dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the HLCA can be used as a reference for cell type deconvolution of bulk RNA expression samples, which have been shown to reflect cell type proportions more accurately than scRNA-seq datasets  49. Inferring cell type proportions from bulk RNA samples from nasal brushings and bronchial biopsies using the HLCA core (Supplementary Table  10 , Supplementary Fig. 8a  and Methods) revealed no significant cell type compositional changes associated with corticosteroid inhalation  50  or asthma  51  , respectively (Supplementary Fig. 8b, c  and Supplementary Table  11 ). In contrast, we find that the proportion of capillary endothelial cells in lung resection tissue from the Lung Tissue Database 52 is higher in samples from patients with severe COPD (GOLD stage 3 or 4) than in those from non-COPD controls matched for age and smoking history (P adj = 0.
Extending the HLCA by projecting new data As knowledge of cell types in the lung expands, and the sizes of newly generated datasets increase, annotations in the HLCA core will need Ref. 6  Jain/Misharin (new data) Ref. 5  Ref. 22  Ref. 26  Ref. 4  (incl. new data) Ref. 4  Ref. 25  Neuroendocrine to be further refined. The HLCA and its annotations can be updated by learning from new data projected onto the reference. We simulated such an HLCA update using the previously projected healthy lung dataset, specifically focusing on the cell identities that were distinguished based on their tissue location in matched spatial transcriptomic data (spatially annotated cell types)  40. These cell identities were present at very low frequencies (median: 0. 005% of all cells; Supplementary Fig. 9a ). Both spatially annotated mesenchymal cell types with more than 40 cells (immune-recruiting fibroblasts and chondrocytes) and two rare cell types (myelinating Schwann cells and perineurial nerve-associated fibroblasts) were recovered in distinct clusters (spatially annotated clusters), and three of these (all except chondrocytes) also contained cells from the HLCA core, thereby enabling a refinement of existing HLCA core annotations using the spatial context from the projected.
Mapping data to the HLCA highlights disease-related states To extend the atlas and include samples from lung disease, we mapped 1,797,714 cells from 380 healthy and diseased individuals from 37 datasets (four unpublished and 33 published  21, 24, 26, 27, 33, 40, 41, [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70]  ) to the HLCA core using scArches  71  , bringing the HLCA to a total of 2. 4 million cells from 486 individuals (Fig. 6a  and Supplementary Table  1 ). Label transfer from the HLCA core to the newly mapped datasets enabled detailed cell type annotation across datasets even for rare cells, including 2,048 migratory dendritic cells identified across 28 datasets with label transfer, whereas this cell type was originally labeled in only two of 12 labeled datasets (Extended Data Fig.
8 ). Out of 37 new datasets, 27 were observed to map well to the HLCA, as evaluated by the mean label transfer uncertainty score (Fig. 6b , Supplementary Fig. 10a  and Methods ). The remaining ten datasets were often from coronavirus disease 2019 (COVID-19) studies or, unlike the HLCA core, contained pediatric samples (Fig. 6b, c  and Supplementary Fig. 10b ). In these datasets, higher uncertainty values may be attributable to true biological differences between the mapped data and the HLCA core adult, healthy lung samples. Overall, the successfully mapped datasets include disease samples, as well as single-nucleus and single-cell data from multiple chemistries (Fig. 6b ), demonstrating the potential of the HLCA core as a universal reference. Pulmonary diseases are characterized by the emergence of unique disease-associated transcriptional phenotypes  4, 21, 22, 24, 72.
We observed higher levels of label transfer uncertainty in datasets from diseased lungs (Fig. 6b , condition), possibly flagging cell types changed in response to disease. Specifically, labels of alveolar fibroblasts and alveolar macrophages, which interact to form a dysregulated cellular circuit in idiopathic pulmonary fibrosis (IPFs)  21, 22, 24  , are transferred with higher uncertainty in IPF samples than in samples from healthy controls from the same dataset  64  (Fig. 6d  and Extended Data Fig. 9a, b ). Furthermore, uncertainty scores separate cells-derived from donors with IPF-within these cell types into more and less affected subsets: the genes more highly expressed in the high-uncertainty subset are also lowly expressed in healthy samples (Fig. 6e ). Genes downregulated in high-uncertainty IPF macrophages are associated with homeostatic functions of tissue-resident alveolar macrophages and lipid metabolism (PPARG, FABP4 and others)  22, 24, 58  , while upregulated genes are associated with extracellular matrix remodeling and scar formation in the context of lung fibrosis (SPP1, PLA2G7 and CCL2; Supplementary Tables  12  and 13  and Extended Data Fig.
