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SCITO-seq: single-cell combinatorial indexed cytometry sequencing

Nature Methods volume 18, pages 903–911 (2021)Cite this article

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Abstract

The development of DNA-barcoded antibodies to tag cell surface molecules has enabled the use of droplet-based single-cell sequencing (dsc-seq) to profile protein abundances from thousands of cells simultaneously. As compared to flow and mass cytometry, the high per cell cost of current dsc-seq-based workflows precludes their use in clinical applications and large-scale pooled screens. Here, we introduce SCITO-seq, a workflow that uses splint oligonucleotides (oligos) to enable combinatorially indexed dsc-seq of DNA-barcoded antibodies from over 105 cells per reaction using commercial microfluidics. By encoding sample barcodes into splint oligos, we demonstrate that multiplexed SCITO-seq produces reproducible estimates of cellular composition and surface protein expression comparable to those from mass cytometry. We further demonstrate two modified splint oligo designs that extend SCITO-seq to achieve compatibility with commercial DNA-barcoded antibodies and simultaneous expression profiling of the transcriptome and surface proteins from the same cell. These results demonstrate SCITO-seq as a flexible and ultra-high-throughput platform for sequencing-based single-cell protein and multimodal profiling.

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Fig. 1: Design of SCITO-seq and mixed-species proof-of-concept.
Fig. 2: Ultra high-throughput PBMC profiling of healthy controls using SCITO-seq.
Fig. 3: Extending SCITO-seq for compatibility with 60-plex custom and 165-plex commercial antibody panels.
Fig. 4: Integrating SCITO-seq and scifi-RNA-seq for simultaneous profiling of transcripts and surface proteins.

Data availability

Single-cell sequencing data generated in this project have been deposited to the Gene Expression Omnibus with the accession code GSE147808. Processed data are also available at the website (https://github.com/yelabucsf/SCITO-seq) with tutorials.

Code availability

All code used to perform simulations and generate figures can be found at the following websites (https://github.com/yelabucsf/SCITO-seq, https://doi.org/10.5281/zenodo.4988182). A useful project-related cost calculation website can also be found at (https://yelabtools.herokuapp.com/scSeqCostCalc/scito.html).

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Acknowledgements

C.J.Y., Y.S.S. and M.H.S. are Chan Zuckerberg Biohub Investigators and C.J.Y. and M.H.S. are members of the PICI. C.J.Y. is further supported by the National Institutes of Health (NIH) grant nos. R01AR071522, R01AI136972 and R01HG011239. E.D.C. is supported by UCSF PBBR grant for Center for Advanced Technology. This work was supported by NIH grant no. DP5 OD023056 and funding from the UCSF PBBR to M.H.S. and NIH grant no. S10 1S10OD018040, which enabled the procurement of the Helios mass cytometer used in this study. We acknowledge the PFCC (RRID:SCR018206) supported in part by grant no. NIH P30 DK063720 and by the NIH S10 Instrumentation grant no. S10 1S10OD021822-01. This study was also supported by NIH grant no. R35-GM134922.

Author information

Author notes

  1. These authors contributed equally: Byungjin Hwang, David S. Lee.

Authors and Affiliations

  1. Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA, USA

    Byungjin Hwang, David S. Lee, Yang Sun, Anton Ogorodnikov, George C. Hartoularos & Chun Jimmie Ye

  2. Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Byungjin Hwang, David S. Lee, Yang Sun, Anton Ogorodnikov, George C. Hartoularos & Chun Jimmie Ye

  3. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Byungjin Hwang & Chun Jimmie Ye

  4. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    Byungjin Hwang & Chun Jimmie Ye

