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Abstract How the lung achieves immune homeostasis after a pulmonary infection is not fully understood. Here, we analyzed the spatiotemporal changes in the lungs over a 2-week natural recovery from severe pneumonia in a Syrian hamster model of SARS-CoV-2 infection. We find that SARS-CoV-2 infects multiple cell types and causes massive cell death at the early stage, including alveolar macrophages. We identify a group of monocyte-derived Slamf9 + macrophages, which are induced after SARS-CoV-2 infection and resistant to impairment caused by SARS-CoV-2. Slamf9 + macrophages contain SARS-CoV-2, recruit and interact with Isg12 + Cst7 + neutrophils to clear the viruses. After viral clearance, Slamf9 + macrophages differentiate into Trem2 + and Fbp1 + macrophages, contributing to inflammation resolution at the late stage, and finally replenish alveolar macrophages. These findings are validated in a SARS-CoV-2-infected hACE2 mouse model and confirmed with publicly available human autopsy single-cell RNA-seq data, demonstrating the potential role of Slamf9 + macrophages and their coordination with neutrophils in post-injury tissue repair and inflammation resolution.

Abstract How immune cells are spatiotemporally coordinated in the lung to effectively monitor, respond to, and resolve infection and inflammation in primed form needs to be fully illustrated. Here we apply immunocartography, a high-resolution technique that integrates spatial and single-cell RNA sequencing (scRNA-seq) through deconvolution and co-localization analyses, to the SARS-CoV-2-infected Syrian hamster model. We generate a comprehensive transcriptome map of the whole process of pulmonary infection from physiological condition, infection initiation, severe pneumonia to natural recovery at organ scale and single-cell resolution, with 142,965 cells and 45 lung lobes from 25 hamsters at 5 time points. Integrative analysis identifies that alveolar dendritic cell–T cell immunity hubs, where Ccr7 + Ido1 + dendritic cells, Cd160 + Cd8 + T cells, and Tnfrsf4 + Cd4 + T cells physiologically co-localize, rapidly expand during SARS-CoV-2 infection, eliminate SARS-CoV-2 with the aid of Slamf9 + macrophages, and then restore to physiological levels after viral clearance. We verify the presence of these cell subpopulations in the immunity hubs in normal and SARS-CoV-2-infected hACE2 mouse models, as well as in publicly available human scRNA-seq datasets, demonstrating the potential broad relevance of our findings in lung immunity.

Abstract Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

Abstract SARS-CoV-2 Omicron variant infection generally gives rise to asymptomatic to moderate COVID-19 in vaccinated people. The immune cells can be reprogrammed or “imprinted” by vaccination and infections to generate protective immunity against subsequent challenges. Considering the immune imprint in Omicron infection is unclear, here we delineate the innate immune landscape of human Omicron infection via single-cell RNA sequencing, surface proteome profiling, and plasma cytokine quantification. We found that monocyte responses predominated in immune imprints of Omicron convalescents, with IL-1β-associated and interferon (IFN)-responsive signatures with mild and moderate symptoms, respectively. Low-density neutrophils increased and exhibited IL-1β-associated and IFN-responsive signatures similarly. Mild convalescents had increased blood IL-1β, CCL4, IL-9 levels and PI3 + neutrophils, indicating a bias to IL-1β responsiveness, while moderate convalescents had increased blood CXCL10 and IFN-responsive monocytes, suggesting durative IFN responses. Therefore, IL-1β- or IFN-responsiveness of myeloid cells may indicate the disease severity of Omicron infection and mediate post-COVID conditions.

