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. F., 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=Subclassification+of+prostate+cancer+circulating+tumor+cells+by+nuclear+size+reveals+very+small+nuclear+circulating+tumor+cells+in+patients+with+visceral+metastases.">Subclassification of prostate cancer circulating tumor cells by nuclear size reveals very small nuclear circulating tumor cells in patients with visceral metastases.</a> Cancer. 121 (18), 3240-3251 (2015).</li><li>Ohlund, D., Elyada, E., Tuveson, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fibroblast+heterogeneity+in+the+cancer+wound.">Fibroblast heterogeneity in the cancer wound.</a> J Exp Med. 211 (8), 1503-1523 (2014).</li><li>Paraiso, K. H., Smalley, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fibroblast-mediated+drug+resistance+in+cancer.">Fibroblast-mediated drug resistance in cancer.</a> Biochem Pharmacol. 85 (8), 1033-1041 (2013).</li><li>Kalluri, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+biology+and+function+of+fibroblasts+in+cancer.">The biology and function of fibroblasts in cancer.</a> Nat Rev Cancer. 16 (9), 582-598 (2016).</li><li>Carpenter, A. 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=CellProfiler:+image+analysis+software+for+identifying+and+quantifying+cell+phenotypes.">CellProfiler: image analysis software for identifying and quantifying cell phenotypes.</a> Genome Biol. 7 (10), R100 (2006).</li><li>Jones, T. R., 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=CellProfiler+Analyst:+data+exploration+and+analysis+software+for+complex+image-based+screens.">CellProfiler Analyst: data exploration and analysis software for complex image-based screens.</a> BMC Bioinformatics. 9, 482 (2008).</li><li>Dao, D., 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=CellProfiler+Analyst:+interactive+data+exploration,+analysis+and+classification+of+large+biological+image+sets.">CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets.</a> Bioinformatics. 32 (20), 3210-3212 (2016).</li><li>Garvey, C. M., 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=A+high-content+image-based+method+for+quantitatively+studying+context-dependent+cell+population+dynamics.">A high-content image-based method for quantitatively studying context-dependent cell population dynamics.</a> Sci Rep. 6, 29752 (2016).</li><li>Juarez, E. F., 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=Quantifying+differences+in+cell+line+population+dynamics+using+CellPD.">Quantifying differences in cell line population dynamics using CellPD.</a> BMC Syst Biol. 10 (1), 92 (2016).</li><li>Mumenthaler, S. M., 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=The+Impact+of+Microenvironmental+Heterogeneity+on+the+Evolution+of+Drug+Resistance+in+Cancer+Cells.">The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells.</a> Cancer Inform. 14, 19-31 (2015).</li></ol>

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><tbody><tr><td>RPMI-1640</td><td>Corning</td><td>10-040-CV</td><td>cell culture medium</td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>Gemini</td><td>100-106</td><td>medium supplement</td></tr><tr><td>Penicillin/streptomycin</td><td>Gibco</td><td>15-140-122</td><td>medium supplement</td></tr><tr><td>TC20</td><td>Biorad</td><td>1450102</td><td>cell counter</td></tr><tr><td>TC20 slides with trypan blue</td><td>Biorad</td><td>1450003</td><td>cell counter slides</td></tr><tr><td>96-well plates</td><td>Corning</td><td>3904</td><td>clear bottom black plates</td></tr><tr><td>Erlotinib</td><td>LC Laboratories</td><td>E-4007</td><td></td></tr><tr><td>DMSO</td><td>VWR</td><td>317275-100ML</td><td>solution to resuspend drug</td></tr><tr><td>Hoechst</td><td>Invitrogen</td><td>H21491</td><td>nuclear dye</td></tr><tr><td>Propidium Iodide (PI)</td><td>Invitrogen</td><td>P1304MP</td><td>dead cell stain</td></tr><tr><td>Cell Tracker Orange CMRA</td><td>Life Technologies</td><td>C34551</td><td>whole cell stain</td></tr><tr><td>Operetta high content imaging system</td><td>Perkin Elmer</td><td></td><td></td></tr><tr><td>CellProfiler</td><td>Broad Institue</td><td>version 2.2.0 (rev 9969f42)</td><td>http://cellprofiler.org/releases/</td></tr><tr><td>Cell culture incubator</td><td></td><td></td><td>Any cell culture incubator will be suitable - cells were cultured under 37 &amp;ordm;C at 5% CO&lt;sub&gt;2.&lt;/sub&gt;</td></tr><tr><td>15 mL Falcon conical tubes</td><td>Falcon</td><td>14-959-53A</td><td></td></tr><tr><td>10 cm&lt;sup&gt;2&lt;/sup&gt; cell culture plates</td><td>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

Research

JoVE Journal

Biology

7.4K 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

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

Developmental Biology

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor1,2, and some of these sub-clones give rise to metastatic (and therefore lethal) disease. 
Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension3, or on macrodissection4. A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue5, providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ.
We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system6. Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index7.
To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.

