Name: Integrate Imaging Flow Cytometry and Transcriptomic Profiling to Evaluate Altered Endocytic CD1d Trafficking (Video) | JoVE Journal
Uploaded: 2018-10-29T13:00:00.000+00:00
Duration: 9 min 1 s
Description: Learn to integrate imaging flow cytometry with transcriptomic profiling to evaluate altered endocytic CD1d trafficking in human dendritic cells. This method connects gene expression changes to protein function for toxicological diagnostics.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

playerTrialDesktop

The authors thank Robert Giulitto (Hoxworth blood center) for human blood samples and Dr. Liang Niu for the normalization of gene expression reads. We also thank grant support from the National Institute of Environmental Health Sciences (ES006096), Center for Environmental Genetics (CEG) pilot project (S.H.), National Institute of Allergy and Infectious Diseases (AI115358) (S.H.), University of Cincinnati University Research Council award (S.H.), and University of Cincinnati College of Medicine Core Enhancement Funding (X. Z.).

Human protocols in this study were approved by the Institutional Review Board of the University of Cincinnati and all methods were performed in accordance with the relevant guidelines and regulations. Blood samples from healthy donors were obtained from the Hoxworth Blood Center at the University of Cincinnati Medical Center.
1. Transcriptomic Profiling of Pollutant-exposed Human Monocyte-derived DCs
<ol>
	<li>Total RNA extraction of BaP-exposed DCs
	<ol>
		<li>Differentiate human DCs using cytokines GM-CSF and IL-4, and expose DCs to BaP for 4 days13.</li>
		<li>Sort BaP-exposed DCs using flow cytometry based on the surface expressed markers (Lineage-HLA-DR+), the majority of which consists of conventional DCs (CD11c+CD1c+)13. Typically, sort 10,000 DCs into culture medium containing 10% FBS, centrifuge DCs at 600 x g and 4 &#176;C for 6 min, and immediately remove the supernatant by aspiration using a 1 mL pipette tip.</li>
		<li>Once the supernatant is removed by aspiration, lyse the cell pellet by adding 0.4 mL of Lysis/Binding Buffer from the RNA isolation kit for storage in -80 &#176;C before RNA extraction.</li>
		<li>Use the RNA isolation kit with the total RNA extraction protocol to extract the total RNA14. Measure the RNA integrity using a Bioanalyzer15.</li>
	</ol>
	</li>
	<li>Transcriptomic analysis of sorted DCs (Figure 1A)
	<ol>
		<li>Prepare the library for RNA-seq using a RNA Library preparation kit. In short, fragment the isolated poly-A RNA (~200 bp), reverse transcribe the fragments to the 1st strand of the cDNA, and follow by the second strand cDNA synthesis labeled with dUTP.</li>
		<li>Ligate double strand cDNA to the adapter with a stem-loop structure after bead purification, end-repairing and dA-tailing. Excise the uracil residues in the 2nd strand of the cDNA and adapter loop with USER (Uracil-Specific Excision Reagent) enzyme to maintain strand specificity and open the adapter loop for PCR.
		<ol>
			<li>Perform 13 cycles of PCR using universal and index-specific primers to enrich the indexed library. Clean up the library and run on a Bioanalyzer DNA High Sensitivity chip to check the library quality and size distribution.</li>
		</ol>
		</li>
		<li>Use a library quantification kit and a real-time PCR system combined with library size information to&#160;calculate library concentration via a standard curve method.&#160;</li>
		<li>Proportionally&#160;pool individually indexed compatible libraries and adjust the final total concentration to 15 pM. Perform library cluster generation and sequence the library at a setting of single read 1x 50 bp to&#160;generate ~25 million reads per sample.</li>
	</ol>
	</li>
	<li>Sequencing
	<ol>
		<li>Load the sequencing and indexing reagents to the SBS and PE reagent racks, respectively. Place the reagents in a laboratory-grade water bath for 1 h until all the ice has melted and the reagents in each bottle/tube are mixed properly.</li>
		<li>Prepare the ICB mix by adding the thawed dye and -20 &#176;C enzyme to the bottle and mix. Prepare a NaOH solution according to the sequencing instructions. Place all reagents at 4 &#176;C until ready to use.</li>
