mcSCRB-seq: sensitive and powerful single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has emerged as the central genome-wide method to characterize cellular identities and processes. While performance of scRNA-seq methods is improving, an optimum in terms of sensitivity, cost-efficiency and flexibility has not yet been reached. Among the flexible plate-based methods “Single-Cell RNA-Barcoding and Sequencing” (SCRB-seq) is one of the most sensitive and efficient ones. Based on this protocol, we systematically evaluated experimental conditions such as reverse transcriptases, reaction enhancers and PCR polymerases. We find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to a new scRNA-seq library protocol we call “molecular crowding SCRB-seq” (mcSCRB-seq), which we show to be the most sensitive and one of the most efficient and flexible scRNA-seq methods to date.

[1]  A. Regev,et al.  Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.

[2]  Valentine Svensson,et al.  Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.

[3]  B. H. Pheiffer,et al.  Macromolecular crowding allows blunt-end ligation by DNA ligases from rat liver or Escherichia coli. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Michael A Quail,et al.  Optimal enzymes for amplifying sequencing libraries , 2011, Nature Methods.

[5]  Shuqiang Li,et al.  CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq , 2016, Genome Biology.

[6]  Tetsutaro Hayashi,et al.  Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads , 2017, Genome Biology.

[7]  W. House,et al.  Detection of mycoplasma in cell cultures. , 1967, The Journal of pathology and bacteriology.

[8]  Andrew C. Adey,et al.  Single-Cell Transcriptional Profiling of a Multicellular Organism , 2017 .

[9]  Christoph Ziegenhain,et al.  The impact of amplification on differential expression analyses by RNA-seq , 2016, Scientific Reports.

[10]  I. Hellmann,et al.  Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.

[11]  Richard A. Muscat,et al.  Scaling single cell transcriptomics through split pool barcoding , 2017, bioRxiv.

[12]  A. Bradley,et al.  Agouti C57BL/6N embryonic stem cells for mouse genetic resources , 2009, Nature Methods.

[13]  Christoph Ziegenhain,et al.  powsimR: Power analysis for bulk and single cell RNA-seq experiments , 2017, bioRxiv.

[14]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[15]  The External Rna Controls Consortium The External RNA Controls Consortium: a progress report , 2005 .

[16]  G. Rivas,et al.  Macromolecular Crowding In Vitro, In Vivo, and In Between. , 2016, Trends in biochemical sciences.

[17]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[18]  R. Sandberg,et al.  Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.

[19]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[20]  P. Barbry,et al.  A cost effective 5΄ selective single cell transcriptome profiling approach with improved UMI design , 2016, Nucleic acids research.

[21]  Aleksandra A. Kolodziejczyk,et al.  The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.

[22]  S. Dudoit,et al.  Normalization of RNA-seq data using factor analysis of control genes or samples , 2014, Nature Biotechnology.

[23]  Kathleen F. Kerr,et al.  The External RNA Controls Consortium: a progress report , 2005, Nature Methods.

[24]  N. Neff,et al.  Quantitative assessment of single-cell RNA-sequencing methods , 2013, Nature Methods.

[25]  David P. Kreil,et al.  A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium , 2014, Nature Biotechnology.

[26]  D. Cacchiarelli,et al.  Characterization of directed differentiation by high-throughput single-cell RNA-Seq , 2014, bioRxiv.

[27]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[28]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[29]  A. Oudenaarden,et al.  Validation of noise models for single-cell transcriptomics , 2014, Nature Methods.

[30]  Alvaro Plaza Reyes,et al.  Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos , 2016, Cell.

[31]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[32]  J. Herzfeld,et al.  Macromolecular diffusion in crowded solutions. , 1993, Biophysical journal.

[33]  J. C. Love,et al.  Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples , 2017, Nature Methods.

[34]  Rudolf Jaenisch,et al.  Targeted mutation of the DNA methyltransferase gene results in embryonic lethality , 1992, Cell.

[35]  I. Macaulay,et al.  Single Cell Genomics: Advances and Future Perspectives , 2014, PLoS genetics.

[36]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[37]  A. Oshlack,et al.  Gene length and detection bias in single cell RNA sequencing protocols , 2017, bioRxiv.

[38]  R. Ellis Macromolecular crowding : obvious but underappreciated , 2022 .

[39]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[40]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[41]  David A. Knowles,et al.  Batch effects and the effective design of single-cell gene expression studies , 2016, Scientific Reports.

[42]  L. Elo,et al.  ROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer , 2015, Nucleic acids research.

[43]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[44]  Gioele La Manno,et al.  Quantitative single-cell RNA-seq with unique molecular identifiers , 2013, Nature Methods.

[45]  Christoph Ziegenhain,et al.  zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs , 2017, bioRxiv.