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An integrated cell atlas of the lung in health and disease
Source paper: PMC10287567 | [
{
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"label": "Tissue",
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"text": "lung"
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] | Single_Cell |
Single-cell technologies have transformed our understanding of human tissues. | [] | Single_Cell |
Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. | [] | Single_Cell |
Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. | [] | Single_Cell |
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. | [
{
"end": 114,
"label": "Tissue",
"start": 90,
"text": "human respiratory system"
}
] | Single_Cell |
The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. | [] | Single_Cell |
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. | [
{
"end": 274,
"label": "Tissue",
"start": 260,
"text": "bronchial tree"
}
] | Single_Cell |
Mapping new data to the HLCA enables rapid data annotation and interpretation. | [] | Single_Cell |
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. | [
{
"end": 173,
"label": "CellType",
"start": 128,
"text": "SPP1 profibrotic monocyte-derived macrophages"
}
] | Single_Cell |
Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. | [] | Single_Cell |
Rapid technological improvements over the past decade have allowed single-cell datasets to grow both in size and number . | [] | Single_Cell |
This has led consortia, such as the Human Cell Atlas, to pursue the generation of large-scale reference atlases of human organs . | [
{
"end": 127,
"label": "Tissue",
"start": 115,
"text": "human organs"
}
] | Single_Cell |
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. | [] | Single_Cell |
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. | [] | Single_Cell |
Several foundational studies have started to map the cellular landscape of the healthy human lung . | [
{
"end": 97,
"label": "Tissue",
"start": 79,
"text": "healthy human lung"
}
] | Single_Cell |
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. | [] | Single_Cell |
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. | [
{
"end": 182,
"label": "Tissue",
"start": 178,
"text": "lung"
}
] | Single_Cell |
Integrated single-cell atlases provide novel insights not obtained in individual studies. | [] | Single_Cell |
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 . | [] | Single_Cell |
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 . | [
{
"end": 130,
"label": "Tissue",
"start": 125,
"text": "organ"
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] | Single_Cell |
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. | [
{
"end": 177,
"label": "Tissue",
"start": 164,
"text": "healthy organ"
}
] | Single_Cell |
Moreover, when integrating data from different sources, it is paramount to correctly separate technical biases from biologically relevant information. | [] | Single_Cell |
Yet, the majority of existing atlases have not assessed the quality of their data integration. | [] | Single_Cell |
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 . | [] | Single_Cell |
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 ). | [
{
"end": 107,
"label": "Tissue",
"start": 83,
"text": "human respiratory system"
},
{
"end": 146,
"label": "Tissue",
"start": 123,
"text": "upper and lower airways"
}
] | Single_Cell |
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. | [] | Single_Cell |
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. | [
{
"end": 54,
"label": "Tissue",
"start": 42,
"text": "healthy lung"
}
] | Single_Cell |
Together, we provide a roadmap for building and using comprehensive, interpretable and up-to-date organ- and population-scale cell atlases. | [] | Single_Cell |
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) . | [] | Single_Cell |
These datasets include samples from 107 individuals, with diversity in age, sex, ethnicity (harmonized as detailed in Methods ), BMI and smoking status (Fig. 2a ). | [] | Single_Cell |
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 ). | [] | Single_Cell |
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 ). | [
{
"end": 188,
"label": "Tissue",
"start": 170,
"text": "respiratory system"
}
] | Single_Cell |
Consensus definitions of cell types based on single-cell transcriptomic data across studies—particularly of transitional cell states—are lacking. | [] | Single_Cell |
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 ). | [] | Single_Cell |
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 ). | [] | Single_Cell |
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 . | [] | Single_Cell |
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 ). | [] | Single_Cell |
A large-scale integrated atlas provides the unique opportunity to systematically investigate the consensus in cell type labeling across datasets. | [] | Single_Cell |