Abstract Gallbladder carcinoma (GBC) is the most common biliary tract malignancy with the lowest survival rate, primarily arising from chronic inflammation. To better characterize the progression from inflammation to cancer to metastasis, we performed single-cell RNA sequencing across samples of 6 chronic cholecystitis, 12 treatment-naive GBCs, and 6 matched metastases. Benign epithelial cells from inflamed gallbladders displayed resting, immune-regulating, and gastrointestinal metaplastic phenotypes. A small amount of PLA2G2A + epithelial cells with copy number variation were identified from a histologically benign sample. We validated significant overexpression of PLA2G2A across in situ GBCs, together with increased proliferation and cancer stemness in PLA2G2A-overexpressing GBC cells, indicating an important role for PLA2G2A during early carcinogenesis. Malignant epithelial cells displayed pervasive cancer hallmarks and cellular plasticity, differentiating into metaplastic, inflammatory, and mesenchymal subtypes with distinct transcriptomic, genomic, and prognostic patterns. Chronic cholecystitis led to an adapted microenvironment characterized by MDSC-like macrophages, CD8+ TRM cells, and CCL2 + immunity-regulating fibroblasts. By contrast, GBC instigated an aggressive and immunosuppressive microenvironment, featured by tumor-associated macrophages, Treg cells, CD8+ TEX cells, and STMN1 + tumor-promoting fibroblasts. Single-cell and bulk RNA-seq profiles consistently showed a more suppressive immune milieu for GBCs with inflammatory epithelial signatures, coupled with strengthened epithelial-immune crosstalk. We further pinpointed a subset of senescence-like fibroblasts (FN1 + TGM2 +) preferentially enriched in metastatic lesions, which promoted GBC migration and invasion via their secretory phenotype. Collectively, this study provides comprehensive insights into epithelial and microenvironmental reprogramming throughout cholecystitis-propelled carcinogenesis and metastasis, laying a new foundation for the precision therapy of GBC.

Abstract Liver metastasis is associated with immunotherapy resistance, although the underlying mechanisms remain incompletely understood. By applying single cell RNA‐sequencing to a concurrent subcutaneous and liver tumor murine model to recapitulate liver metastases, it is identified that subsets within tumor‐infiltrating exhausted CD8+ T (Tex) cells and immunosuppressive tumor‐associated macrophages (TAMs) display opposite responses to concurrent liver tumors and anti‐PD‐1 treatment, suggesting a complex immune regulating network. Both angiogenic and interferon‐reactive TAMs show increased frequencies in implanted liver tumors, and anti‐PD‐1 treatment further elevates the frequencies of angiogenic TAMs. Such TAMs frequencies negatively correlate with the proportions of cytotoxic T cell subsets. Further, expression of interferon‐stimulated genes in TAMs is dramatically reduced under effective anti‐PD‐1 treatment, while such tendencies are diminished in mice with implanted liver tumors. Therefore, the study indicates that liver metastases could increase immunosuppressive TAMs frequencies and inhibit Tex responses to PD‐1 blockade, resulting in compromised systemic antitumor immunity and limited immunotherapy efficacy.

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry. Public summary: • Single-cell sequencing can reveal molecular characteristics at the cell level • Spatial omics can reconstruct the organization of cells and their interactions • Single-cell multi-omics will picture the cell in a comprehensive way

Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45− cells, we systematically evaluated their features using a wide spectrum of analyses. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data more. For 10X-based data, we observed higher noise for mRNAs with low expression levels. Approximately 10%−30% of all detected transcripts by both platforms were from non-coding genes, with long non-coding RNAs (lncRNAs) accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected distinct groups of differentially expressed genes between cell clusters, indicating the different characteristics of these technologies. Our study promotes better understanding of these two platforms and offers the basis for an informed choice of these widely used technologies.

The cell-mediated protective and pathogenic immune responses to SARS-CoV-2 infection remain largely elusive. Here we identified 76 distinct cell subsets in the PBMC samples that were associated with various clinical presentations of COVID-19 using scRNA-seq technology coupled with a deep and comprehensive analysis of unique cell surface markers and differentially expressed genes. We revealed that (TRAV1-2+CD8+)MAIT cells and (NCAM1hiCD160+)NK cells significantly enriched in the asymptomatic subjects whereas (LAG3+CD160+CD8+)NKT cells increased in the symptomatic patients. We also observed that (CD68-CSF1R-IL1BhiCD14+)classical monocytes were positively correlated with the disease severity. Moreover, (CD33-HLA-DMA-CD14+)classical monocytes and (CLEC10A-S100A9lo)pDC were associated with the viral persistence. The GO and KEGG analyses identified enriched pathways related to immune responses, inflammation, and apoptosis. These findings may enhance our understanding of the immunopathogenesis of COVID-19 and help develop novel strategies against SARS-CoV-2 infection.