		<li>During the 1 h waiting period, power on the sequencer. Wait for the DONOTEJECT drive to appear and connect the computer to a network drive.</li>
		<li>Launch the sequencer control software.</li>
		<li>Prepare 2 L of Maintenance Wash solution that contains 0.5% Tween 20 and 0.03% ProClin 300 in laboratory-grade water.</li>
		<li>In the SBS reagent rack, add ~100 mL of Maintenance Wash solution to each of the 8 bottles, and screw funnel caps to the bottles. In the PE reagent rack, add ~12 mL of Maintenance Wash solution to each of the ten 15 mL conical tubes, and discard the caps.</li>
		<li>Load the two racks with the solution filled bottles/tubes to the sequencer.</li>
		<li>From the sequencer control software, choose the Maintenance Wash; follow the instructions on screen to clean the sequencer fluid system until the process is completed.</li>
		<li>Start a New Run in the &#34;SEQUENCE&#34; tab from the software; direct the output data to a network drive. Choose parameters for single read 1x 50 bp with single index multiplexed libraries.</li>
		<li>Optionally, log into the BaseSpace Sequence Hub so that the sequencing status can be remotely monitored via a computer or smart phone.</li>
		<li>Upload a Sample Sheet for demultiplexing and provide reagent information according to the software requirement.</li>
		<li>Load SBS and PE reagents to the sequencer. Prime the system with a used flow cell (~15 min).</li>
		<li>Once the cluster generation is completed (~4.5 h), take the flow cell out, lightly spray the flow cell with water, and wipe it dry using lens paper. Lightly spray the flow cell with 95% ethanol and wipe it dry. Check against a light to make sure that the surface is clean without debris or salt residue.</li>
		<li>After the Prime step is completed, load the clustered flow cell and start the sequencing. The Sequence Analysis Viewer software will automatically be started.</li>
		<li>Monitor the sequencing data quality via SAV including cluster density, reads pass filter, cluster pass filter %, % &#8805;Q30, Legacy phase/prophase %, indexing QC, etc. This helps to understand the data quality and troubleshoot.</li>
		<li>Change the flow cell gasket and perform a Maintenance Wash after the sequencing is completed. The sequencer is ready for the next run.</li>
	</ol>
	</li>
	<li>Bioinformatic analysis
	<ol>
		<li>Perform bioinformatics RNA-seq data analysis13.</li>
	</ol>
	</li>
</ol>
2. Pathway Analysis of Transcriptomic Profiles (Figure 1B)
<ol>
	<li>Use an edgeR Bioconductor to compare resultant gene expression intensity counts between BaP-exposed and non-exposed DCs from three donors. Then, identify differentially expressed genes between BaP-exposed and non-exposed DCs based on the absolute fold change (&#62;2 folds) and the false discovery rate&#160;(FDR)-adjusted p-values (&#60;0.05).</li>
	<li>To predict the functional clusters of differentially expressed genes, use the ToppCluster software package to search the altered genes against several databases including KEGG and REACTOME and generate clustering data. ToppCluster uses the hypergeometric test to obtain functional enrichment&#160;via the gene list enrichment analysis16.</li>
	<li>Further input the results from these function clusters to Cytoscape17,18 Version 3.3.0, a broadly used open source software platform for visualizing complex networks. Thus, the genes involved in different clusters or pathways, including endocytic clusters and lipid metabolism, are shown in a network with color annotation of the averaged fold change of gene expression (Figure 1B)13.</li>
</ol>
3. Imaging Flow Cytometry of CD1d and Lamp1 Colocalization
<ol>
	<li>Antibody labeling of BaP-exposed DCs
	<ol>
		<li>Expose 0.5 x 106 human DCs to 5.94 &#956;M BaP for 4 days at 37 &#176;C in 2 mL of complete Dulbecco&#8217;s Modified Eagle&#8217;s Medium. Harvest DCs by centrifugation at 400 x g for 10 min. Block the differentiated DCs by incubating cells with human serum blocker and anti-human Fc receptor antibodies for 10 min in ice, including anti-human CD16, CD32, and CD64 antibodies.</li>
		<li>Incubate Brilliant Violet 421-anti-CD1c (L161), phycoerythrin/cyanine dye 7 (PE/Cy7)-anti-HLA-DR, and PE/Cy5-anti-CD11c with DCs for 20 min in ice. Table 1 shows this list of antibodies.</li>
