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Evaluation the effects of cell sorting on gene expression ABRF 2016 Ft
Evaluation the effects of cell sorting on gene expression ABRF Ft. Lauderdale FL
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FCRG Members Co-Chairs: Monica DeLay Cincinnati Children’s Hospital
Andrew Box Stowers Institute Members: Alan Bergeron Dartmouth School of Medicine Kathleen Brundage West Virginia University Matthew Cochran University of Rochester Roxana del Rio-Guerra University of Vermont Maris Handley Massachusetts General Hospital Mike Meyer University of Pittsburgh Alan Saluk Scripps Research Institute EB Liaison: Frances Weis-Garcia Memorial Sloan Kettering Peter Lopez NYU Med Center
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Acknowledgements eBioscience Affymetrix - Quantigene Plex gene expression assays Brian McLucas & Stefan Jellbauer - Assay design, optimization and analysis support Cinicinatti Children’s Flow Core Monica DeLay – FCRG co-chair and CCH core manager Alyssa Sproles - CCH core staff and Luminex expert Stowers Institute Tissue Culture Core Chongbei Zhao – tissue culture core staff and ES/iPS cell expert Maria Katt – tissue culture core manager
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Study Goals: Determine if cell sorting induces significant or severe gene expression changes by studying several model cell types. If sorting is found to induce gene expression/pathway changes, seek to find optimal sorter configurations to minimize or eliminate these effects and report findings to the community.
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Last Year’s Study Design
Do this workflow with ‘harsh’ and ‘gentle’ sorter configurations (pressure/nozzle). Ask if there are transcriptome changes across time points more strongly associated with the ‘harsh’ configuration compared to ‘gentle’. Because unsorted primary B cell control is impossible to obtain and we were interested in a pure, primary cell type. RNA Isolation using Qiagen RNeasy Micro kit Spleen Time: 0 Time: 4h FLOW SORTING Y Time: 8h NuGEN Ovation Pico WTA v2 Staining with Anti-CD19 Identified 7 genes as candidates for differentially expressed in harsh sorting conditions. Affymetrix Mouse Gene ST 2.0 Microarray
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QuantiGene Assay Workflow
Current phase – validate candidates using Quantigene Plex QuantiGene Assay Workflow Cell lysate or RNA from sorted cells Target hybridization and signal amplification using QuantiGene Plex Detection using Bio-Plex 200 Note – For lysates, cells were used at a concentration of 1500/l. For RNA, 100 ng was used for each replicate. Some replicates were pooled to achieve this amount.
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Our Quantigene Plex Panel
Microarray hits Symbol abcg1 microarray candidate fos klf4 rgs1 s1pr3 plk2 gm129 Other egr1 shear stress responsive baff cAMP & MAPK pathway blimp-1 tnf-a NFkB pathway il1 il6 p53 targets Symbol AEN apoptosis APAF1 BAX BBC3 CYFIP2 FAS PHLDA3 TNFRSF10B TP53I11 TRAF4 UNC5B TP53I13 CFLAR XIAP p53 targets Symbol BTG2 cell cycle arrest CDKN1A FBXW7 DDB2 DNA repair POLH XPC RRM2B GPR87 survival TNFRSF10B TRIAP1 C12orf5 metabolism PANK1 DRAM1 autophagy PRKAB1 TP53INP1 ALOX5 ROS control FDXR SESN1 SESN2 Heat Shock Proteins/Unfolded Protein Response Symbol Atf4 Atf6 Atf6b Bid Calr Ddit3 Dnajc3 Hsp90aa1 Hsp90b1 Hspa4 Hspa5 Xbp1
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Candidate Genes – QGP validation of uArray results
Expression profiles strongly correlated with time point post-sorting. Some clustering of gene expression by site (different animals). Pressure/nozzle does not appear to be correlated with large expression changes in candidate genes, but maybe very small changes Max fold-changes are small.
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QGP results – full gene panel
Similar trends as with candidate gene subset. Gene expression profiles cluster predominately with time point and site, not so much with pressure/nozzle. Initial analysis doesn’t indicate significant activation of pathways assayed (heat shock, p53 targets).
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Conclusions We observed minimal expression changes (number of genes and fold change) as a result of cell sorting across all instruments and settings. Genes previously identified by microarray to be differentially expressed in sorted B cells were evaluated across a larger sample set using the Quantigene plex assay in addition to several stress and cell damage pathway genes. The QuantiGene plex assay is a simple and flexible alternative to qPCR since sample input can be simplified to using cell lysate and avoid RNA and cDNA processing steps. Similar results were obtained for mouse V6.5 ES cells and CD11c enriched mouse spleen cells (see FCRG poster #57 here at ABRF) Next… Run QGP data through IPA for better pathway analysis. PCA to find genes contribution most to variance between samples/conditions. Finish analysis and begin writing up the series of experiments on this work from the last 2-3 years for publication so results can be shared with the community.
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