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PMC10914349
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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling.
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The experiments were not randomized and investigators were not blinded to allocation during experiments and outcome assessment.
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PMC10914349
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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling.
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Two-sided Wilcoxon test with Holm correction for multiple comparisons was performed in Figure 3D, Figure 6B, and Figure 6—figure supplement 1B.
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PMC10914349
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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling.
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Detailed summary statistics were provided in corresponding Source data files.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Single-cell technologies have transformed our understanding of human tissues.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Yet, studies typically capture only a limited number of donors and disagree on cell type definitions.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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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.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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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.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Mapping new data to the HLCA enables rapid data annotation and interpretation.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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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.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Rapid technological improvements over the past decade have allowed single-cell datasets to grow both in size and number.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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This has led consortia, such as the Human Cell Atlas, to pursue the generation of large-scale reference atlases of human organs.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To advance our understanding of health and disease, such atlases must capture variation between individuals that is expected to impact the molecular phenotypes of the cells in a tissue.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Whereas the generation of atlases at this scale by single research groups is currently not feasible, integrating datasets generated by the research community at large will enable capture of the diversity of the cellular landscape across individuals.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Several foundational studies have started to map the cellular landscape of the healthy human lung.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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These studies each have a specific bias due to their choice of experimental protocol and technologies, and are therefore not tailored to serve as a universal reference.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The studies moreover include only a limited number of samples and individuals, thus lacking the scale and diversity to capture the full cellular heterogeneity present within the lung as well as across individuals.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Integrated single-cell atlases provide novel insights not obtained in individual studies.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Recent reference atlases have led to the discovery of unknown cell types, the identification of marker genes that are reproducible across studies, the comparison of animal and in vitro models with human healthy and diseased tissue and patient stratification for disease endotypes.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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However, many currently available integrated atlases are limited in the number of human samples, datasets or cell types per organ, as well as donor metadata (for example, age, body mass index (BMI) and smoking status), or focus mainly on a specific disease.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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These limitations constrain the potential of atlases to serve as a reference, as they fail to represent and catalog the diversity of cellular phenotypes within the healthy organ and across individuals.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Moreover, when integrating data from different sources, it is paramount to correctly separate technical biases from biologically relevant information.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Yet, the majority of existing atlases have not assessed the quality of their data integration.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Nonetheless, successful integration of the available datasets into a single tissue atlas is a critical step in achieving the goals of the Human Cell Atlas.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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In this resource, we present an integrated single-cell transcriptomic atlas of the human respiratory system, including the upper and lower airways, from published and newly generated datasets (Fig. 1).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The Human Lung Cell Atlas (HLCA) comprises data from 486 donors and 49 datasets, including 2.4 million cells, which we re-annotated to generate a consensus cell type reference.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The HLCA expands our understanding of the healthy lung and its changes in disease and can be used as a reference for analyzing future lung data.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
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Together, we provide a roadmap for building and using comprehensive, interpretable and up-to-date organ- and population-scale cell atlases.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Fig. 1HLCA study overview.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Harmonized cell annotations, raw count data, harmonized patient and sample metadata and sample anatomical locations encoded into a CCF were collected and generated as input for the HLCA core (left).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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After integration of the core datasets, the atlas was extended by mapping 35 additional datasets, including disease samples, to the HLCA core, bringing the total number of cells in the extended HLCA to 2.4 million (M).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
