The following study was conducted by Scientists from Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, USA; BD Biosciences, La Jolla, USA; Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, USA; Department of Statistics, University of Washington, Seattle, USA; Department of Global Health and Department of Immunology, University of Washington, Seattle, USA. Study is published in Cell Reports Journal – Cell Press Publishing as detailed below.
Cell Reports Journal – Cell Press Publishing
A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level
Highlights
- Targeted transcriptomics captures immune cell heterogeneity at a low sequencing depth
- Antibody panels for sequencing-based protein measurement require validation
- Combined protein and transcript measurements highlight T cell heterogeneity
- One-SENSE provides an intuitive visualization tool for protein-transcript datasets
Summary
High-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 to visualize 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.
Source:
Cell Reports – Cell Press Publishing
URL: https://www.cell.com/cell-reports/fulltext/S2211-1247(20)30388-0
Citation:
Mair, F., J. R. Erickson, et al. (2020). “A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level.” Cell Reports 31(1).
