologic neoplasms. New individualized treatment strategies based on specific genomic alterations are rapidly being introduced in many tumor types (reviewed in references1,2). Despite significant progress in lymphoma genomics, the genome of the neoplastic HRS cells in classical Hodgkin lymphoma (CHL) had been underexplored. The investigations have been hampered by the scarcity of neoplastic HRS cells within a reactive microenvironment, making it difficult to isolate purified HRS cell populations3.
The method for isolating viable HRS cells from primary tumors was developed by Fromm et al.4,5,6. The method uses an eight-antibody cocktail, consisting of CD30, CD15, CD40, CD95, CD45 CD20, CD5, and CD64, to unequivocally identify HRS cells from a CHL tumor suspension. Using this methodology, we are able to isolate at least 1,000 viable HRS cells from fresh or frozen cell suspensions from tumor biopsies consisting of at least 107 cells (approximately 10 mg of tissue). The purity is greater than 90% by flow cytometric analysis and is estimated to be least 80% by exome genomic analysis of ten consecutive cases.
We have refined a flow cytometric cell isolation technique that has greatly eased the process, allowing for the rapid isolation of thousands of viable HRS cells from primary CHL tumors7. We have utilized the technique to produce what is believed to be the first whole-exome sequence of the tumor cells in primary cases of Hodgkin lymphoma. Our studies demonstrate the feasibility of high-throughput, genome-wide studies of individual CHL cases and have already led to the identification of novel genomic alterations with the potential to explain aspects of CHL pathogenesis.
We further developed a pipeline to utilize the extracted DNA for high-throughput genomic studies. In order to achieve reliable results from as few as 1,000 sorted HRS cells (thhe minimum obtained from sequential cases), we further developed a modified next-generation DNA library construction procedure8 that allowed us to increase adaptor ligation efficiency and to generate DNA fragment libraries without excessive amplification. This method allows for the analysis of routine clinical samples and the detection of recurrent mutations and chromosomal alterations7.