To identify areas of consensus and disagreement, we iteratively clustered the HLCA core and investigated donor diversity and cell type label agreement in these clusters using entropy scores (see Methods ). | [] | Single_Cell |
Most clusters contained cells from many donors (Extended Data Fig. 1a ). | [] | Single_Cell |
Clusters with low donor diversity ( n = 14) were largely immune cell clusters ( n = 13), representing donor- or donor group-specific phenotypes. | [] | Single_Cell |
Similarly, a high diversity of (contradictory) cell type labels (high label entropy) can identify both annotation disagreements between studies and clusters of doublets ( Methods ). | [] | Single_Cell |
Most clusters (61 out of 94) showed low label entropy, suggesting overall agreement of coarse cell type labels across datasets (Fig. 3b ). | [] | Single_Cell |
The remaining 33 clusters exhibited high label entropy, highlighting cellular phenotypes that were differently labeled across datasets (Fig. 3b ). | [] | Single_Cell |
For example, the immune cluster with the highest label entropy contained many cells that were originally mislabeled as monocytes and macrophages but were actually type 2 dendritic cells (Fig. 3c and Extended Data Fig. 1b ). | [
{
"end": 128,
"label": "CellType",
"start": 119,
"text": "monocytes"
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{
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"label": "CellType",
"start": 133,
"text": "macrophages"
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{
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"label": "CellType",
"start": 163,
"text": "type 2 dendritic cells "
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] | Single_Cell |
Thus, populations with high label entropy identify mislabeled cell types, indicating the need for consensus re-annotation of the integrated atlas. | [] | Single_Cell |
As a first step to achieve such a consensus on the diversity of cell types present in the HLCA core, we performed a full re-annotation of the integrated data on the basis of the original annotations and six expert opinions (consensus annotation; Methods and Fig. 3d ). | [] | Single_Cell |
Each of the 61 annotated cell types (Supplementary Table 5 ) was detected in at least four datasets out of 14, often in specific parts of the respiratory system, and different cell types showed varying fractions of proliferating ( MKI67 ) cells (Extended Data Fig. 2a–c ). | [
{
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"label": "Tissue",
"start": 142,
"text": "respiratory system"
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{
"end": 244,
"label": "CellType",
"start": 215,
"text": "proliferating ( MKI67 ) cells"
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] | Single_Cell |
While our consensus cell type annotations partly correspond to original labels (41% of cells), there were also refinements (28%) and substantial re-annotations (31%; Fig. 3e and Supplementary Fig. 2 ). | [] | Single_Cell |
To robustly characterize the cell types, we established a universal set of marker genes that generalizes across individuals and studies ( Methods , Extended Data Fig. 3 and Supplementary Table 6 ). | [] | Single_Cell |
The fully re-annotated HLCA core thus combines data from a diverse set of studies to provide a carefully curated reference for cell type annotations and marker genes in healthy lung tissue. | [
{
"end": 188,
"label": "Tissue",
"start": 169,
"text": "healthy lung tissue"
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Rare cell types, such as ionocytes, tuft cells, neuroendocrine cells and specific immune cell subsets, are often difficult to identify in individual datasets. | [
{
"end": 34,
"label": "CellType",
"start": 25,
"text": "ionocytes"
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{
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"label": "CellType",
"start": 36,
"text": "tuft cells"
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"label": "CellType",
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"text": "neuroendocrine cells"
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{
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"label": "Ce... | Single_Cell |
Yet, combining datasets in the HLCA core provides better power for identifying these rare cell types. | [] | Single_Cell |
Ionocytes, tuft and neuroendocrine cells make up only 0.08, 0.01 and 0.02% of the cells in the HLCA core according to the original labels, and were originally identified in only seven, two and four datasets out of 14, respectively. | [
{
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"label": "CellType",
"start": 0,
"text": "Ionocytes"
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"label": "CellType",
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"text": "tuft"
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{
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"label": "CellType",
"start": 20,
"text": "neuroendocrine cells"
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] | Single_Cell |
Despite their low abundance, these cells formed three separate clusters of the HLCA core (Fig. 3f ). | [] | Single_Cell |
