Targeted Multiomics: Visualise Transcript and Protein Data Together
Discrepancies between low-abundance transcripts and proteins make data visualisation challenging. Transcripts tend to be expressed at a much lower level than proteins. The range of expression is higher for proteins. To overcome these challenges, Mair et al. used targeted multiomics to study immune-cell heterogeneity at a low sequencing depth.
Read the paper, A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level.
Measuring Low-Abundance Transcripts and Protein Expression
Working with sorted pan CD45+ live cells derived from cryopreserved peripheral blood mononuclear cells (PBMCs), the authors used a high-throughput (>104 single cells) approach in which they captured the cells in nano wells in the BD Rhapsody™ Single-Cell Analysis System, combined with BD® AbSeq Antibody-Oligonucleotide Conjugates. They simultaneously interrogated 492 immune-related genes and 41 surface proteins commonly used for immunophenotyping. Also, they performed 30-parameter flow cytometry to measure the expression of the same targets.
Targeted Multiomics Found to be More Efficient than WTA
The authors found that the targeted approach was more efficient than whole-transcriptome analysis (WTA) in detecting some of the low-abundance transcripts, requiring only one-tenth of the reading depth. This approach also enabled them to separate memory T-cell subsets, as well as regulatory T cells (Tregs), something that is normally difficult to do due to the low amounts of messenger ribonucleic acid (mRNA) that is recovered from T lymphocytes. The protein and transcription data were visualised in a single plot by adapting the one-dimensional soli expression by nonlinear stochastic embedding (One-SENSE).
Targeted Multiomics and High-dimensional Flow Cytometry: a Powerful Combination
This technique allows for effective visualisation and identification of cellular phenotypes differing by transcripts and proteins. The authors propose that the approach of targeted multiomics combined with high-dimensional flow cytometry and the combined visualisation can constitute a methodological toolset for generating high-throughput, multiomic, single-cell data with a focus on selected targets at a minimal read depth.
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