Here we describe a method to quantify molecular heterogeneity in histological sections of tumor material using quantitative immunofluorescence, image analysis, and a statistical measure of heterogeneity. The method is intended for use in clinical biomarker development and analysis.

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of ...

Medicine

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 ...

Genetics

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a combination of bright field and differential interference contrast imagery. Two primary approaches are presented: noninterferometric quantitative phase microscopy (NIQPM), to perform measurements of total cell mass and subcellular density distribution, and Hilbert transform differential interference contrast microscopy (HTDIC)&#160;to determine volume. NIQPM is based on a simplified model of wave propagation, termed the paraxial approximation, with three underlying assumptions: low numerical aperture (NA) illumination, weak scattering, and weak absorption of light by the specimen. Fortunately, unstained cellular specimens satisfy these assumptions and low NA illumination is easily achieved on commercial microscopes. HTDIC is used to obtain volumetric information from through-focus DIC imagery under high NA illumination conditions. High NA illumination enables enhanced sectioning of the specimen along the optical axis. Hilbert transform processing on the DIC image stacks greatly enhances edge detection algorithms for localization of the specimen borders in three dimensions by separating the gray values of the specimen intensity from those of the background. The primary advantages of NIQPM and HTDIC lay in their technological accessibility using &#8220;off-the-shelf&#8221; microscopes. There are two basic limitations of these methods: slow z-stack acquisition time on commercial scopes currently abrogates the investigation of phenomena faster than 1 frame/minute, and secondly, diffraction effects restrict the utility of NIQPM and HTDIC to objects from 0.2 up to 10 (NIQPM) and 20 (HTDIC) &#956;m in diameter, respectively. Hence, the specimen and its associated time dynamics of interest must meet certain size and temporal constraints to enable the use of these methods. Excitingly, most fixed cellular specimens are readily investigated with these methods.

We describe the use of a standard optical microscope to perform quantitative measurements of cellular mass, volume, and density through a combination of bright field and differential interference contrast imagery.

We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a ...

Bioengineering

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro when stem cells are spatially confined onto micropatterns, thus setting up experimental models which offer unique opportunities to identify, in vitro, the fundamental principles of biological organisation.
Here we describe our own implementation of the methodology. We adapted a photo-patterning technique to reduce the need for specialized equipment to make it easier to establish the method in a standard cell biology laboratory. We also developed a free, open-source and easy to install image analysis framework in order to precisely measure the preferential positioning of sub-populations of cells within colonies of standard shapes and sizes. This method makes it possible to reveal the existence of patterning events even in seemingly disorganized populations of cells. The technique provides quantitative insights and can be used to decouple influences of the environment (e.g., physical cues or endogenous signaling), on a given patterning process.

The method presented here uses micropatterning together with quantitative imaging to reveal spatial organization within mammalian cultures. The technique is easy to establish in a standard cell biology laboratory and offers a tractable system to study patterning in vitro.

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro ...

High Throughput Characterization of Adult Stem Cells Engineered for Delivery of Therapeutic Factors for Neuroprotective Strategies

