This work was funded by National Cancer Institute (NCI) Grants U54CA143798 and U54CA143907 to establish Physical Sciences-Oncology Centers (PS-OCs) at the Dana-Farber Cancer Institute and University of Southern California, respectively. S.M. Mumenthaler received a PS-OC transnetwork award that supported some of this work.
We would like to express our deepest gratitude to our philanthropic supporters, particularly the Stephenson family, Emmet, Toni and Tessa, for their donation of the Operetta HCS platform. We would also like to thank J. Foo for guidance, and the Center for Applied Molecular Medicine team members: D. Agus for clinical guidance and mentorship, K. Patsch for meaningful discussions with experimental design, R. Rawat for aid in image analysis protocols, J. Katz for technical assistance with the Operetta, and P. Macklin and D. Ruderman for helpful discussions and feedback.

1. Cell Culture
NOTE: Here phenotypic responses of NSCLC cells (H3255) to the EGFR-targeting agent, erlotinib, when co-cultured with lung fibroblasts (CCD-19Lu GFP) were investigated. For the same data set, fluorescence-based and morphology-based classification of the two cell populations (H3255 and CCD-19Lu GFP) to exemplify their concordance was performed. However, only one classification method needs to be used and should be chosen based upon the application.
<ol>
	<li>Prepare 500 mL of RPMI-1640 supplemented with 10% heat-inactivated FBS and 1% penicillin/streptomycin. 
	NOTE: The growth medium and supplements can be replaced as required for other cell types.</li>
	<li>Culture cells in 10 cm3 plates as pure populations in 5% CO2 at 37 &#176;C and passage the at appropriate ratio every 3-4 days.</li>
</ol>
2. Preparation of Cells
<ol>
	<li>To prepare a cell suspension, remove cell media and wash cells with 5 mL 1X Phosphate-Buffered Saline (PBS).</li>
	<li>Aspirate off the PBS, add 1 mL of 0.05% trypsin warmed to 37 &#176;C and incubate for 5 min (or until cells are no longer adhered to plate) at 37 &#176;C.</li>
	<li>To neutralize trypsin, add 4 mL of RPMI to H3255 and CCD-19Lu GFP plates and pipet the cell solutions into 15 mL conical tubes.</li>
	<li>Centrifuge cells at 150 x g for 5 min at RT.</li>
	<li>Aspirate off the supernatant and resuspend cell pellets in 10 mL of RPMI media in conical tubes to prepare for seeding.</li>
</ol>
3. Cell Plating
<ol>
	<li>Count the cells to standardize seeding.
	<ol>
		<li>Invert tubes to mix cells in solution. Pipette 10 &#181;L of each cell suspension into a microcentrifuge tube and add 10 &#181;L trypan blue.</li>
		<li>Pipette 10 &#181;L of this solution into a cell counting slide and insert into automated cell counter. 
		NOTE: Other methods of cell counting (i.e. hemocytometer) may be used.</li>
		<li>Repeat the cell count at least three times, or until obtained counts are consistent.</li>
	</ol>
	</li>
	<li>Seed cells onto a multi-well plate 
	NOTE: The number of plates to seed is dependent on the number of time points that will be imaged. Alternatively, one plate can be re-imaged over time if the cells are transduced with histone-2B-GFP (or other stable lentiviral nuclear stains that provide adequate nuclear segmentation).
	<ol>
		<li>Seed a total of 1,500 cells/well in 100 &#181;L of RPMI. Prepare three cell suspensions to plate three cell populations: H3255, CCD-19Lu GFP, and 50% H3255 + 50% CCD-19Lu GFP. Perform each in triplicate with identical plate setups for each time point. 
		NOTE: Initial seeding densities should be optimized for each cell line and plate size.</li>
		<li>Seed cells in 3 x 96-well plates using a multichannel pipette. Incubate cells O/N at 37 &#176;C, 5% CO2.</li>
	</ol>
	</li>
</ol>
4. Drug Dosing
<ol>
	<li>Add DMSO to erlotinib powder to make a final erlotinib stock solution of 10 mM concentration. 
	NOTE: The drug can be substituted as desired.</li>
	<li>Dilute the drug with cell culture medium to 2x the final concentration with the highest final concentration at 10 &#181;M and serially dilute four times at a 1:10 ratio, for a total of five drug concentrations and a no drug control. 
	NOTE: If concentration of DMSO equals or exceeds 0.1% v/v at the highest drug dose, the no drug control should contain equivalent amount of DMSO to ensure the observed effects observed are not due to DMSO toxicity.</li>
	<li>Pipette 100 &#181;L of drug solution into the appropriate well for a final drug concentration of 1x diluted in media.</li>
</ol>
5. Image Acquisition
NOTE: Images were acquired on days 0, &#160;2 and 3. Depending on cell types and cellular processes being studied, other desired time points may be studied.
<ol>
	<li>Stain cells to prepare for imaging.
	<ol>
		<li>Make up the dye solution at the following final concentrations.
		<ol>
			<li>For fluorescence-based classification, prepare 5 &#181;g/mL nuclear stain and 5 &#181;M dead cell stain in PBS (see Table of Materials).</li>
			<li>For morphology-based classification, prepare 5 &#181;g/mL nuclear stain, 5 &#181;M dead cell stain, and 5 &#181;M cell stain in PBS (see Table of Materials).</li>
		</ol>
		</li>
		<li>Add 20 &#181;L dye solution to each well. Incubate for 30 min at 37 &#176;C protected from light.</li>
	</ol>
	</li>
	<li>Optimize and acquire images.
	<ol>
		<li>Remove the well plate from incubator, wipe the bottom of plate with 70% EtOH, and place the plate in the imaging chamber.</li>
		<li>In the &#39;Setup&#39; tab, click the &#39;+&#39; button under &#39;Channel Selection&#39; to add appropriate channels to image (i.e. brightfield, nuclear stain, dead cell stain, GFP, and RFP signals).</li>
		<li>Click &#39;Layout Selection&#39;, and take z-stacked test images every 2 &#181;m starting at 0 &#181;m and ending at 20 &#181;m to identify the plane of focus. Input this distance for every channel under &#39;Height&#39;.</li>
		<li>Click &#39;Snapshot&#39; under each channel to evaluate intensities and optimize exposure times. Enter higher or lower values under &#39;Time&#39; as required.</li>
		<li>On right side of screen, highlight appropriate wells (to be imaged) on the plate schematic. In the wells schematic below, highlight the twenty-five fields to be imaged in a similar manner.</li>
