An integrated cell atlas of the lung in health and disease.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
10
03
2022
accepted:
30
03
2023
medline:
26
6
2023
pubmed:
9
6
2023
entrez:
8
6
2023
Statut:
ppublish
Résumé
Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. 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. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. 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. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1
Identifiants
pubmed: 37291214
doi: 10.1038/s41591-023-02327-2
pii: 10.1038/s41591-023-02327-2
pmc: PMC10287567
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1563-1577Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL153375
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL127349
Pays : United States
Organisme : NHLBI NIH HHS
ID : U54 HL165443
Pays : United States
Organisme : NHLBI NIH HHS
ID : P01 HL107202
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL148856
Pays : United States
Organisme : NHLBI NIH HHS
ID : R21 HL156124
Pays : United States
Organisme : NIA NIH HHS
ID : U54 AG075931
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL146557
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL123766
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL148861
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL141852
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES034350
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL126176
Pays : United States
Organisme : NHLBI NIH HHS
ID : R21 HL161760
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL145372
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG049665
Pays : United States
Organisme : NICHD NIH HHS
ID : K12 HD105271
Pays : United States
Organisme : NIAID NIH HHS
ID : U19 AI135964
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL142568
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL153312
Pays : United States
Organisme : NIA NIH HHS
ID : U54 AG079754
Pays : United States
Organisme : NHLBI NIH HHS
ID : R56 HL157632
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL158139
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL135156
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL153045
Pays : United States
Organisme : NHLBI NIH HHS
ID : U54 HL145608
Pays : United States
Organisme : NIAMS NIH HHS
ID : P50 AR060780
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL128439
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL146519
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL117004
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL068702
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL145567
Pays : United States
Organisme : NHLBI NIH HHS
ID : P01 HL132821
Pays : United States
Organisme : Medical Research Council
ID : MR/R015635/1
Pays : United Kingdom
Organisme : NIMHD NIH HHS
ID : R01 MD010443
Pays : United States
Investigateurs
Yuexin Chen
(Y)
James S Hagood
(JS)
Ahmed Agami
(A)
Peter Horvath
(P)
Joakim Lundeberg
(J)
Charles-Hugo Marquette
(CH)
Gloria Pryhuber
(G)
Chistos Samakovlis
(C)
Xin Sun
(X)
Lorraine B Ware
(LB)
Kun Zhang
(K)
Informations de copyright
© 2023. The Author(s).
Références
Angerer, P. et al. Single cells make big data: new challenges and opportunities in transcriptomics. Curr. Opin. Syst. Biol. 4, 85–91 (2017).
doi: 10.1016/j.coisb.2017.07.004
Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).
pubmed: 29206104
pmcid: 5762154
doi: 10.7554/eLife.27041
HuBMAP Consortium. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187–192 (2019).
doi: 10.1038/s41586-019-1629-x
Vieira Braga, F. A. et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25, 1153–1163 (2019).
pubmed: 31209336
doi: 10.1038/s41591-019-0468-5
Travaglini, K. J. et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 619–625 (2020).
pubmed: 33208946
pmcid: 7704697
doi: 10.1038/s41586-020-2922-4
Deprez, M. et al. A single-cell atlas of the human healthy airways. Am. J. Respir. Crit. Care Med. 15, 1636–1645 (2020).
doi: 10.1164/rccm.201911-2199OC
Hrovatin, K. et al. Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas. Preprint at bioRxiv https://doi.org/10.1101/2022.12.22.521557 (2022).
Steuernagel, L. et al. HypoMap—a unified single-cell gene expression atlas of the murine hypothalamus. Nat. Metab. 4, 1402–1419 (2022).
pubmed: 36266547
pmcid: 9584816
doi: 10.1038/s42255-022-00657-y
Schupp, J. C. et al. Integrated single-cell atlas of endothelial cells of the human lung. Circulation 144, 286–302 (2021).
pubmed: 34030460
pmcid: 8300155
doi: 10.1161/CIRCULATIONAHA.120.052318
Novella-Rausell, C., Grudniewska, M., Peters, D. J. M. & Mahfouz, A. A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery. Preprint at bioRxiv https://doi.org/10.1101/2022.07.02.498501 (2022).
Herpelinck, T. et al. An integrated single-cell atlas of the skeleton from development through adulthood. Preprint at bioRxiv https://doi.org/10.1101/2022.03.14.484345 (2022).
Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).
pubmed: 33981032
doi: 10.1038/s41586-021-03549-5
Swamy, V. S., Fufa, T. D., Hufnagel, R. B. & McGaughey, D. M. Building the mega single-cell transcriptome ocular meta-atlas. Gigascience 10, giab061 (2021).
pubmed: 34651173
pmcid: 8514335
doi: 10.1093/gigascience/giab061
Ruiz-Moreno, C. et al. Harmonized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastoma. Preprint at bioRxiv https://doi.org/10.1101/2022.08.27.505439 (2022).
Salcher, S. et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell 40, 1503–1520.e8 (2022).
pubmed: 36368318
pmcid: 9767679
doi: 10.1016/j.ccell.2022.10.008
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Nieto, P. et al. A single-cell tumor immune atlas for precision oncology. Genome Res. 31, 1913–1926 (2021).
pubmed: 34548323
pmcid: 8494216
doi: 10.1101/gr.273300.120
Suo, C. et al. Mapping the developing human immune system across organs. Science 376, eabo0510 (2022).
pubmed: 35549310
doi: 10.1126/science.abo0510
Muus, C. et al. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics. Nat. Med. 27, 546–559 (2021).
pubmed: 33654293
pmcid: 9469728
doi: 10.1038/s41591-020-01227-z
Li, M. et al. DISCO: a database of Deeply Integrated human Single-Cell Omics data. Nucleic Acids Res. 50, D596–D602 (2021).
pmcid: 8728243
doi: 10.1093/nar/gkab1020
Habermann, A. C. et al. Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis. Sci. Adv. 6, eaba1972 (2020).
pubmed: 32832598
pmcid: 7439444
doi: 10.1126/sciadv.aba1972
Morse, C. et al. Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis. Eur. Respir. J. 54, 1802441 (2019).
pubmed: 31221805
pmcid: 8025672
doi: 10.1183/13993003.02441-2018
Madissoon, E. et al. scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Genome Biol. 21, 1 (2019).
pubmed: 31892341
pmcid: 6937944
doi: 10.1186/s13059-019-1906-x
Reyfman, P. A. et al. Single-cell transcriptomic analysis of human lung provides insights into the pathobiology of pulmonary fibrosis. Am. J. Respir. Crit. Care Med. 199, 1517–1536 (2019).
pubmed: 30554520
pmcid: 6580683
doi: 10.1164/rccm.201712-2410OC
Goldfarbmuren, K. C. et al. Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium. Nat. Commun. 11, 2485 (2020).
pubmed: 32427931
pmcid: 7237663
doi: 10.1038/s41467-020-16239-z
Bharat, A. et al. Lung transplantation for patients with severe COVID-19. Sci. Transl. Med. 12, eabe4282 (2020).
pubmed: 33257409
pmcid: 8050952
doi: 10.1126/scitranslmed.abe4282
Natri et al. Cell type-specific and disease-associated eQTL in the human lung. Preprint at bioRxiv https://doi.org/10.1101/2023.03.17.533161 (2023).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2021).
pubmed: 34949812
pmcid: 8748196
doi: 10.1038/s41592-021-01336-8
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Förster, R. et al. CCR7 coordinates the primary immune response by establishing functional microenvironments in secondary lymphoid organs. Cell 99, 23–33 (1999).
pubmed: 10520991
doi: 10.1016/S0092-8674(00)80059-8
Hauser, M. A. Inflammation-induced CCR7 oligomers form scaffolds to integrate distinct signaling pathways for efficient cell migration. Immunity 44, 59–72 (2016).
pubmed: 26789922
doi: 10.1016/j.immuni.2015.12.010
Kadur Lakshminarasimha Murthy, P. et al. Human distal lung maps and lineage hierarchies reveal a bipotent progenitor. Nature 604, 111–119 (2022).
pubmed: 35355018
pmcid: 9169066
doi: 10.1038/s41586-022-04541-3
Basil, M. C. et al. Human distal airways contain a multipotent secretory cell that can regenerate alveoli. Nature 604, 120–126 (2022).
pubmed: 35355013
pmcid: 9297319
doi: 10.1038/s41586-022-04552-0
Pujantell, M. & Altfeld, M. Consequences of sex differences in type I IFN responses for the regulation of antiviral immunity. Front. Immunol. 13, 986840 (2022).
