The spatio-temporal landscape of lung pathology in SARS-CoV-2 infection

SummaryRecent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19)1–8. However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.

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The spatio-temporal program of liver zonal regeneration

10.1101/2021.08.11.455924 ◽

2021 ◽

Author(s):

Shani Ben-Moshe ◽

Tamar Veg ◽

Rita Manco ◽

Stav Dan ◽

Aleksandra A. Kolodziejczyk ◽

...

Keyword(s):

Liver Regeneration ◽

Single Cell ◽

Stellate Cell ◽

Cell Types ◽

Matrix Remodeling ◽

Spatially Resolved ◽

Single Cell Rna Sequencing ◽

Ability To Regenerate ◽

Spatio Temporal ◽

Macrophage Populations

The liver carries a remarkable ability to regenerate rapidly after acute zonal damage. Single-cell approaches are necessary to study this process, given the spatial heterogeneity of multiple liver cell types. Here, we use spatially-resolved single cell RNA sequencing (scRNAseq) to study the dynamics of mouse liver regeneration after acute acetaminophen (APAP) intoxication. We find that hepatocytes proliferate throughout the liver lobule, creating the mitotic pressure required to repopulate the necrotic pericentral zone rapidly. A subset of hepatocytes located at the regenerating front transiently up-regulate fetal-specific genes, including Afp and Cdh17, as they reprogram to a pericentral state. Zonated endothelial, hepatic-stellate cell (HSC) and macrophage populations are differentially involved in immune recruitment, proliferation and matrix remodeling. We observe massive transient infiltration of myeloid cells, yet stability of lymphoid cell abundance, in accordance with global decline in antigen presentation. Our study provides a resource for understanding the coordinated programs of zonal liver regeneration.

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Annotation of Spatially Resolved Single-cell Data with STELLAR

10.1101/2021.11.24.469947 ◽

2021 ◽

Author(s):

Maria Brbic ◽

Kaidi Cao ◽

John W Hickey ◽

Yuqi Tan ◽

Michael Snyder ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Computational Methods ◽

Spatial Organization ◽

Cell Types ◽

Learning Method ◽

Spatially Resolved ◽

Reference Set ◽

Time Savings ◽

Cell Data

Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.

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Single-Cell Transcriptomics Identifies Dysregulated Metabolic Programs of Aging Alveolar Progenitor Cells in Lung Fibrosis

10.1101/2020.07.30.227892 ◽

2020 ◽

Author(s):

Jiurong Liang ◽

Guanling Huang ◽

Xue Liu ◽

Forough Taghavifar ◽

Ningshan Liu ◽

...

Keyword(s):

Lipid Metabolism ◽

Epithelial Cells ◽

Lung Injury ◽

Single Cell ◽

Cell Types ◽

Alveolar Epithelial Cells ◽

Lung Epithelial Cells ◽

Alveolar Epithelial ◽

Metabolic Defects ◽

Lung Epithelial

ABSTRACTAging is a critical risk factor in progressive lung fibrotic diseases such as idiopathic pulmonary fibrosis (IPF). Loss of integrity of type 2 alveolar epithelial cells (AEC2s) is the main causal event in the pathogenesis of IPF. To systematically examine the genomic program changes of AEC2s with aging and lung injury, we performed unbiased single cell RNA-seq analyses of lung epithelial cells from either uninjured or bleomycin-injured young and old mice. Major lung epithelial cell types were readily identified with canonical cell markers in our dataset. Heterogenecity of AEC2s was apparent, and AEC2s were then classified into three subsets according to their gene signatures. Genes related to lipid metabolism and glycolysis were significantly altered within these three clusters of AEC2s, and also affected by aging and lung injury. Importantly, IPF AEC2s showed similar genomic programming and metabolic changes as that of AEC2s from bleomycin injured old mouse lungs relative to controls. Furthermore, perturbation of both lipid metabolism and glycolysis significantly changed progenitor renewal capacity in 3-Demensional organoid culture of AEC2s. Taken togather, this work identified metabolic defects of AEC2s in aging and during lung injury. Strategies to rectify these altered programs would promote AEC2 renewal which in turn improves lung repair.One sentence summaryMetabolic defects of alveolar progenitors in aging and during lung injury impair their renewal.

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Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

Nature Protocols ◽

10.1038/s41596-021-00561-x ◽

2021 ◽

Author(s):

Jordan W. Squair ◽

Michael A. Skinnider ◽

Matthieu Gautier ◽

Leonard J. Foster ◽

Grégoire Courtine

Keyword(s):

Single Cell ◽

Cell Types ◽

Cell Data

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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Single-cell transcriptomic analysis of mIHC images via antigen mapping

Science Advances ◽

10.1126/sciadv.abc5464 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabc5464

Author(s):

Kiya W. Govek ◽

Emma C. Troisi ◽

Zhen Miao ◽

Rachael G. Aubin ◽

Steven Woodhouse ◽

...