<table><tbody><tr><td>Petri or Cell Culture Dish (sterile)</td><td></td><td></td><td></td></tr><tr><td>RPMI-1640 Media</td><td>Roswell Park Memorial Institute</td><td></td><td></td></tr><tr><td>Fetal Calf Serum (FCS), (heat inactivated)</td><td></td><td></td><td></td></tr><tr><td>Freezing Media (RPMI, 20% FCS, 10% dimethylsulfoxide (DMSO))-make fresh and keep sterile</td><td></td><td></td><td></td></tr><tr><td>RPMI with 2% FCS (make fresh or store for up to 1 month)</td><td></td><td></td><td></td></tr><tr><td>scalpel with fresh blade</td><td></td><td></td><td></td></tr><tr><td>10 mL syringe (no needle)</td><td></td><td></td><td></td></tr><tr><td>Cryogenic vials</td><td></td><td></td><td></td></tr><tr><td>50 mL conical centrifuge tubes, force</td><td></td><td></td><td></td></tr><tr><td>Centrifuge</td><td></td><td></td><td>capable of handling 50 mL conical centrifuge tubes and providing 400g</td></tr><tr><td>Hepes buffer(1 M, cell culture grade)</td><td></td><td></td><td></td></tr><tr><td>phosphate buffered saline (PBS)</td><td></td><td></td><td></td></tr><tr><td>Pluoronic-F68</td><td>Thermo-Fisher</td><td>24040-032</td><td></td></tr><tr><td>DNAase-I</td><td>Sigma-Aldrich, St. Louis, MO</td><td>D4527-10KU</td><td>store as 5 mg/mL in RPMI in -200 &amp;#176;C</td></tr><tr><td>Bovine Serum Albumin (BSA)</td><td></td><td></td><td></td></tr><tr><td>Sort Media (PBS + 2% BSA + 25 mM HEPES + Pluoronic &amp;ndash;F68 (1x))</td><td></td><td></td><td></td></tr><tr><td>CD64-FITC (22)</td><td>Beckman Coulter, Miami, FL</td><td></td><td>20 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD30-PE (BerH83)</td><td>BD Biosciences, San Jose, CA</td><td></td><td>20 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD5-ECD (BL1a)</td><td>Beckman Coulter, Miami, FL</td><td></td><td>10 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD40-PerCP-eFluor 710 (1C10)</td><td>Ebiosciences, San Diego, CA</td><td></td><td>5 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD20-PC7 (B9E9)</td><td>Beckman Coulter, Miami, FL</td><td></td><td>10 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD15-APC (HI98)</td><td>BD Biosciences, San Jose, CA</td><td></td><td>20 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD45 APC-H7 (2D1)</td><td>BD Biosciences, San Jose, CA</td><td></td><td>Can be substituted with 10 &amp;mu;L suggested volume of CD45-Krome Orange (J.33, Beckman Coulter); Titering is suggested</td></tr><tr><td>CD95-Pacific Blue (DX2)</td><td>Life Technologies, Grand Island, NY</td><td></td><td>5 &amp;mu;L suggested starting volume; Titering is suggested</td></tr><tr><td>CD2 (5 &amp;mu;g; clone RPA-2.10)</td><td>Biolegend, San Diego, CA</td><td></td><td>For optional protocol; Titering is suggested</td></tr><tr><td>CD54 (10 &amp;mu;g; clone 84H10)</td><td>Serotec, Oxford, United Kingdom</td><td></td><td>For optional protocol; Titering is suggested</td></tr><tr><td>CD58 (10 &amp;mu;g; clone TS2/9)</td><td>eBioscience, San Diego, CA</td><td></td><td>For optional protocol; Titering is suggested</td></tr><tr><td>LFA-1 (12 &amp;mu;g; clone MHM23)</td><td>Novus Biologicals, Littleton, CO</td><td></td><td>For optional protocol; Titering is suggested</td></tr><tr><td>BD CS&amp;amp;T Beads</td><td>BD Biosciences, San Jose, CA</td><td></td><td></td></tr><tr><td>BD Accudrop Beads</td><td>BD Biosciences, San Jose, CA</td><td></td><td></td></tr><tr><td>BC Versa Comp antibody capture beads</td><td>Beckman Coulter, Miami, FL</td><td></td><td>Compensation Beads</td></tr><tr><td>BD-FACS ARIA special research order instrument using 5 lasers</td><td>BD Biosciences, San Jose, CA</td><td></td><td>any BD-FACS aria with capabilities to detect the fluorochromes in the antibody panel should be sufficient</td></tr><tr><td>Wizard</td><td>Promega</td><td>A2360</td><td></td></tr><tr><td>10 mM Tris-Cl buffer</td><td>NA</td><td></td><td></td></tr><tr><td>Qubit dsDNA HS Assay kit</td><td>Life Technologies, Carlsbad, CA</td><td></td><td></td></tr><tr><td>S2 Sonicator</td><td>Covaris, Woburn, MA</td><td></td><td>Alternatives may be substituted</td></tr><tr><td>microTUBE</td><td>Covaris, Woburn, MA</td><td></td><td></td></tr><tr><td>Low-Throughput Library Preparation Kit</td><td>Kapa Biosystems, Wilmington, MA</td><td>KK8221</td><td></td></tr><tr><td>Sybr Green</td><td>Sigma-Aldrich, St. Louis, MO</td><td>S9430</td><td></td></tr><tr><td>Agencourt AMPure XP Beads</td><td>Beckman Coulter, Miami, FL</td><td></td><td></td></tr><tr><td>Bioanalyzer</td><td>Agilent Technologies, Santa Clara, CA</td><td></td><td></td></tr><tr><td>SeqCap EZ Exome v.3.0</td><td>Roche Nimblegen</td><td>6465684001</td><td></td></tr><tr><td>HiSeq</td><td>Illumina</td><td></td><td></td></tr><tr><td>TruSeq-style Universal adapter</td><td>Integrated DNA Technologies (IDT), Coralville, Iowa</td><td></td><td>HPLC purification; AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGAT*C*T</td></tr><tr><td>TruSeq-style index adapter</td><td>Integrated DNA Technologies (IDT), Coralville, Iowa</td><td></td><td>HPLC purification; /5Phos/GATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATGCCGTCTTCTGCTTG</td></tr><tr><td>TruSeq-style PCR primer 1</td><td>Integrated DNA Technologies (IDT), Coralville, Iowa</td><td></td><td>AATGATACGGCGACCACCGAGA</td></tr><tr><td>TruSeq-style PCR primer 2</td><td>Integrated DNA Technologies (IDT), Coralville, Iowa</td><td></td><td>CAAGCAGAAGACGGCATACGAG</td></tr><tr><td>Nuclease Free Duplex Buffer</td><td>Integrated DNA Technologies (IDT), Coralville, Iowa</td><td></td><td></td></tr><tr><td>BD FACSDIVA software</td><td>BD Biosciences, San Jose, CA</td><td></td><td></td></tr><tr><td>BD Falcon Tubes</td><td>BD Biosciences, San Jose, CA</td><td></td><td></td></tr><tr><td>BD Flow Tubes</td><td>BD Biosciences, San Jose, CA</td><td></td><td></td></tr></tbody></table>

