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Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

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Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology Travis K Hughes1,2,3,4,5,9, Marc H Wadsworth II1,3,4,5,9, Todd M Gierahn5,6,9, Tran Do7, David Weiss7, Priscilla R. Andrade7, Feiyang Ma7, Bruno J. de Andrade Silva7, Shuai Shao8, Lam C Tsoi8, Jose Ordovas-Montanes1,3,4,5, Johann E Gudjonsson8, Robert L Modlin9, J Christopher Love3,4,5,6,10, and Alex K Shalek1,2,3,4,5,10 Affiliations: 1 Institute for Medical Engineering & Science (IMES) and Department of Chemistry, MIT, Cambridge, Massachusetts, USA 2 Department of Immunology, Harvard Medical School, Boston, MA 02115, USA 3 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA 4 Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA 5 Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA 6 Department of Chemical Engineering, MIT, Cambridge, Massachusetts, USA 7 Division of Dermatology and Department of Microbiology, Immunology and Molecular Biology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA 8 Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA 9 These authors contributed equally to this work 10 These senior authors contributed equally to this work 10 To whom correspondence should be addressed: [email protected] (AKS), [email protected] (JCL) bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 SUMMARY 2 The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies 3 has empowered the characterization of complex biological samples by dramatically increasing the 4 number of constituent cells that can be examined concurrently. Nevertheless, these approaches 5 typically recover substantially less information per-cell as compared to lower-throughput microtiter 6 plate-based strategies. To uncover critical phenotypic differences among cells and effectively link 7 scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts 8 – such as transcription factors, affinity receptors, and signaling molecules – by these methods is 9 essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we 10 term Seq-Well S^3 (“Second-Strand Synthesis”) that increases the efficiency of transcript capture 11 and gene detection by up to 10- and 5-fold, respectively, relative to previous iterations, surpassing 12 best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell 13 lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of 14 distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell 15 states. Our work presents an essential methodological advance as well as a valuable resource 16 for studying the cellular and molecular features that inform human skin inflammation. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 17 INTRODUCTION 18 Although a nascent technology, single-cell RNA-sequencing (scRNA-Seq) has already 19 helped define, at unprecedented resolution, the cellular composition of many healthy and 20 diseased tissues (Klein et al., 2015; Macosko et al., 2015; Montoro et al., 2018; Ordovas- 21 Montanes et al., 2018; Vento-Tormo et al., 2018). The development of high-throughput 22 methodologies has been crucial to this process, empowering the characterization of increasingly 23 complex cellular samples. Unfortunately, current scRNA-Seq platforms typically demonstrate an 24 inverse relationship between the number of cells that can be profiled at once and the amount of 25 biological information that can be recovered from each cell. As a result, one must choose between 26 quantity and quality – and thus comprehensiveness and fidelity – or alternatively employ two 27 distinct approaches in parallel (Tabula Muris Consortium et al., 2018). Indeed, inefficiencies in 28 transcript capture among massively-parallel methods have limited our ability to resolve the distinct 29 cell states that comprise broad cell types (Braga et al., 2019), as well as their essential molecular 30 attributes and often lowly-expressed molecular features, such as transcription factors, affinity 31 receptors, and signaling molecules (Figure 1A). 32 Improving the fidelity of these methodologies is particularly important for resolving 33 differences within heterogeneous populations of immune cells like lymphocytes and myeloid cells 34 (Villani et al., 2017). Here, subtle differences in surface receptor, transcription factor and/or 35 cytokine expression can profoundly impact cellular function, particularly in the setting of human 36 pathology (Puel et al., 1998). Enhancing data quality in high-throughput scRNA-Seq would 37 facilitate a greater appreciation of the underlying molecular features that describe such cellular 38 variation. Similarly, it would ease integration with legacy datasets that often rely on lowly- 39 expressed biomarkers, such as transcription factors, that are false-negative prone to discriminate 40 subsets of cells. bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 41 Most high-throughput scRNA-Seq methods currently rely on early barcoding of cellular 42 contents to achieve scale. Typically, these techniques recover single-cell transcriptomes for 43 thousands of cells at once by leveraging reverse-emulsion droplets or microwells to isolate 44 individual cells with uniquely barcoded poly-dT oligonucleotides which can then capture and tag 45 cellular mRNAs during reverse transcription (Prakadan et al., 2017). Afterward, an additional 46 priming site is added to the 3’ end of the synthesized cDNA to enable PCR-based amplification 47 of all transcripts using a single primer (whole transcriptome amplification, WTA). A number of 48 techniques have been described to add this second priming site (Sasagawa et al., 2013; Shishkin 49 et al., 2015). The most common uses the terminal transferase activity of certain reverse 50 transcription enzymes to facilitate a “template-switch” from the original mRNA to a second defined 51 oligonucleotide handle (Picelli et al., 2013). While simple to implement, this process has the 52 potential to be highly inefficient, leading to the loss of molecules that have been captured and 53 converted to cDNA but not successfully tagged with a secondary PCR priming site (Figure 1A 54 and S1A) (Islam et al., 2012; Kapteyn et al., 2010; Zajac et al., 2013). 55 To overcome these limitations, we have developed a new massively-parallel scRNA-Seq 56 protocol we call Seq-Well S^3 (for “Second-Strand Synthesis”). Seq-Well S^3 increases the 57 efficiency of the second PCR handle addition by amending it through a randomly-primed second- 58 strand synthesis after reverse transcription (Figure 1A). Working with cell lines and peripheral 59 blood mononuclear cells (PBMCs), we demonstrate that Seq-Well S^3 enables significant 60 improvements in transcript and gene capture across sample types, facilitating studies of complex 61 immune tissues at enhanced resolution (Figures 1, S1, and S2). 62 To illustrate the utility of S^3, we apply it to generate a resource of single-cell 63 transcriptional states spanning multiple inflammatory skin conditions. Skin represents the largest 64 barrier tissue in the human body and is comprised of numerous specialized cell-types that help 65 maintain both immunological and physical boundaries between our inner and outer worlds bioRxiv preprint doi: https://doi.org/10.1101/689273; this version posted July 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 66 (Kabashima et al., 2019). The dermis and epidermis – the two primary compartments of human 67 skin – play complementary roles in tissue structure and function (Figure 2A) (Kabashima et al., 68 2019). The epidermis consists primarily of keratinized epithelial cells, which provide a physical 69 barrier to the outside world; the dermis, meanwhile, provides structural support for the skin, with 70 fibroblasts producing collagen and elastin fibrils along with the other components of the 71 extracellular matrix. Crucially, within the cellular ecosystem of human skin, there are numerous 72 tissue-resident
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