Our re-annotation increases the number of datasets in which these cells are detected up to threefold and identifies both cells falsely annotated as monocytes, tuft cells or neuroendocrine cells, as well as originally undetected rare cells (Fig. 3f and Supplementary Fig. 3a ). | [
{
"end": 157,
"label": "CellType",
"start": 148,
"text": "monocytes"
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{
"end": 169,
"label": "CellType",
"start": 159,
"text": "tuft cells"
},
{
"end": 193,
"label": "CellType",
"start": 173,
"text": "neuroendocrine cells"
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] | Single_Cell |
Importantly, other integration methods tested during our benchmarking, such as Harmony and Seurat’s RPCA , failed to separate these rare cells into distinct clusters (Supplementary Fig. 3b ). | [] | Single_Cell |
We were further able to detect six cell identities that were not previously found in the human lung or were only recently described in individual studies. | [
{
"end": 99,
"label": "Tissue",
"start": 89,
"text": "human lung"
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] | Single_Cell |
These cell types include migratory dendritic cells ( n = 312 cells, expressing CCR7 , LAD1 and COL19 ), hematopoietic stem cells ( n = 60, expressing SPINK2 , STMN , PRSS57 and CD34 ), highly proliferative hillock-like epithelial cells not previously reported in adult human lung ( n = 4,600, expressing KRT6A , KRT13 an... | [
{
"end": 50,
"label": "CellType",
"start": 25,
"text": "migratory dendritic cells"
},
{
"end": 128,
"label": "CellType",
"start": 104,
"text": "hematopoietic stem cells"
},
{
"end": 235,
"label": "CellType",
"start": 185,
"text": "highly proliferative hillock-... | Single_Cell |
SFTPC and SCGB3A1 ) and the closely related preterminal bronchiole secretory cells ( n = 4,393, expressing SFTPB , SCGB3A2 , SFTPC and SCGB3A1 , together with alveolar type 0 cells called transitional club-AT2 cells) and a subset of smooth muscle cells ( n = 335) that to our knowledge have not previously been described... | [
{
"end": 82,
"label": "CellType",
"start": 44,
"text": "preterminal bronchiole secretory cells"
},
{
"end": 180,
"label": "CellType",
"start": 159,
"text": "alveolar type 0 cells"
},
{
"end": 215,
"label": "CellType",
"start": 188,
"text": "transitional club-A... | Single_Cell |
These smooth muscle cells, predominantly found in the airways, express canonical smooth muscle markers ( CNN1 and MYH11 ) and also uniquely and consistently express FAM83D across datasets (Extended Data Fig. 4e,f ). | [
{
"end": 25,
"label": "CellType",
"start": 6,
"text": "smooth muscle cells"
},
{
"end": 61,
"label": "Tissue",
"start": 54,
"text": "airways"
}
] | Single_Cell |
The HLCA core thus enables improved detection and identification of rare cell types, as well as the discovery of unknown cell types. | [] | Single_Cell |
Demographic and other metadata covariates affect cellular transcriptional phenotypes . | [] | Single_Cell |
Better insight into the impact of these covariates (for example, sex, BMI and smoking) on cell type gene expression can shed light on the contribution of these factors to progression from healthy to diseased states. | [] | Single_Cell |
In addition, technical covariates such as ribosomal and mitochondrial genes exhibit batch-specific variation in expression ( Methods and Supplementary Table 7 ). | [] | Single_Cell |
The diversity in demographics (for example, smoking status, age, harmonized ethnicity and BMI) and experimental protocols represented in the HLCA core enables us to explore the contribution of each technical or biological covariate to cell type-specific gene expression variation ( Methods and Supplementary Fig. 4 ). | [] | Single_Cell |
For many cell types, anatomical location is the biological variable explaining most of the variance between samples (Fig. 4a ). | [] | Single_Cell |
Furthermore, sex is most associated with transcriptomic variation in lymphatic endothelial cells, whereas BMI is most associated with variation in B and T cells, harmonized ethnicity in transitional club-AT2 cells and smoking status in innate lymphoid/natural killer cells. | [
{
"end": 96,
"label": "CellType",
"start": 69,
"text": "lymphatic endothelial cells"
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{
"end": 148,
"label": "CellType",
"start": 147,
"text": "B"
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{
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"label": "CellType",
"start": 153,
"text": "T cells"
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{
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"label": "Ce... | Single_Cell |
Furthermore, for several cell types (for example, mast, AT1 and smooth muscle cells), the tissue dissociation protocol explains most of the variance of all technical as well as biological covariates recorded. | [
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"label": "CellType",
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"text": "mast"