flow-sorting and exome sequencing of the reed-sternberg cells of classical hodgkin lymphoma

The Hodgkin Reed-Sternberg cells of classical Hodgkin lymphoma are sparsely distributed within a background of inflammatory lymphocytes and typically comprise less than 1% of the tumor mass. Material derived from bulk tumor contains tumor content at a concentration insufficient for characterization. Therefore, fluorescence activated cell sorting using eight antibodies, as well as side- and forward-scatter, is described here as a method of rapidly separating and concentrating with high purity thousands of HRS cells from the tumor for subsequent study. At the same time, because standard protocols for exome sequencing typically require 100-1,000 ng of input DNA, which is often too high, even with flow sorting, we also provide an optimized, low-input library construction protocol capable of producing high-quality data from as little as 10 ng of input DNA. This combination is capable of producing next-generation libraries suitable for hybridization capture of whole-exome baits or more focused targeted panels, as desired. Exome sequencing of the HRS cells, when compared against healthy intratumor T or B cells, can identify somatic alterations, including mutations, insertions and deletions, and copy number alterations. These findings elucidate the molecular biology of HRS cells and may reveal avenues for targeted drug treatments.

A bioanalyzer plot should be taken after library amplification and 0.8x bead cleanup. One should see a &#34;normal-like&#34; distribution of fragment sizes in the desired range (Figure 2a). Deviations from this shape, such as a visible &#34;shoulder&#34; in the curve, indicate the presence of a high or low molecular weight artifact. For example, Figure 2b-2d shows examples of libraries containing visible artifact...

Watch this Scientific Journal Video about Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma at JoVE.com

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Here, we describe a combined flow cytometric cell sorting and low-input, next-generation library construction protocol designed to produce high-quality, whole-exome data from the Hodgkin Reed-Sternberg (HRS) cells of classical Hodgkin lymphoma (CHL).

The Hodgkin Reed-Sternberg cells of classical Hodgkin lymphoma are sparsely distributed within a background of inflammatory lymphocytes and typically ...

flow-sorting-exome-sequencing-reed-sternberg-cells-classical-hodgkin

Research

JoVE Journal

Genetics

10.0K Views.  Memorial Sloan Kettering Cancer Center.  Hier beschrijven we een gecombineerde flow cytometrische celsortering en een laag ingevoerde, volgende generatie bibliotheekconstructieprotocol dat is ontworpen om hoogwaardige, volledige exome data van de Hodgkin Reed-Sternberg (HRS) cellen van klassiek Hodgkin lymfoom (CHL) te produceren. 

Video: Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Acute myeloid leukemia (AML) is a heterogeneous, and if not treated, fatal disease. It is the most common cause of leukemia-associated mortality in adults. Initially, AML is a disease of hematopoietic stem cells (HSC) characterized by arrest of differentiation, subsequent accumulation of leukemia blast cells, and reduced production of functional hematopoietic elements. Heterogeneity extends to the presence of leukemia stem cells (LSC), with this dynamic cell compartment evolving to overcome various selection pressures imposed upon during leukemia progression and treatment. To further define the LSC population, the addition of CD90 and CD45RA allows the discrimination of normal HSCs and multipotent progenitors within the CD34+CD38- cell compartment. Here, we outline a protocol to detect simultaneous expression of several putative LSC markers (CD34, CD38, CD45RA, CD90) on primary blast cells of human AML by multiparametric flow cytometry. Furthermore, we show how to quantify three progenitor populations and a putative LSC population with increasing degree of maturation. We confirmed the presence of these populations in corresponding patient-derived-xenografts. This method of detection and quantification of putative LSC may be used for clinical follow-up of chemotherapy response (i.e., minimal residual disease), as residual LSC may cause AML relapse.