		<li>Under &#39;Run Experiment&#39;, identify the plate in &#39;Plate Name&#39; on left tab, and click the &#39;Start&#39; button (underneath) to run the image acquisition protocol with a 10X objective to generate gray-scale TIFF images in the aforementioned channels. 
		NOTE: Step-by-step instructions for imaging protocol may differ between instruments, but parameter values should still be optimized.</li>
		<li>Repeat the imaging on days two and three.</li>
	</ol>
	</li>
</ol>
6. Image Analysis
NOTE: All image analysis was performed using proprietary software. However, because this is not a publicly available, comparable analyses were also designed on CellProfiler 2.2 and CellProfiler Analyst 2.0, with a brief protocol listed below and detailed protocol provided as supplementary material (test images are already loaded into pipelines to test workflow). The images from this experiment were grouped together on a per-well basis so that each well could be loaded individually. The CellProfiler pipelines below contain a regular-expression that parses metadata information from each image filename and allows the images to be further grouped by channel.
<ol>
	<li>Download and install the open source software: &#39;CellProfiler&#39; and &#39;CellProfiler Analyst&#39;. Accept all default options and add-ons during installation.</li>
	<li>Classify heterocellular cultures into subpopulations.
	<ol>
		<li>Open &#39;CellProfiler&#39; software, click &#39;File&#39;| &#39;Import Pipeline&#39;| &#39;From File,&#39; and select the appropriate file (&#34;Fluorescence_Classification.cppipe&#34; for fluorescence-based classification, or &#34;Morphology_Classification.cppipe&#34; for morphology-based classification). 
		NOTE: These files contain protocols for basic image processing and nuclei/cell segmentation. Because of the uneven Hoechst stain present across these cells, nuclei were segmented and enlarged, and then used to mask the image to allow for segmentation of nuclei with lower intensities.
		<ol>
			<li>Select the fluorescence-based classification pipeline to classify cells as either H3255 or CCD-19Lu based upon eGFP intensity, and to calculate morphology features. 
			NOTE: CCD-19Lu cells were transduced with GFP.</li>
			<li>Select the morphology-based classification pipeline to segment nuclei and cells, and to calculate morphology features. 
			NOTE: Further downstream analysis in &#39;CellProfiler Analyst&#39; is needed for classification. Dead cells are classified via fluorescence-based classification.</li>
		</ol>
		</li>
		<li>Click &#39;View Output Settings&#39; at the bottom left of the screen. Select the location of the image files for analysis (&#39;Default Input Folder&#39;) and the destination for the extracted data (&#39;Default Output Folder&#39;).</li>
		<li>Click &#39;Analyze Images&#39; to begin the analysis. When analysis is complete, click &#39;OK&#39; and go to the &#39;Default Output Folder&#39; to view the calculated data. 
		NOTE: Example parameter values and images are available in Supplemental File 1-CellProfilerProtocol.pdf.</li>
		<li>For morphology-based classification (only) do the following.
		<ol>
			<li>Open &#39;CellProfiler Analyst&#39; software, select the database properties file generated in CellProfiler, and click &#39;Open&#39;.</li>
			<li>Click the &#39;Classifier&#39; tab at the top-left of screen. To call random cell images from experiment, click &#39;Fetch&#39;; images will appear in the unclassified window.</li>
		</ol>
		</li>
		<li>Manually classify cells as positive (H3255) or negative (CCD-19Lu) by selecting and dragging cells to appropriate bin (see Supplemental File 2-Pipeline.pdf: Figure B). After classifying at least 50 cells per subpopulation, click &#39;Train Classifier&#39;, and then click &#39;Check Progress&#39;. 
		NOTE: Cells are classified via a user-supervised random forest machine learning algorithm12. If the accuracy is not above approved threshold (&#62;90%), it may be necessary to go back and optimize cell segmentation on &#39;CellProfiler&#39;, or the cells may not be ideal for classifying by morphology.</li>
		<li>Click &#39;Score All&#39; to generate a table with cell counts for each subpopulation.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Kanemura, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+in+vitro+proliferative+activity+of+human+fetal+neural+stem/progenitor+cells+using+indirect+measurements+of+viable+cells+based+on+cellular+metabolic+activity.">Evaluation of in vitro proliferative activity of human fetal neural stem/progenitor cells using indirect measurements of viable cells based on cellular metabolic activity.</a> J Neurosci Res. 69 (6), 869-879 (2002).</li><li>Plumb, J. A., Milroy, R., Kaye, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+the+pH+dependence+of+3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium+bromide-formazan+absorption+on+chemosensitivity+determined+by+a+novel+tetrazolium-based+assay.">Effects of the pH dependence of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide-formazan absorption on chemosensitivity determined by a novel tetrazolium-based assay.</a> Cancer Res. 49 (16), 4435-4440 (1989).</li><li>Hsu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Green+tea+polyphenols+induce+differentiation+and+proliferation+in+epidermal+keratinocytes.">Green tea polyphenols induce differentiation and proliferation in epidermal keratinocytes.</a> J Pharmacol Exp Ther. 306 (1), 29-34 (2003).</li><li>Caicedo, J. C., Singh, S., Carpenter, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+in+image-based+profiling+of+perturbations.">Applications in image-based profiling of perturbations.</a> Curr Opin Biotechnol. 39, 134-142 (2016).</li><li>Sero, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+shape+and+the+microenvironment+regulate+nuclear+translocation+of+NF-kappaB+in+breast+epithelial+and+tumor+cells.">Cell shape and the microenvironment regulate nuclear translocation of NF-kappaB in breast epithelial and tumor cells.</a> Mol Syst Biol. 11 (3), 790 (2015).</li><li>Chen, J. 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The authors have nothing to disclose.

