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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antigen+Processing+and+Presentation+Mechanisms+in+Myeloid+Cells.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MR1+uses+an+endocytic+pathway+to+activate+mucosal-associated+invariant+T+cells.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Endosomes+are+specialized+platforms+for+bacterial+sensing+and+NOD2+signalling.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pathways+of+antigen+processing.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Invariant+natural+killer+T+cells:+an+innate+activation+scheme+linked+to+diverse+effector+functions.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CD1+mediated+T+cell+recognition+of+glycolipids.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+cellular+lipids+bound+to+human+CD1d+molecules.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Natural+lipid+ligands+associated+with+human+CD1d+targeted+to+different+subcellular+compartments.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovery+of+deoxyceramides+and+diacylglycerols+as+CD1b+scaffold+lipids+among+diverse+groove-blocking+lipids+of+the+human+CD1+system.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CD1a-autoreactive+T+cells+recognize+natural+skin+oils+that+function+as+headless+antigens.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+biology+of+antigen+processing+in+vitro+and+in+vivo.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardizing+immunophenotyping+for+the+Human+Immunology+Project.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibition+of+endocytic+lipid+antigen+presentation+by+common+lipophilic+environmental+pollutants.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+and+simple+method+for+purification+of+nucleic+acids.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agilent+2100+bioanalyzer+for+restriction+fragment+length+polymorphism+analysis+of+the+Campylobacter+jejuni+flagellin+gene.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ToppCluster:+a+multiple+gene+list+feature+analyzer+for+comparative+enrichment+clustering+and+network-based+dissection+of+biological+systems.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+genomic+information+with+biological+networks+using+Cytoscape).">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+biological+networks+and+gene+expression+data+using+Cytoscape.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Donor-unrestricted+T+cells+in+the+human+CD1+system.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+Innate-Like+T+Cells+in+Tuberculosis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracellular+pathways+of+CD1+antigen+presentation.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stabilization+of+cooperative+virulence+by+the+expression+of+an+avirulent+phenotype.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+proliferative+and+mature+beta-cells+in+the+islets+of+Langerhans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biological+imaging+software+tools.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+and+quantitative+measurement+of+protein-protein+colocalization+in+live+cells.">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 border="0" cellpadding="0" cellspacing="0" style="width:1340px;" width="1340">
	<tbody>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">Transcriptomics</td>
			<td style="width:448px;">Illumina</td>
			<td style="width:417px;">HiSeq 2500 v4</td>
			<td style="width:191px;">Illumina HiSeq system</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">ImagingStream X</td>
			<td style="width:448px;">Millipore</td>
			<td>100220</td>
			<td>Imaging flow cytometry</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">mirVana miRNA Isolation Kit</td>
			<td style="width:448px;">Thermo Fisher</td>
			<td></td>
			<td>Total RNA extraction</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">Agilent RNA 6000 Pico Kit</td>
			<td style="width:448px;">Agilent</td>
			<td></td>
			<td>Total RNA QC analysis</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">Veriti 96-Well Fast Thermal Cycler</td>
			<td style="width:448px;">Thermo Fisher</td>
			<td></td>
			<td>PCR, enzyme reaction</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">NEBNext Poly(A) mRNA Magnetic Isolation Module&#160;</td>
			<td style="width:448px;">New England Biolab</td>
			<td></td>
			<td>PolyA RNA purification</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">Automated SMARTer Apollo system</td>
			<td style="width:448px;">Takara</td>
			<td></td>
			<td>PolyA RNA purification</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">NEBNext Ultra RNA Library Prep Kit for Illumina</td>
			<td style="width:448px;">New England Biolab</td>
			<td></td>
			<td>Library preparation</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">Agencourt AMPure XP magnetic beads</td>
			<td style="width:448px;">Beckman Coulter</td>
			<td></td>
			<td>DNA purification&#160;</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">2100 Bioanalyzer</td>
			<td style="width:448px;">Agilent</td>
			<td></td>
			<td>Library QC, size distribution analysis</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">Agilent High Sensitivity DNA Kit</td>
			<td style="width:448px;">Agilent</td>
			<td></td>
			<td>Library QC, size distribution analysis</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">QuantStudio 5 Real-Time PCR System (Thermo Fisher)</td>
			<td style="width:448px;">Thermo Fisher</td>
			<td></td>
			<td>Library quantification</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">NEBNext Library Quant Kit</td>
			<td style="width:448px;">New England Biolab</td>
			<td></td>
			<td>Library quantification</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">cBot</td>
			<td style="width:448px;">Illumina</td>
			<td></td>
			<td>Library cluster generation</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">TruSeq SR Cluster Kit v3 - cBot &#8211; HS</td>
			<td style="width:448px;">Illumina</td>
			<td></td>
			<td>Library cluster generation</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">HiSeq 1000</td>
			<td style="width:448px;">Illumina</td>
			<td></td>
			<td>Sequencing</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">TruSeq SBS Kit v3 - HS (50-cycles)</td>
			<td style="width:448px;">Illumina</td>
			<td></td>
			<td>Sequencing</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">Phycoerythrin/Cyanine 7 (PE/Cy7)</td>
			<td style="width:448px;">Bio Legend</td>
			<td style="width:417px;">L243</td>
			<td></td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">Phycoerythrin/Cyanine 5 (PE/Cy5)</td>
			<td style="width:448px;">Bio Legend</td>
			<td align="right" style="width:417px;">3.9</td>
			<td></td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">Brilliant violet 421</td>
			<td style="width:448px;">Bio Legend</td>
			<td style="width:417px;">L161</td>
			<td></td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">PE-anti-mouse IgG2b</td>
			<td style="width:448px;">Bio Legend</td>
			<td align="right" style="width:417px;">51.1</td>
			<td></td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:284px;">Alexa Fluor 647 (AF647)</td>
			<td style="width:448px;">Bio Legend</td>
			<td style="width:417px;">CD107a(H4A3)</td>
			<td></td>
		</tr>
	</tbody>
</table>

