Shivangi Wani, Nicole Cloonan

MicroRNAs (miRNAs) are predominantly negative regulators of gene expression that act through the RNA-induced Silencing Complex (RISC) to suppress the translation of protein coding mRNAs. Despite intense study of these regulatory molecules, the specific molecular functions of most miRNAs remain unknown, largely due to the challenge of accurately identifying miRNA targets. Reporter gene assays can determine direct interactions, but are laborious and do not scale to genome-wide screens. Genomic scale methods such as HITS-CLIP do not preserve the direct interactions, and rely on computationally derived predictions of interactions that are plagued by high false positive rates. Here we describe a protocol for the isolation of direct targets of a mature miRNA, using synthetic biotinylated miRNA duplexes. This approach allows sensitive and specific detection of miRNA-mRNA interactions, isolating high quality mRNA suitable for analysis by microarray or RNAseq.

Xiannian Zhang, Tianqi Li, Feng Liu, Yaqi Chen, Jiacheng Yao, Zeyao Li, Yanyi Huang, Jianbin Wang

Since its debut in 2009, single-cell RNA-seq has been a major propeller behind biomedical research progress. Developmental biology and stem cell studies especially benefit from the ability to profile single cells. While most studies still focus on individual tissues or organs, recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power to depict more complexed system or even the entire body. Though multiple ultra-high-throughput single-cell RNA-seq systems have acquired attention, systematic comparison of these systems is yet available. Here we focus on three prevalent droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. While each system is capable of profiling single-cell transcriptome, detailed comparison revealed distinguishing features and suitable application scenario for each system.

Grace X.Y. Zheng, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W. Bent, Ryan Wilson, Solongo B. Ziraldo, Tobias D. Wheeler, Geoff P. McDermott, Junjie Zhu, Mark T. Gregory, Joe Shuga, Luz Montesclaros, Donald A Masquelier, Stefanie Y. Nishimura, Michael Schnall-Levin, Paul W Wyatt, Christopher M. Hindson, Rajiv Bharadwaj, Alexander Wong, Kevin D. Ness, Lan W. Beppu, H. Joachim Deeg, Christopher McFarland, Keith R. Loeb, William J. Valente, Nolan G. Ericson, Emily A. Stevens, Jerald P. Radich, Tarjei S. Mikkelsen, Benjamin J. Hindson, Jason H Bielas

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 up to tens of thousands of single cells per sample. Cell encapsulation in droplets takes place in ~6 minutes, with ~50% cell capture efficiency, up to 8 samples at a time. The speed and efficiency allow the processing of precious samples while minimizing stress to cells. To demonstrate the system′s technical performance and its applications, we collected transcriptome data from ~¼ million single cells across 29 samples. First, we validate the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. Then, we profile 68k peripheral blood mononuclear cells (PBMCs) to demonstrate the system′s ability to characterize large immune populations. Finally, we use sequence variation in the transcriptome data to determine host and donor chimerism at single cell resolution in bone marrow mononuclear cells (BMMCs) of transplant patients. This analysis enables characterization of the complex interplay between donor and host cells and monitoring of treatment response. This high-throughput system is robust and enables characterization of diverse biological systems with single cell mRNA analysis.

Magali Soumillon, Davide Cacchiarelli, Stefan Semrau, Alexander van Oudenaarden, Tarjei S. Mikkelsen

Directed differentiation of cells in vitro is a powerful approach for dissection of developmental pathways, disease modeling and regenerative medicine, but analysis of such systems is complicated by heterogeneous and asynchronous cellular responses to differentiation-inducing stimuli. To enable deep characterization of heterogeneous cell populations, we developed an efficient digital gene expression profiling protocol that enables surveying of mRNA in thousands of single cells at a time. We then applied this protocol to profile 12,832 cells collected at multiple time points during directed adipogenic differentiation of human adipose-derived stem/stromal cells in vitro. The resulting data reveal the major axes of cell-to-cell variation within and between time points, and an inverse relationship between inflammatory gene expression and lipid accumulation across cells from a single donor.