We outline a protocol to detect simultaneous expression of leukemia stem cell markers on primary acute myeloid leukemia cells by flow cytometry. We show how to quantify three progenitor populations and a putative LSC population with increasing degree of maturation. We confirmed the presence of these populations in corresponding patient-derived-xenografts.

Acute myeloid leukemia (AML) is a heterogeneous, and if not treated, fatal disease. It is the most common cause of leukemia-associated mortality in ...

Cancer Research

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting

Brain tumors are typically comprised of morphologically diverse cells that express a variety of neural lineage markers. Only a relatively small fraction of cells in the tumor with stem cell properties, termed brain tumor initiating cells (BTICs), possess an ability to differentiate along multiple lineages, self-renew, and initiate tumors in vivo. We applied culture conditions originally used for normal neural stem cells (NSCs) to a variety of human brain tumors and found that this culture method specifically selects for stem-like populations. Serum-free medium (NSC) allows for the maintenance of an undifferentiated stem cell state, and the addition of bFGF and EGF allows for the proliferation of multi-potent, self-renewing, and expandable tumorspheres.
To further characterize each tumor's BTIC population, we evaluate cell surface markers by flow cytometry. We may also sort populations of interest for more specific characterization. Self-renewal assays are performed on single BTICs sorted into 96 well plates; the formation of tumorspheres following incubation at 37 &deg;C indicates the presence of a stem or progenitor cell. Multiple cell numbers of a particular population can also be sorted in different wells for limiting dilution analysis, to analyze self-renewal capacity. We can also study differential gene expression within a particular cell population by using single cell RT-PCR.
The following protocols describe our procedures for the dissociation and culturing of primary human samples to enrich for BTIC populations, as well as the dissociation of tumorspheres. Also included are protocols for staining for flow cytometry analysis or sorting, self-renewal assays, and single cell RT-PCR.

The identification of brain tumor initiating cells (BTICs), the rare cells within a heterogeneous tumor possessing stem cell properties, provides new insights into human brain tumor pathogenesis. We have refined specific culture conditions to enrich for BTICs, and we routinely use flow cytometry to further enrich these populations. Self-renewal assays and transcript analysis by single cell RT-PCR can subsequently be performed on these isolated cells.

Brain tumors are typically comprised of morphologically diverse cells that express a variety of neural lineage markers. Only a relatively small fraction ...

Medicine

Detection of Residual Donor Erythroid Progenitor Cells after Hematopoietic Stem Cell Transplantation for Patients with Hemoglobinopathies

The presence of incomplete chimerism is noted in a large proportion of patients following bone marrow transplant for thalassemia major or sickle cell disease. This observation has tremendous implications, as subsequent therapeutic immunomodulation strategies can improve clinical outcome. Conventionally, polymerase chain reaction-based analysis of short tandem repeats is used to identify chimerism in donor-derived blood cells. However, this method is restricted to nucleated cells and cannot distinguish between dissociated single-cell lineages. We applied the analysis of short tandem repeats to flow cytometric-sorted hematopoietic progenitor cells and compared this with the analysis of short tandem repeats obtained from selected burst-forming unit - erythroid colonies, both collected from the bone marrow. With this method we are able to demonstrate the different proliferation and differentiation of donor cells in the erythroid compartment. This technique is eligible to complete current monitoring of chimerism in the stem cell transplant setting and thus may be applied in future clinical studies, stem cell research and design of gene therapy trials.

Quantification of donor-derived cells is required to monitor engraftment after stem cell transplantation in patients with hemoglobinopathies. A combination of flow cytometry-based cell sorting, colony formation assay, and subsequent analysis of short tandem repeats may be used to assess the proliferation and differentiation of progenitors in the erythroid compartment.

The presence of incomplete chimerism is noted in a large proportion of patients following bone marrow transplant for thalassemia major or sickle cell ...

Developmental Biology

Isolation of Precursor B-cell Subsets from Umbilical Cord Blood

Umbilical cord blood is highly enriched for hematopoietic progenitor cells at different lineage commitment stages. We have developed a protocol for isolating precursor B-cells at four different stages of differentiation. Because genes are expressed and epigenetic modifications occur in a tissue specific manner, it is vital to discriminate between tissues and cell types in order to be able to identify alterations in the genome and the epigenome that may lead to the development of disease. This method can be adapted to any type of cell present in umbilical cord blood at any stage of differentiation.	
This method comprises 4 main steps. First, mononuclear cells are separated by density centrifugation. Second, B-cells are enriched using biotin conjugated antibodies that recognize and remove non B-cells from the mononuclear cells. Third the B-cells are fluorescently labeled with cell surface protein antibodies specific to individual stages of B-cell development. Finally, the fluorescently labeled cells are sorted and individual populations are recovered. The recovered cells are of sufficient quantity and quality to be utilized in downstream nucleic acid assays.