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"text": "AT1"
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{
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"label": "CellType",
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"text": "smooth muscle cells"
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] | Single_Cell |
These associations provide a systematic overview of the effects of biological and technical factors on the transcriptional state of lung cell types. | [] | Single_Cell |
To better characterize how biological variables affect cellular phenotypes, we modeled their cell type-specific effects on the transcriptome at the gene level ( Methods ). | [] | Single_Cell |
Sex-related differences in lymphatic endothelial cells are dominated by differential expression of genes located on the X and Y chromosomes, but also include a decrease in IFNAR1 in females (Supplementary Table 8 ), which may be linked to differential interferon responses between the biological sexes . | [
{
"end": 54,
"label": "CellType",
"start": 27,
"text": "lymphatic endothelial cells"
}
] | Single_Cell |
We furthermore found cell type-specific programs that change with proximal (low CCF score) to distal (high CCF score) location along the respiratory tract (Supplementary Tables 8 and 9 ). | [
{
"end": 154,
"label": "Tissue",
"start": 137,
"text": "respiratory tract"
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] | Single_Cell |
For instance, oxidative phosphorylation (including cytochrome c oxidase genes such as COX7A1 ), antigen presentation by major histocompatibility complex class I molecules (including proteasome and protease subunit genes such as PSMD14 and PSMB4 ), signaling by interleukin-1 and tumor necrosis factor α, as well as plana... | [
{
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"label": "CellType",
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"text": "secretory"
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"label": "CellType",
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"text": "multiciliated"
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{
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"label": "CellType",
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"text": "basal cells"
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] | Single_Cell |
Some gene programs were specific for a subset of airway epithelial cell types (for example, cornification and keratinization, which were programs that were downregulated in distal multiciliated and secretory cells; including genes such as KRT8 and KRT19 ). | [
{
"end": 193,
"label": "CellType",
"start": 173,
"text": "distal multiciliated"
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{
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"label": "CellType",
"start": 198,
"text": "secretory cells"
}
] | Single_Cell |
The changes in airway epithelial cell states toward the terminal airways are further illustrated by increased expression of developmental pathway genes such as NKX2-1 , NFIB , GATA6 , BMP4 and SOX9 in multiciliated cells along the proximal-to-distal axis (Fig. 4b ), whereas basal cells decrease in number (Fig. 4c ) . | [
{
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"label": "CellType",
"start": 201,
"text": "multiciliated cells"
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{
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"label": "Tissue",
"start": 231,
"text": "proximal-to-distal axis"
},
{
"end": 286,
"label": "CellType",
"start": 275,
"text": "basal cells"
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] | Single_Cell |
Similarly, several cell types display transcriptomic changes in donors with increasing BMI (Fig. 4d and Supplementary Tables 8 and 9 ). | [] | Single_Cell |
AT2 cells, secretory cells and alveolar macrophages exhibit downregulation of a range of biological processes (Supplementary Fig. 5 ), including cellular respiration, differentiation and synthesis of peptides and other molecules. | [
{
"end": 9,
"label": "CellType",
"start": 0,
"text": "AT2 cells"
},
{
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"label": "CellType",
"start": 11,
"text": "secretory cells"
},
{
"end": 51,
"label": "CellType",
"start": 31,
"text": "alveolar macrophages"
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] | Single_Cell |
In secretory cells, a downregulation of the insulin response pathway is also associated with higher BMI, in line with the insulin resistance observed in donors with obesity . | [
{
"end": 18,
"label": "CellType",
"start": 3,
"text": "secretory cells"
}
] | Single_Cell |
In alveolar macrophages, inflammatory responses involving JAK/STAT signaling (previously associated with obesity-induced chronic systemic inflammation ) are associated with higher BMI. | [
{
"end": 23,
"label": "CellType",
"start": 3,
"text": "alveolar macrophages"
}
] | Single_Cell |
