Total-mRNA-Aware Analysis for Droplet-Based Single Cell Sequencing

Abstract

Controlling total mRNA content differences between cell populations is critical in comparative transcriptomic measurements. Due to poor compatibility with ERCC, a good control for droplet-based scRNA-seq is yet to be discovered. Normalizing cells to a common count distribution has been adopted as a silent compromise. Such practice profoundly confounds downstream analysis and mislead discoveries. We present TOMAS, a computational framework that derives total mRNA content ratios between cell populations via deconvoluting their heterotypic doublets. Experiments showed that cell types can have total mRNA differences by many folds and TOMAS can accurately infer the ratios between them. We demonstrate that TOMAS corrects bias in downstream analysis and rectifies a plethora of previously counter-intuitive or inconclusive analytical results. We argue against the opinion that doublets are undesired scale-limiting factors and revealed the unique value of doublets as controls in scRNA-seq. We advocate for their essential role in future large-scale scRNA-seq experiments.

A revised single-cell transcriptomic atlas ofXenopusembryo reveals new differentiation dynamics

“…Note that customary filtering of cells based on thresholding the total mRNA levels does not take into account the fact that cell types differ widely in how much mRNA they harbor. Cells that are actively synthesizing proteins will have a lot of mRNA [35,36]. This includes cells that are undergoing rapid growth and division, cells that are responding to stimuli or stress, and cells that are specialized for protein synthesis, such as secretory cells and neurons.…”