		<li>Fix DCs with 4% paraformaldehyde in PBS and permeabilize them with Permeabilization Wash Buffer. Perform the intracellular staining with a mixture of PE-labeled purified anti-human CD1d (51.1) and&#160;Alexa Fluor 647-labeled anti-Lamp1 (CD107a) (H4A3).</li>
	</ol>
	</li>
	<li>Imaging flow cytometry measurement (Figure 2)
	<ol>
		<li>Analyze the stained samples using an imaging flow cytometer at the flow cytometry core of Cincinnati Children&#8217;s Hospital using a 40X objective to yield 300 pixels for a cell with around 10 &#956;m in diameter. One-pixel size is approximately 0.5 &#956;m by 0.5 &#956;m of the object.</li>
		<li>Acquire 10,000 cellular images in an unbiased manner according to the manufacturer&#8217;s instruction.</li>
	</ol>
	</li>
</ol>
4. Imaging Analyses of Flow Cytometry Images
<ol>
	<li>Colocalization analysis using IDEAS software (Figure 2)
	<ol>
		<li>Perform compensation with single-stained versus non-stained samples by setting the fluorescence intensity of non-stained samples below the threshold, including non-stained BaP-exposed DCs with BaP autofluorescence, as in routine flow cytometry data analysis.</li>
		<li>Gate stained cells to obtain the subsets of HLA-DR+CD11c+CD1d+Lamp1+ cells (Figure 2A) and show cellular images in the gated subsets (Figure 2B).</li>
		<li>Save cell images in a png format based on two technical inclusion criteria, the visual presence of strong co-stained signals and subcellular localization of CD1d and Lamp1 proteins, for the colocalization analyses using ImageJ-Fiji (Figure 2B).</li>
	</ol>
	</li>
	<li>Use software ImageJ-Fiji to perform colocalization analysis (Figure 3).
	<ol>
		<li>Prepare the input image files by merging the 100 saved cell images for non-exposed and BaP-exposed human DCs, respectively (Figure 3A).</li>
		<li>Analyze CD1d (Red) and Lamp1 (Green) colocalization using a scatterplot.
		<ol>
			<li>Run ImageJ-Fiji program. Open the .png file with 100 cell images (Figure 3B and Figure 4A): &#34;File | Open&#34;.</li>
			<li>Split the image with merged red and green channels into two images with either a red or a green channel: &#34;Image | Color | Split channels&#34;.</li>
			<li>Draw a scatterplot using the commands &#34;Analyze | Colocalization | Colocalization Threshold&#34;. Save the scatterplot using the &#34;PrintScreen&#34; key.</li>
		</ol>
		</li>
		<li>Calculate Mander&#39;s colocalization coefficients for each single cellular image (n=100) (Figure 4B)
		<ol>
			<li>Select a single cell image on the image file with split channels using the &#34;Oval&#34; selection tool.</li>
			<li>Use the commands &#34;Analyze | Colocalization | Colocalization Threshold&#34;. Select &#34;Channel 1&#34; from the dialogue box of region of interest (ROI). Keep all calculation options including &#34;Mander&#8217;s using thresholds&#34; for each cell image.</li>
			<li>Repeat this calculation for all 100 cell images.</li>
			<li>Save and open the results using a spreadsheet.</li>
			<li>Plot the average and standard errors for &#8220;thresholded Mander&#8217;s coefficients&#8221; (n=100, 0 means no colocalization and 1 means perfect colocalization). Use Student&#8217;s t-test to calculate the p value for the comparison between BaP-exposed and non-exposed groups (Figure 4B).</li>
		</ol>
		</li>
		<li>Calculate the percent of thresholded pixel intensity co-localized between CD1d and Lamp1 for multiple cell images (n=100) (Figure 4C).
		<ol>
			<li>Use the same analysis protocol in 4.2.3 and additionally select the result option &#34;% intensity above threshold colocalized&#34;.</li>
			<li>Perform this analysis together with Mander&#8217;s colocalization coefficients.</li>
			<li>Similarly plot the average and standard errors for % intensity colocalized between CD1d and Lamp1. Use Student&#8217;s t-test to calculate the p value for comparison between BaP-exposed and non-exposed groups (Figure 4C).</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol><li>Roche, P. A., Cresswell, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Antigen+Processing+and+Presentation+Mechanisms+in+Myeloid+Cells%5BTitle%5D)+AND+%22Microbiology+Spectrum%22%5BJournal%5D)">Antigen Processing and Presentation Mechanisms in Myeloid Cells.</a> Microbiology Spectrum. 4 (3), (2016).</li>