|
The HLCA core provides detailed consensus cell annotations with matched consensus cell type markers (top right), gene modules associated with technical, demographic and anatomical covariates in various cell types (middle right), GWAS-based association of lung conditions with cell types (middle right) and a reference projection model to annotate new data (middle right) and discover previously undescribed cell types, transitional cell states and disease-associated cell states (right, bottom).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Harmonized cell annotations, raw count data, harmonized patient and sample metadata and sample anatomical locations encoded into a CCF were collected and generated as input for the HLCA core (left).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
After integration of the core datasets, the atlas was extended by mapping 35 additional datasets, including disease samples, to the HLCA core, bringing the total number of cells in the extended HLCA to 2.4 million (M).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
The HLCA core provides detailed consensus cell annotations with matched consensus cell type markers (top right), gene modules associated with technical, demographic and anatomical covariates in various cell types (middle right), GWAS-based association of lung conditions with cell types (middle right) and a reference projection model to annotate new data (middle right) and discover previously undescribed cell types, transitional cell states and disease-associated cell states (right, bottom).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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To build the HLCA, we collected single-cell RNA sequencing (scRNA-seq) data and detailed, harmonized technical, biological and demographic metadata from 14 datasets (11 published and three unpublished).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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These datasets include samples from 107 individuals, with diversity in age, sex, ethnicity (harmonized as detailed in Methods), BMI and smoking status (Fig. 2a).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Cells were obtained from 166 tissue samples using a variety of tissue donors, sampling methods, experimental protocols and sequencing platforms (Supplementary Tables 1 and 2).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Anatomical locations of the samples were projected onto a one-dimensional (1D) common coordinate framework (CCF), representing the proximal (0) to distal (1) axis of the respiratory system, to standardize the anatomical location of origin (Fig. 2a and Supplementary Tables 2 and 3).Fig.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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2Composition and construction of the HLCA core.a, Donor and sample composition in the HLCA core for demographic and anatomical variables.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Donors/samples without annotation are shown as not available (NA; gray bars) for each variable.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
For the anatomical region CCF score, 0 represents the most proximal part of the lung and airways (nose) and 1 represents the most distal (distal parenchyma).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Donors show diversity in ethnicity (harmonized metadata proportions: 65% European, 14% African, 2% admixed American, 2% mixed, 2% Asian, 0.4% Pacific Islander and 14% unannotated; see Methods), smoking status (52% never, 16% former, 15% active and 17% NA), sex (60% male and 40% female), age (ranging from 10–76 years) and BMI (20–49; 30% NA).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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b, Overview of the HLCA core cell type composition for the first three levels of cell annotation, based on harmonized original labels.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
In the cell type hierarchy, the lowest level (1) consists of the coarsest possible annotations (that is, epithelial (48% of cells), immune (38%), endothelial (9%) and stromal (4%)).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Higher levels (2–5) recursively break up coarser-level labels into finer ones (Methods).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Cells were set to ‘none’ if no cell type label was available at the level.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Cell labels making up less than 0.02% of all cells are not shown.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Overall, 94, 66 and 7% of cells were annotated at levels 3, 4 and 5, respectively.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
|
c, Cell type composition per sample, based on level 2 labels.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
|
Samples are ordered by anatomical region CCF score.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
|
d, Summary of the dataset integration benchmarking results.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Batch correction score and biological conservation score each show the mean across metrics of that type, as shown in Supplementary Fig. 1, with metric scores scaled to range from 0 to 1.
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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Both Scanorama and fastMNN were benchmarked on two distinct outputs: the integrated gene expression matrix and integrated embedding (see output).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
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The methods are ordered by overall score.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
For each method, the results are shown only for their best-performing data preprocessing.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Methods marked with an asterisk use coarse cell type labels as input.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Preprocessing is specified under HVG (that is, whether or not genes were subsetted to the 2,000 (HVG) or 6,000 (FULL) most highly variable genes before integration) and scaling (whether genes were left unscaled or scaled to have a mean of 0 and a standard deviation of 1 across all cells).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
EC, endothelial cell; NK, natural killer; Bioconserv.,
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
conservation of biological signal.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
a, Donor and sample composition in the HLCA core for demographic and anatomical variables.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Donors/samples without annotation are shown as not available (NA; gray bars) for each variable.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