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen, William M. Mauck, Shiwei Zheng, Andrew Butler, Maddie J. Lee, Aaron J. Wilk, Charlotte Darby, Michael Zagar, Paul Hoffman, Marlon Stoeckius, Efthymia Papalexi, Eleni P. Mimitou, Jaison Jain, Avi Srivastava, Tim Stuart, Lamar B. Fleming, Bertrand Yeung, Angela J. Rogers, M. Juliana McElrath, Catherine A. Blish, Raphael Gottardo, Peter Smibert, Rahul Satija

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce "weighted-nearest neighbor analysis", an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability: Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat ### Competing Interest Statement In the past three years, RS has worked as a consultant for Bristol-Myers Squibb, Regeneron, and Kallyope, and served as an SAB member for ImmunAI, Apollo Life Sciences GmbH, Nanostring and the NYC Pandemic Response Lab. R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene, Merck and has received research support from Janssen Pharmaceuticals and Juno Therapeutics, and declares ownership in CellSpace Biosciences. PS is a co-inventor of a patent related to this work. BZY is an employee at BioLegend Inc., which is the exclusive licensee of the New York Genome Center patent application related to this work.

Armin Raznahan, Neelroop Parikshak, Vijayendran Chandran, Jonathan Blumenthal, Liv Clasen, Aaron Alexander-Bloch, Andrew Zinn, Danny Wangsa, Jasen Wise, Declan Murphy, Patrick Bolton, Thomas Ried, Judith Ross, Jay Giedd, Daniel Geschwind

A fundamental question in the biology of sex-differences has eluded direct study in humans: how does sex chromosome dosage (SCD) shape genome function? To address this, we developed a systematic map of SCD effects on gene function by analyzing genome-wide expression data in humans with diverse sex chromosome aneuploidies (XO, XXX, XXY, XYY, XXYY). For sex chromosomes, we demonstrate a pattern of obligate dosage sensitivity amongst evolutionarily preserved X-Y homologs, and update prevailing theoretical models for SCD compensation by detecting X-linked genes whose expression increases with decreasing X- and/or Y-chromosome dosage. We further show that SCD-sensitive sex chromosome genes regulate specific co-expression networks of SCD-sensitive autosomal genes with critical cellular functions and a demonstrable potential to mediate previously documented SCD effects on disease. Our findings detail wide-ranging effects of SCD on genome function with implications for human phenotypic variation.

Yaniv Erlich

Genetic privacy is an area of active research. While it is important to identify new risks, it is equally crucial to supply policymakers with accurate information based on scientific evidence. Recently, Lippert et al. (PNAS, 2017) investigated the status of genetic privacy using trait-predictions from whole genome sequencing. The authors sequenced a cohort of about 1000 individuals and collected a range of demographic, visible, and digital traits such as age, sex, height, face morphology, and a voice signature. They attempted to use the genetic features in order to predict those traits and re-identify the individuals from small pool using the trait predictions. Here, I report major flaws in the Lippert et al. manuscript. In short, the authors' technique performs similarly to a simple baseline procedure, does not utilize the power of whole genome markers, uses technically wrong metrics, and finally does not really identify anyone.

Chuan-Chao Wang, Sabine Reinhold, Alexey Kalmykov, Antje Wissgott, Guido Brandt, Choongwon Jeong, Olivia Cheronet, Matthew Ferry, Eadaoin Harney, Denise Keating, Swapan Mallick, Nadin Rohland, Kristin Stewardson, Anatoly R. Kantorovich, Vladimir E. Maslov, Vladimira G. Petrenko, Vladimir R. Erlikh, Biaslan Ch. Atabiev, Rabadan G. Magomedov, Philipp L. Kohl, Kurt W. Alt, Sandra L. Pichler, Claudia Gerling, Harald Meller, Benik Vardanyan, Larisa Yeganyan, Alexey D. Rezepkin, Dirk Mariaschk, Natalia Berezina, Julia Gresky, Katharina Fuchs, Corina Knipper, Stephan Schiffels, Elena Balanovska, Oleg Balanovsky, Iain Mathieson, Thomas Higham, Yakov B. Berezin, Alexandra Buzhilova, Viktor Trifonov, Ron Pinhasi, Andrej B. Belinskiy, David Reich, Svend Hansen, Johannes Krause, Wolfgang Haak