<li>Huang, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((MR1+uses+an+endocytic+pathway+to+activate+mucosal-associated+invariant+T+cells%5BTitle%5D)+AND+%22The+Journal+of+experimental+medicine%22%5BJournal%5D)">MR1 uses an endocytic pathway to activate mucosal-associated invariant T cells.</a> The Journal of experimental medicine. 205 (5), 1201-1211 (2008).</li>
<li>Nakamura, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Endosomes+are+specialized+platforms+for+bacterial+sensing+and+NOD2+signalling%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Endosomes are specialized platforms for bacterial sensing and NOD2 signalling.</a> Nature. 509 (7499), 240-244 (2014).</li>
<li>Blum, J. S., Wearsch, P. A., Cresswell, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pathways+of+antigen+processing%5BTitle%5D)+AND+%22Annual+review+of+immunology%22%5BJournal%5D)">Pathways of antigen processing.</a> Annual review of immunology. 31, 443-473 (2013).</li>
<li>Brennan, P. J., Brigl, M., Brenner, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Invariant+natural+killer+T+cells%3A+an+innate+activation+scheme+linked+to+diverse+effector+functions%5BTitle%5D)+AND+%22Nature+reviews.+Immunology%22%5BJournal%5D)">Invariant natural killer T cells: an innate activation scheme linked to diverse effector functions.</a> Nature reviews. Immunology. 13 (2), 101-117 (2013).</li>
<li>Zajonc, D. M., Kronenberg, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CD1+mediated+T+cell+recognition+of+glycolipids%5BTitle%5D)+AND+%22Current+opinion+in+structural+biology%22%5BJournal%5D)">CD1 mediated T cell recognition of glycolipids.</a> Current opinion in structural biology. 17 (5), 521-529 (2007).</li>
<li>Cox, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Determination+of+cellular+lipids+bound+to+human+CD1d+molecules%5BTitle%5D)+AND+%22PloS+one%22%5BJournal%5D)">Determination of cellular lipids bound to human CD1d molecules.</a> PloS one. 4 (5), e5325(2009).</li>
<li>Yuan, W., Kang, S. J., Evans, J. E., Cresswell, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Natural+lipid+ligands+associated+with+human+CD1d+targeted+to+different+subcellular+compartments%5BTitle%5D)+AND+%22Journal+of+immunology%22%5BJournal%5D)">Natural lipid ligands associated with human CD1d targeted to different subcellular compartments.</a> Journal of immunology. 182 (8), 4784-4791 (2009).</li>
<li>Huang, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Discovery+of+deoxyceramides+and+diacylglycerols+as+CD1b+scaffold+lipids+among+diverse+groove-blocking+lipids+of+the+human+CD1+system%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Discovery of deoxyceramides and diacylglycerols as CD1b scaffold lipids among diverse groove-blocking lipids of the human CD1 system.</a> Proceedings of the National Academy of Sciences of the United States of America. 108 (48), 19335-19340 (2011).</li>
<li>de Jong, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CD1a-autoreactive+T+cells+recognize+natural+skin+oils+that+function+as+headless+antigens%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">CD1a-autoreactive T cells recognize natural skin oils that function as headless antigens.</a> Nature. 15 (2), 177-185 (2014).</li>
<li>Trombetta, E. S., Mellman, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cell+biology+of+antigen+processing+in+vitro+and+in+vivo%5BTitle%5D)+AND+%22Annual+review+of+immunology%22%5BJournal%5D)">Cell biology of antigen processing in vitro and in vivo.</a> Annual review of immunology. 23, 975-1028 (2005).</li>
<li>Maecker, H. T., McCoy, J. P., Nussenblatt, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Standardizing+immunophenotyping+for+the+Human+Immunology+Project%5BTitle%5D)+AND+%22Nature+reviews.+Immunology%22%5BJournal%5D)">Standardizing immunophenotyping for the Human Immunology Project.</a> Nature reviews. Immunology. 12 (3), 191-200 (2012).</li>
<li>Sharma, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Inhibition+of+endocytic+lipid+antigen+presentation+by+common+lipophilic+environmental+pollutants%5BTitle%5D)+AND+%22Science+Reports%22%5BJournal%5D)">Inhibition of endocytic lipid antigen presentation by common lipophilic environmental pollutants.</a> Science Reports. 7 (1), 2085(2017).</li>
