Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

Cell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed “single-cell combinatorial fluidic indexing” (scifi) and applied it to single-cell RNA sequencing. The resulting scifi-RNA-seq assay combines one-step combinatorial pre-indexing of single-cell transcriptomes with subsequent single-cell RNA-seq using widely available droplet microfluidics. Pre-indexing allows us to load multiple cells per droplet, which increases the throughput of droplet-based single-cell RNA-seq up to 15-fold, and it provides a straightforward way of multiplexing hundreds of samples in a single scifi-RNA-seq experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow, thereby enabling massive-scale scRNA-seq experiments for a broad range of applications ranging from population genomics to drug screens with scRNA-seq readout. We benchmarked scifi-RNA-seq on various human and mouse cell lines, and we demonstrated its feasibility for human primary material by profiling TCR activation in T cells.

[1]  Nathan C. Sheffield,et al.  Multi-Omics of Single Cells: Strategies and Applications , 2016, Trends in biotechnology.

[2]  Andrew J. Hill,et al.  The single cell transcriptional landscape of mammalian organogenesis , 2019, Nature.

[3]  S. Orkin,et al.  Mapping the Mouse Cell Atlas by Microwell-Seq , 2018, Cell.

[4]  Jennifer L Hu,et al.  MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices , 2019, Nature Methods.

[5]  Thomas M. Norman,et al.  A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response , 2016, Cell.

[6]  Martin J. Aryee,et al.  Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility , 2019, Nature Biotechnology.

[7]  J. C. Love,et al.  Erratum: Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput , 2017, Nature Methods.

[8]  Bhuvan Unhelkar,et al.  Strategies and Applications , 2011 .

[9]  Kun Zhang,et al.  High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell , 2019, Nature Biotechnology.

[10]  Andrew C. Adey,et al.  Sequencing thousands of single-cell genomes with combinatorial indexing , 2017 .

[11]  Richard A. Muscat,et al.  Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding , 2018, Science.

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

[13]  Andrew C. Adey,et al.  Highly scalable generation of DNA methylation profiles in single cells , 2018, Nature Biotechnology.

[14]  I. Amit,et al.  Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq , 2016, Cell.

[15]  Samantha A. Morris,et al.  CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics , 2019, Genome Biology.

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

[17]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[18]  Michael J. T. Stubbington,et al.  The Human Cell Atlas: from vision to reality , 2017, Nature.

[19]  Thomas M. Norman,et al.  Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens , 2016, Cell.

[20]  Andrew C. Adey,et al.  Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing , 2015, Science.

[21]  P. Reddien,et al.  Fundamentals of planarian regeneration. , 2004, Annual review of cell and developmental biology.

[22]  William Stafford Noble,et al.  Massively multiplex single-cell Hi-C , 2016, Nature Methods.

[23]  André F. Rendeiro,et al.  Pooled CRISPR screening with single-cell transcriptome read-out , 2017, Nature Methods.

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

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

[26]  Silas S. Brown,et al.  PrimerPooler: automated primer pooling to prepare library for targeted sequencing , 2017, Biology methods & protocols.

[27]  E. Shapiro,et al.  Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.

[28]  Shiwei Zheng,et al.  Cell “hashing” with barcoded antibodies enables multiplexing and doublet detection for single cell genomics , 2017, bioRxiv.

[29]  S. P. Fodor,et al.  Combinatorial labeling of single cells for gene expression cytometry , 2015, Science.

[30]  Jonathan S. Packer,et al.  Massively multiplex chemical transcriptomics at single-cell resolution , 2019, Science.

[31]  J. C. Love,et al.  Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput , 2017, Nature Methods.

[32]  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.

[33]  qPCR-based characterization of DNA fragmentation efficiency of Tn5 transposomes , 2017, Biology methods & protocols.

[34]  Andrew C. Adey,et al.  Joint profiling of chromatin accessibility and gene expression in thousands of single cells , 2018, Science.

[35]  Bertrand Z. Yeung,et al.  Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics , 2018, Genome Biology.

[36]  B. Ren,et al.  An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome , 2019, Nature Structural & Molecular Biology.