The protocol described above improves upon current cell biology assays by providing more comprehensive insights into phenotypic dynamics of multiple cell types in response to environmental perturbations while using reduced reagents and time. A major advantage of this experimental design is the ability to analyze multiple phenotypes with a single setup and generate quantitative data characterizing these phenotypes on a single cell level. One technical advantage to this platform is the ease of initial troubleshooting compared to other assays. Because this method is image-based, one is able to visualize the wells for apparent over/under seeding. It is advisable to have cells in the exponential growth phase for the duration of the experiment and not be limited by nutrient or spatial constraints or confounded by senescence due to scarce seeding. Otherwise, birth and death rates may not be reproducible between experiments. For reference, CellPD is a publicly available program for computation of birth and death rates14. Additionally, one can visualize whether adequate concentrations of dyes were added to each well. Pipetting issues in an individual well could result in missegmentation and skewed data, but can easily be detected with the aforementioned protocol.
Unfortunately, not all cell types may be amenable to this application. It is important to be able to accurately segment the nuclei and cells, therefore analysis of cells that organize in more sphere-like or clumped structures may not be suitable. For some cells, it also may be advantageous to use a cell strainer prior to seeding to ensure initial seeding of single cells. In addition, the linear classifier technique is only applicable to cell types that can be readily distinguished based on morphology features. 
For the success of the protocol, it is important to first optimize the imaging conditions, as the validity of downstream analyses is dependent on the quality of the images. While here we performed experiments using a high-content screening platform, image acquisition can also be performed using any fluorescent microscope (although an automated imaging platform is ideal for high-throughput approaches). Test images should be taken prior to each imaging time point to ensure that there are no problems with the microscope or protocol. The signal to noise ratio should be high, especially for the channels that will be used for segmentation (i.e. nuclei, cell stains). Additionally, it is important to image in the optimal plane of focus. If the images are out of focus, segmentation becomes much more difficult and the calculated morphological features will likely be inaccurate. Illumination differences between fields can cause problems with image segmentation as well. Large differences in brightness make the automated selection of threshold values difficult. Additionally, if there are heterogeneous fluorescence intensities between cells, a single threshold value may not sufficiently segment all the cells in an image. In this analysis, these problems were overcome by creating masks around brighter and dimmer cell populations and segmenting each population separately. 
While the phenotypes under investigation in this protocol are limited to live, dead, and morphological characterization, they can easily be expanded to investigate other features. For example, functional genetic studies can be added with RNAi, overexpression, or other chemical perturbations. 
In this paper, the capabilities of the protocol to measure the response of non-small cell lung cancer cells to erlotinib in the presence and absence of CAFs was demonstrated. However, this is merely one example of the many cell types and microenvironmental parameters that can be tested. We have extended this protocol to be used with other cell types and drug studies, including primary cells isolated from patient tumors13,15.

