Abstract
Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4–9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2–3 d. Sequencing and processing the data through the analytical pipeline take another 1–2 d.
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Data availability
The sequenced data analyzed in this study have been deposited in GEO: GSE123392. Published data analyzed in this paper are available at https://doi.org/10.1016/j.cell.2015.11.013. Access to all published sequenced data generated using the method described here can be found within the relevant publications.
Code availability
Users can access the code freely on our website: http://compgenomics.weizmann.ac.il/tanay/?page_id=672.
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Acknowledgements
We thank the J. H. Hanna lab for the donation of v6.5 mouse embryonic stem (ES) cells, DR4 mouse embryonic fibroblast (MEF) cells and WIBR3 human ES cells. We thank G. Brodsky for help with the artwork. This work was funded by the Chan Zuckerberg Initiative (CZI) (A.T. and I.A.), European Research Council consolidator grants (A.T. and I.A.), ERC-COG (724824-scAssembly; A.T.), ERC-COG (724471-HemTree2.0; I.A.) and the I-Core program (A.T. and I.A.).
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Contributions
H.K.-S. developed the experimental protocols; designed, performed, and analyzed experiments; and wrote the manuscript. E.K. designed experiments, developed the computational methods, and performed bioinformatics analysis. D.A.J. developed experimental protocols. E.D. performed bioinformatic analysis. F.P. contributed to the experimental design. A.T. directed the project and developed computational methods. I.A. directed the project and designed the experiments.
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Key references using this protocol
Jaitin, D. A. et al. Cell 167, 1883–1896.e15 (2016): https://doi.org/10.1016/j.cell.2016.11.039
Keren-Shaul, H. et al. Cell 169, 1276–1290.e17 (2017): https://doi.org/10.1016/j.cell.2017.05.018
Medaglia, C. et al. Science 358, 1622–1626 (2017): https://doi.org/10.1126/science.aao4277
Integrated supplementary information
Supplementary Figure 1 Statistics for 1 million cells analyzed by MARS-seq2.0.
UMI and gene counts distributions on 1,033,944 mouse cells using genome assembly mm9 and 16,488 empty wells analyzed by MARS-seq2.0. Cells analyzed were either immune, epithelial, fibroblast, stromal, hepatocytes or cancer mouse cells as well as cell lines (mouse ES and MEF). (A-B) A histogram showing the fraction of cells (black) and empty wells (red) binned by logarithmic intervals of UMI-counts (A) or by gene-counts (B). (C) Empty wells are enriched with lowly detected genes. Histogram showing the fraction of cells/wells (similar to A-B) binned by the average number of UMIs observed per gene (log2 scale).
Supplementary Figure 2 Optimization of MARS-seq.
(A) Background noise estimation for high and low volume protocol versions. Shown is the average number of UMIs in wells containing single mouse ES (y-axis) vs. an average number of UMIs in negative control wells (x-axis) for two protocol versions with high (green) and low (black) liquid volumes in the RT step. (B) Background noise estimation for different second strand synthesis enzymes mixtures. Shown is the average number of UMIs in common myeloid progenitor cells (CMP; y-axis) vs. an average number of UMIs in negative controls wells (x-axis). Second strand synthesis was performed with ‘A’ composition (green symbols), with addition of RNaseH or without DNA polymerase, or with ‘B’ composition (black symbols) in dilutions of 1:4 or 1:8. Dashed lines demarcate background noise levels of 5% (red) and 10% (magenta).
Supplementary Figure 3 MARS-seq2.0: method overview.
(A) Molecular yield. Shown is a cumulative cell percentage (y-axis) vs. the total number of detected molecules per cell (x-axis). (B) Sequencing depth. Shown is a cumulative cell percentage (y-axis) vs. the total number of reads per cell (x-axis) for amplification batches of FACS sorted mouse ES (black curves) and MEF (red curves). (C) Total number of molecules detected vs. flow cytometry forward scatter values. Data was analyzed from 5500 bone marrow myeloid progenitors index sorted single cells (Lineage- c-Kit+ Sca1+) generated as previously reported9. (D) Average gene expression (number of detected molecules per gene divided by the total number of molecules) for two randomly selected groups of 100 mouse ES single-cells (x and y axis) is shown for 7567 genes with average expression higher than 10−6 (E) Similarly to D, showing average gene expression over groups of MEF (y-axis) vs. mouse ES (x-axis) single cells for 2350 differentially expressed genes (fold change >4 and FDR<10-5; red dots) and other genes (black dots). (F-I) Absolute molecules count for TPM1 (y-axis) vs. KLF5 (x-axis) (F), Id3 vs. Oct4 (G), Lgals1 vs. Dppa4 (H), and Actn1 vs. Dphs1 (I) in 1128 mouse ES cells (black dots) and 752 MEF cells (red dots).
Supplementary Figure 4 Evaluation of doublet rate in MARS-seq2.0.
Total number of molecules identified for each species in a 1:1 mouse/human mixing experiment (mouse ES/ human ES). Two out of 1041 single cells analyzed were identified as doublets.
Supplementary Figure 5 Modeling of single-cell RNA-seq data.