Archaeogenetic studies have described the formation of Eurasian 'steppe ancestry' as a mixture of Eastern and Caucasus hunter-gatherers. However, it remains unclear when and where this ancestry arose and whether it was related to a horizon of cultural innovations in the 4th millennium BCE that subsequently facilitated the advance of pastoral societies likely linked to the dispersal of Indo-European languages. To address this, we generated genome-wide SNP data from 45 prehistoric individuals along a 3000-year temporal transect in the North Caucasus. We observe a genetic separation between the groups of the Caucasus and those of the adjacent steppe. The Caucasus groups are genetically similar to contemporaneous populations south of it, suggesting that - unlike today - the Caucasus acted as a bridge rather than an insurmountable barrier to human movement. The steppe groups from Yamnaya and subsequent pastoralist cultures show evidence for previously undetected Anatolian farmer-related ancestry from different contact zones, while Steppe Maykop individuals harbour additional Upper Palaeolithic Siberian and Native American related ancestry.

John M. Urban, Jacob Bliss, Charles E. Lawrence, Susan A Gerbi

Oxford Nanopore Technologies' nanopore sequencing device, the MinION, holds the promise of sequencing ultra-long DNA fragments >100kb. An obstacle to realizing this promise is delivering ultra-long DNA molecules to the nanopores. We present our progress in developing cost-effective ways to overcome this obstacle and our resulting MinION data, including multiple reads >100kb.

Georgios D Koutsovoulos, Sujai Kumar, Dominik R Laetsch, Lewis Stevens, Jennifer Daub, Claire Conlon, Habib Maroon, Fran Thomas, A. Aziz Aboobaker, Mark Blaxter

Tardigrades are meiofaunal ecdysozoans that are key to understanding the origins of Arthropoda. Many species of Tardigrada can survive extreme conditions through cryptobiosis. In a recent paper (Boothby TC et al (2015) Evidence for extensive horizontal gene transfer from the draft genome of a tardigrade. Proc Natl Acad Sci USA 112:15976-15981) the authors concluded that the tardigrade Hypsibius dujardini had an unprecedented proportion (17%) of genes originating through functional horizontal gene transfer (fHGT), and speculated that fHGT was likely formative in the evolution of cryptobiosis. We independently sequenced the genome of H. dujardini. As expected from whole-organism DNA sampling, our raw data contained reads from non-target genomes. Filtering using metagenomics approaches generated a draft H. dujardini genome assembly of 135 Mb with superior assembly metrics to the previously published assembly. Additional microbial contamination likely remains. We found no support for extensive fHGT. Among 23,021 gene predictions we identified 0.2% strong candidates for fHGT from bacteria, and 0.2% strong candidates for fHGT from non-metazoan eukaryotes. Cross-comparison of assemblies showed that the overwhelming majority of HGT candidates in the Boothby et al. genome derived from contaminants. We conclude that fHGT into H. dujardini accounts for at most 1-2% of genes and that the proposal that one sixth of tardigrade genes originate from functional HGT events is an artefact of undetected contamination.

Paul Datlinger, Andre F Rendeiro, Thorina Boenke, Thomas Krausgruber, Daniele Barreca, Christoph Bock

Cell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed "single-cell combinatorial fluidic indexing" (scifi) and applied it to single-cell RNA sequencing. The resulting scifi-RNA-seq assay combines one-step combinatorial pre-indexing of single-cell transcriptomes with subsequent single-cell RNA-seq using widely available droplet microfluidics. Pre-indexing allows us to load multiple cells per droplet, which increases the throughput of droplet-based single-cell RNA-seq up to 15-fold, and it provides a straightforward way of multiplexing hundreds of samples in a single scifi-RNA-seq experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow, thereby enabling massive-scale scRNA-seq experiments for a broad range of applications ranging from population genomics to drug screens with scRNA-seq readout. We benchmarked scifi-RNA-seq on various human and mouse cell lines, and we demonstrated its feasibility for human primary material by profiling TCR activation in T cells.