Cell-based assays have been a workhorse in basic research and drug development settings. However, the limitations of these standard assays have become increasingly apparent with the discordance between in vitro and clinical data and the failure of most drugs to receive FDA approval. Here, we present a novel method for utilizing quantitative imaging to simultaneously analyze heterocellular phenotypes in response to relevant, co-occurring environmental stimuli.
Traditional cell-based assays that are used to measure cell viability include: trypan blue exclusion assays, MTT/MTS, and Annexin V-FITC flow cytometry staining. Trypan blue exclusion assays, while simple and inexpensive, require a large number of cells, are time consuming, and are often influenced by user bias1. MTT and MTS assays indirectly measure cell viability through measurements of mitochondrial metabolic rate. However, the metabolic activity of cells can be affected by different culture conditions (such as media or oxygen concentration), which leads to inaccurate results and prevents standardization across cell types and conditions2,3. Another major disadvantage of these techniques is their inability to distinguish between multiple cell types &#8211; most biological systems are heterocellular. While flow cytometry methods have the ability to distinguish between multiple cell populations, cell labels are required, dynamic sampling is challenging, and when using adherent cells, this application becomes time-consuming and error prone.
Other important cellular phenotypes, including morphological changes, occur in response to environmental stimuli but are not captured by traditional cell-based assays. Profiling cell states through morphological characterization and mapping similarities across samples is a powerful, unbiased tool with the ability to provide novel insights into many aspects of basic and translational research, including basic cell biology and drug discovery4. Furthermore, tumor cell morphology has been shown to correlate with tumor subtypes5 and aggressiveness6. Hence, it is of great interest to study these cellular features and how they relate to specific environmental perturbations. Additionally, one can use differences in morphological features to discriminate between subpopulations in co-culture systems. Fluorescently labeling cells has downfalls (i.e. altering inherent cell properties, time-consuming) and therefore additional methods to classify cell types are advantageous.
Microscopy-based imaging is an alternative method for profiling cellular phenotypes in a multiplexed, quantitative, and robust manner. In this manuscript, we apply our quantitative imaging pipeline to highlight the evolutionary dynamics of heterogeneous cell populations within a tumor. We focus on the interaction between Non-Small Cell Lung Cancer (NSCLC) cells and Cancer-Associated Fibroblasts (CAFs), the most prevalent stromal cell type found in tumors. CAFs have been implicated in tumor initiation, progression, and therapeutic response; therefore, performing phenotypic assays on tumor cells in the absence of CAFs can be misleading7,8,9. Specifically, we evaluated effects of CAFs on tumor cells in response to erlotinib, a small molecule targeting the Epidermal Growth Factor Receptor (EGFR) that is often used in clinical treatment of NSCLC. We utilized a high-content screening platform and its accompanying image analysis software for evaluation; however, in an attempt to make this methodology accessible to other researchers we have also developed a comparable downstream protocol using the open-source software: CellProfiler10 and CellProfiler Analyst11. Most image-based high-content screening assays are analyzed with commercialized software specific to a given instrument model. Results are difficult to replicate in other labs with different software because the underlying algorithms are often proprietary. Using this image-based pipeline, cell proliferation, death, and morphology of each subpopulation of a heterocellular culture in response to drug treatment using both fluorescence- and morphology-based classification were measured. The following protocol provides a robust methodology for probing complex cellular processes.

<table border="0" cellpadding="0" cellspacing="0" width="677">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">RPMI-1640</td>
			<td style="width:127px;">Corning</td>
			<td style="width:119px;">10-040-CV</td>
			<td style="width:191px;">cell culture medium</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">FBS</td>
			<td style="width:127px;">Gemini</td>
			<td style="width:119px;">100-106</td>
			<td>medium supplement</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">Penicillin/streptomycin</td>
			<td style="width:127px;">Gibco</td>
			<td style="width:119px;">15-140-122</td>
			<td>medium supplement</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">TC20</td>
			<td style="width:127px;">Biorad</td>
			<td style="width:119px;">1450102</td>
			<td>cell counter</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">TC20 slides with trypan blue</td>
			<td style="width:127px;">Biorad</td>
			<td style="width:119px;">1450003</td>
			<td>cell counter slides</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">96-well plates</td>
			<td style="width:127px;">Corning</td>
			<td style="width:119px;">3904</td>
			<td>clear bottom black plates</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">Erlotinib</td>
			<td style="width:127px;">LC Laboratories</td>
			<td style="width:119px;">E-4007</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">DMSO</td>
			<td style="width:127px;">VWR</td>
			<td style="width:119px;">317275-100ML</td>
			<td>solution to resuspend drug</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">Hoechst</td>
			<td style="width:127px;">Invitrogen</td>
			<td style="width:119px;">H21491</td>
			<td>nuclear dye</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">Propidium Iodide</td>
			<td style="width:127px;">Invitrogen</td>
			<td style="width:119px;">P1304MP</td>
			<td>dead cell stain</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:240px;">Cell Tracker Orange CMRA</td>
			<td style="width:127px;">Life Technologies</td>
			<td style="width:119px;">C34551</td>
			<td>whole cell stain</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:240px;">Operetta high content imaging system</td>
			<td style="width:127px;">Perkin Elmer</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:240px;">CellProfiler</td>
			<td style="width:127px;">Broad Institue</td>
			<td style="width:119px;">version 2.2.0 (rev 9969f42)</td>
			<td>http://cellprofiler.org/releases/</td>
		</tr>
		<tr height="86">
			<td height="86" style="height:86px;width:240px;">Cell culture incubator</td>
			<td></td>
			<td></td>
			<td style="width:191px;">Any cell culture incubator will be suitable - cells were cultured under 37 &#186;C at 5% CO2.</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:240px;">15 mL Falcon conical tubes</td>
			<td style="width:127px;">Falcon</td>
			<td style="width:119px;">14-959-53A</td>
			<td></td>
		</tr>
		<tr height="24">
			<td height="24" style="height:24px;width:240px;">10 cm2 cell culture plates</td>
			<td style="width:127px;">TPP</td>
			<td>93040</td>
			<td></td>
		</tr>
	</tbody>
</table>

discrimination and characterization of heterocellular populations using quantitative imaging techniques

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.

