Human distal lung maps and lineage hierarchies reveal a bipotent progenitor

Murthy, Preetish Kadur Lakshminarasimha; Sontake, Vishwaraj; Tata, Aleksandra; Kobayashi, Yoshihiko; Macadlo, Lauren; Okuda, Kenichi; Conchola, Ansley S.; Nakano, Satoko; Gregory, Simon; Miller, Lisa; Spence, Jason R.; Engelhardt, John F.; Boucher, Richard C.; Rock, Jason R.; Randell, Scott H.; Tata, Purushothama Rao

doi:10.1038/s41586-022-04541-3

Cited by 201 publications

(207 citation statements)

References 59 publications

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“…Six published scRNA-seq datasets of normal human lung samples were collected to independently evaluate the accuracy of the automated cell type annotation using the LungMAP CellRefs. The datasets were from normal human lung samples of 2 months to 45 years of age and were generated using 10X chromium 3’ (GSM5388411/12/13 35 and GSM4504966/67 36 ; aligned to hg38 reference genome) and 5’ (GSM4035472 1 ; aligned to hg19 reference genome) platforms. For each test dataset, we used both the “LungMAP Human Lung CellRef Seed” and the “LungMAP Human Lung CellRef” to predict cell type annotations using Azimuth’s mapping algorithm 5 .…”

Section: Resultsmentioning

confidence: 99%

Guided construction of single cell reference for human and mouse lung

Guo

Morley

et al. 2022

Preprint

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Accurate cell type identification is a key and rate-limiting step in single cell data analysis. Single cell references with comprehensive cell types, reproducible and functional validated cell identities, and common nomenclatures are much needed by the research community to optimize automated cell type annotation and facilitate data integration, sharing, and collaboration. In the present study, we developed a novel computational pipeline to utilize the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples and constructed "LungMAP CellRef" and "LungMAP CellRef Seed" for both normal human and mouse lungs. "CellRef Seed" has an equivalent prediction power and produces consistent cell annotation as does "CellRef" but improves computational efficiency and simplifies its utilization for fast automated cell type annotation and online visualization. This atlas set incorporates 48 human and 40 mouse well-defined lung cell types catalogued from diverse anatomic locations and developmental time points. Using independent datasets, we demonstrated the utility of our CellRefs for automated cell type annotation analysis of both normal and disease lungs. User-friendly web interfaces were developed to support easy access and maximal utilization of the LungMAP CellRefs. LungMAP CellRefs are freely available to the pulmonary research community through fast interactive web interfaces to facilitate hypothesis generation, research discovery, and identification of cell type alterations in disease conditions.

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Section: Resultsmentioning

confidence: 99%

Guided construction of single cell reference for human and mouse lung

Guo

Morley

et al. 2022

Preprint

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“…This revealed highly localized immunosuppressive niches containing PDL1-expressing myeloid cells in contact with PD1-expressing T cells, often juxtaposed by areas of T cell immunoediting. Finally, a recent study of distal regions of the healthy human lung discovered new types of fibroblast, alveolar, and secretory cells [ 33 ]. Thus, spatial technologies are providing new information on specific tissue niches in health and disease.…”

Section: Spatial Transcriptomics In Current Biomedical Researchmentioning

confidence: 99%

An introduction to spatial transcriptomics for biomedical research

et al. 2022

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Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as ‘spatial transcriptomics’. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.

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“…Mucus accumulation with increased MUC5B expression in terminal bronchioles that do not normally express MUC5B and "bronchiolized microcysts" has also previously been reported in IPF (25,29,57,58). Extensive molecular profiling of these affected regions in IPF has revealed complex and dynamic interrelationships between terminal bronchiolar secretory cells, termed terminal airway-enriched secretory cells (TASCs) or respiratory airway secretory cells (RAS) cells, basal cells, and ATII/AT0 cells (59)(60)(61)(62)(63). Our molecular analyses of cell types lining distal airspace structures in SARS-CoV-2 infected lungs were limited by virus-and/or inflammation-induced suppression of defining distal bronchiolar cell genes, e.g., SCGB3A2…”

Section: Studies Of Proximal Airway Epithelia In Covid-19 Autopsy Lun...mentioning

confidence: 73%

Prevalence and Mechanisms of Mucus Accumulation in COVID-19 Lung Disease

Kato

Asakura

Edwards³

et al. 2022

Am J Respir Crit Care Med

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SHR provided primary human bronchial epithelial (HBE) cells and excised lungs. KO and FBA provided human control lung sections. LBT, ACB, SS, FJM, SMH, DSC, and NIHCAC provided COVID-19 autopsy lung sections. TK, TA, and PH reviewed these sections. CEE, MRC and ABB performed in vitro SARS-CoV-2 infection to HBE cell cultures and virus titering. HD and YM contributed to bioinformatic and statistical analysis. RCG performed RNA in situ hybridization. LS performed western blotting. TK, TA and YM quantified the images. TK and TA performed all the other experiments. CE provided mouse anti-MUC5B antibody. GDC supervised the GeoMx assays. GC and WKO provided guidance and feedback to the overall work. WKO, SHR, RSB, and RCB designed and supervised the project. TK and TA drafted the

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Human distal lung maps and lineage hierarchies reveal a bipotent progenitor

Cited by 201 publications

References 59 publications

Guided construction of single cell reference for human and mouse lung

Guided construction of single cell reference for human and mouse lung

An introduction to spatial transcriptomics for biomedical research

Prevalence and Mechanisms of Mucus Accumulation in COVID-19 Lung Disease

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