Ryan L. Collins, Harrison Brand, Konrad Karczewski, Xuefang Zhao, Jessica Alföldi, Laurent C Francioli, Amit V Khera, Chelsea Lowther, Laura D Gauthier, Harold Wang, Nicholas A Watts, Matthew Solomonson, Anne H. O’Donnell-Luria, Alexander Baumann, Ruchi Munshi, Mark Walker, Christopher Whelan, Yongqing Huang, Ted Brookings, Ted Sharpe, Matthew R Stone, Elise Valkanas, Jack Fu, Grace Tiao, Kristen M Laricchia, Valentin Ruano-Rubio, Christine Stevens, Namrata Gupta, Lauren Margolin, Genome Aggregation Database Production Team, Genome Aggregation Database Consortium, Kent D Taylor, Henry J Lin, Stephen S Rich, Wendy Post, Yii-Der Ida Chen, Jerome I. Rotter, Chad Nusbaum, Anthony Philippakis, Eric Lander, Stacey Gabriel, Benjamin M Neale, Sekar Kathiresan, M. Daly, Eric Banks, Daniel G MacArthur, Michael E. Talkowski

Structural variants (SVs) rearrange large segments of the genome and can have profound consequences for evolution and human diseases. As national biobanks, disease association studies, and clinical genetic testing grow increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral for interpreting genetic variation. To date, no large-scale reference maps of SVs exist from high-coverage sequencing comparable to those available for point mutations in protein-coding genes. Here, we constructed a reference atlas of SVs across 14,891 genomes from diverse global populations (54% non-European) as a component of gnomAD. We discovered a rich landscape of 433,371 distinct SVs, including 5,295 multi-breakpoint complex SVs across 11 mutational subclasses, and examples of localized chromosome shattering, as in chromothripsis. The average individual harbored 7,439 SVs, which accounted for 25-29% of all rare protein-truncating events per genome. We found strong correlations between constraint against damaging point mutations and rare SVs that both disrupt and duplicate protein-coding sequence, suggesting intolerance to reciprocal dosage alterations for a subset of tightly regulated genes. We also uncovered modest selection against noncoding SVs in cis -regulatory elements, although selection against protein-truncating SVs was stronger than any effect on noncoding SVs. Finally, we benchmarked carrier rates for medically relevant SVs, finding very large (≥1Mb) rare SVs in 3.8% of genomes (~1:26 individuals) and clinically reportable incidental SVs in 0.18% of genomes (~1:556 individuals). These data have been integrated directly into the gnomAD browser (<https://gnomad.broadinstitute.org>) and will have broad utility for population genetics, disease association, and diagnostic screening.

Steven Henikoff, Jorja G. Henikoff, Hatice S. Kaya-Okur, Kami Ahmad

Chromatin accessibility mapping is a powerful approach to identify potential regulatory elements. A popular example is ATAC-seq, whereby Tn5 transposase inserts sequencing adapters into accessible DNA ("tagmentation"). CUT&Tag is a tagmentation-based epigenomic profiling method in which antibody tethering of Tn5 to a chromatin epitope of interest profiles specific chromatin features in small samples and single cells. Here we show that by simply modifying the tagmentation conditions for histone H3K4me2 or H3K4me3 CUT&Tag, antibody-tethered tagmentation of accessible DNA sites is redirected to produce chromatin accessibility maps that are indistinguishable from the best ATAC-seq maps. Thus, chromatin accessibility maps can be produced in parallel with CUT&Tag maps of other epitopes with all steps from nuclei to amplified sequencing-ready libraries performed in single PCR tubes in the laboratory or on a home workbench. As H3K4 methylation is produced by transcription at promoters and enhancers, our method identifies transcription-coupled accessible regulatory sites. ### Competing Interest Statement S.H. and H.S.K. have filed patent applications on related work.