We generated an image set consisting of 25&#160;fields/well, 54 wells/plate (3 cell populations x 6 drug concentrations x 3 replicates), across three plates for a total of 4,050 individual images. The image sets generated over the course of the experiment were analyzed using proprietary software (see table of materials) to extract various quantitative properties of cells (i.e. morphology, fluorescence) which could then be used to classify cell subpopulations. However, because the commercial software used has limited access, comparable downstream pipelines in CellProfiler and CellProfiler Analyst were created.
Heterocellular Classification into Subpopulations
Nuclei were identified and segmented based on the DNA stain (here Hoechst) and cell populations were classified either based upon fluorescence or morphology (Figure 1). For fluorescence-based classification, the fibroblasts (CCD-19Lu) were previously transduced with GFP-lentivirus. The GFP intensity levels were measured for each nucleus, and those that were calculated above the accepted threshold (based on the background signal) were classified as CCD-19Lu while those below were identified as tumor cells (H3255). For morphology-based classification, cells were previously stained with a non-toxic cellular stain (see the table of materials) and this was used to identify and segment the cytoplasm. A machine learning algorithm was trained with ~50-100 cells from each population. Morphological features were identified that were significantly different between the populations, which were then used to design a linear classifier to distinguish between CCD-19Lu and H3255 cells. The fluorescence and morphology classification protocols were 97.4% (n = 1403) concordant at distinguishing between the two cell populations in untreated conditions and 92.5% (n = 916) concordant in drug treated conditions (1 &#181;M erlotinib) (Figure 2).
Phenotypic Analyses of Subpopulations
In addition to discriminating between cell types, we aimed to characterize phenotypic properties of each subpopulation. Multiplexing assays saves time and reagents, adds consistency, and provides additional information regarding the system being studied. There are many potential phenotypic outputs and one should choose them based upon the questions of interest. Here, changes in the cell morphology and viability status in response to erlotinib treatment were investigated. After three days of drug treatment, a decrease in nuclear area and an increase in cellular area of the H3255 cells (Figure 3A) was observed. The mean difference in nuclear area between the &#34;no drug&#34; and &#34;drug&#34; treated populations was found to be statistically significant via a two-sided type-2 (equal variance) t-test (p = 7.92 x 10-16). We hypothesize that this observation is a cellular response to the stress imposed by drug treatment.
It is also of interest to study whether a drug has a cytotoxic (i.e. increase in number of dead cells over time) or cytostatic (i.e. decrease in number of cell births over time) effect on cells, as this has profound clinical impact. For example, a cytostatic drug effect induces growth arrest yet does not eliminate the cells from the tumor, thus there is the potential for cancer cells to reinitiate cell proliferation once the drug is removed. Drug effects can often be context, concentration, and cell type dependent. We previously observed erlotinib eliciting a cytotoxic response in one cell type, while showing a cytostatic response in another13.
Traditional viability assays output relative cell number and therefore, do not discriminate between growth arrests and cell death. Herein, dead cells were identified based upon propidium iodide stain (Figure 3B). Both cytotoxic and cytostatic effects of erlotinib on H3255 cells were observed, with an increase in the number of deaths and a decrease in the number of births following drug treatment (Figure 3C). It is worth noting that the number of dead cells drops following day 1 likely due to cell clearance. CCD-19Lu cells were not affected by the drug. An additional advantage of this platform is the generation of quantitative data. For example, in our co-culture experiment, an initial subpopulation of 1,118 (75.8%) H3255 cells was found to be 2,817 (87.9%) or 396 (57.2%) after three days without or with erlotinib treatment, respectively (Figure 4). Because we can generate actual cell counts instead of relative percentage (as with flow cytometry methods), we conclude that the change in composition during drug treatment is due to a decrease in H3255 cells and not an increase in CCD-19Lu. It is worth nothing that death rates may be underestimated due to cell clearance, which is difficult to assess experimentally and likely differs across cell types.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55844/55844fig1.jpg" /> 
Figure 1: Overview of the Image Analysis Protocol. Two potential downstream image analysis pipelines to classify heterocellular populations using either morphology-based classification or fluorescence-based classification. Scale bars = 100 &#181;m. <a href="http://cloudflare.jove.com/files/ftp_upload/55844/55844fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55844/55844fig2.jpg" /> 
Figure 2: Concordance between Morphology and Fluorescence-based Classification. (A) Concordance plot displaying the overlap of the two classification protocols. The same cells were classified as H3255 using both morphology- and fluorescence-based classification. The two protocols were in agreement, with classification for 97.4% (n = 1403) of untreated cells and 92.5% (n = 916) of cells treated with erlotinib (Note: white area is too small to visualize). (B) 10X images depicting examples of good and poor concordance between fluorescence-based and morphology-based classification. The white arrows point out cells that were inconsistently classified between platforms. Input image: blue &#8211; nuclei (Hoechst); green &#8211; CCD19Lu (GFP). Classification images: Red &#8211; H3255; green &#8211; CCD-19Lu. Scale bar = 100 &#181;m. <a href="http://cloudflare.jove.com/files/ftp_upload/55844/55844fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55844/55844fig3.jpg" /> 
Figure 3: Multiplexed Phenotypic Measurements from a Single Experimental Setup. (A) Morphological features, such as nuclei and cell area, were calculated on the single cell level in the presence and absence of drug. Note: Cell areas measuring smaller than 100 &#181;m2 were considered debris and excluded from analyses. Box plot depicts median with first and third quartile ranges and 95% confidence interval error bars. (B) H3255 (blue) and CCD-19Lu (green) cells were co-cultured and dead cells were identified based upon the intensity of propidium iodide stain (red) and imaged using a 10X objective. Scale bar = 1 mm (top panel); 100 &#181;m (bottom images). (C) Total number of live and dead cells were calculated over three days with or without drug treatment, with an obvious decrease in number of live cells and increase in dead cells with the addition of erlotinib. Error bars represent standard error of the mean based on of three replicates. <a href="http://cloudflare.jove.com/files/ftp_upload/55844/55844fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55844/55844fig4.jpg" /> 
Figure 4: Subpopulation Dynamics over Time. Representative 10X images of wells containing H3255 (blue) and CCD-19Lu (green) on day 0 or day 3 with and without drug. Cells belonging to each subpopulation were counted and proportional pie charts show the actual change in population composition across samples. Scale bars = 1 mm (middle panels, shpwn in ;eftmost panel), 1 mm (top image), 100 &#181;m&#160; (bottom images). <a href="http://cloudflare.jove.com/files/ftp_upload/55844/55844fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

