Multimodal single-cell assays provide high-resolution snapshots of complex cell populations, but are mostly limited to transcriptome plus an additional modality. Here, we describe expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell. We demonstrate application of ECCITE-seq to multimodal CRISPR screens with robust direct single-guide RNA capture and to clonotype-aware multimodal phenotyping of cancer samples.
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Data generated in this project have been deposited to the Gene Expression Omnibus with the accession code GSE126310.
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We thank N. Ødum (University of Copenhagen) for the kind gift of cell lines. Samples from patients were obtained with the help of M.B. Natan Zommer and J.-A. Latkowski. Work in S.B.K.’s laboratory was supported by the NIH R01 grant no. HL-125816, the Colton Center for Autoimmunity, funding from the Hirschl/Weill-Coulier Trust and a grant from the Spatz Foundation. Work in the NYGC Technology Innovation lab was supported by the NIH R21 grant no. HG-009748 to P.S. and the Chan Zuckerberg Initiative grant no. HCA-A-1704-01895 to P.S. and R.S. N.E.S. is supported by NYU and NYGC startup funds, NIH/NHGRI (R00HG008171 and DP2HG010099), NIH/NCI (R01CA218668), DARPA (D18AP00053), the Sidney Kimmel Foundation, the Melanoma Research Alliance, and the Brain and Behavior Foundation. M.L. is supported by a Hope Funds for Cancer Research postdoctoral fellowship. Work in Z.O.’s laboratory was supported by NIH R35 grant GM124998. We thank L. Yang, W. Stephenson, S. Jaini and K. Pandit for helpful discussions. We thank B. Fritz from 10x Genomics for providing kits for development of 5P compatible CITE-seq reagents and B. Yeung and K. Nazor from BioLegend for providing some of the unconjugated antibodies used in this study.
M.S. and P.S. are listed as co-inventors on a patent application related to this work (US provisional patent application 62/515–180).
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Integrated supplementary information
a, Species-mixing proof-of-principle experiment: protein tag reads associated with each cell barcode. Points are colored based on species classification using transcripts as shown in Fig. 1b. About 1.2% of either human or mouse cells show cross-reactivity with mouse or human antibodies respectively. b, Mixed-species ECCITE-seq experiment demonstrating measurement of six cellular modalities. Cells of different origin were stained with ECCITE-seq and hashing antibodies, washed and combined to a single 10x run. All cells were stained with a mix of anti-human CD29 and anti-mouse CD29 antibodies. NIH-3T3 cells were split into 7 tubes and stained with 7 barcoded hashing antibodies (Hashtag-A to Hashtag-G), followed by washing and pooling. MyLa, Sez4 and PBMCs were stained with Hashtag_1, Hashtag_2 and Hashtag_3 respectively. Eventually, a cell mix comprising 65% NIH-3T3 cells, 25% PBMCs, and 5% each MyLa and Sez4 cells was loaded into the 10x Chromium for droplet formation and reverse transcription. After emulsion breakage and cDNA amplification, the distinctly sized products were separated with size selection capture beads and amplified separately. A pool consisting 88% cDNA, 7% guide-tag, 3% hashtag and 2% protein-tag library was submitted for NGS sequencing. c,d, sgRNA representation as measured by direct sgRNA capture or genomic DNA amplification of the guide variable region in two different cell lines. To assess the direct capture, we assigned a guide to each cell with a single sgRNA and quantified their proportion in the total cell population (orange). This ratio is compared to the ratio of genomic reads matching each sgRNA to the total genomic sgRNA reads. e, scRNA-seq saturation curves for the ECCITE-seq versus standard 10x V(D)J run using the K562 cells expressing the targeting sgRNA library. The same cell suspension was used into two parallel experiments; one underwent a standard 10x V(D)J run while the other was preceded by staining with CITE-seq and hashing antibodies, as well as spike-in of guide RT primer in the reverse transcription reaction. For this analysis, data were downsampled to the same total reads per cell after correcting for the different fraction of reads in cell. f. Levels of target mRNA or protein (moving median smoothing window = 151) in 1,000 cells ranked on decreasing sgRNA counts for CD46 sgRNA 2 or non-targeting sgRNA 4. g, Adjusted p-values of detecting the indicated gene expression or protein abundance change as a function of cell number. Different-size, randomized cell samples assigned to unique targeting sgRNAs were tested against equal size, randomized cell samples from the non-targeting sgRNAs groups, using the Wilcoxon rank sum test.
Supplementary Fig. 2 Surface protein and clonotype detection on PBMCs from a healthy donor and a CTCL patient.
a, Transcriptome-based clustering of PBMCs from healthy donor after removing cell doublets. Projected is the protein signal for 36 out of 49 antibodies used to stain the cells, as well as the 2nd and 3rd most abundant CD4+ or CD8+ TCR α/β or the 3rd, 4th, 5th and 6th most abundant TCR γ/δ clonotype (red). In total, we detected 1,606 TCR α/β clonotypes from 2,796 barcodes and 183 TCR γ/δ clonotypes from 269 barcodes. The top CD4+ TCR α/β clonotype (defined by TRB CDR3 sequence: CASSTLQGKETQYF, shown in Fig. 2a) accounts for ~1% of recovered clonotype-associated barcodes. b, Same analysis as panel a, using data from CTCL patient PBMCs. In total, we detected 1,738 TCR α/β clonotypes from 3,857 barcodes, and 87 TCR γ/δ clonotypes from 243 barcodes. Clonal expansion is readily apparent by the top CD4+ TCR α/β clonotype (defined by TRB CDR3 sequence: CSARFLRGGYNEQFF, shown in Fig. 2a) present in 36% of cells for which we recovered clonotype information.
a, Unsupervised clustering of PBMCs from both healthy and CTCL donors (n = 9,816) after removing cell duplicates, merging, depth normalization and cell alignment13. Each cell was colored and labelled based on unsupervised clustering information on gene expression (left) or surface marker (right). b, Heatmap of genes differentially expressed across gene expression-based (left) or surface marker-based (right) cluster assignments.
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Mimitou, E.P., Cheng, A., Montalbano, A. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat Methods 16, 409–412 (2019). https://doi.org/10.1038/s41592-019-0392-0
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