pubmed: 36189206
pmcid: 9522975
doi: 10.3389/fimmu.2022.986840
Boers, J. E., Ambergen, A. W. & Thunnissen, F. B. Number and proliferation of basal and parabasal cells in normal human airway epithelium. Am. J. Respir. Crit. Care Med. 157, 2000–2006 (1998).
pubmed: 9620938
doi: 10.1164/ajrccm.157.6.9707011
Kahn, S. E., Hull, R. L. & Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 (2006).
pubmed: 17167471
doi: 10.1038/nature05482
Zatterale, F. et al. Chronic adipose tissue inflammation linking obesity to insulin resistance and type 2 diabetes. Front. Physiol. 10, 1607 (2019).
pubmed: 32063863
doi: 10.3389/fphys.2019.01607
Parikh, R., Tariq, S. M., Marinac, C. R. & Shah, U. A. A comprehensive review of the impact of obesity on plasma cell disorders. Leukemia 36, 301–314 (2021).
pubmed: 34654885
pmcid: 8810701
doi: 10.1038/s41375-021-01443-7
Madissoon, E. et al. A spatially resolved atlas of the human lung characterizes a gland-associated immune niche. Nat. Genet. 55, 66–77 (2023).
pubmed: 36543915
doi: 10.1038/s41588-022-01243-4
Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).
pubmed: 29988129
doi: 10.1038/s41591-018-0096-5
Zhang, K. A single-cell atlas of chromatin accessibility in the human genome. Cell 184, 5985–6001.e19 (2021).
pubmed: 34774128
pmcid: 8664161
doi: 10.1016/j.cell.2021.10.024
Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290 (2022).
pubmed: 35549429
pmcid: 9383269
doi: 10.1126/science.abl4290
Han, Y. et al. Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma. Nat. Commun. 11, 1776 (2020).
pubmed: 32296059
pmcid: 7160128
doi: 10.1038/s41467-020-15649-3
McKay, J. D. et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat. Genet. 49, 1126–1132 (2017).
pubmed: 28604730
pmcid: 5510465
doi: 10.1038/ng.3892
Sakornsakolpat, P. et al. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat. Genet. 51, 494–505 (2019).
pubmed: 30804561
pmcid: 6546635
doi: 10.1038/s41588-018-0342-2
Shrine, N. et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat. Genet. 51, 481–493 (2019).
pubmed: 30804560
pmcid: 6397078
doi: 10.1038/s41588-018-0321-7
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
pubmed: 29632380
pmcid: 5896795
doi: 10.1038/s41588-018-0081-4
Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).
pubmed: 32487174
pmcid: 7265231
doi: 10.1186/s13059-020-02048-6
Boudewijn, I. M. et al. Nasal gene expression changes with inhaled corticosteroid treatment in asthma. Allergy 75, 191–194 (2020).
pubmed: 31230369
doi: 10.1111/all.13952
Roffel, M. P. et al. Identification of asthma-associated microRNAs in bronchial biopsies. Eur. Respir. J. 59, 2101294 (2022).
pubmed: 34446467
doi: 10.1183/13993003.01294-2021
Hao, K. et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet. 8, e1003029 (2012).
pubmed: 23209423
pmcid: 3510026
doi: 10.1371/journal.pgen.1003029
Chung, K. F. The role of airway smooth muscle in the pathogenesis of airway wall remodeling in chronic obstructive pulmonary disease. Proc. Am. Thorac. Soc. 2, 347–354 (2005).
pubmed: 16267361
pmcid: 2713326
doi: 10.1513/pats.200504-028SR
Lukassen, S. et al. SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells. EMBO J. 39, e105114 (2020).
pubmed: 32246845
pmcid: 7232010
doi: 10.15252/embj.20105114
Carraro, G. et al. Transcriptional analysis of cystic fibrosis airways at single-cell resolution reveals altered epithelial cell states and composition. Nat. Med. 27, 806–814 (2021).
pubmed: 33958799
pmcid: 9009537
doi: 10.1038/s41591-021-01332-7
Guo, M. et al. Single-cell transcriptomic analysis identifies a unique pulmonary lymphangioleiomyomatosis cell. Am. J. Respir. Crit. Care Med. 202, 1373–1387 (2020).
pubmed: 32603599
pmcid: 7667901
doi: 10.1164/rccm.201912-2445OC
Mould, K. J. et al. Airspace macrophages and monocytes exist in transcriptionally distinct subsets in healthy adults. Am. J. Respir. Crit. Care Med. 203, 946–956 (2021).