Single cell RNA-seq is a powerful method to assign cell identity, but the purity of cell clusters arising from this data is not clear. Here the authors present an entropy-based statistic called ROGUE to quantify the purity of cell clusters, and identify subtypes within clusters.

The increasing size of single cell sequencing data sets calls for scalable cell annotation methods. Here, the authors introduce SciBet, which uses a multinomial distribution model and maximum likelihood estimation for fast and accurate single cell identification.

Abstract Background Tumor-reactive CD8+ tumor-infiltrating lymphocytes (TILs) represent a subtype of T cells that can recognize and destroy tumor specifically. Understanding the regulatory mechanism of tumor-reactive CD8+ T cells has important therapeutic implications. Yet the DNA methylation status of this T cell subtype has not been elucidated. Results In this study, we segregate tumor-reactive and bystander CD8+ TILs, as well as naïve and effector memory CD8+ T cell subtypes as controls from colorectal cancer patients, to compare their transcriptome and methylome characteristics. Transcriptome profiling confirms previous conclusions that tumor-reactive TILs have an exhausted tissue-resident memory signature. Whole-genome methylation profiling identifies a distinct methylome pattern of tumor-reactive CD8+ T cells, with tumor-reactive markers CD39 and CD103 being specifically demethylated. In addition, dynamic changes are observed during the transition of naïve T cells into tumor-reactive CD8+ T cells. Transcription factor binding motif enrichment analysis identifies several immune-related transcription factors, including three exhaustion-related genes (NR4A1, BATF, and EGR2) and VDR, which potentially play an important regulatory role in tumor-reactive CD8+ T cells. Conclusion Our study supports the involvement of DNA methylation in shaping tumor-reactive and bystander CD8+ TILs, and provides a valuable resource for the development of novel DNA methylation markers and future therapeutics.

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. Keywords: Single cell, RNA-seq, Clustering, Subsampling, Classification

Abstract Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Abstract Background Rodents represent around 43% of all mammalian species, are widely distributed, and are the natural reservoirs of a diverse group of zoonotic viruses, including hantaviruses, Lassa viruses, and tick-borne encephalitis viruses. Thus, analyzing the viral diversity harbored by rodents could assist efforts to predict and reduce the risk of future emergence of zoonotic viral diseases. Results We used next-generation sequencing metagenomic analysis to survey for a range of mammalian viral families in rodents and other small animals of the orders Rodentia, Lagomorpha, and Soricomorpha in China. We sampled 3,055 small animals from 20 provinces and then outlined the spectra of mammalian viruses within these individuals and the basic ecological and genetic characteristics of novel rodent and shrew viruses among the viral spectra. Further analysis revealed that host taxonomy plays a primary role and geographical location plays a secondary role in determining viral diversity. Many viruses were reported for the first time with distinct evolutionary lineages, and viruses related to known human or animal pathogens were identified. Phylogram comparison between viruses and hosts indicated that host shifts commonly happened in many different species during viral evolutionary history. Conclusions These results expand our understanding of the viromes of rodents and insectivores in China and suggest that there is high diversity of viruses awaiting discovery in these species in Asia. These findings, combined with our previous bat virome data, greatly increase our knowledge of the viral community in wildlife in a densely populated country in an emerging disease hotspot.

Acute respiratory infections by influenza viruses are commonly causes of severe pneumonia, which can further deteriorate if secondary bacterial infections occur. Although the viral and bacterial agents are quite diverse, defensins, a set of antimicrobial peptides expressed by the host, may provide promising biomarkers that would greatly improve the diagnosis and treatment. We examined the correlations between the gene expression levels of defensins and the viral and bacterial loads in the blood on a longitudinal, precision-medical study of a severe pneumonia patient infected by influenza A H7N9 virus. We found that DEFA5 is positively correlated to the blood load of influenza A H7N9 virus (r = 0.735, p < 0.05, Spearman correlation). DEFB116 and DEFB127 are positively and DEFB108B and DEFB114 are negatively correlated to the bacterial load. Then the diagnostic potential of defensins to discriminate bacterial and viral infections was evaluated on an independent dataset with 61 bacterial pneumonia patients and 39 viral pneumonia patients infected by influenza A viruses and reached 93% accuracy. Expression levels of defensins in the blood may be of important diagnostic values in clinic to indicate viral and bacterial infections.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22248-3