We have developed a novel protocol for studying heterocellular population dynamics in response to perturbations. This manuscript describes an imaging-based platform that produces quantitative datasets for simultaneous characterization of multiple cellular phenotypes of heterocellular populations in a robust manner.

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do ...

discrimination-characterization-heterocellular-populations-using

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7.3K Views.  University of Southern California (USC). We have developed a novel protocol for studying heterocellular population dynamics in response to perturbations. This manuscript describes an imaging-based platform that produces quantitative datasets for simultaneous characterization of multiple cellular phenotypes of heterocellular populations in a robust manner.

Video: Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes in cell surface area and cell height, and where cells undergo mitosis and migrate. Furthermore, epithelial cells can also regulate morphogenetic movements in adjacent tissues1. A traditional method to study epithelial cells and tissues involve chemical fixation and histological methods to determine cell morphology or localization of particular proteins of interest. These approaches continue to be useful and provide "snapshots" of cell shapes and tissue architecture, however, much remains to be understood about how cells acquire specific shapes, how various proteins move or localize to specific positions, and what paths cells follow toward their final differentiated fate. High resolution live imaging complements traditional methods and also allows more direct investigation into the dynamic cellular processes involved in the formation, maintenance, and morphogenesis of multicellular epithelial sheets. Here we demonstrate experimental methods from the isolation of animal cap tissues from Xenopus laevis embryos to confocal imaging of epithelial cells and simple measurement approaches that together can augment molecular and cellular studies of epithelial morphogenesis.

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Xenopus embryonic epithelia are an ideal model system to study cell behaviors such as polarity development and shape change during epithelial morphogenesis. Traditional histology of fixed samples is increasingly being complemented by live-cell confocal imaging. Here we demonstrate methods to isolate frog tissues and visualize live epithelial cells and their cytoskeleton using live-cell confocal microscopy.

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes ...

Techniques for Imaging Ca2+ Signaling in Human Sperm

Fluorescence microscopy of cells loaded with fluorescent, Ca2+-sensitive dyes is used for measurement of spatial and temporal aspects of Ca2+ signaling in live cells. Here we describe the method used in our laboratories for loading suspensions of human sperm with Ca2+-reporting dyes and measuring the fluorescence signal during physiological stimulation. Motile cells are isolated by direct swim-up and incubated under capacitating conditions for 0-24 h, depending upon the experiment. The cell-permeant AM (acetoxy methyl ester) ester form of the Ca2+-reporting dye is then added to a cell aliquot and a period of 1 h is allowed for loading of the dye into the cytoplasm. We use visible wavelength dyes to minimize photo-damage to the cells, but this means that ratiometric recording is not possible. Advantages and disadvantages of this approach are discussed. During the loading period cells are introduced into an imaging chamber and allowed to adhere to a poly-D-lysine coated coverslip. At the end of the loading period excess dye and loose cells are removed by connection of the chamber to the perfusion apparatus. The chamber is perfused continuously, stimuli and modified salines are then added to the perfusion header. Experiments are recorded by time-lapse acquisition of fluorescence images and analyzed in detail offline, by manually drawing regions of interest. Data are normalized to pre-stimulus levels such that, for each cell (or part of a cell), a graph showing the Ca2+ response as % change in fluorescence is obtained.

Techniques for Imaging Ca2+ Signaling in Human Sperm

Stimulus-evoked [Ca2+]i signals of individual human sperm are assessed. Motile cells are loaded with Ca2+-sensitive fluorescent dye (AM-ester method) and immobilised in a perfusable chamber. Cells are imaged by time-lapse fluorescence microscopy and stimulated via the perfusing medium. Responses of single cells (or regions) are analysed offline using Excel.

Fluorescence microscopy of cells loaded with fluorescent, Ca2+-sensitive dyes is used for measurement of spatial and temporal aspects of Ca2+ signaling in ...