<li>Boom, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Rapid+and+simple+method+for+purification+of+nucleic+acids%5BTitle%5D)+AND+%22Journal+of+clinical+microbiology%22%5BJournal%5D)">Rapid and simple method for purification of nucleic acids.</a> Journal of clinical microbiology. 28 (3), 495-503 (1990).</li>
<li>Nachamkin, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Agilent+2100+bioanalyzer+for+restriction+fragment+length+polymorphism+analysis+of+the+Campylobacter+jejuni+flagellin+gene%5BTitle%5D)+AND+%22Journal+of+clinical+microbiology%22%5BJournal%5D)">Agilent 2100 bioanalyzer for restriction fragment length polymorphism analysis of the Campylobacter jejuni flagellin gene.</a> Journal of clinical microbiology. 39 (2), 754-757 (2001).</li>
<li>Kaimal, V., Bardes, E. E., Tabar, S. C., Jegga, A. G., Aronow, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ToppCluster%3A+a+multiple+gene+list+feature+analyzer+for+comparative+enrichment+clustering+and+network-based+dissection+of+biological+systems%5BTitle%5D)+AND+%22Nucleic+acids+research%22%5BJournal%5D)">ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems.</a> Nucleic acids research. 38 (Web Server issue), W96-W102 (2010).</li>
<li>Bauer-Mehren, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integration+of+genomic+information+with+biological+networks+using+Cytoscape%29%5BTitle%5D)+AND+%22Methods+in+molecular+biology%22%5BJournal%5D)">Integration of genomic information with biological networks using Cytoscape).</a> Methods in molecular biology. 1021, 37-61 (2013).</li>
<li>Cline, M. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integration+of+biological+networks+and+gene+expression+data+using+Cytoscape%5BTitle%5D)+AND+%22Nature+protocols%22%5BJournal%5D)">Integration of biological networks and gene expression data using Cytoscape.</a> Nature protocols. 2 (10), 2366-2382 (2007).</li>
<li>Huang, S., Moody, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Donor-unrestricted+T+cells+in+the+human+CD1+system%5BTitle%5D)+AND+%22Immunogenetics%22%5BJournal%5D)">Donor-unrestricted T cells in the human CD1 system.</a> Immunogenetics. 68 (8), 577-596 (2016).</li>
<li>Huang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Targeting+Innate-Like+T+Cells+in+Tuberculosis%5BTitle%5D)+AND+%22Frontiers+in+immunology%22%5BJournal%5D)">Targeting Innate-Like T Cells in Tuberculosis.</a> Frontiers in immunology. 7, 594(2016).</li>
<li>Moody, D. B., Porcelli, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Intracellular+pathways+of+CD1+antigen+presentation%5BTitle%5D)+AND+%22Nature+reviews.+Immunology%22%5BJournal%5D)">Intracellular pathways of CD1 antigen presentation.</a> Nature reviews. Immunology. 3 (1), 11-22 (2003).</li>
<li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fiji%3A+an+open-source+platform+for+biological-image+analysis%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Fiji: an open-source platform for biological-image analysis.</a> Nature. 9 (7), 676-682 (2012).</li>
<li>Diard, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stabilization+of+cooperative+virulence+by+the+expression+of+an+avirulent+phenotype%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Stabilization of cooperative virulence by the expression of an avirulent phenotype.</a> Nature. 494 (7437), 353-356 (2013).</li>
<li>Bader, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identification+of+proliferative+and+mature+beta-cells+in+the+islets+of+Langerhans%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Identification of proliferative and mature beta-cells in the islets of Langerhans.</a> Nature. 535 (7612), 430-434 (2016).</li>
<li>Eliceiri, K. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biological+imaging+software+tools%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Biological imaging software tools.</a> Nature. 9 (7), 697-710 (2012).</li>
<li>Costes, S. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automatic+and+quantitative+measurement+of+protein-protein+colocalization+in+live+cells%5BTitle%5D)+AND+%22Biophysical+journal%22%5BJournal%5D)">Automatic and quantitative measurement of protein-protein colocalization in live cells.</a> Biophysical journal. 86 (6), 3993-4003 (2004).</li>
</ol>