A schematic diagram of MARS-seq2.0 Library preparation is shown as a sequential sampling process. We model our RNA-seq data as a sequential process of multiple samplings and amplifications steps. During the first RT conversion and second strand synthesis, the mRNA molecules (labeled mRNA A, B, C, D, E and F) of the cell (transcriptome) are sampled and tagged with a barcode and a unique molecular identifier (UMI, ‘tagged mRNA pool’). Tagged molecules which are schematically represented by UMI A, C, and D are stochastically linearly amplified by IVT to produce multiple copies of each molecule (‘IVT products’) that are sheared at random positions (see Pos 1–6). IVT products will be further exponentially amplified using PCR which involves repeated cycles of sampling and amplification that will be followed by an additional random sampling of ~10K PCR products (reads) per cell that will be sequenced (x5, x10, x1, and x2 represent the number of PCR products of each IVT product). The pipeline quantifies the number of tagged mRNA molecules per gene that went through this complex sampling process while eliminating several types of experimental artifacts.
Supplementary Figure 6 A representative diagnostics report for an amplification batch of mouse ES cells.
(A) Sequencing depth per cell. Shown is a cumulative cell percentage (y-axis) vs. the total number of reads per cell (x-axis). (B) Mapping analysis. Shown are the fractions of reads per cell mapped to exonic loci, spike-ins, or unmapped due to multiple mapping or low MAPQ score. Cells are ordered according to gene mapping fractions. (C) Oligo contamination gauge. Shown are fractions of RT primer, poly A sequences and other oligo sequences within the unmapped reads pool. (D) UMI nucleotide composition. Nucleotide composition (y-axis) for all UMI positions (x-axis). (E-G) Error distributions. Cumulative cell percentage (y-axis) vs. the fraction of molecules that were filtered (x-axis) due to sequencing errors in the UMI (E), cell barcode (F) or template switching errors (G). (H) Negative control wells. The number of unique UMIs mapped to genes (blue) and spike-ins (red) that have a cell barcode associated with four negative control wells. (I) Molecular yield vs. technical efficiency. Shown is the number of detected mouse mRNA molecules (y-axis) vs. the number of spike-ins molecules (x-axis) detected in four wells following sorting of single cells (black dots) and four negative control wells that do not contain single cells (red signs). This visualization highlights potential problems of background noise and failed sorting. (J) Proportion molecules with a single IVT product. The percentage of detected molecules with a single IVT product (single offset; y-axis) vs. the total number of detected molecules (x-axis) per cell (black dots) or in empty wells (red signs). (K-L) Plate visualization. Normalized number of extracted gene (K) or spike-in (L) molecules (blue - low, red - high) in wells (ordered according to the physical plate positions) that were pooled and amplified together (single amplification batch). This visualization allows identification of sorting or robot related problems. (M-N) Molecules per cell. Shown is a cumulative cell percentage (y-axis) vs. the total number of detected gene (M) or spike-in (N) molecules per cell (x-axis). (O) IVT products per molecule. A histogram of the number of IVT products (y-axis, logarithmic scale) per UMI. (P) Reads per molecule. A histogram of the number of reads (y-axis, logarithmic scale) per UMI. (Q) Highly expressed genes. The average number of detected molecules (log2, y-axis) for the 25 genes (x-axis) with the highest expression levels. (R) Highly variable genes. The variance of detected number of molecules divided by the average number of detected molecules (log2, y-axis) for the 25 genes (x-axis) with the highest variance/mean score.
Supplementary Figure 7 A representative diagnostics report for an amplification batch of MEF cells.
Same as supplementary figure 6.
Supplementary Figure 8 A representative diagnostics report for an amplification batch of mixed mouse ES and MEF cells.
Same as supplementary figure 6.
Supplementary Figure 9 Sequencing depth of mouse ES single cells.
(A) Marginal sequencing efficiency. Shown is the number of molecules detected (y-axis) vs. the total number of reads following logarithmic down-sampling of the reads (black dots) as well as exponential fit (dashed red curve). Dashed gray line demarcates the saturation level of the library (8250 molecules). (B) Similarly to A, showing saturation curves for six cell groups divided according to their saturation level (color coded lines), indicating sequencing depth that was sufficient to sequence 50%, 75%, and 90% of the molecules in the library (circle, triangle and diamond, respectively). Inferred number of molecules per cell assuming saturation is indicated on the right margin for the six cell groups. A magnified view (right panel) of the marked area in the bottom is highlighting saturation curves for cells with low complexity.
Supplementary Figure 10 A representative successful MARS-seq2.0 library profile.
TapeStation profile of a MARS-seq library generated from mouse ES cells. ‘Lower’ and ‘Upper’ represent internal instrument markers. The average library size (384 bp) is indicated.
Supplementary information
Supplementary Information
Supplementary Figures 1–10, Supplementary Manuals 1–3 and Supplementary Methods
Supplementary Software
Bravo robot scripts for MARS-seq2.0
Supplementary Table 1
List of RT1 primers.
Supplementary Table 2
List of ligation adaptor primers.
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Keren-Shaul, H., Kenigsberg, E., Jaitin, D.A. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat Protoc 14, 1841–1862 (2019). https://doi.org/10.1038/s41596-019-0164-4
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DOI: https://doi.org/10.1038/s41596-019-0164-4
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