Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

Nucleic Acids Res. 2022 Jan 25;50(2):e12. doi: 10.1093/nar/gkab1071.


Considerable effort has been devoted to refining experimental protocols to reduce levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods
  • Databases, Genetic
  • Gene Expression Profiling / methods
  • Gene Library*
  • Humans
  • RNA-Seq / methods*
  • RNA-Seq / standards
  • Sequence Analysis, RNA / methods
  • Single-Cell Analysis / methods*
  • Single-Cell Analysis / standards
  • Software