Abstract 4290: Multi-omic single cell sequencing for deep cell immune profiling and identification of potential biomarkers for cell therapy and immunotherapy
Authors: Shi Wenge , Laing Christian , Gao Jane , Burns Kerri , Sarikonda Shyam , Pollner Reinhold , Gong Hua 
Published: September 2020

Introduction: Rapidly advancing single cell genomics technology enables uncovering cell heterogeneity missed by bulk sequencing/analysis. Multi-omic single cell sequencing has ability to assess up to a hundred cell surface proteins, hundreds of transcripts to whole transcriptome, or TCR/BCR gene rearrangement in thousands of cells. Here we report deep cell immune profiling of PBMC by assessing selected cell surface proteins by AbSeq and targeted immune response genes using two single cell sequencing platforms.

Experimental procedures: For targeted RNASeq on 10xGenomics Chromium platform, droplets containing single PBMC cell/bead were generated. After cDNA synthesis, libraries with genes of interest were generated using BD Rhapsody Human Immune Response Targeted Panel. For BD Rhapsody multi-omic single cell analysis, PBMC were stained using tagged AbSeq antibodies and sample specific cell tag antibodies. Then viable cells after incubation with cell viability dyes were counted on BD Rhapsody scanner. Some samples were processed for removal of apoptotic cells using EasySep Dead Cell Removal (Annexin V) Kit (Stem Cell Tech). Labeled PBMC from multiple subjects were pooled and loaded to a BD Rhapsody chip. Followed by cell capture beads loading and lysis, mRNA and antibody tags were captured. cDNA was synthesized and libraries containing genes of interest were generated using Human immune response panel along with AbSeq and sample tag libraries. Sequencing was performed on NextSeq 500 (Illumina). Data analysis from 10xGenomics were conduct using its Cell Ranger software. Data analysis from BD Rhapsody workflow were conduct using Seven Bridges Genomics Pipeline and SeqGeq (FlowJo). For comparison, PBMC were also profiled for general major cell populations by flow cytometry.

Summary and Conclusions: Targeted RNASeq using both single cell sequencing platforms enables deep cell profiling with benefits of lower cost and higher sensitivity compared with whole transcriptome RNASeq. The results for general cell populations generated by single cell sequencing are comparable to those generated by flow cytometry, however, single cell sequencing classifies sub-populations in greater details based on gene expression markers. Including tagged AbSeq antibodies to assess cell surface proteins in the workflow further helps classification in the case of post-transcriptional regulation, while identifying alternatively spliced isoforms (CD45RO and CD45RA) elusive by RNASeq. One of the challenges is the requirement for viable cells. Although some apoptotic cells could be removed, less viable cells leftover could interfere single cell sequencing and thus careful data QC bioinformatics is warranted. Overall, multi-omic single cell sequencing could enable deep cell profiling and assessing patient samples has great potential to provide biological insights and identify predictive biomarkers.

Citation Format: Wenge Shi, Christian Laing, Jane Gao, Kerri Burns, Shyam Sarikonda, Reinhold Pollner, Hua Gong. Multi-omic single cell sequencing for deep cell immune profiling and identification of potential biomarkers for cell therapy and immunotherapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4290.