Molecular spikes: a gold standard for single-cell RNA counting

Single-cell sequencing methods rely on molecule-counting strategies to account for amplification biases, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes-RNA spike-ins containing built-in unique molecular identifiers (UMIs) that we use to identify critical experimental and computational conditions for accurate RNA counting in single-cell RNA-sequencing (scRNA-seq). Using molecular spikes, we uncovered impaired RNA counting in methods that were not informative for cellular RNA abundances due to inflated UMI counts. We further leverage molecular spikes to improve estimates of total endogenous RNA amounts in cells, and introduce a strategy to correct experiments with impaired RNA counting. The molecular spikes and the accompanying R package UMIcountR (https://github.com/cziegenhain/UMIcountR) will improve the validation of new methods, better estimate and adjust for cellular mRNA amounts and enable more indepth characterization of RNA counting in scRNA-seq.

Automated summary

This study introduces a strategy using molecular spikes with unique molecular identifiers (UMIs) to evaluate the accuracy of RNA counting in single-cell sequencing. The authors uncover and correct impaired counting methods, as well as improve estimates of cellular mRNA amounts. These molecular spikes and the accompanying software will benefit the validation of new methods and enable more accurate characterization of RNA counting in single-cell sequencing.