The AIMe registry for artificial intelligence in biomedical research

peer reviewe

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

bulk RNA-seq data processed by Sanger

bulk RNA-seq data processed by SangerThe metadata is downloaded from bulk RNA-seq data processed by Sanger (https://cellmodelpassports.sanger.ac.uk/downloads, rnaseq_all_20220624.zip).converted to a csv file where the rows are models names and the columns are the symbols.random white space is removed.</ul

combosciplex

Combosciplex subset of sciplex v3</p

Proteomics data processed by Sanger

Proteomics data processed by Sanger: The metadata is downloaded from https://cellmodelpassports.sanger.ac.uk/downloads, Proteomics_20221214.zip, proteomics_all_20221214.csv</p

scverse/spatialdata: v0.0.8

An open and universal framework for processing spatial omics dat

scverse/spatialdata: 0.0.6

This release adds polygon spatial query

nf-core/mhcquant: nf-core/mhcquant V1.3 "Red Perrot"

Identify and quantify peptides from mass spectrometry raw dat

scverse/spatialdata: 0.0.9

An open and universal framework for processing spatial omics dat