Identification and Characterization of Protein Glycosylation using Specific Endo- and Exoglycosidases

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes such as cell adhesion, molecular trafficking, clearance, and signal transduction1-4. In eukaryotes, the most common glycosylation modifications in the secretory pathway are additions at consensus asparagine residues (N-linked); or at serine or threonine residues (O-linked) (Figure 1). Initiation of N-glycan synthesis is highly conserved in eukaryotes, while the end products can vary greatly among different species, tissues, or proteins. Some glycans remain unmodified ("high mannose N-glycans") or are further processed in the Golgi ("complex N-glycans"). Greater diversity is found for O-glycans, which start with a common N-Acetylgalactosamine (GalNAc) residue in animal cells but differ in lower organisms1.
The detailed analysis of the glycosylation of proteins is a field unto itself and requires extensive resources and expertise to execute properly. However a variety of available enzymes that remove sugars (glycosidases) makes possible to have a general idea of the glycosylation status of a protein in a standard laboratory setting. Here we illustrate the use of glycosidases for the analysis of a model glycoprotein: recombinant human chorionic gonadotropin beta (hCG&beta;), which carries two N-glycans and four O-glycans 5. The technique requires only simple instrumentation and typical consumables, and it can be readily adapted to the analysis of multiple glycoprotein samples.
Several enzymes can be used in parallel to study a glycoprotein. PNGase F is able to remove almost all types of N-linked glycans6,7. For O-glycans, there is no available enzyme that can cleave an intact oligosaccharide from the protein backbone. Instead, O-glycans are trimmed by exoglycosidases to a short core, which is then easily removed by O-Glycosidase. The Protein Deglycosylation Mix contains PNGase F, O-Glycosidase, Neuraminidase (sialidase), &beta;1-4 Galactosidase, and &beta;-N-Acetylglucosaminidase. It is used to simultaneously remove N-glycans and some O-glycans8 . Finally, the Deglycosylation Mix was supplemented with a mixture of other exoglycosidases (&alpha;-N-Acetylgalactosaminidase, &alpha;1-2 Fucosidase, &alpha;1-3,6 Galactosidase, and &beta;1-3 Galactosidase ), which help remove otherwise resistant monosaccharides that could be present in certain O-glycans.
SDS-PAGE/Coomasie blue is used to visualize differences in protein migration before and after glycosidase treatment. In addition, a sugar-specific staining method, ProQ Emerald-300, shows diminished signal as glycans are successively removed. This protocol is designed for the analysis of small amounts of glycoprotein (0.5 to 2 &mu;g), although enzymatic deglycosylation can be scaled up to accommodate larger quantities of protein as needed.

Using specific glycosidases to remove sugars from glycoproteins followed by SDS-PAGE is a valuable method to detect glycan modifications on protein samples and is a good choice for initial glycobiology studies. Changes following deglycosylation can be detected as shifts in gel mobility or by staining with glycan sensitive reagents.

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Spermatozoa are male reproductive cells especially designed to reach, recognize and fuse with the egg. To perform these tasks, sperm cells must be prepared to face a constantly changing environment and to overcome several physical barriers. Being in essence transcriptionally and translationally silent, these motile cells rely profoundly on diverse signaling mechanisms to orient themselves and swim in a directed fashion, and to contend with challenging environmental conditions during their journey to find the egg. In particular, Ca2+-mediated signaling is pivotal for several sperm functions: activation of motility, capacitation (a complex process that prepares sperm for the acrosome reaction) and the acrosome reaction (an exocytotic event that allows sperm-egg fusion). The use of fluorescent dyes to track intracellular fluctuations of this ion is of remarkable importance due to their ease of application, sensitivity, and versatility of detection. Using one single dye-loading protocol we utilize four different fluorometric techniques to monitor sperm Ca2+ dynamics. Each technique provides distinct information that enables spatial and/or temporal resolution, generating data both at single cell and cell population levels.

Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

Intracellular Ca2+ dynamics are very important in sperm physiology and Ca2+-sensitive fluorescent dyes constitute a versatile tool to study them. Population experiments (fluorometry and stopped flow fluorometry) and single cell experiments (flow cytometry and single cell imaging) are used to track spatio-temporal [Ca2+] changes in human sperm cells.

Spermatozoa  are male reproductive cells especially designed to reach, recognize and fuse  with the egg. To perform these tasks, sperm cells must be ...

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for using FISH to quantify the number of mRNAs in single yeast cells. Cells can be grown in any condition of interest and then fixed and made permeable. Subsequently, multiple single-stranded deoxyoligonucleotides conjugated to fluorescent dyes are used to label and visualize mRNAs. Diffraction-limited fluorescence from single mRNA molecules is quantified using a spot-detection algorithm to identify and count the number of mRNAs per cell. While the more standard quantification methods of northern blots, RT-PCR and gene expression microarrays provide information on average mRNAs in the bulk population, FISH facilitates both the counting and localization of these mRNAs in single cells at single-molecule resolution.

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

This protocol describes an experimental procedure for performing Fluorescence in situ Hybridization (FISH) for counting mRNAs in single cells at single-molecule resolution.

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for ...

Techniques for the Analysis of Extracellular Vesicles Using Flow Cytometry

Extracellular Vesicles (EVs) are small, membrane-derived vesicles found in bodily fluids that are highly involved in cell-cell communication and help regulate a diverse range of biological processes. Analysis of EVs using flow cytometry (FCM) has been notoriously difficult due to their small size and lack of discrete populations positive for markers of interest. Methods for EV analysis, while considerably improved over the last decade, are still a work in progress. Unfortunately, there is no one-size-fits-all protocol, and several aspects must be considered when determining the most appropriate method to use. Presented here are several different techniques for processing EVs and two protocols for analyzing EVs using either individual detection or a bead-based approach. The methods described here will assist with eliminating the antibody aggregates commonly found in commercial preparations, increasing signal&#8211;to-noise ratio, and setting gates in a rational fashion that minimizes detection of background fluorescence. The first protocol uses an individual detection method that is especially well suited for analyzing a high volume of clinical samples, while the second protocol uses a bead-based approach to capture and detect smaller EVs and exosomes.

