Evaluation of Cell Sorting Effects on Gene Expression

1. Evaluation the effects of cell sorting on gene expressionABRF 2016 Ft. Lauderdale FL

2. 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

3. Acknowledgements • eBioscience Affymetrix- QuantigenePlex 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

4. 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.

5. 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 Time: 8h NuGEN Ovation Pico WTA v2 Staining with Anti-CD19 Y Y Y Y Identified 7 genes as candidates for differentially expressed in harsh sorting conditions. Affymetrix Mouse Gene ST 2.0 Microarray

6. 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.

7. Our Quantigene Plex Panel

8. 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.

9. 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).

10. 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 Quantigeneplex assay in addition to several stress and cell damage pathway genes. • The QuantiGeneplex 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.