9b, c )  22, 24, 58. Thus, the HLCA core can be used to annotate new data, identify previously unreported populations, and-using label transfer uncertainty scores-help to detect disease-affected cell states and corresponding gene expression programs. This vastly speeds up analysis and interpretation of new data, automatically prioritizing the most relevant populations. Automated mapping of new data to the HLCA core can be done by any user via an interactive web portal (https://github. com/LungCellAtlas/ HLCA) or using code tutorials as provided online.
The HLCA reveals common aberrant cell states across diseases Similar to healthy cellular states, the HLCA can provide insight into disease-specific states that are consistent across demographics and experimental protocols. To demonstrate this, we determined which cell types are consistently affected by IPF across datasets, extending the previous IPF analysis to five independent datasets. We found that cells labeled as alveolar fibroblasts consistently show high uncertainty levels in IPF samples compared with controls across all mapped IPF datasets that include controls  58, 62, 64  (Extended Data Fig. 10a ). Clustering of alveolar fibroblasts from the HLCA core and all IPF datasets  21, 24, 58, 62, 64  shows that cells from patients with IPF predominantly cluster together in a single cluster (Fig.
6f ,g and Extended Data Fig. 10b ) characterized by high expression of genes previously associated with IPF  64, 73, 74  (CCL2, COL1A1, CTHRC1 and MMP19), as well as further fibrosis-associated markers (SERPINE1, an inhibitor of extracellular matrix breakdown  75  , and HIF1A, a chronic hypoxia response gene  76  ; Fig. 6h  and Supplementary Table  14 ). These marker genes are consistently expressed across datasets (Extended Data Fig. 10c ), confirming that the identification of this IPF-specific alveolar fibroblast state is reproducible. The HLCA contains data across more than ten lung diseases, providing the unique opportunity to discover cellular states shared across diseases. Discovering such common diseased cellular states could improve our understanding of lung diseases and accelerate the identification of effective treatments.
Discussion In this study, we built the HLCA: an integrated reference atlas of the human respiratory system. While previous studies have described the cellular heterogeneity within the human lung  [4] [5] [6] 24, 58  , study-specific biases due to experimental design and a limited number of sampled individuals constrain their capacity to capture population variation and serve as a universal reference. The H CA integrates data from 49 datasets to produce such a reference of 2. 4 million cells, covering all major lung scRNA-seq studies published to date. The core of this atlas consists of a fully integrated healthy reference of 14 datasets with 61 cell identities, including rare and novel cell types, representing a data-derived consensus annotation of the cellular landscape of the human lung.
We leveraged the unprecedented complexity of the HLCA to recover cell type-specific gene modules associated with covariates such as lung anatomical location, age, sex, BMI and smoking status. By projecting data onto the HLCA, we showed that the HLCA enables a fast and detailed annotation of new datasets, as well as the identification of unique, disease-associated cell states and cell states common to multiple diseases. The HLCA is publicly available as a resource for the community, together with an online platform for automated mapping of new data. Taken together, the HLCA is a universal reference for single-cell lung research that promises to accelerate future studies into pulmonary health and disease. The ultimate goal of a human lung cell atlas reference is to provide a comprehensive overview of all cells in the healthy human lung, as well as their variation from individual to individual.
Despite its overall diversity, the HLCA is limited by the biological, demographic and experimental diversity in the foundational single-cell studies. For example, 65% of the HLCA core data are from individuals of European harmonized ethnicity, highlighting the urgent need for diversification of the population sampled in lung studies. Moreover, ethnicity metadata were based on self-reports and harmonized across datasets, which is an imperfect approach to representing the diversity of the atlas. SNP-based inference of genetic ancestry constitutes a more objective and therefore preferable approach to the grouping of individuals based on genetic background and would aid in better assessing the genetic diversity captured in the atlas. Overall, more diverse samples will enrich the atlas, diversify captured cell identities and improve the quality of the HLCA as a reference for new datasets.
