What is the Human Lung Cell Atlas?

The Human Lung Cell Atlas (HLCA) has a core consensus reference model of the cellular landscape of the lung and nose. This reference was built by integrating data of 107 individuals from 14 datasets, including major published datasets as well as unpublished data. The cells in the integrated atlas were re-annotated in a consensus manner based on originally published labels and inputs from 6 experts, resulting in a consensus re-annotation of, and robust marker genes for 58 cell identities in healthy human lung.

The Human Lung Cell Atlas is the largest, most diverse, and most detailed integrated atlas of any organ generated to date, and the extended atlas includes data from healthy and diseased lungs with more than 2.2M cells from 444 individuals in the extended atlas. The HLCA recovers rare cell types that could not be annotated in individual datasets and identifies cell-type specific gene modules associated with relevant covariates such as lung anatomical location, and age, sex, BMI and smoking status of the tissue donor. The HLCA can be explored here.

The HLCA also can be used as a tool by the respiratory community to explore the cell identities in healthy lung, to transfer labels as a way to speed up cell-type annotation workflows and to identify cellular changes in lung disease.

Why map my data to the HLCA?

Mapping your own lung single-cell or single-nucleus data to the HLCA will greatly speed up the analysis of your dataset. Upon mapping we can project consensus labels from the HLCA to your data and, using mapping uncertainty, highlight which cells seem different from healthy cells in the atlas. Depending on your experimental setup, these cells might represent novel cellular identities or cells particularly affected by disease. 

Get started

To get started, watch the video or visit the project page of the Human Lung Cell Atlas on FASTGenomics at https://beta.fastgenomics.org/p/hlca. For more information, download our flyer below. 

This project is funded by
Grant no. 874656

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