An integrated cell atlas of the human lung in health and disease

Abstract

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

“…Coarse, lineage-specific clusters were iteratively sub-clustered to identify cell-types at a more fine-grained resolution. Cell type clusters were annotated based on previously reported marker genes (Madissoon et al, 2019; Schupp et al, 2021; Sikkema et al, 2022) (Figure S1A).…”

SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data

“…Advances in scRNA-seq technology and atlas projects such as the Human Cell Atlas 1 are prompting the generation of single-cell datasets spanning millions of cells [38][39][40][41][42][43] and hundreds of individuals, rendering even the most fundamental analyses such as dimensionality reduction and clustering computationally infeasible. SEACells identifies robust, well-defined metacells from any sample, and thus can be used to integrate large-scale single-cell datasets in a computationally efficient manner.…”

The rise of sparser single-cell RNAseq datasets; consequences and opportunities

“…In contrast, binarizing the same dataset and storing it as bits requires only 300 Megabytes, which is a 17-fold reduction in storage requirements. This potentially boosts scalability of downstream analyses to larger numbers of cells, opening possibilities to get a more fine-grained resolution of biological heterogeneity (32).…”