  5. Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA

    Byungjin Hwang & Chun Jimmie Ye

  6. Graduate Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, CA, USA

    Whitney Tamaki

  7. Graduate Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, USA

    George C. Hartoularos & Aidan Winters

  8. BioLegend Inc, San Diego, CA, USA

    Bertrand Z. Yeung & Kristopher L. Nazor

  9. Computer Science Division, University of California, Berkeley, Berkeley, CA, USA

    Yun S. Song

  10. Department of Statistics, University of California, Berkeley, CA, USA

    Yun S. Song

  11. Center for Advanced Technology, Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA

    Eric D. Chow

  12. Graduate Program in Biomedical Sciences, University of California, San Francisco, San Francisco, CA, USA

    Matthew H. Spitzer

  13. Departments of Otolaryngology and Microbiology and Immunology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

    Matthew H. Spitzer

  14. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

    Matthew H. Spitzer & Chun Jimmie Ye

  15. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Yun S. Song, Matthew H. Spitzer & Chun Jimmie Ye

  16. J. David Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

    Chun Jimmie Ye

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  1. Byungjin Hwang
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Contributions

C.J.Y., B.H. and D.S.L. conceived the experiments. B.H. and D.S.L. designed and conducted the experiment(s). B.Z.Y. and K.L.N. kindly provided antibodies for commercial compatibility experiments. C.J.Y., B.H., D.S.L., W.T., A.O., G.C.H., A.W., Y.S.S., Y.S., E.D.C. and M.H.S. analyzed the results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Chun Jimmie Ye.

Ethics declarations

Competing interests

C.J.Y. is a SAB member for and hold equity in Related Sciences and ImmunAI, a consultant for and hold equity in Maze Therapeutics and a consultant for Trex Bio. C.J.Y. has received research support from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub and Genentech.

Additional information

Peer review information Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Simulation and cost analysis of SCITO-seq.

a, Collision rate (y-axis, denoted as CRS) as a function of the number of cells loaded and the number of pools (denoted by different colors). Here, number of simulations were performed as follows: maxSim = 1000 for cells_loaded < = 1e4, maxSim = 100 for 1e4 < cells_loaded < = 1e5, and maxSim = 10 for 1e5 < cells_loaded. Expected (solid lines) and simulated (dotted lines) collision rates are based on the Poisson statistics for 100,000 droplets and the number of droplets containing cells is modeled as 0.6 × 100,000 in simulation. When the number of cells loaded is not large (for example, less than 10,000), there is noticeable variance in the number of collisions, so multiple simulation runs were used to estimate the collision rates shown in dotted lines. b, Number of droplets (y-axis) containing no cells (blue), exactly one cell (green) or greater than one cell (red) as a function of the number of cells loaded (x axis). Singlets refer to droplets that contain one cell, multiplets contain more than one cell (>=2) and empties contain no cells in the droplet. c, Distribution (Y-axis, counts) of number of cells per droplet (X-axis) for different cell loading numbers (cells_loaded) based on Poisson distribution. d, (Left) Droplet collision rate, depicting proportion of droplets with at least one barcode collision. (right) Barcode collision rate, estimating proportion of batches (pools) with a collision in a given droplet. Collision rates were calculated using simulations of a Poisson Point Process (solid lines) or a closed form solution (dashed lines; see Methods). Estimates from a closed form solution robustly and almost identically recapitulate simulations and can be used to calculate collision rates for an experiment. e, Total cost estimates (purple) including library prep (green), antibody prep (red) and sequencing cost (blue) assuming 40 reads/Ab/cell and a panel of 30 antibodies for different number of SCITO-seq pools.