Here we describe a protocol for isolating subsets of precursor B-cells from umbilical cord blood. A sufficient quantity and quality of nucleic acids may be extracted from the cells and used in subsequent assays utilizing DNA or RNA.

Umbilical cord blood is highly enriched for hematopoietic progenitor cells at different lineage commitment stages. We have developed a protocol for ...

Immunology and Infection

Clonal Analysis of Embryonic Hematopoietic Stem Cell Precursors Using Single Cell Index Sorting Combined with Endothelial Cell Niche Co-culture

The ability to study hematopoietic stem cell (HSC) genesis during embryonic development has been limited by the rarity of HSC precursors in the early embryo and the lack of assays that functionally identify the long-term multilineage engraftment potential of individual putative HSC precursors. Here, we describe methodology that enables the isolation and characterization of functionally validated HSC precursors at the single cell level. First, we utilize index sorting to catalog the precise phenotypic parameter of each individually sorted cell, using a combination of phenotypic markers to enrich for HSC precursors with additional markers for experimental analysis. Second, each index-sorted cell is co-cultured with vascular niche stroma from the aorta-gonad-mesonephros (AGM) region, which supports the maturation of non-engrafting HSC precursors to functional HSC with multilineage, long-term engraftment potential in transplantation assays. This methodology enables correlation of phenotypic properties of clonal hemogenic precursors with their functional engraftment potential or other properties such as transcriptional profile, providing a means for the detailed analysis of HSC precursor development at the single cell level.

Here we present methodology for the clonal analysis of hematopoietic stem cell precursors during murine embryonic development. We combine index sorting of single cells from the embryonic aorta-gonad-mesonephros region with endothelial cell co-culture and transplantation to characterize the phenotypic properties and engraftment potential of single hematopoietic precursors.

The ability to study hematopoietic stem cell (HSC) genesis during embryonic development has been limited by the rarity of HSC precursors in the early ...

From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia

The identification of molecules involved in tumor initiation and progression is fundamental for understanding disease&#8217;s biology and, as a consequence, for the clinical management of patients. In the present work we will describe an optimized proteomic approach for the identification of molecules involved in the progression of Chronic Lymphocytic Leukemia (CLL). In detail, leukemic cell lysates are resolved by 2-dimensional Electrophoresis (2DE) and visualized as &#8220;spots&#8221; on the 2DE gels. Comparative analysis of proteomic maps allows the identification of differentially expressed proteins (in terms of abundance and post-translational modifications) that are picked, isolated and identified by Mass Spectrometry (MS). The biological function of the identified candidates can be tested by different assays (i.e. migration, adhesion and F-actin polymerization), that we have optimized for primary leukemic cells.

Here we describe a protocol that couples two proteomic techniques, namely 2-dimensional Electrophoresis (2DE) and Mass Spectrometry (MS), to identify differentially expressed/post-translational modified proteins among two or more groups of primary samples. This approach, together with functional experiments, allows the identification and characterization of prognostic markers/therapeutic targets.

The identification of molecules involved in tumor initiation and progression is fundamental for understanding disease&#8217;s biology and, as a ...