Mesenchymal stem cells (MSCs) derived from bone marrow are a powerful cellular resource and have been used in numerous studies as potential candidates to develop strategies for treating a variety of diseases. The purpose of this study was to develop and characterize MSCs as cellular vehicles engineered for delivery of therapeutic factors as part of a neuroprotective strategy for rescuing the damaged or diseased nervous system. In this study we used mouse MSCs that were genetically modified using lentiviral vectors, which encoded brain-derived neurotrophic factor (BDNF) or glial cell-derived neurotrophic factor (GDNF), together with green fluorescent protein (GFP). 
Before proceeding with in vivo transplant studies it was important to characterize the engineered cells to determine whether or not the genetic modification altered aspects of normal cell behavior. Different culture substrates were examined for their ability to support cell adhesion, proliferation, survival, and cell migration of the four subpopulations of engineered MSCs. High content screening (HCS) was conducted and image analysis performed. 
Substrates examined included: poly-L-lysine, fibronectin, collagen type I, laminin, entactin-collagen IV-laminin (ECL). Ki67 immunolabeling was used to investigate cell proliferation and Propidium Iodide staining was used to investigate cell viability. Time-lapse imaging was conducted using a transmitted light/environmental chamber system on the high content screening system.
Our results demonstrated that the different subpopulations of the genetically modified MSCs displayed similar behaviors that were in general comparable to that of the original, non-modified MSCs. The influence of different culture substrates on cell growth and cell migration was not dramatically different between groups comparing the different MSC subtypes, as well as culture substrates. 
This study provides an experimental strategy to rapidly characterize engineered stem cells and their behaviors before their application in long-term in vivo transplant studies for nervous system rescue and repair.

This study describes an experimental platform to rapidly characterize engineered stem cells and their behaviors before their application in long-term in vivo transplant studies for nervous system rescue and repair.

Mesenchymal stem cells (MSCs) derived from bone marrow are a powerful cellular resource and have been used in numerous studies as potential candidates to ...

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are well orchestrated and are crucial in regulating cell functions in a living system. Although a number of researchers have attempted to investigate the correlation between environmental factors and desired cellular functions, much remains unknown. This is largely due to the lack of a proper methodology to mimic such environmental cues in vitro, and simultaneously test different environmental cues on cells. Here, we report an integrated platform of microfluidic channels and a nanofiber array, followed by high-content single-cell analysis, to examine stem cell phenotypes altered by distinct environmental factors. To demonstrate the application of this platform, this study focuses on the phenotypes of self-renewing human pluripotent stem cells (hPSCs). Here, we present the preparation procedures for a nanofiber array and the microfluidic structure in the fabrication of a Multiplexed Artificial Cellular MicroEnvironment (MACME) array. Moreover, overall steps of the single-cell profiling, cell staining with multiple fluorescent markers, multiple fluorescence imaging, and statistical analyses, are described.

This article describes the detailed methodology to prepare a Multiplexed Artificial Cellular MicroEnvironment (MACME) array for high-throughput manipulation of physical and chemical cues mimicking in vivo cellular microenvironments and to identify the optimal cellular environment for human pluripotent stem cells (hPSCs) with single-cell profiling.

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry

There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the heart, and improving strategies to accurately model heart disease. The potential of exploiting human induced pluripotent stem cell (hiPSC) technology to generate cardiac muscle &#8220;in a dish&#8221; for these applications continues to generate high enthusiasm. In recent years, the ability to efficiently generate cardiomyogenic cells from human pluripotent stem cells (hPSCs) has greatly improved, offering us new opportunities to model very early stages of human cardiac development not otherwise accessible. In contrast to many previous methods, the cardiomyocyte differentiation protocol described here does not require cell aggregation or the addition of Activin A or BMP4 and robustly generates cultures of cells that are highly positive for cardiac troponin I and T (TNNI3, TNNT2), iroquois-class homeodomain protein IRX-4 (IRX4), myosin regulatory light chain 2, ventricular/cardiac muscle isoform (MLC2v) and myosin regulatory light chain 2, atrial isoform (MLC2a) by day 10 across all human embryonic stem cell (hESC) and hiPSC lines tested to date. Cells can be passaged and maintained for more than 90 days in culture. The strategy is technically simple to implement and cost-effective. Characterization of cardiomyocytes derived from pluripotent cells often includes the analysis of reference markers, both at the mRNA and protein level. For protein analysis, flow cytometry is a powerful analytical tool for assessing quality of cells in culture and determining subpopulation homogeneity. However, technical variation in sample preparation can significantly affect quality of flow cytometry data. Thus, standardization of staining protocols should facilitate comparisons among various differentiation strategies. Accordingly, optimized staining protocols for the analysis of IRX4, MLC2v, MLC2a, TNNI3, and TNNT2 by flow cytometry are described.

The article describes the detailed methodology to efficiently differentiate human pluripotent stem cells into cardiomyocytes by selectively modulating the Wnt pathway, followed by flow cytometry analysis of reference markers to assess homogeneity and identity of the population.

There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the ...

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.

Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.