Such a reference will also enable comparison with model systems such as mice, cell lines or organoids, although further method development may be required to map across diverse in vitro and clinical datasets. The constituent datasets of the HLCA vary widely in experimental design, such as the sample handling protocol or single-cell platform used, causing dataset-specific batch effects. The quality of the HLCA hinges on the choice of data integration method, with methods such as Seurat's RPCA  30  and Harmony 29 failing to correctly group rare cell identities into separate clusters. Nevertheless, also in the HLCA, certain subsets of T cells (regulatory T cells and γδ T cells) could not be identified as separate clusters, showing the limitations of the current HLCA in capturing cellular heterogeneity for a subset of immune cell types.
Mapping additional datasets with high-resolution annotations (for example, derived from multimodal data) could provide the power to detect these cell identities in the atlas. Indeed, the HLCA must be viewed as a live resource that requires continuous updates. While we showed that mapping new, spatially annotated data to the HLCA core can refine HLCA annotations, this new knowledge must be consolidated by regular updates of the HLCA with new datasets (including epigenomic, spatial and imaging data) and refinements of HLCA annotations based on additional expert opinions. Thereby, the HLCA can serve as a community-and data-driven platform for open discussion on lung cell identities as the respiratory community progresses in charting the cellular landscape of the lung. In this process, we envision that the HLCA will be completed in two phases: first on the level of cellular variation (when no new consensus cell types can be found) and then in the description of individual variation (when population diversity is fully represented).
Online content , split into cells from healthy donors (blue) and donors with IPF (orange). e, Uncertainty-based disease signature scores among alveolar fibroblasts and alveolar macrophages, split into cells from control donors (n = 10,453 and 1,812, respectively), and low-uncertainty cells (n = 1,419 and 200, respectively) and high-uncertainty cells (n = 1,172 and 162, respectively) from donors with IPF. f, UMAP embedding of alveolar fibroblasts (labeled with manual annotation (core) or label transfer (five IPF datasets)) colored by Leiden cluster. g, Composition of the clusters shown in f by study, with cells from control samples colored in gray. h, Expression of marker genes for IPF-enriched cluster 0 per alveolar fibroblast cluster. Cluster 5 was excluded as 96% of its cells were from a single donor.
Single-cell sequencing and preprocessing of data Several previously unpublished datasets were used for the HLCA and generated as follows. Barbry_unpubl. Participants recruited by the Pneumology Unit of Nice University Hospital were sampled between 1 and 15 December 2020. The full procedure, including patient inclusion criteria, is detailed at https://www. clinicaltrials. gov/ct2/show/NCT04529993. Nasal and tracheobronchial samples were collected from patients with IPF after obtaining their informed consent, following a protocol approved by the Centre Hospitalier Universitaire de Nice. The data were derived from the clinical trial registered at ClinicalTrials. gov under reference NCT04529993. This study was described as an interventional study instead of an observational study because the participants were volunteers and all assigned to a specific bronchoscopy not related to routine medical care.
Participants were prospectively assigned to a procedure (bronchoscopy) according to a specific protocol to assess our ability to sample the airway. No other procedures were included in this study. Metadata of the donors' sex was based on self-report. The libraries were prepared as described in Deprez et al. 6  and yielded an average of 61,000 ± 11,000 cells per sample, with a viability above 95%. The single-cell suspension was used to generate single-cell libraries following the v3. 1 protocol for 3′ chemistry from 10x Genomics (CG000204). Sequencing was performed on a NextSeq 500/550 sequencer (Illumina). Raw sequencing data were processed using the Cell Ranger 6. 0. 0 pipeline, with the reference genome GRCh38 and annotation using Ensembl98. For each sample, cells with fewer than 200 transcripts or more than 40,000 transcripts were filtered out, as well as genes expressed in fewer than three cells.