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding the clonal composition changes that occur within a tumor in response to certain micro-environment or treatments may lead to the design of more sophisticated and effective approaches to eradicate tumor cells. However, tracking tumor clonal sub-populations has been challenging due to the lack of distinguishable markers. To address this problem, a VDJ-seq protocol was created to trace the clonal evolution patterns of diffuse large B cell lymphoma (DLBCL) relapse by exploiting VDJ recombination and somatic hypermutation (SHM), two unique features of B cell lymphomas.
In this protocol, Next-Generation sequencing (NGS) libraries with indexing potential were constructed from amplified rearranged immunoglobulin heavy chain (IgH) VDJ region from pairs of primary diagnosis and relapse DLBCL samples. On average more than half million VDJ sequences per sample were obtained after sequencing, which contain both VDJ rearrangement and SHM information. In addition, customized bioinformatics pipelines were developed to fully utilize sequence information for the characterization of IgH-VDJ repertoire within these samples. Furthermore, the pipeline allows the reconstruction and comparison of the clonal architecture of individual tumors, which enables the examination of the clonal heterogeneity within the diagnosis tumors and deduction of clonal evolution patterns between diagnosis and relapse tumor pairs. When applying this analysis to several diagnosis-relapse pairs, we uncovered key evidence that multiple distinctive tumor evolutionary patterns could lead to DLBCL relapse. Additionally, this approach can be expanded into other clinical aspects, such as identification of minimal residual disease, monitoring relapse progress and treatment response, and investigation of immune repertoires in non-lymphoma contexts.

This protocol describes an approach to interrogate the recombined immunoglobulin heavy chain VDJ regions of lymphomas by deep-sequencing and retrieve VDJ rearrangement and somatic hypermutation status to delineate clonal architecture of individual tumor. Comparing clonal architecture between paired diagnosis and relapse samples reveals lymphoma relapse clonal evolution modes.

Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding ...

Separation of Immune Cell Subpopulations in Peripheral Blood Samples from Children with Infectious Mononucleosis

Infectious mononucleosis (IM) is an acute syndrome mostly associated with primary Epstein&#8211;Barr virus (EBV) infection. The main clinical symptoms include irregular fever, lymphadenopathy, and significantly increased lymphocytes in peripheral blood. The pathogenic mechanism of IM is still unclear; there is no effective treatment method for it, with mainly symptomatic therapies being available. The main question in EBV immunobiology is why only a small subset of infected individuals shows severe clinical symptoms and even develop EBV-associated malignancies, whilemost individuals are asymptomatic for life with the virus.
B cells are first involved in IM because EBV receptors are presented on their surface. Natural killer (NK) cells are cytotoxic innate lymphocytes that are important for killing EBV-infected cells. The proportion of CD4+ T cells decreases while that of CD8+ T cells expands dramatically during acute EBV infection, and the persistence of CD8+ T cells is important for lifelong control of IM. Those immune cells play important roles in IM, and their functions need to be identified separately. For this purpose, monocytes are separated first from peripheral blood mononuclear cells (PBMCs) of IM individuals using CD14 microbeads, a column, and a magnetic separator.
The remaining PBMCs are stained with peridinin-chlorophyll-protein (PerCP)/Cyanine 5.5 anti-CD3, allophycocyanin (APC)/Cyanine 7 anti-CD4, phycoerythrin (PE) anti-CD8, fluorescein isothiocyanate (FITC) anti-CD19, APC anti-CD56, and APC anti-CD16 antibodies to sort CD4+ T cells, CD8+ T cells, B cells, and NK cells using a flow cytometer. Furthermore, transcriptome sequencing of five subpopulations was performed to explore their functions and pathogenic mechanisms in IM.

We describe a method combining immunomagnetic beads and fluorescence-activated cell sorting to isolate and analyze defined immune cell subpopulations of peripheral blood mononuclear cells (monocytes, CD4+ T cells, CD8+ T cells, B cells, and natural killer cells). Using this method, magnetic and fluorescently labeled cells can be purified and analyzed.

Infectious mononucleosis (IM) is an acute syndrome mostly associated with primary Epstein&#8211;Barr virus (EBV) infection. The main clinical symptoms ...

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able to generate large datasets, necessitate the destruction of the cell of interest, making it impossible to assess its functional fate. Heterogeneous biological differentiating systems like hematopoiesis are therefore difficult to describe. Building on cell division tracking dyes, we further developed a protocol to simultaneously determine kinship, division number, and differentiation status for many single hematopoietic progenitors. This protocol allows the assessment of the ex vivo differentiation potential of murine and human hematopoietic progenitors, isolated from various biological sources. Moreover, as it is based on flow cytometry and a limited number of reagents, it can quickly generate a large amount of data, at the single-cell level, in a relatively inexpensive manner. We also provide the analytical pipeline for single-cell analysis, combined with a robust statistical framework. As this protocol allows the linking of cell division and differentiation at the single-cell level, it can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. Altogether, this protocol can be used in experimental designs aiming to unravel the biological differences between hematopoietic progenitors, from a single-cell perspective.

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework.

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able ...

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

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