Extended Data Fig. 2 Species mixing QC analysis (Human/Mouse).

a, (Upper) Transcriptomic UMAP of human and mouse cells, distinguished by transcript alignment. (Lower) ADT staining of mouse and human cells overlaid on the transcriptomic UMAP for 100k loading experiment. Pool barcodes per antibody were merged (that is CD29_h_merged-1 = CD29_h_barcode-1 + ….+CD29_h_barcode-5, where the latter number represents the pool number). Species classification was transcriptomically determined by a > 95% cutoff based on normalized counts specific to either species. Cells which did not meet the threshold were classified as Multiplets. Overlaid normalized ADT counts shows human and mouse antibody staining. b, Scatterplot showing within species multiplets (shown on double-positive axes) across batches when loading 100k cells. Resolution of cell types with a single batch barcode and annotation of Multiplets (positive for both pool barcodes, such as CD29h_barcode1 and CD29h_barcode2 positive or CD29m_barcode1 and CD29m_barcode2). c, Scatterplots for species mixing 20k (left) and 100k (right) loading experiments colored by pool showing pool specific staining level. Resolved hCD29 or mCD29 on the axes refers to normalized antibody counts after resolution into single cells. If a droplet contained a mixture of hCD29 from pool 1 and pool 2, the droplet was resolved as two cells with the pool-normalized counts. d, Scatter plots of SCITO-seq normalized counts from 2 × 104 loading of species mixing to determine cross pool or within pool background level. e, Scatter plots of directly-conjugated hCD29 and mCD29 antibody-based normalized counts from 2 × 104 loading of species mixing to show cross pool or within pool background level using direct conjugation. Hg19, mm10, and Multiplet define cell populations based on respective transcriptomic alignment. Direct conjugates provide a baseline for noise in the SCITO-seq system.

Extended Data Fig. 3 Species mixing QC analysis (Human B/T cells).

a, UMAP projection (left) of T/B cell experiments with 2 × 105 loading colored by cell types as determined transcriptomically (cutoff value of 0.9 for differences in highly variable genes). Doublets (multiplets) represent a mixture of T and B and are colored in green. The other two panels demonstrate specific staining of merged (merging all 5 pool barcodes) and normalized SCITO-seq counts. b, Scatterplot for 200k T/B experiments loading colored by pool. Resolved hCD4 or hCD20 on the axes refers the normalized antibody count after resolution into single cells. For example, if droplet is a mixture of hCD20 from pool 1 to 5, the resolved count should be either of the normalized counts from specific pool only (for Pool1 legend, the Resolved axes are represented by the normalized pool 1 counts). c, Estimated (x-axis) versus expected (y-axis) frequencies of Multiplets (frequencies of droplets that contain 1 cell to 5 cells) between estimated (observed) vs expected (simulated) for 2 × 105 loading experiment (left). The five dots represent the number of the cells in the droplets (from single to five cells). Expected (x-axis) versus observed (y-axis) frequencies (right) of co-occurrences between antibody pool barcodes for loading concentrations of 2 × 105 cells. Expected frequencies were calculated based on the frequencies of barcodes in singlets. Correlation value and p-value are also shown. Observed co-occurrence of antibody pool barcodes is calculated using R package mixtools (v1.0.4) implementing normalmixEM function with default parameters (epsilon = 1e-08, maxit = 1000). d, Distribution of the normalized UMI counts for each antibody in cells resolved from droplet containing single pool barcodes (S) and multiple pool barcodes (M) per donor for 200k loading experiment. Distribution of the antibodies in pool multiplets shows expected prior mixture proportions (5:1 for donor1 (D1) and 1:3 for donor2 (D2)) and overlaps with the corresponding distribution in pool singlets.

Extended Data Fig. 4 Human donor PBMC QC analysis (28plex).

a, Ridgeplots of 28-plex experiment of the pool specific antibodies. The normalized expression values of 60 antibodies within each pool was summed for thresholding. An individual plot contains the batch specific normalized expression values for demonstrating the signal to noise distribution of the expected specificity. b, UMAP projections for 100k PBMC loading experiment for representative markers. UMAP projection comparison using RNA expression (left), merged antibody counts before resolution (middle) and after resolution (right). The scale represents the normalized merged ADT counts (left and the middle) and resolved ADT counts (right).

