A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level

Florian Mair,Jami R Erickson,Valentin Voillet,Yannick Simoni,Timothy Bi,Aaron J Tyznik,Jody Martin,Raphael Gottardo,Evan W Newell,Martin Prlic

doi:10.1016/j.celrep.2020.03.063

Abstract

SUMMARYHigh-throughput single-cell RNA sequencing (scRNA-seq) has become a frequently used tool to assess immune cell heterogeneity. Recently, the combined measurement of RNA and protein expression was developed, commonly known as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcripts but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools tovisualize combined transcript-protein datasets. Here, we describe a targeted transcriptomics approach that combines an analysis of over 400 genes with simultaneous measurement of over 40 proteins on 2 × 104 cells in a single experiment. This targeted approach requires only about one-tenth of the read depth compared to a whole-transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic datasets, we adapted one-dimensional soli expression by nonlinear stochastic embedding (One-SENSE) for intuitive visualization of protein-transcript relationships on a single-cell level.

Highlights

Pioneering work almost 20 years ago illustrated the ability to study transcript expression at the single-cell level (Chiang and Melton, 2003; Phillips and Eberwine, 1996), but recent advances in microfluidics and reagents allow the highthroughput analysis of transcripts of 104 single cells in one experiment (Jaitin et al, 2014; Klein et al, 2015; Macosko et al, 2015)
Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcripts and nearly doubles the sequencing read depth required per single cell
We describe a targeted transcriptomics approach that combines an analysis of over 400 genes with simultaneous measurement of over 40 proteins on 2 3 104 cells in a single experiment. This targeted approach requires only about one-tenth of the read depth compared to a whole-transcriptome approach while retaining high sensitivity for low abundance transcripts

Summary

Introduction

Pioneering work almost 20 years ago illustrated the ability to study transcript expression at the single-cell level (Chiang and Melton, 2003; Phillips and Eberwine, 1996), but recent advances in microfluidics and reagents allow the highthroughput analysis of transcripts of 104 single cells in one experiment (Jaitin et al, 2014; Klein et al, 2015; Macosko et al, 2015). The correlation of gene expression and protein expression has been estimated to have a Pearson correlation coefficient between 0.4 (Schwanhausser et al, 2011) and 0.6 (Azimifar et al, 2014). These discrepancies in transcript and protein expression patterns are relevant for the biological interpretation of single-cell transcriptome data and pose analytical challenges. Suitable approaches are required to visualize the data despite the pronounced differences in abundance and dynamic range of expression

Methods

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Discussion

Conclusion