Many different methods exist for the measurement of extracellular vesicles (EVs) using flow cytometry (FCM). Several aspects should be considered when determining the most appropriate method to use. Two protocols for measuring EVs are presented, using either individual detection or a bead-based approach.

Extracellular Vesicles (EVs) are small, membrane-derived vesicles found in bodily fluids that are highly involved in cell-cell communication and help ...

Development and Characterization of In Vitro Microvessel Network and Quantitative Measurements of Endothelial [Ca2+]i and Nitric Oxide Production

Endothelial cells (ECs) lining the blood vessel walls in vivo are constantly exposed to flow, but cultured ECs are often grown under static conditions and exhibit a pro-inflammatory phenotype. Although the development of microfluidic devices has been embraced by engineers over two decades, their biological applications remain limited. A more physiologically relevant in vitro microvessel model validated by biological applications is important to advance the field and bridge the gaps between in vivo and in vitro studies. Here, we present detailed procedures for the development of cultured microvessel network using a microfluidic device with a long-term perfusion capability. We also demonstrate its applications for quantitative measurements of agonist-induced changes in EC [Ca2+]i and nitric oxide (NO) production in real time using confocal and conventional fluorescence microscopy. The formed microvessel network with continuous perfusion showed well-developed junctions between ECs. VE-cadherin distribution was closer to that observed in intact microvessels than statically cultured EC monolayers. ATP-induced transient increases in EC [Ca2+]i and NO production were quantitatively measured at individual cell levels, which validated the functionality of the cultured microvessels. This microfluidic device allows ECs to grow under a well-controlled, physiologically relevant flow, which makes the cell culture environment closer to in vivo than that in the conventional, static 2D cultures. The microchannel network design is highly versatile, and the fabrication process is simple and repeatable. The device can be easily integrated to the confocal or conventional microscopic system enabling high resolution imaging. Most importantly, because the cultured microvessel network can be formed by primary human ECs, this approach will serve as a useful tool to investigate how pathologically altered blood components from patient samples affect human ECs and provide insight into clinical issues. It also can be developed as a platform for drug screening.

Development and Characterization of In Vitro Microvessel Network and Quantitative Measurements of Endothelial [Ca2+]i and Nitric Oxide Production

Primary human umbilical vein endothelial cells (HUVECs) were grown to confluence within a microfluidic network device. The endothelial cell junction and F-actin distributions were illustrated and the changes in intracellular calcium concentration and nitric oxide production in response to adenosine triphosphate (ATP) were quantified in real-time at individual cell levels.

Endothelial cells (ECs) lining the blood vessel walls in vivo are constantly exposed to flow, but cultured ECs are often grown under static conditions and ...

Techniques for Imaging Prometaphase and Metaphase of Meiosis I in Fixed Drosophila Oocytes

Chromosome segregation in human oocytes is error prone, resulting in aneuploidy, which is the leading genetic cause of miscarriage and birth defects. The study of chromosome behavior in oocytes from model organisms holds much promise to uncover the molecular basis of the susceptibility of human oocytes to aneuploidy. Drosophila melanogaster is amenable to genetic manipulation, with over 100 years of research, community, and technique development. Visualizing chromosome behavior and spindle assembly in Drosophila oocytes has particular challenges, however, due primarily to the presence of membranes surrounding the oocyte that are impenetrable to antibodies. We describe here protocols for the collection, preparation, and imaging of meiosis I spindle assembly and chromosome behavior in Drosophila oocytes, which allow the molecular dissection of chromosome segregation in this important model organism.

Techniques for Imaging Prometaphase and Metaphase of Meiosis I in Fixed Drosophila Oocytes

We present protocols for the collection, preparation, and imaging of mature Drosophila oocytes. These methods allow the visualization of chromosome behavior and spindle assembly and function during meiosis.

Chromosome segregation in human oocytes is error prone, resulting in aneuploidy, which is the leading genetic cause of miscarriage and birth defects. The ...

Characterization of Calcification Events Using Live Optical and Electron Microscopy Techniques in a Marine Tubeworm

Characterizing the first event of biological production of calcium carbonate requires a combination of microscopy approaches. First, intracellular pH distribution and calcium ions can be observed using live microscopy over time. This allows identification of the life stage and the tissue with the feature of interest for further electron microscopy studies. Life stage and tissues of interest are typically higher in pH and Ca signals. 
Here, using H. elegans, we present a protocol to characterize the presence of calcium carbonate structures in a biological specimen on the scanning electron microscope (SEM), using energy-dispersive X-ray spectroscopy (EDS) to visualize elemental composition, using electron backscatter diffraction (EBSD) to determine the presence of crystalline structures, and using transmission electron microscopy (TEM) to analyze the composition and structure of the material. In this protocol, a focused ion beam (FIB) is used to isolate samples with dimension suitable for TEM analysis. As FIB is a site specific technique, we demonstrate how information from the previous techniques can be used to identify the region of interest, where Ca signals are highest.

We demonstrate the use of various microscopy methods that are useful in observing the calcification of a tubeworm, Hydroides elegans, as well as locating and characterizing the first calcified material. Live microscopy and electron microscopy are used together to provide functional and material information that are important in studying biomineralization.

Characterizing the first event of biological production of calcium carbonate requires a combination of microscopy approaches. First, intracellular pH ...