Extended Data Fig. 5 Human donor PBMC QC analysis 2.

a, RNA expression based UMAP projections for representative markers of 200k PBMC loading. Since the RNA molecules are not combinatorially indexed, these UMAPs show stark contrast with the resolved UMAP based on normalized ADT counts where we see clear distinction of all clusters. b, UMAP ADT projection of 200k loading PBMC data colored by different pools (color numbers 0 through 9). Two pooled donors prior to aliquoting into 10 different pools to investigate batch effects across all stained wells and found no apparent batch effects. c, ADT UMAP clusters overlaid on ADT UMAP (left) and ADT UMAP clusters overlaid on transcriptomic UMAP. d, (top) Protein expression on ADT UMAP of CD4/8 and CD45RA/RO. (down) Protein expression on transcriptomic UMAP of CD4/8 and CD45RA/RO.

Extended Data Fig. 6 Human donor PBMC QC analysis 3.

a, UMAP (x-, y- axis with UMAP1 and UMAP2 dimensions) with representative PBMC markers based on CyTOF experiment using the same donor and antibody panel as in SCITO-seq. The scale shows arcsinh (hyperbolic inverse sine) transformed normalized values. b, Comparisons of SCITO-seq with CyTOF per donor (D1: top, D2: middle) for 100k loading data (SNG:singlet, DBL:doublet, TRI:triplet). Pairwise correlation heatmap plot (bottom) is also shown (similar to Fig. 2d and e in the main figure). Within donors, the proportion each Leiden cluster was highly correlated (Cosine similarity within donor1:0.95, donor2:0.94).

Extended Data Fig. 7 Scalability experiment and QC analysis (60- and 165-plex).

a, Design of splint oligo with Totalseq-C compatible system. The splint oligos (FBC RC (reverse complement) + Well + Ab BC (5 + 5,10 bp) + Read2 + Totalseq-C Barcode RC) are hybridized to the barcode region of the Totalseq-C oligo conjugated antibodies (right dotted lines around the blue region). Splint oligo (1 μM) was incubated with antibody for 15 min for hybridization (same workflow as conventional SCITO-seq). The well and the antibody barcode sequences are encoded in orange and blue above. b, Ridgeplots of 60-plex experiment showing the specificity of the pool specific antibodies. The normalized expression values of the sum of counts for all 60 antibodies per pool. Individual plots contain batch specific normalized expression values to show the signal-to-noise distribution of the expected specificity. c, Ridgeplots of 165-plex experiment showing the specificity of the pool specific antibodies. The normalized expression values of the sum of all 165 antibodies per pool. Individual plots contain batch specific normalized expression values to show signal to noise distribution of the expected specificity. d, Barplots of 165-plex experiment showing low UMI counts of an example Isotype control ADT counts (Rat IgG) for all 10 pools (top 1-5 batches, bottom 6-10 batches). The percentage of cells (y-axis) that express the ADT less than 2 UMI or over is calculated. Background noise across batches shows less than 2 UMI counts in ~92% of the cells. e, Overlay density histograms of the example CD8a vs Isotype control Ab (Rat IgG) to assess the ‘noise’ level for all 10 pools (top 1-5 batches, bottom 6-10 batches) in 165-plex data. X-axis for log1p(raw counts) transformed values and y-axis for density. f, Overlay density histograms of the example antibodies aggregated over all 10 pools (CD3, CD11c, CD45, CD127, CD8a, CD4, CD19) vs Isotype control Ab (Rat IgG) to assess the ‘noise’ level in 165-plex data. X-axis for log1p(raw counts) transformed values and y-axis for density.

Extended Data Fig. 8 Scalability experiment and QC analysis 2 (60- and 165-plex).

a, UMAPs of 60-plex (upper) and 165-plex (lower) experiment showing normalized expression of cDC1, cDC2 and pDC markers. CD141 and CD370 for cDC1 and CD1c for cDC2 and CD123 and CD303 for pDC markers. b, Schematic of sample multiplexed SCITO-seq where different samples are hashed with different pool barcodes (Red, Blue, Purple). Droplets containing cells from different individuals (two different colors) can be resolved into separate cells. c, Pairwise correlation plots (using the ggpair function from the GGally R package) of normalized expression values of all combinations for CD4 antibody in the 60-plex experiment. d, Pairwise correlation plots of normalized expression values of all combinations for CD4 antibody in the TSC 165-plex experiment.