For the anatomical region CCF score, 0 represents the most proximal part of the lung and airways (nose) and 1 represents the most distal (distal parenchyma).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Donors show diversity in ethnicity (harmonized metadata proportions: 65% European, 14% African, 2% admixed American, 2% mixed, 2% Asian, 0.4% Pacific Islander and 14% unannotated; see Methods), smoking status (52% never, 16% former, 15% active and 17% NA), sex (60% male and 40% female), age (ranging from 10–76 years) and BMI (20–49; 30% NA).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
b, Overview of the HLCA core cell type composition for the first three levels of cell annotation, based on harmonized original labels.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
In the cell type hierarchy, the lowest level (1) consists of the coarsest possible annotations (that is, epithelial (48% of cells), immune (38%), endothelial (9%) and stromal (4%)).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Higher levels (2–5) recursively break up coarser-level labels into finer ones (Methods).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Cells were set to ‘none’ if no cell type label was available at the level.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Cell labels making up less than 0.02% of all cells are not shown.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Overall, 94, 66 and 7% of cells were annotated at levels 3, 4 and 5, respectively.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
c, Cell type composition per sample, based on level 2 labels.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Samples are ordered by anatomical region CCF score.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
d, Summary of the dataset integration benchmarking results.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Batch correction score and biological conservation score each show the mean across metrics of that type, as shown in Supplementary Fig. 1, with metric scores scaled to range from 0 to 1.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Both Scanorama and fastMNN were benchmarked on two distinct outputs: the integrated gene expression matrix and integrated embedding (see output).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
The methods are ordered by overall score.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
For each method, the results are shown only for their best-performing data preprocessing.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Methods marked with an asterisk use coarse cell type labels as input.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Preprocessing is specified under HVG (that is, whether or not genes were subsetted to the 2,000 (HVG) or 6,000 (FULL) most highly variable genes before integration) and scaling (whether genes were left unscaled or scaled to have a mean of 0 and a standard deviation of 1 across all cells).
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
EC, endothelial cell; NK, natural killer; Bioconserv.,
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
conservation of biological signal.
|
PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Consensus definitions of cell types based on single-cell transcriptomic data across studies—particularly of transitional cell states—are lacking.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
To enable supervised data integration and downstream integrated analysis, we harmonized cell type nomenclature by building a five-level hierarchical cell identity reference framework (Methods, Supplementary Table 4 and Fig. 2b).
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PMC10287567
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An integrated cell atlas of the lung in health and disease.
|
We then unified cell type labeling across datasets by mapping the collected cell identity labels for every dataset as provided by the data generator to the hierarchical reference framework, showing varying cell type proportions per sample (Fig. 2c).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
To optimally remove dataset-specific batch effects, we evaluated 12 different data integration methods on 12 datasets (Fig. 2d and Supplementary Fig. 1) using our previously established benchmarking pipeline.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
We used the top-performing integration method, scANVI, to create an integrated embedding of all 584,444 cells of 107 individuals from the collected datasets: the HLCA core (Fig. 3a).Fig.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
3The HLCA core conserves detailed biology and enables consensus-driven annotation.a, A UMAP of the integrated HLCA, colored by level 1 annotation.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
b, Cluster label disagreement (label entropy) of Leiden 3 clusters of the HLCA.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
The HLCA was split into three parts (immune, epithelial and endothelial/stromal) for ease of visualization.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Cells from every cluster are colored by label entropy.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Clusters with less than 20% of cells annotated at level 3 are colored gray.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
c, Cell type label composition of the immune cluster with the most label disagreement (left), with original labels (middle left) and matching manual re-annotations (middle right).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
A zoom-in on the UMAP from b shows the final re-annotations (right).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
d, UMAPs of the immune, epithelial and endothelial/stromal parts of the HLCA core with cell annotations from the expert manual re-annotation.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
e, Percentage of cells originally labeled correctly, mislabeled or underlabeled (that is, only labeled at a coarser level) compared with final manual re-annotations.
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
The percentages were calculated per manual annotation, as well as across all cells (right bar).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
f, UMAP of HLCA clusters annotated as rare epithelial cell types (that is, ionocytes, neuroendocrine cells and tuft cells).
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PMC10287567
|
An integrated cell atlas of the lung in health and disease.
|
Final annotations, original labels and the study of origin are shown (top), as well as the expression of ionocyte marker FOXI1, tuft cell marker LRMP and neuroendocrine marker CALCA (bottom).
|
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