The authors have nothing to disclose

Functional confirmation of an altered gene pathway is challenging, because of widely impacted gene expression involving multiple pathways and the difficulty to&#160;integrate individual and populational cellular activities. We employed imaging flow cytometry to specifically test CD1d trafficking in endocytic compartments. Imaging flow cytometry integrates the populational measurement of cells and the individual demonstration of subcellular colocalization of multiple proteins. Confocal microscopy has previously provided h...

Antigen presentation typically involves intracellular protein trafficking, which has been often investigated using morphological characterization and phenotypic profiling of antigen presenting cells1,2,3. To integrate the advantages of imaging and phenotyping methods, we describe an imaging analysis platform at both single cell and population levels to demonstrate an altered protein colocalization in human dendritic cells (DCs). In peptide antigen presentation, major histocompatibility complex (MHC) class I molecules bind a short peptide (8-10 residues) in the endoplasmic reticulum to activate conventional CD8+ T cells, while MHC class II molecules bind a relatively longer peptide (~20 residues) in endocytic compartments to activate conventional CD4+ T cells1,4. In contrast, lipid-specific T cells are activated by CD1 proteins with lipid antigens loaded mainly in endocytic compartments5,6. Lipid antigen presentation requires the supply of lipid metabolites produced in lipid metabolism7,8,9,10 and the loading of functional lipid metabolites to CD1 proteins in endocytic compartments5,6. In this context, various cellular factors modulating lipid antigen presentation, especially in environmental exposure of lipophilic pollutants and immune disorders, are critical to be defined. In this study, we used transcriptomic analysis, image profiling, and cellular and populational imaging analysis of human monocyte-derived DCs to determine the endocytic protein trafficking contributing to lipid antigen presentation in pollutant exposure. Certainly, this combined platform can be applied to studying subcellular protein trafficking and colocalization in different biological processes.
Technically, subcellular protein localization was usually demonstrated using confocal microscopy and statistically analyzed in a limited number of detected cells1,2,3,11. Moreover, flow cytometry has been broadly applied to gate cell populations with co-stained signals of multiple proteins at a cellular level12; however, this lacks a detailed visualization of subcellular protein colocalization. To achieve comprehensive and statistical analyses of percentage protein colocalization at both cellular and population levels, we incorporate imaging profiling and analysis approaches to determine the features of protein colocalization with biological relevance. Specifically, we use imaging flow cytometry to detect the colocalization of CD1d and lysosomal-associated membrane protein 1 (Lamp1) proteins in this study. Quantitative analysis of colocalized molecules was previously difficult to be performed at a populational scale. In this study, we adapted the ImageJ-Fiji program to examine the percentage of protein colocalization with a large number of co-stained cells at both populational and individual cellular levels. Specifically, we measured co-localized areas, intensity, and populational size to support the conclusion that the CD1d protein was largely retained in late endocytic compartments of human DCs in exposure to a lipophilic pollutant benzo[a]pyrene (BaP)13. This combined cellular and population imaging analysis provided highly reproducible, comprehensive, and statistically significant results of CD1d-Lamp1 colocalization relevant to inhibited lipid antigen presentation.
The transcriptome of BaP-exposed human DCs strongly supported the hypothesis that endocytic lipid metabolism and CD1d endocytic trafficking were impaired in BaP exposure. To test this hypothesis, we applied imaging flow cytometry to profile the images of DC population that were co-stained with multiple proteins including CD1d, endocytic markers, and DC markers. Finally, co-stained cells were statistically analyzed to demonstrate the percentage of intensity and areas of CD1d and Lamp1 colocalization.