Extended Data Fig. 9 Development of comodality experiment and QC analysis.

a, Proof-of-concept experiment to analyze SCITO-seq using ATAC-kit. UMAP of PBMCs stained with 12 representative surface markers (CD4, 8a, 14, 16, 45, 45RA, 45RO, 19, 20, 56, 11c, and HLA-DR) in 5 separate pools loading 5x104 cells (nCM: non-conventional monocytes, cMono: conventional monocytes). B, Schematic of GEM ligation step in the comodality experiment using the 10x Genomics ATAC-seq kit. Detailed sequence structure of the RNA and ADT capture during the GEM reaction using the scifi-RNA-seq workflow. A more detailed workflow for the RNA can be found in the Supplementary Figure 2 in the scifi-RNA-seq paper. 10x_round2 refers to the 16 bp droplet barcode, round1 barcode refers to the well barcode (11 bp) used in the in situ reverse transcription reaction. Untemplated ‘CCC’ is added at the end of the reverse transcription reaction (MMLV variant). Antibody barcode (Ab BC 10-bp) and antibody handle (Ab handle 20-bp, conjugated directly to the blue antibody) sequences are specific to each antibody. Read2n represents Read2 Nextera sequence. Compared to the bridge oligo 1 (used to capture in-situ RT mRNA molecules), bridge oligo 2 has an extra 10-bp (AACGTATCGA between red and blue colored sequences). ddC (dideoxy C) and InvdT (inverted dT) for preventing extension. Arrow indicates the ligation site during the GEM reaction. c, Dimensional reduction using UMAP with normalized RNA counts and corresponding cell line specific ADT marker expressions on the UMAP space. d, Dimensional reduction using UMAP with normalized RNA counts and corresponding single RNA marker expressions on the UMAP space.

Extended Data Fig. 10 Flow validation experiment of SCITO-seq.

a, To reduce the non-specific staining of secondary oligonucleotides, we titrated oligos at 1 μM (right) and 100 μM (left). After hybridization of oligo conjugated antibodies with a Cy5 conjugated reverse complementary oligo for 15 minutes, a mixture of LCLs and primary monocytes were stained with the hybridized material and CD13-BV421 for 30 minutes, washed twice and analyzed on a LSRII. CD13 BV421 antibody was captured by the Violet-F channel (x-axis) and Cy5 tagged secondary oligos was captured on the Red-C channel to check the level of background staining (Q6 gated population refers to the spillover of non-cognate secondary oligonucleotides in the primary monocyte population). b, To determine if 1 μl of 1 μM reverse complementary oligonucleotide would saturate 1 μg antibody, we first hybridized 1 μg of oligonucleotide conjugated CD3 with 1 μl of 1 μM reverse complementary oligonucleotide conjugated to Cy5. Following this, another 1 μl of 1 μM reverse complementary FAM conjugated oligo was added for 15 minutes prior to washing and analysis. The red population represents cells stained with sequentially hybridized antibody. The blue population represents cells stained with a conjugated CD3 antibody but without the hybridization sequence for the Cy5 or FAM oligo. c, Lymphocytes were gated for singlets and live cells (PI signal captured in the YG C-A channel) prior to binning samples across CD8a expression for sorting. Red-C represents CD8a-APC and Blue-B represents isotype control-AF488.

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Hwang, B., Lee, D.S., Tamaki, W. et al. SCITO-seq: single-cell combinatorial indexed cytometry sequencing. Nat Methods 18, 903–911 (2021). https://doi.org/10.1038/s41592-021-01222-3

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