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&#160;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...

Watch this Scientific Journal Video about Integrate Imaging Flow Cytometry and Transcriptomic Profiling to Evaluate Altered Endocytic CD1d Trafficking at JoVE.com

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

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

6.6K Views.  University of Cincinnati College of Medicine. 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.

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

Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Regulatory T cells (Tregs) are critical mediators of immune tolerance to self-antigens. In addition, they are crucial regulators of the immune response following an infection. Despite efforts to identify unique surface marker on Tregs, the only unique feature is their ability to suppress the proliferation and function of effector T cells. While it is clear that only in vitro assays can be used in assessing human Treg function, this becomes problematic when assessing the results from cross-sectional studies where healthy cells and cells isolated from subjects with autoimmune diseases (like Type 1 Diabetes-T1D) need to be compared. There is a great variability among laboratories in the number and type of responder T cells, nature and strength of stimulation, Treg:responder ratios and the number and type of antigen-presenting cells (APC) used in human in vitro suppression assays. This variability makes comparison between studies measuring Treg function difficult. The Treg field needs a standardized suppression assay that will work well with both healthy subjects and those with autoimmune diseases. We have developed an in vitro suppression assay that shows very little intra-assay variability in the stimulation of T cells isolated from healthy volunteers compared to subjects with underlying autoimmune destruction of pancreatic &beta;-cells. The main goal of this piece is to describe an in vitro human suppression assay that allows comparison between different subject groups. Additionally, this assay has the potential to delineate a small loss in nTreg function and anticipate further loss in the future, thus identifying subjects who could benefit from preventive immunomodulatory therapy1. Below, we provide thorough description of the steps involved in this procedure. We hope to contribute to the standardization of the in vitro suppression assay used to measure Treg function. In addition, we offer this assay as a tool to recognize an early state of immune imbalance and a potential functional biomarker for T1D.

Human In Vitro Suppression as Screening Tool for the Recognition of an Early State of Immune Imbalance

Tregs are potent suppressors of the immune system. There is a lack of unique surface markers to define them, hence, definitions of Tregs are primarily functional. Here we describe an optimized in vitro assay capable of identifying immune imbalance in subjects at risk to develop T1D.

Regulatory T cells (Tregs) are critical mediators of immune tolerance to self-antigens. In addition, they are crucial regulators of the immune response ...

Cell-based Flow Cytometry Assay to Measure Cytotoxic Activity

Cytolytic activity of CD8+ T cells is rarely evaluated. We describe here a new cell-based assay to measure the capacity of antigen-specific CD8+ T cells to kill CD4+ T cells loaded with their cognate peptide. Target CD4+ T cells are divided into two populations, labeled with two different concentrations of CFSE. One population is pulsed with the peptide of interest (CFSE-low) while the other remains un-pulsed (CFSE-high). Pulsed and un-pulsed CD4+ T cells are mixed at an equal ratio and incubated with an increasing number of purified CD8+ T cells. The specific killing of autologous target CD4+ T cells is analyzed by flow cytometry after coculture with CD8+ T cells containing the antigen-specific effector CD8+ T cells detected by peptide/MHCI tetramer staining. The specific lysis of target CD4+ T cells measured at different effector versus target ratios, allows for the calculation of lytic units, LU30/106 cells. This simple and straightforward assay allows for the accurate measurement of the intrinsic capacity of CD8+ T cells to kill target CD4+ T cells.