<table><tbody><tr><td>Transcriptomics</td><td>Illumina</td><td>HiSeq 2500 v4</td><td>Illumina HiSeq system</td></tr><tr><td>ImagingStream X</td><td>Millipore</td><td>100220</td><td>Imaging flow cytometry</td></tr><tr><td>mirVana miRNA Isolation Kit</td><td>Thermo Fisher</td><td></td><td>Total RNA extraction</td></tr><tr><td>Agilent RNA 6000 Pico Kit</td><td>Agilent</td><td></td><td>Total RNA QC analysis</td></tr><tr><td>Veriti 96-Well Fast Thermal Cycler</td><td>Thermo Fisher</td><td></td><td>PCR, enzyme reaction</td></tr><tr><td>NEBNext Poly(A) mRNA Magnetic Isolation Module&amp;nbsp;</td><td>New England Biolab</td><td></td><td>PolyA RNA purification</td></tr><tr><td>Automated SMARTer Apollo system</td><td>Takara</td><td></td><td>PolyA RNA purification</td></tr><tr><td>NEBNext Ultra RNA Library Prep Kit for Illumina</td><td>New England Biolab</td><td></td><td>Library preparation</td></tr><tr><td>Agencourt AMPure XP magnetic beads</td><td>Beckman Coulter</td><td></td><td>DNA purification&amp;nbsp;</td></tr><tr><td>2100 Bioanalyzer</td><td>Agilent</td><td></td><td>Library QC, size distribution analysis</td></tr><tr><td>Agilent High Sensitivity DNA Kit</td><td>Agilent</td><td></td><td>Library QC, size distribution analysis</td></tr><tr><td>QuantStudio 5 Real-Time PCR System (Thermo Fisher)</td><td>Thermo Fisher</td><td></td><td>Library quantification</td></tr><tr><td>NEBNext Library Quant Kit</td><td>New England Biolab</td><td></td><td>Library quantification</td></tr><tr><td>cBot</td><td>Illumina</td><td></td><td>Library cluster generation</td></tr><tr><td>TruSeq SR Cluster Kit v3 - cBot &amp;ndash; HS</td><td>Illumina</td><td></td><td>Library cluster generation</td></tr><tr><td>HiSeq 1000</td><td>Illumina</td><td></td><td>Sequencing</td></tr><tr><td>TruSeq SBS Kit v3 - HS (50-cycles)</td><td>Illumina</td><td></td><td>Sequencing</td></tr><tr><td>Phycoerythrin/Cyanine 7 (PE/Cy7)</td><td>Bio Legend</td><td>L243</td><td></td></tr><tr><td>Phycoerythrin/Cyanine 5 (PE/Cy5)</td><td>Bio Legend</td><td>3.9</td><td></td></tr><tr><td>Brilliant violet 421</td><td>Bio Legend</td><td>L161</td><td></td></tr><tr><td>PE-anti-mouse IgG2b</td><td>Bio Legend</td><td>51.1</td><td></td></tr><tr><td>Alexa Fluor 647 (AF647)</td><td>Bio Legend</td><td>CD107a(H4A3)</td><td></td></tr></tbody></table>

integrate imaging flow cytometry and transcriptomic profiling to evaluate altered endocytic cd1d trafficking

Integrate Imaging Flow Cytometry and Transcriptomic Profiling to Evaluate Altered Endocytic CD1d Trafficking

Populational analyses of the morphological and functional alteration of endocytic proteins are challenging due to the demand of image capture at a single cell level and statistical image analysis at a populational level. To overcome this difficulty, we used imaging flow cytometry and transcriptomic profiling (RNA-seq) to determine altered subcellular localization of the cluster of differentiation 1d protein (CD1d) associated with impaired endocytic gene expression in human dendritic cells (DCs), which were exposed to the common lipophilic air pollutant benzo[a]pyrene. The colocalization of CD1d and endocytic marker Lamp1 proteins from thousands of cell images captured with imaging flow cytometry was analyzed using IDEAS and ImageJ-Fiji programs. Numerous cellular images with co-stained CD1d and Lamp1 proteins were visualized after gating on CD1d+Lamp1+ DCs using IDEAS. The enhanced CD1d and Lamp1 colocalization upon BaP exposure was further demonstrated using thresholded scatterplots, tested with Mander&#39;s coefficients for co-localized intensity, and plotted based on the percentage of co-localized areas using ImageJ-Fiji. Our data provide an advantageous instrumental and bioinformatic approach to measure protein colocalization at both single and populational cellular levels, supporting an impaired functional outcome of transcriptomic alteration in pollutant-exposed human DCs.

