Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

40Characterizing the transcriptome of individual cells is fundamental to understanding complex 41 biological systems. We describe a droplet-based system that enables 3' mRNA counting of up 56peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/065912 doi: bioRxiv preprint first posted online 84 RESULTS 86Droplet-based platform enables barcoding of tens of thousands of cells 88The scRNA-seq microfluidics platform builds on the GemCode ® technology, which has 89 been used for genome haplotyping, structural variant analysis and de novo assembly of a 90human genome [10][11][12] . The core of the technology is a Gel bead in Emulsion (GEM). GEM 91 peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/065912 doi: bioRxiv preprint first posted online 5 generation takes place in an 8-channel microfluidic chip that encapsulates single gel beads at ~80% fill rate (Fig. 1a-c). Each gel bead is functionalized with barcoded oligonucleotides that 93 consist of: i) sequencing adapters and primers, ii) a 14bp barcode drawn from ~750,000 94 designed sequences to index GEMs, iii) a 10bp randomer to index molecules (unique molecular 95 identifier, UMI), and iv) an anchored 30bp oligo-dT to prime poly-adenylated RNA transcripts 96 (Fig. 1d). Within each microfluidic channel, ~100,000 GEMs are formed per ~6-min run, 97encapsulating thousands of cells in GEMs. Cells are loaded at a limiting dilution to minimize co- 98occurrence of multiple cells in the same GEM. 100Cell lysis begins immediately after encapsulation. Gel beads automatically dissolve to 101 release their oligonucleotides for reverse transcription of poly-adenylated RNAs. Each cDNA 102 molecule contains a UMI and shared barcode per GEM, and ends with a template switching 103 oligo at the 3' end (Fig. 1e). Next, the emulsion is broken and barcoded cDNA is pooled for 104PCR amplification, using primers complementary to the switch oligos and sequencing adapters. Methods, Fig. 1f). Briefly, 98-nt of Read1s were aligned against the union of human (hg19) 123peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/065912 doi: bioRxiv preprint first posted online 6 Based on the distribution of total UMI counts for each barcode (Online Methods), we 124 estimated that 1,012 GEMs contained cells, of which 482 and 538 contained reads that mapped 125 primarily to the human and mouse transcriptome, respectively (and will be referred to as human 126 and mouse GEMs) (Fig. 2a). >83% of UMI counts were associated with cell barcodes, 127indicating low background of cell-free RNA. Eight cell-containing GEMs had a substantial 128 fraction of human and mouse UMI counts (the UMI count is >1% of each species' UMI...

As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95–99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.

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The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.