This protocol describes a sensitive, cell-based cytotoxicity assay. By enumerating the decrease in frequency of live target CD4+ T cells in the presence of an increasing number of effector CD8+ T cells, this assay allows for the direct assessment of cytolytic activity of antigen-specific CD8+ T cells.

Cytolytic activity of CD8+ T cells is rarely evaluated. We describe here a new cell-based assay to measure the capacity of antigen-specific CD8+ T cells ...

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

Granulocytes play a key role in the body&#8217;s innate immune response to bacterial and viral infections. While methods exist to measure granulocyte function, in general these are limited in terms of the information they can provide. For example, most existing assays merely provide a percentage of how many granulocytes are activated following a single, fixed length incubation. Complicating matters, most assays focus on only one aspect of function due to limitations in detection technology. This report demonstrates a technique for simultaneous measurement of granulocyte phagocytosis of bacteria and oxidative burst. By measuring both of these functions at the same time, three unique phenotypes of activated granulocytes were identified: 1) Low Activation (minimal phagocytosis, no oxidative burst), 2) Moderate Activation (moderate phagocytosis, some oxidative burst, but no co-localization of the two functional events), and 3) High Activation (high phagocytosis, high oxidative burst, co-localization of phagocytosis and oxidative burst). A fourth population that consisted of inactivated granulocytes was also identified. Using assay incubations of 10, 20, and 40-min the effect of assay incubation duration on the redistribution of activated granulocyte phenotypes was assessed. A fourth incubation was completed on ice as a control. By using serial time incubations, the assay may be able to able to detect how a treatment spatially affects granulocyte function. All samples were measured using an image-based flow cytometer equipped with a quantitative imaging (QI) option, autosampler, and multiple lasers (488, 642, and 785 nm).

Image-based Flow Cytometry Technique to Evaluate Changes in Granulocyte Function In Vitro

This method demonstrates a technique for assessing granulocyte function by simultaneously measuring phagocytosis of bacteria and oxidative burst. Image-based flow cytometry allowed for the identification of three distinct subsets of activated granulocytes that all differed in their relative functional capacity.

Granulocytes play a key role in the body&#8217;s innate immune response to bacterial and viral infections. While methods exist to measure granulocyte ...

A Simple and Rapid Protocol to Non-enzymatically Dissociate Fresh Human Tissues for the Analysis of Infiltrating Lymphocytes

The ability of malignant cells to evade the immune system, characterized by tumor escape from both innate and adaptive immune responses, is now accepted as an important hallmark of cancer. Our research on breast cancer focuses on the active role that tumor infiltrating lymphocytes play in tumor progression and patient outcome. Toward this goal, we developed a methodology for the rapid isolation of intact lymphoid cells from normal and abnormal tissues in an effort to evaluate them proximate to their native state. Homogenates prepared using a mechanical dissociator show both increased viability and cell recovery while preserving surface receptor expression compared to enzyme-digested tissues. Furthermore, enzymatic digestion of the remaining insoluble material did not recover additional CD45+ cells indicating that quantitative and qualitative measurements in the primary homogenate likely genuinely reflect infiltrating subpopulations in the tissue fragment. The lymphoid cells in these homogenates can be easily characterized using immunological (phenotype, proliferation, etc.) or molecular (DNA, RNA and/or protein) approaches. CD45+ cells can also be used for subpopulation purification, in vitro expansion or cryopreservation. An additional benefit of this approach is that the primary tissue supernatant from the homogenates can be used to characterize and compare cytokines, chemokines, immunoglobulins and antigens present in normal and malignant tissues. This protocol functions extremely well for human breast tissues and should be applicable to a wide variety of normal and abnormal tissues.

This protocol describes the rapid non-enzymatic dissociation of fresh human tissue fragments for qualitative and quantitative assessment of CD45+ cells (lymphocytes/leukocytes) present in various normal and malignant human tissues. Additionally, the supernatant obtained from the primary tissue homogenate can be collected and stored for further analysis or experimentation.

The ability of malignant cells to evade the immune system, characterized by tumor escape from both innate and adaptive immune responses, is now accepted ...