Keyword(s):

Single Cell ◽

Spatial Patterns ◽

Cell Types ◽

Level Of Detail ◽

Cell Populations ◽

Sequencing Data ◽

Spatially Resolved ◽

Murine Spleen ◽

Single Cell Rna Sequencing ◽

Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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484 Bioturing browser: interactively explore public single cell sequencing data

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0484 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A520-A520

Author(s):

Son Pham ◽

Tri Le ◽

Tan Phan ◽

Minh Pham ◽

Huy Nguyen ◽

...

Keyword(s):

Single Cell ◽

Immune Cell ◽

Expression Profiles ◽

Meta Analysis ◽

Cell Types ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Data Formats ◽

Cancer Types ◽

Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

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Mapping single-cell atlases throughout Metazoa unravels cell type evolution

eLife ◽

10.7554/elife.66747 ◽

2021 ◽

Vol 10 ◽

Author(s):

Alexander J Tarashansky ◽

Jacob M Musser ◽

Margarita Khariton ◽

Pengyang Li ◽

Detlev Arendt ◽

...

Keyword(s):

Stem Cell ◽

Single Cell ◽

Cell Types ◽

The Self ◽

Cell Type ◽

Germ Layers ◽

Animal Evolution ◽

Self Assembling ◽

Animal Phyla ◽

Cell Data

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning mouse to sponge, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

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Spatial and single-cell transcriptional landscape of human cerebellar development

10.1101/2020.06.30.174391 ◽

2020 ◽

Author(s):

Kimberly A. Aldinger ◽

Zach Thomson ◽

Parthiv Haldipur ◽

Mei Deng ◽

Andrew E. Timms ◽

...

Keyword(s):

Single Cell ◽

Emotional Regulation ◽

Cell Types ◽

Molecular Organization ◽

Cerebellar Development ◽

Disease Genes ◽

Spatially Resolved ◽

Human Cerebellum ◽

Spatial Composition ◽

And Function

ABSTRACTCerebellar development and function require precise regulation of molecular and cellular programs to coordinate motor functions and integrate network signals required for cognition and emotional regulation. However, molecular understanding of human cerebellar development is limited. Here, we combined spatially resolved and single-cell transcriptomics to systematically map the molecular, cellular, and spatial composition of early and mid-gestational human cerebellum. This enabled us to transcriptionally profile major cell types and examine the dynamics of gene expression within cell types and lineages across development. The resulting ‘Developmental Cell Atlas of the Human Cerebellum’ demonstrates that the molecular organization of the cerebellar anlage reflects cytoarchitecturally distinct regions and developmentally transient cell types that are insufficiently captured in bulk transcriptional profiles. By mapping disease genes onto cell types, we implicate the dysregulation of specific cerebellar cell types, especially Purkinje cells, in pediatric and adult neurological disorders. These data provide a critical resource for understanding human cerebellar development with implications for the cellular basis of cerebellar diseases.

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Cell Atlas technologies and insights into tissue architecture

Biochemical Journal ◽

10.1042/bcj20190341 ◽

2020 ◽

Vol 477 (8) ◽

pp. 1427-1442 ◽

Cited By ~ 4

Author(s):

Anna Wilbrey-Clark ◽

Kenny Roberts ◽

Sarah A. Teichmann

Keyword(s):

Single Cell ◽

Molecular Level ◽

Cell Types ◽

Tissue Architecture ◽

Robert Hooke ◽

Spatially Resolved ◽

Gene And Protein Expression ◽

Basic Biology ◽

And Function ◽

Fundamental Units

Since Robert Hooke first described the existence of ‘cells’ in 1665, scientists have sought to identify and further characterise these fundamental units of life. While our understanding of cell location, morphology and function has expanded greatly; our understanding of cell types and states at the molecular level, and how these function within tissue architecture, is still limited. A greater understanding of our cells could revolutionise basic biology and medicine. Atlasing initiatives like the Human Cell Atlas aim to identify all cell types at the molecular level, including their physical locations, and to make this reference data openly available to the scientific community. This is made possible by a recent technology revolution: both in single-cell molecular profiling, particularly single-cell RNA sequencing, and in spatially resolved methods for assessing gene and protein expression. Here, we review available and upcoming atlasing technologies, the biological insights gained to date and the promise of this field for the future.

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