Downstream analysis was performed using the Scanpy 83 package (version 1. 8. 0). We assessed the quality of our libraries and excluded barcodes with fewer than 300 genes detected, while retaining those with a number of transcripts between 500 and 30,000. Furthermore, cells with a high proportion (>15%) of transcript counts derived from mitochondrial-encoded genes were removed. Genes were considered if they were expressed in at least five cells. Raw counts of cells that passed filtering were used as input for the HLCA. Duong_lungMAP_unpubl. All postmortem human donor lung samples were obtained from BRINDL, supported by the NHLBI LungMAP Human Tissue Core housed at the University of Rochester. Consent, tissue acquisition and storage protocols can be found on the repository's website (brindl.
urmc. rochester. edu/). Data were collected as part of the Human Biomolecular Atlas Program (HuBMAP). Metadata of the donor's sex were based on self-report. For isolation of single nuclei, ten cryosections (40 µm thickness) from O. C. T. -embedded tissue blocks stored at -80 °C were shipped on dry ice and processed according to a published protocol  84. Single-nucleus RNA-seq was completed using 10x Chromium Single Cell 3' Reagent Kits v3, according to a published protocol  84, 85. Raw sequencing data were processed using the 10x Cell Ranger v3 pipeline and the GRCh38 reference genome. For downstream analysis, mitochondrial transcripts and doublets identified by DoubletDetection 86 version 2. 4. 0 were removed. Samples were then combined and cell barcodes were filtered based on the genes detected (>200 and <7,500) and the gene unique molecular identifier (UMI) ratio (gene.
vs. molecule. cell. filter function) using Pagoda2 (github. com/ hms-dbmi/pagoda2). Also using Pagoda2 for clustering, counts were normalized to total counts per nucleus. For batch correction, gene expression was scaled to dataset average expression. After variance normalization, all significantly variant genes (n = 4,519) were used for PCA. Clustering was done at different k values (50, 100 or 200) using the top 50 principal components and the infomap community detection Resource https://doi. org/10. 1038/s41591-023-02327-2 algorithm. Then, principal component and cluster annotations were imported into Seurat 30 version 4. 0. 0. Differentially expressed genes for all clusters were generated for each k resolution using Seurat FindAllMarkers (only. pos = TRUE, max. cells. per. ident = 1000, logfc.
Ambient RNA correction was performed with FastCAR 88 , using an empty library cutoff of 100 UMI and a maximum allowed contamination chance of 0. 05, ignoring the mitochondrial RNA. Data were merged and processed using Seurat  30  , filtering to libraries with >500 UMIs and >200 genes and to the libraries containing the lowest 95% of mitochondrial RNA per sample and <25% mitochondrial RNA, normalized using sctransform  89  while regressing out variation correlating with the percentage of mitochondrial RNA per cell. In general, 15 principal components were used for the clustering, at a resolution of 0. 5 to facilitate manual annotation of the dataset. Clusters in the final object that were driven by single donors were removed. Raw counts and cell annotations were used as input for the HLCA. Jain_Misharin_2021.
The Seurat v3. 2 pipeline was used to further analyze the data. Genes in fewer than five cells in the dataset, as well as the mitochondrial genes MT-RNR1 and MT-RNR2, were removed. Cells with fewer than 200 genes were discarded, whereas cells with <5% mitochondrial genes or <30% intronic reads were kept for further analysis. The data were log normalized and 2,000 variable genes were calculated for each sample for integration with Seurat's Canonical Correlation Analysis algorithm  93. The data were scaled, 50 principle components were selected and the data were clustered with 0. 6 resolution. Cluster annotation revealed a low-quality cluster that was subsequently removed and the process (the calculation of variable genes, calculation of 30 principal components, clustering with 0. 4 resolution) was repeated.
HLCA core data collection To accommodate data protection legislation, scRNA-seq datasets of healthy lung tissue were shared by dataset generators as raw count matrices, thereby obviating the need to share genetic information. Count matrices were generated using varying software (Supplementary Table  1 ). Previously published scRNA-seq data were partly realigned by the dataset generators: the raw sequencing data of four previously published studies were realigned to GRCh38 using Ensembl84 for the HLCA  5, 6, 25, 40. For two of these studies  5, 6  , the Cell Ranger 3. 1. 0-based HLCA pipeline was used for realignment. For the remaining two  25, 40  , data were processed as previously described  25, 40  , but with the reference genome and genome annotation adapted to the HLCA (GRCh38; Ensembl84).