In contrast, in plasma cells, high BMI is associated with downregulation of gene sets associated with immune response and upregulation of gene sets associated with cellular respiration, the cell cycle and DNA repair. | [
{
"end": 28,
"label": "CellType",
"start": 16,
"text": "plasma cells"
}
] | Single_Cell |
This is consistent with obesity being a known risk factor for multiple myeloma—a plasma cell malignancy . | [] | Single_Cell |
Thus, the HLCA enables a detailed understanding of the effects of anatomical and demographic covariates on the cellular landscape of the lung and their relation to disease. | [
{
"end": 141,
"label": "Tissue",
"start": 137,
"text": "lung"
}
] | Single_Cell |
Biological and technical factors can also affect cell type proportions. | [] | Single_Cell |
Indeed, all cell types show changes in abundance as a function of anatomical location ( Fig. 4c and Extended Data Fig. 5 ). | [] | Single_Cell |
For example, ionocytes are present at comparable proportions in the airway epithelium, from the larger lower airways (CCF score = 0.36) down to the distal lobular airways (CCF score = 0.81), while being largely absent in the lung parenchyma (CCF score = 0.97). | [
{
"end": 22,
"label": "CellType",
"start": 13,
"text": "ionocytes"
},
{
"end": 85,
"label": "Tissue",
"start": 68,
"text": "airway epithelium"
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{
"end": 116,
"label": "Tissue",
"start": 96,
"text": "larger lower airways"
},
{
"end": 170,
"label":... | Single_Cell |
In contrast, neuroendocrine cells are predominantly observed in the larger lower airways but are absent from more distal parts of the bronchial tree (Fig. 4c ). | [
{
"end": 33,
"label": "CellType",
"start": 13,
"text": "neuroendocrine cells"
},
{
"end": 88,
"label": "Tissue",
"start": 68,
"text": "larger lower airways"
},
{
"end": 148,
"label": "Tissue",
"start": 134,
"text": "bronchial tree"
}
] | Single_Cell |
In some cases, these proportions are highly dependent on the tissue sampling method and the dissociation protocol used (for example, for smooth muscle FAM83D cells; Extended Data Fig. 5 ). | [
{
"end": 163,
"label": "CellType",
"start": 137,
"text": "smooth muscle FAM83D cells"
}
] | Single_Cell |
These observations shed light on the effects of biological and technical factors on the abundance of cell types in different parts of the lung and can help guide important choices in study design. | [
{
"end": 142,
"label": "Tissue",
"start": 138,
"text": "lung"
}
] | Single_Cell |
The HLCA core contains an unprecedented diversity of donors, sampling protocols and cell identities, and can serve as a transcriptomic reference for lung research. | [] | Single_Cell |
New datasets can be mapped to this reference to substantially speed up data analysis by transferring consensus cell identity annotations to the new data. | [] | Single_Cell |
We tested this on a recently released multimodal lung dataset ( Methods , Fig. 6a and Extended Data Fig. 6 ). | [] | Single_Cell |
Overall, the transferred labels were correct in the majority of cases, with 68% of the cells correctly labeled, 14% of labels incorrectly labeled and 18% set to unknown due to highly uncertain labeling (Fig. 5b and Methods ). | [] | Single_Cell |
Uncertain labels were observed specifically in continuous transitions from one cell type to another and among cellular identities not present in the HLCA core, including rare cell identities (erythrocytes ( n = 328), chondrocytes ( n = 42), myelinating Schwann cells ( n = 7), nonmyelinating Schwann cells ( n = 29) and ... | [
{
"end": 204,
"label": "CellType",
"start": 192,
"text": "erythrocytes"
},
{
"end": 229,
"label": "CellType",
"start": 217,
"text": "chondrocytes"
},
{
"end": 266,
"label": "CellType",
"start": 241,
"text": "myelinating Schwann cells"
},
{
"end": 305,
... | Single_Cell |
Taken together, these results show that the HLCA core can be used for highly detailed annotation of new datasets, while allowing for the identification of unknown cell types in these datasets based on label transfer uncertainty. | [] | Single_Cell |
Single-cell studies of disease rely on adequate, matching control samples to allow correct identification of disease-specific changes. | [] | Single_Cell |
To demonstrate the ability of the HLCA core to serve as a comprehensive healthy control and contextualize disease data, we mapped scRNA-seq data from lung cancer samples to the HLCA core ( Methods and Extended Data Fig. 7a–c ). | [] | Single_Cell |
Using HLCA label transfer, we correctly identified cell states missing from the HLCA core as unknown (cancer cells and erythroblasts). | [
{
"end": 132,
"label": "CellType",
"start": 119,
"text": "erythroblasts"
}
] | Single_Cell |
The remaining cells were annotated correctly in 77%, incorrectly in 1% and as unknown in 22% of cases (Extended Data Fig. 7d–g ). | [] | Single_Cell |
A finding of the original study was the separation of endothelial cells into tumor-associated and normal cells . | [
{
"end": 71,
"label": "CellType",
"start": 54,
"text": "endothelial cells"
}
] | Single_Cell |
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