Stephanie C Hicks, F. William Townes, Mingxiang Teng, Rafael A. Irizarry

Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-Seq and scRNA-seq data are markedly different. In particular, unlike RNA-Seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, gene expressing RNA, but not at a sufficient level to detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.

Neville E Sanjana, Ophir Shalem, Feng Zhang

Genome-wide, targeted loss-of-function pooled screens using the CRISPR (clustered regularly interspaced short palindrome repeats)?associated nuclease Cas9 in human and mouse cells provide an alternative screening system to RNA interference (RNAi). Initial lentiviral delivery systems for CRISPR screening had low viral titer or required a cell line already expressing Cas9, limiting the range of biological systems amenable to screening. In this work, we present 1- and 2-vector lentiCRISPR systems capable of producing higher viral titers and, in these vectors, new human and mouse libraries for genome-scale CRISPR knock-out (GeCKO) screening.

Junyue Cao, Jonathan S Packer, Vijay Ramani, Darren A. Cusanovich, Chau Huynh, Riza Daza, Xiaojie Qiu, Choli Lee, Scott N. Furlan, Frank J Steemers, Andrew Adey, Robert H. Waterston, Cole Trapnell, Jay Shendure

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold "shotgun cellular coverage" of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major leap towards a comprehensive, single-cell molecular atlas of a whole animal.

Jase Gehring, Jong Hwee Park, Sisi Chen, Matthew Thomson, Lior Pachter

We describe a universal sample multiplexing method for single-cell RNA-seq in which cells are chemically labeled with identifying DNA oligonucleotides. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing a cost effective means to survey cell populations from large experiments and clinical samples with the depth and resolution of single-cell RNA-seq.

Xiaoqiang Chai, Longfei Hu, Yan Zhang, Weiyu Han, Zhou Lu, Aiwu Ke, Jian Zhou, Guoming Shi, Nan Fang, Jia Fan, Jiabin Cai, Jue Fan, Fei Lan

A newly identified coronavirus, 2019-nCoV, has been posing significant threats to public health since December 2019. ACE2, the host cell receptor for severe acute respiratory syndrome coronavirus (SARS), has recently been demonstrated in mediating 2019-nCoV infection. Interestingly, besides the respiratory system, substantial proportion of SARS and 2019-nCoV patients showed signs of various degrees of liver damage, the mechanism and implication of which have not yet been determined. Here, we performed an unbiased evaluation of cell type specific expression of ACE2 in healthy liver tissues using single cell RNA-seq data of two independent cohorts, and identified specific expression in cholangiocytes. The results indicated that virus might directly bind to ACE2 positive cholangiocytes but not necessarily hepatocytes. This finding suggested the liver abnormalities of SARS and 2019-nCoV patients may not be due to hepatocyte damage, but cholangiocyte dysfunction and other causes such as drug induced and systemic inflammatory response induced liver injury. Our findings indicate that special care of liver dysfunction should be installed in treating 2019-nCoV patients during the hospitalization and shortly after cure.

Sissel Juul, Fernando Izquierdo, Adam Hurst, Xiaoguang Dai, Amber Wright, Eugene Kulesha, Roger Pettett, Daniel J. Turner

Whole genome sequencing on next-generation instruments provides an unbiased way to identify the organisms present in complex metagenomic samples. However, the time-to-result can be protracted because of fixed-time sequencing runs and cumbersome bioinformatics workflows. This limits the utility of the approach in settings where rapid species identification is crucial, such as in the quality control of food-chain components, or in during an outbreak of an infectious disease. Here we present What′s in my Pot? (WIMP), a laboratory and analysis workflow in which, starting with an unprocessed sample, sequence data is generated and bacteria, viruses and fungi present in the sample are classified to subspecies and strain level in a quantitative manner, without prior knowledge of the sample composition, in approximately 3.5 hours. This workflow relies on the combination of Oxford Nanopore Technologies′ MinION ™ sensing device with a real-time species identification bioinformatics application.

Yue J Wang, Jonathan Schug, Jerome Lin, Zhiping Wang, Andrew Kossenkov, the HPAP Consortium, Klaus H. Kaestner

The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.