Metadata collection (HLCA core) For all of the datasets from the HLCA core, a preformatted sample metadata form was filled out by the dataset providers for every sample, containing metadata such as the ID of the donor from whom the sample came, the donor's self-reported ethnicity, the type of sample, the sequencing platform and so on (Supplementary Table  2 ). Ethnicity metadata were based on self-reported ethnicity for live donors or retrieved from medical records or assigned by the organ procurement team in the case of organ donors, as collected in the individual studies. For donor ethnicity, the following categories of self-reported ethnicity were used during metadata collection: Black, white, Latino, Asian, Pacific Islander and mixed. To conform to pre-existing 1,000 Genomes ancestry superpopulations  94  , these self-reported ethnicity categories were then harmonized with the superpopulation categories as follows: Black was categorized as African, white as European and Latino as admixed American, while keeping the category Asian (merging the superpopulations East Asians and South Asians as this granularity was missing from the collected self-reported ethnicity data) and keeping Pacific Islander, as this category did not correspond to any of the superpopulations but does constitute a separate population in HANCESTRO  95.
We refer to the resulting categories as harmonized ethnicity. Both self-reported ethnicity (as collected) and harmonized ethnicity per donor are detailed in Supplementary Table  2. Cell type annotations from dataset providers were included in all datasets. For tissue dissociation protocols, protocols were grouped based on: (1) enzyme(s) used for tissue dissociation; and (2) the digestion time in cases where large differences were observed between protocols (that is, cold protease protocols were split into two groups: 30-60 min versus overnight).
Cell type reference creation and metadata harmonization To harmonize cell type labels from different datasets in the HLCA core, a common reference was created to which original cell type labels were mapped (Supplementary Table  4 ). To accommodate labels at different levels of detail, the cell type reference was made hierarchical, with level 1 containing the coarsest possible labels (immune, epithelial and so on) and level 5 containing the finest possible labels (for example, naive CD4 T cells). Levels were created in a data-driven fashion, recursively breaking up coarser-level labels into finer ones where finer labels were available. To map anatomical location to a 1D CCF score that could be used for modeling, a distinction was made between upper and lower airways. First, an anatomical coordinate score was applied to the upper airways, starting at 0 and increasing linearly (with a value of 0.
Data integration benchmarking For computational efficiency, benchmarking was performed on a subset of the total atlas, including data from ten studies split into 13 datasets (ref. 22 was split into 10xv1 and 10xv2 data; ref. 25 was split into 10xv2 and 10xv3 data; and the pooled data from ref. 21 and associated unpublished data were split into two based on the processing site). The data came from 72 donors, 124 samples and 372,111 cells. Preprocessing of the benchmarking data included the filtering of cells (minimum number of total UMI counts: 500) and genes (minimum number of cells expressing the gene: 5). For integration benchmarking, the scIB benchmarking framework was used  99  with default integration parameter settings unless otherwise specified. All benchmarked methods except scGen (that is, BBKNN, ComBat, Conos, fas  99  tMNN, Harmony, Scanorama, scANVI, scVI and Seurat RPCA) were run at least twice: on the 2,000 most HVGs; and on the 6,000 most HVGs.
Of these methods, all that did not require raw counts as input were run twice on each gene set: once with gene counts scaled to have a mean of 0 and standard deviation of 1; and once with unscaled gene counts. scVI and scANVI, which require raw counts as input, were not run on scaled gene counts. scGen was only tested on 2,000 unscaled HVGs. For HVG selection, first, HVGs were calculated per dataset using Cell Ranger-based HVG selection  100  (default parameter settings: min_disp=0. 5, min_mean=0. 0125, max_mean=3, span=0. 3, n_bins=20). Then, genes that were highly variable in all datasets were considered overall highly variable, followed by genes highly variable in all datasets but one, in all datasets but two and so on until a predetermined number of genes were selected (2,000 or 6,000 genes).