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...
Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.