PT  - JOURNAL ARTICLE
AU  - Byungjin Hwang
AU  - David S. Lee
AU  - Whitney Tamaki
AU  - Yang Sun
AU  - Anton Ogorodnikov
AU  - George Hartoularos
AU  - Aidan Winters
AU  - Yun S. Song
AU  - Eric D. Chow
AU  - Matthew H. Spitzer
AU  - Chun Jimmie Ye
TI  - SCITO-seq: single-cell combinatorial indexed cytometry sequencing
AID  - 10.1101/2020.03.27.012633
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.27.012633
4099  - http://biorxiv.org/content/early/2020/03/30/2020.03.27.012633.short
4100  - http://biorxiv.org/content/early/2020/03/30/2020.03.27.012633.full
AB  - 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 the surface proteomes of cells. Compared to flow and mass cytometry, the major limitation of current dsc-seq-based workflows is the high cost associated with profiling each cell, thus precluding its use in applications where millions of cells are required. Here, we introduce SCITO-seq, a new workflow that combines combinatorial indexing and commercially available dsc-seq to enable cost-effective cell surface proteomic sequencing of greater than 105 cells per microfluidic reaction. We demonstrate SCITO-seq’s feasibility and scalability by profiling mixed species cell lines and mixed human T and B lymphocytes. To further demonstrate its applicability, we show comparable cellular composition estimates in peripheral blood mononuclear cells obtained with SCITO-seq and mass cytometry. SCITO-seq can be extended to include simultaneous profiling of additional modalities such as transcripts and accessible chromatin or tracking of experimental perturbations such as genome edits or extracellular stimuli.