An epidemic caused by influenza A (H7N9) virus was recently reported in China. Deep sequencing revealed the full genome of the virus obtained directly from a patient’s sputum without virus culture. The full genome showed substantial sequence heterogeneity and large differences compared with that from embryonated chicken eggs.

The recent emergence of human infection with influenza A(H10N8) virus is an urgent public health concern. Genomic analysis showed that the virus was conserved in chicken eggs but presented substantial adaptive mutations in MDCK cells. Our results provide additional evidence for the avian origin of this influenza virus.

To clarify the evolutionary relationships among betavoronaviruses that infect bats, we analyzed samples collected during 2010–2011 from 14 insectivorous bat species in China. We identified complete genomes of 2 novel betacoronaviruses in Rhinolophus pusillus and Chaerephon plicata bats, which showed close genetic relationships with severe acute respiratory syndrome coronaviruses.

BACKGROUND: Enterovirus 71 (EV71) infection can lead to a rapidly progressing, life-threatening, and severe neurological disease in young children, including the development of human hand, foot, and mouth disease (HFMD). This study aims to further characterize the specific immunological features in EV71-mediated HFMD patients presenting with differing degrees of disease severity. METHODOLOGY: Comprehensive cytokine and chemokine expression were broadly evaluated by cytokine antibody array in EV71-infected patients hospitalized for HFMD compared to Coxsackievirus A16-infected patients and age-matched healthy controls. More detailed analysis using Luminex-based cytokine bead array was performed in EV71-infected patients stratified into diverse clinic outcomes. Additionally, immune cell frequencies in peripheral blood and EV71-specific antibodies in plasma were also examined. PRINCIPAL FINDINGS: Expression of several cytokines and chemokines were significantly increased in plasma from EV71-infected patients compared to healthy controls, which further indicated that: (1) GM-CSF, MIP-1β, IL-2, IL-33, and IL-23 secretion was elevated in patients who rapidly developed disease and presented with uncomplicated neurological damage; (2) G-CSF and MCP-1 were distinguishably secreted in EV71 infected very severe patients presenting with acute respiratory failure; (3) IP-10, MCP-1, IL-6, IL-8, and G-CSF levels were much higher in cerebrospinal fluid than in plasma from patients with neurological damage; (4) FACS analysis revealed that the frequency of CD19(+)HLADR(+) mature B cells dynamically changed over time during the course of hospitalization and was accompanied by dramatically increased EV71-specific antibodies. Our data provide a panoramic view of specific immune mediator and cellular immune responses of HFMD and may provide useful immunological profiles for monitoring the progress of EV71-induced fatal neurological symptoms with acute respiratory failure.

Next generation sequencing (NGS) technologies have greatly changed the landscape of transcriptomic studies of non-model organisms. Since there is no reference genome available, de novo assembly methods play key roles in the analysis of these data sets. Because of the huge amount of data generated by NGS technologies for each run, many assemblers, e.g., ABySS, Velvet and Trinity, are developed based on a de Bruijn graph due to its time- and space-efficiency. However, most of these assemblers were developed initially for the Illumina/Solexa platform. The performance of these assemblers on 454 transcriptomic data is unknown. In this study, we evaluated and compared the relative performance of these de Bruijn graph based assemblers on both simulated and real 454 transcriptomic data. The results suggest that Trinity, the Illumina/Solexa-specialized transcriptomic assembler, performs the best among the multiple de Bruijn graph assemblers, comparable to or even outperforming the standard 454 assembler Newbler which is based on the overlap-layout-consensus algorithm. Our evaluation is expected to provide helpful guidance for researchers to choose assemblers when analyzing 454 transcriptomic data.

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms. [ABSTRACT FROM AUTHOR]