Fluorescence-activated Cell Sorting for Purification of Plasmacytoid Dendritic Cells from the Mouse Bone Marrow

Fluorescence-activated cell sorting (FACS) is a technique to purify specific cell populations based on phenotypes detected by flow cytometry. This method enables researchers to better understand the characteristics of a single cell population without the influence of other cells. Compared to other methods of cell enrichment, such as magnetic-activated cell sorting (MCS), FACS is more flexible and accurate for cell separation due to the ability of phenotype detection by flow cytometry. In addition, FACS is usually capable of separating multiple cell populations simultaneously, which improves the efficiency and diversity of experiments. Although FACS has some limitations, it has been broadly used to purify cells for functional studies in both in vitro and in vivo settings. Here we report a protocol using fluorescence-activated cell sorting to isolate a very rare population of immune cells, plasmacytoid dendritic cells (pDC), with high purity from the bone marrow of lupus-prone mice for in vitro functional studies of pDC.

We report a protocol using fluorescence-activated cell sorting to isolate plasmacytoid dendritic cells (pDC) with high purity from the bone marrow of lupus-prone mice for functional studies of pDC.

Fluorescence-activated cell sorting (FACS) is a technique to purify specific cell populations based on phenotypes detected by flow cytometry. This method ...

Murine Lymphocyte Labeling by 64Cu-Antibody Receptor Targeting for In Vivo Cell Trafficking by PET/CT

This protocol illustrates the production of 64Cu and the chelator conjugation/radiolabeling of a monoclonal antibody (mAb) followed by murine lymphocyte cell culture and 64Cu-antibody receptor targeting of the cells. In vitro evaluation of the radiolabel and non-invasive in vivo cell tracking in an animal model of an airway delayed-type hypersensitivity reaction (DTHR) by PET/CT are described.
In detail, the conjugation of a mAb with the chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) is shown. Following the production of radioactive 64Cu, radiolabeling of the DOTA-conjugated mAb is described. Next, the expansion of chicken ovalbumin (cOVA)-specific CD4+ interferon (IFN)-&#947;-producing T helper cells (cOVA-TH1) and the subsequent radiolabeling of the cOVA-TH1 cells are depicted. Various in vitro techniques are presented to evaluate the effects of 64Cu-radiolabeling on the cells, such as the determination of cell viability by trypan blue exclusion, the staining for apoptosis with Annexin V for flow cytometry, and the assessment of functionality by IFN-&#947; enzyme-linked immunosorbent assay (ELISA). Furthermore, the determination of the radioactive uptake into the cells and the labeling stability are described in detail. This protocol further describes how to perform cell tracking studies in an animal model for an airway DTHR and, therefore, the induction of cOVA-induced acute airway DHTR in BALB/c mice is included. Finally, a robust PET/CT workflow including image acquisition, reconstruction, and analysis is presented.
The 64Cu-antibody receptor targeting approach with subsequent receptor internalization provides high specificity and stability, reduced cellular toxicity, and low efflux rates compared to common PET-tracers for cell labeling, e.g.64Cu-pyruvaldehyde bis(N4-methylthiosemicarbazone) (64Cu-PTSM). Finally, our approach enables non-invasive in vivo cell tracking by PET/CT with an optimal signal-to-background ratio for 48 h. This experimental approach can be transferred to different animal models and cell types with membrane-bound receptors that are internalized.

Murine Lymphocyte Labeling by 64Cu-Antibody Receptor Targeting for In Vivo  Cell Trafficking by PET/CT

Following the preparation of a 64Cu-modified monoclonal antibody binding to a transgenic murine T cell receptor, T cells are radiolabeled in vivo, analyzed for viability, functionality, labeling stability and apoptosis, and adoptively transferred into mice with an airway delayed-type hypersensitivity reaction for non-invasive imaging by positron emission tomography/computed tomography (PET/CT).

This protocol illustrates the production of 64Cu and the chelator conjugation/radiolabeling of a monoclonal antibody (mAb) followed by murine lymphocyte ...

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

The measurement of immunological reactivity to donor antigens in transplant recipients is likely to be crucial for the successful reduction or withdrawal of immunosuppression. The mixed leukocyte reaction (MLR), limiting dilution assays, and trans-vivo delayed-type hypersensitivity (DTH) assay have all been applied to this question, but these methods have limited predictive ability and/or significant practical limitations that reduce their usefulness. Imaging flow cytometry is a technique that combines the multiparametric quantitative powers of flow cytometry with the imaging capabilities of fluorescent microscopy. We recently made use of an imaging flow cytometry approach to define the proportion of recipient T cells capable of forming mature immune synapses with donor antigen-presenting cells (APCs). Using a well-characterized mouse heart transplant model, we have shown that the frequency of in vitro immune synapses among T-APC membrane contact events strongly predicted allograft outcome in rejection, tolerance, and a situation where transplant survival depends on induced regulatory T cells. The frequency of T-APC contacts increased with T cells from mice during acute rejection and decreased with T cells from mice rendered unresponsive to alloantigen. The addition of regulatory T cells to the in vitro system reduced prolonged T-APC contacts. Critically, this effect was also seen with human polyclonally expanded, naturally occurring regulatory T cells, which are known to control the rejection of human tissues in humanized mouse models. Further development of this approach may allow for a deeper characterization of the alloreactive T-cell compartment in transplant recipients. In the future, further development and evaluation of this method using human cells may form the basis for assays used to select patients for immunosuppression minimization, and it can be used to measure the impact of tolerogenic therapies in the clinic.

