Spatiotemporal landscape of SARS-CoV-2 pulmonary infection reveals Slamf9+Spp1+ macrophages promoting viral clearance and inflammation resolution

While SARS-CoV-2 pathogenesis has been intensively investigated, the host mechanisms of viral clearance and inflammation resolution are still elusive because of the ethical limitation of human studies based on COVID-19 convalescents. Here we infected Syrian hamsters by authentic SARS-CoV-2 and built an ideal model to simulate the natural recovery process of SARS-CoV-2 infection from severe pneumonia1,2. We developed and applied a spatial transcriptomic sequencing technique with subcellular resolution and tissue-scale extensibility, i.e., Stereo-seq3, together with single-cell RNA sequencing (scRNA-seq), to the entire lung lobes of 45 hamsters and obtained an elaborate map of the pulmonary spatiotemporal changes from acute infection, severe pneumonia to the late viral clearance and inflammation resolution. While SARS-CoV-2 infection caused massive damages to the hamster lungs, including naive T cell infection and deaths related to lymphopenia, we identified a group of monocyte-derived proliferating Slamf9+Spp1+ macrophages, which were SARS-CoV-2 infection-inducible and cell death-resistant, recruiting neutrophils to clear viruses together. After viral clearance, the Slamf9+Spp1+ macrophages differentiated into Trem2+ and Fbp1+ macrophages, both responsible for inflammation resolution and replenishment of alveolar macrophages. The existence of this specific macrophage subpopulation and its descendants were validated by RNAscope in hamsters, immunofluorescence in hACE2 mice, and public human autopsy scRNA-seq data of COVID-19 patients. The spatiotemporal landscape of SARS-CoV-2 infection in hamster lungs and the identification of Slamf9+Spp1+ macrophages that is pivotal to viral clearance and inflammation resolution are important to better understand the critical molecular and cellular players of COVID-19 host defense and also develop potential interventions of COVID-19 immunopathology.

Human genetic variants associated with COVID-19 severity are enriched in immune and epithelium regulatory networks

Human genetic variants can influence the severity of symptoms being infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk SNPs associated with COVID-19 severity. However, the causal tissues or cell types of COVID-19 severity are uncertain and candidate genes associated with these human risk SNPs were investigated in genomic proximity instead of their functional cellular contexts. Here, we compiled regulatory networks of 77 human contexts and revealed those risk SNPs enriched cellular contexts and associated transcript factors, regulatory elements, and target genes. Twenty-one human contexts were identified and grouped into two categories: immune cells and epithelium cells. We further aggregated the regulatory networks of immune cells, epithelium cells, and immune-epithelium crosstalk and investigated their association with risk SNPs regulation. Two genomic clusters, chemokine receptors cluster and OAS cluster, showed the strongest association with COVID-19 severity and different regulations in immune and epithelium contexts. Our findings were supported by analysis on both microarray and whole genome sequencing based GWAS summary statistics.

Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients

Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19. HIGHLIGHTSO_LILarge-scale scRNA-seq analysis depicts the immune landscape of COVID-19 C_LIO_LILymphopenia and active T and B cell responses coexist and are shaped by age and sex C_LIO_LISARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responses C_LIO_LICytokine storms with systemic S100A8/A9 are associated with COVID-19 severity C_LI

SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data / 基因组蛋白质组与生物信息学报·英文版

Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC), a new clustering framework based on random projection and feature construction, for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy, robustness, and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, SSCC achieved 20% improvement for clustering accuracy and 50-fold acceleration, but only consumed 66% memory usage, compared to the widelyused software package SC3. Compared to k-means, the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https://github.com/Japrin/sscClust.