1The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies 2 has empowered the characterization of complex biological samples by dramatically increasing the 3 number of constituent cells that can be examined concurrently. Nevertheless, these approaches 4 typically recover substantially less information per-cell as compared to lower-throughput microtiter 5 plate-based strategies. To uncover critical phenotypic differences among cells and effectively link 6 scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts 7 -such as transcription factors, affinity receptors, and signaling molecules -by these methods is 8 essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we 9 term Seq-Well S^3 ("Second-Strand Synthesis") that increases the efficiency of transcript capture 10 and gene detection by up to 10-and 5-fold, respectively, relative to previous iterations, surpassing 11 best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell 12 lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of 13 distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell 14 states. Our work presents an essential methodological advance as well as a valuable resource 15 for studying the cellular and molecular features that inform human skin inflammation. 109Mann-Whitney U Test & Linear Regression; Figure 1B-C). To confirm that these overall 110 improvements were not driven by changes in the relative frequencies of different cell types 111 captured by each technology, we also examined each subset independently (Figure S2A-B). For 112 each cell type detected, we observed significant increases in the numbers of transcripts captured 113 and genes detected using S^3 for each pairwise comparison between techniques (P < 0.05, 114 Mann-Whitney U Test; CD4 + T cells, Seq-Well V1: 1,044 ± 62.3 UMIs/cell; 10x v2: 7,671 ± 103.9 115 UMIs/cell; Seq-Well S^3: 13,390 ± 253.4 UMIs/cell; Mean ± Standard Error of the Median (SEM); 116 Figure S2). Both Seq-Well S^3 and 10x v2 displayed increased sensitivity for transcripts and 117 genes relative to Seq-Well v1, but Seq-Well S^3 showed the greatest efficiency (defined as genes 118 recovered at matched read depth) to detect genes for each cell type (Figure 1D-E; Figure S2). 119We sought to further understand whether these improvements resulted in enhanced 120 detection of biologically relevant genes typically under-represented in high-throughput single-cell 121 sequencing libraries (Tabula Muris Consortium et al., 2018). Importantly, genes that were 122 differentially detected (i.e., higher in S^3) within each cell type include numerous transcription 123 factors, cytokines and cell-surface receptors (Figure 1D-E). For example, among CD4 + T cells, 124we observe significantly increased detection of cytokines (e.g., TGFB1 and TNF), surface 125 receptors (e.g., TGFBR and CCR4) and transcription fact...
Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.
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