This paper describes a method for measuring alloreactivity in a mixed population of T cells using imaging flow cytometry.

The measurement of immunological reactivity to donor antigens in transplant recipients is likely to be crucial for the successful reduction or withdrawal ...

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

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins towards the immune synapse to assure a stable binding and signal exchange. Classical confocal, TIRF, or super-resolution microscopy have been used to study the immune synapse. Since these methods require manual image acquisition and time-consuming quantification, the imaging of rare events is challenging. Here, we describe a workflow that enables the morphological analysis of tens of thousands of cells. Immune synapses are induced between primary human T cells in pan-leukocyte preparations and Staphylococcus aureus enterotoxin B (SEB)-loaded Raji cells as APCs. Image acquisition is performed with imaging flow cytometry, also called In-Flow microscopy, which combines features of a flow cytometer and a fluorescence microscope. A complete gating strategy for identifying T cell/APC couples and analyzing the immune synapses is provided. As this workflow allows the analysis of immune synapses in unpurified pan-leukocyte preparations and hence requires only a small volume of blood (i.e., 1 mL), it can be applied to samples from patients. Importantly, several samples can be prepared, measured, and analyzed in parallel.

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.

The immune synapse is the area of communication between T cells and antigen-presenting cells (APCs). T cells polarize surface receptors and proteins ...

Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy

Soluble N-ethylmaleimide sensitive fusion protein (NSF) attachment protein receptor (SNARE) proteins are key for membrane trafficking, as they catalyze membrane fusion within eukaryotic cells. The SNARE protein family consists of about 36 different members. Specific intracellular transport routes are catalyzed by specific sets of 3 or 4 SNARE proteins that thereby contribute to the specificity and fidelity of membrane trafficking. However, studying the precise function of SNARE proteins is technically challenging, because SNAREs are highly abundant and functionally redundant, with most SNAREs having multiple and overlapping functions. In this protocol, a new method for the visualization of SNARE complex formation in live cells is described. This method is based on expressing SNARE proteins C-terminally fused to fluorescent proteins and measuring their interaction by F&#246;rster resonance energy transfer (FRET) employing fluorescence lifetime imaging microscopy (FLIM). By fitting the fluorescence lifetime histograms with a multicomponent decay model, FRET-FLIM allows (semi-)quantitative estimation of the fraction of the SNARE complex formation at different vesicles. This protocol has been successfully applied to visualize SNARE complex formation at the plasma membrane and at endosomal compartments in mammalian cell lines and primary immune cells, and can be readily extended to study SNARE functions at other organelles in animal, plant, and fungal cells.

This protocol describes a new method allowing for the quantitative visualization of complex formation of SNARE proteins, based on F&#246;rster resonance energy transfer, and fluorescence lifetime imaging microscopy.

Soluble N-ethylmaleimide sensitive fusion protein (NSF) attachment protein receptor (SNARE) proteins are key for membrane trafficking, as they catalyze ...

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and their important role in lung development and function, as well as their lung-localized responses to infection and inflammation. To date, no unified method for identification, isolation, and handling of alveolar macrophages from humans and mice exists. Such a method is needed for studies on these important innate immune cells in various experimental settings. The method described here, which can be easily adopted by any laboratory, is a simplified approach to harvesting alveolar macrophages from bronchoalveolar lavage fluid or from lung tissue and maintaining them in vitro. Because alveolar macrophages primarily occur as adherent cells in the alveoli, the focus of this method is on dislodging them prior to harvest and identification. The lung is a highly vascularized organ, and various cell types of myeloid and lymphoid origin inhabit, interact, and are influenced by the lung microenvironment. By using the set of surface markers described here, researchers can easily and unambiguously distinguish alveolar macrophages from other leukocytes, and purify them for downstream applications. The culture method developed herein supports both human and mouse alveolar macrophages for in vitro growth, and is compatible with cellular and molecular studies.

Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

This communication describes methodologies for isolation and culture of alveolar macrophages from humans and murine models for experimental purposes.

Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and ...