The lipophilic pollutant BaP alters endocytic gene clusters in human DCs. Human monocyte-derived DCs from each donor (n=3) were incubated with BaP and sorted for conventional DCs, which were further used for RNA extraction and transcriptomic analysis as described. Upon the normalization of gene expression, altered genes between BaP-exposed and non-exposed groups were clustered according to the functional correlation of differentially expressed genes. We input the altered gene list to the ...

Imaging flow cytometry provides an ideal approach to detect the morphological and functional alteration of cells at individual and populational levels. Disrupted endocytic function for lipid antigen presentation in pollutant-exposed human dendritic cells was demonstrated with a combined transcriptomic profiling of gene expression and morphological demonstration of protein trafficking.

Populational analyses of the morphological and functional alteration of endocytic proteins are challenging due to the demand of image capture at a single ...

integrate-imaging-flow-cytometry-transcriptomic-profiling-to-evaluate

Research

JoVE Journal

Immunology and Infection

The IP-FCM method is presented, which allows a sensitive, robust, biochemical assessment of native protein-protein interactions, without requiring genetic engineering or large sample sizes.

IP-FCM:  Immunoprecipitation Detected by Flow Cytometry

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

The mechanisms that govern the interstitial motility of CD4 effector T cells at sites of inflammation are relatively unknown. We present a non-invasive approach to visualize and manipulate in vitro-primed CD4 T cells in the inflamed ear dermis, allowing for study of the dynamic behavior of these cells in situ.

Imaging CD4 T Cell Interstitial Migration in the Inflamed Dermis

Microglial phagocytosis is critical for the maintenance of tissue homeostasis and inadequate phagocytic function has been implicated in pathology. However, assessing microglia function in vivo is technically challenging. We have developed a simple but robust technique for precisely monitoring and quantifying the phagocytic potential of microglia in a physiological setting.

Assessing Retinal Microglial Phagocytic Function In Vivo Using a Flow Cytometry-based Assay

We describe here an isolation method to obtain human endocervical intraepithelial lymphocytes for the analysis of intraepithelial gamma delta T cells. This protocol can be extended for the purification of endocervical gamma delta T cells by magnetic beads or by cell sorting.

Isolation and Flow Cytometric Analysis of Human Endocervical Gamma Delta T Cells

Here, we describe a complete workflow for the qualitative and quantitative analysis of immune synapses between primary human T cells and antigen-presenting cells. The method is based on imaging flow cytometry, which allows the acquisition and evaluation of several thousand cell images within a relatively short period of time.

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

A flow cytometry-based cellular binding assay is described that is primarily used as a screening tool to identify compounds that inhibit the binding of a fluorescently labeled CXC chemokine ligand 12 (CXCL12) to the CXC chemokine receptor 4 (CXCR4).

A Flow Cytometry-based Assay to Identify Compounds That Disrupt Binding of Fluorescently-labeled CXC Chemokine Ligand 12 to CXC Chemokine Receptor 4

Isolation of cells from dissected implants and their characterization by flow cytometry can significantly contribute to understanding the pattern of immune response against implants. This paper describes a precise method for the isolation of cells from dissected implants and their staining for flow cytometric analysis.

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

This protocol describes a flow cytometry-based, high-throughput screening method to identify small-molecule drugs that inhibit β2 integrin activation on human neutrophils.

A Flow Cytometry-Based High-Throughput Technique for Screening Integrin-Inhibitory Drugs

Calcium influx, a measure of T-cell signaling, is an effective way to analyze responses to T-cell receptor stimulation. This protocol for multiplexing Indo-1 with panels of antibodies directed at cell surface molecules takes advantage of the highly flexible capabilities of full spectrum flow cytometry.

Analysis of T-cell Receptor-Induced Calcium Influx in Primary Murine T-cells by Full Spectrum Flow Cytometry

This protocol combines flow cytometry and single-cell RNA sequencing to isolate and characterize microglial cell states in the cerebellum of early postnatal mouse brains. It uses enzymatic dissociation, Percoll centrifugation, and immunostaining to reveal microglia heterogeneity and improve understanding of their roles in cerebellar development and disease.

Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development

Learn to integrate imaging flow cytometry with transcriptomic profiling to evaluate altered endocytic CD1d trafficking in human dendritic cells. This method connects gene expression changes to protein function for toxicological diagnostics.

Integrate Imaging Flow Cytometry and Transcriptomic Profiling to Evaluate Altered Endocytic CD1d Trafficking

Explore More Videos