Nature Medicine · 2023 · 760 citations · 110 references
Life SciencesBioinformaticsCell BiologyGenomicsMolecular Cell BiologyTranscriptomicsCell Type AnnotationRare Cell Population DetectionSingle-cell AnalysisSingle-cell GenomicsSingle-cell IntegrationSingle-cell TranscriptomicsCell Atlas ConstructionCell Type HeterogeneityCell Type Reference Atlas ConstructionCell-type IdentificationDemographic CovariatesIntegration Of Single-cell DatasetsLung DiseasesRare Cell Type Discovery
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. The authors aim to integrate 49 single‑cell datasets into the Human Lung Cell Atlas, spanning 2.4 million cells from 486 individuals, to overcome limited donor numbers and inconsistent cell type definitions. Using the HLCA’s diverse donor cohort, the authors identify gene modules linked to demographics and bronchial axis expression, and demonstrate that mapping new data to the atlas enables rapid annotation and interpretation. The atlas provides consensus cell‑type re‑annotation with marker genes, annotating rare and previously undescribed cell types, and reveals shared cell states across multiple lung diseases, such as SPP1.
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
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