Single cell profiling of COVID-19 patients: an international data resource from multiple tissues

Abstract

[Abstract]In late 2019 and through 2020, the COVID-19 pandemic swept the world, presenting both scientific and medical challenges associated with understanding and treating a previously unknown disease. To help address the need for great understanding of COVID-19, the scientific community mobilized and banded together rapidly to characterize SARS-CoV-2 infection, pathogenesis and its distinct disease trajectories. The urgency of COVID-19 provided a pressing use-case for leveraging relatively new tools, technologies, and nascent collaborative networks. Single-cell biology is one such example that has emerged over the last decade as a powerful approach that provides unprecedented resolution to the cellular and molecular underpinnings of biological processes. Early foundational work within the single-cell community, including the Human Cell Atlas, utilized published and unpublished data to characterize the putative target cells of SARS-CoV-2 sampled from diverse organs based on expression of the viral receptor ACE2 and associated entry factors TMPRSS2 and CTSL (Muus et al., 2020; Sungnak et al., 2020; Ziegler et al., 2020). This initial characterization of reference data provided an important foundation for framing infection and pathology in the airway as well as other organs. However, initial community analysis was limited to samples derived from uninfected donors and other previously-sampled disease indications. This report provides an overview of a single-cell data resource derived from samples from COVID-19 patients along with initial observations and guidance on data reuse and exploration.

Immune response to SARS-CoV-2 in the nasal mucosa in children and adults

“…Ultimately, the use of techniques that enable analysis at the single-cell level, such as single-cell RNA-seq, will allow a more precise assessment of cell-type-specific changes associated with SARS-CoV-2 or other viral infections. Recently, single-cell RNA- seq has been applied to understand virus-host interactions in children infected with IV (59), and similar efforts are underway in SARS-CoV-2 (60).…”

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

“…Many single-cell studies have been conducted to analyze COVID-19 patient immune responses [57][58][59][60][61][62][63][64] . However, these studies often suffer from small sample size and/or limited sampling of various disease states 58,64 .…”

“…However, these studies often suffer from small sample size and/or limited sampling of various disease states 58,64 . For a comprehensive study, we collected data from multiple COVID-19 PBMC studies, involving 860,746 single cells, and 10 batches from 9 studies [57][58][59][60][61][62][63] (Fig. 5a, Fig.…”

In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

“…However, one of the major limitations is the gene expression variability due to the cellular heterogeneity of organs (Gawel et al, 2019). Single-cell biology is a powerful approach that provides unprecedented resolution to the cellular and molecular underpinnings of biological processes and signaling pathways of diseases in order to find therapeutic targets (Ballestar et al, 2020). For instance, the significant overexpression of programmed death 1 (PD-1) in innate lymphoid cells as therapeutic target for cancer immunotherapy (Yu et al, 2016).…”

“…Regarding COVID-19, there are several single-cell studies focused on understanding the transcriptional and proteomics insights into the host response for drug discovery (Ballestar et al, 2020;Di Giorgio et al, 2020;Wu M et al, 2020;Park and Lee, 2020;Prokop et al, 2020). Ziegler et al discovered ACE2 and TMPRSS2 co-expressing cells in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes through scRNA-seq data analyses (Ziegler et al, 2020).…”