pubmed: 33079572
pmcid: 8048748
doi: 10.1164/rccm.202005-1989OC
Adams, T. S. et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Sci. Adv. 6, eaba1983 (2020).
pubmed: 32832599
pmcid: 7439502
doi: 10.1126/sciadv.aba1983
Wauters, E. et al. Discriminating mild from critical COVID-19 by innate and adaptive immune single-cell profiling of bronchoalveolar lavages. Cell Res. 31, 272–290 (2021).
pubmed: 33473155
pmcid: 8027624
doi: 10.1038/s41422-020-00455-9
Valenzi, E. et al. Single-cell analysis reveals fibroblast heterogeneity and myofibroblasts in systemic sclerosis-associated interstitial lung disease. Ann. Rheum. Dis. 78, 1379–1387 (2019).
pubmed: 31405848
doi: 10.1136/annrheumdis-2018-214865
Laughney, A. M. et al. Regenerative lineages and immune-mediated pruning in lung cancer metastasis. Nat. Med. 26, 259–269 (2020).
pubmed: 32042191
pmcid: 7021003
doi: 10.1038/s41591-019-0750-6
Mayr, C. H. et al. Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers. EMBO Mol. Med. 13, e12871 (2021).
pubmed: 33650774
pmcid: 8033531
doi: 10.15252/emmm.202012871
Ordovas-Montanes, J. et al. Allergic inflammatory memory in human respiratory epithelial progenitor cells. Nature 560, 649–654 (2018).
pubmed: 30135581
pmcid: 6133715
doi: 10.1038/s41586-018-0449-8
Tsukui, T. et al. Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis. Nat. Commun. 11, 1920 (2020).
pubmed: 32317643
pmcid: 7174390
doi: 10.1038/s41467-020-15647-5
Szabo, P. A. et al. Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease. Nat. Commun. 10, 4706 (2019).
pubmed: 31624246
pmcid: 6797728
doi: 10.1038/s41467-019-12464-3
Wang, A. et al. Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. eLife 9, e62522 (2020).
pubmed: 33164753
pmcid: 7688309
doi: 10.7554/eLife.62522
Grant, R. A. et al. Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia. Nature 590, 635–641 (2021).
pubmed: 33429418
pmcid: 7987233
doi: 10.1038/s41586-020-03148-w
Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).
pubmed: 32398875
doi: 10.1038/s41591-020-0901-9
Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595, 107–113 (2021).
pubmed: 33915569
pmcid: 8919505
doi: 10.1038/s41586-021-03570-8
Yoshida, M. et al. Local and systemic responses to SARS-CoV-2 infection in children and adults. Nature 602, 321–327 (2022).
pubmed: 34937051
doi: 10.1038/s41586-021-04345-x
Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40, 121–130 (2022).
pubmed: 34462589
doi: 10.1038/s41587-021-01001-7
Strunz, M. et al. Alveolar regeneration through a Krt8
pubmed: 32678092
pmcid: 7366678
doi: 10.1038/s41467-020-17358-3
Jara, P. et al. Matrix metalloproteinase (MMP)-19-deficient fibroblasts display a profibrotic phenotype. Am. J. Physiol. Lung Cell. Mol. Physiol. 308, L511–L522 (2015).
pubmed: 25575513
pmcid: 5243210
doi: 10.1152/ajplung.00043.2014
Moore, B. B. et al. Protection from pulmonary fibrosis in the absence of CCR2 signaling. J. Immunol. 167, 4368–4377 (2001).
pubmed: 11591761
doi: 10.4049/jimmunol.167.8.4368
Ghosh, A. K. & Vaughan, D. E. PAI-1 in tissue fibrosis. J. Cell. Physiol. 227, 493–507 (2012).
pubmed: 21465481
pmcid: 3204398
doi: 10.1002/jcp.22783
Xiong, A. & Liu, Y. Targeting hypoxia inducible factors-1α as a novel therapy in fibrosis. Front. Pharmacol. 8, 326 (2017).
pubmed: 28611671
pmcid: 5447768
doi: 10.3389/fphar.2017.00326
Wendisch, D. et al. SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis. Cell 184, 6243–6261.e27 (2021).
pubmed: 34914922
pmcid: 8626230
doi: 10.1016/j.cell.2021.11.033
Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809.e23 (2021).
pubmed: 33545035
doi: 10.1016/j.cell.2021.01.010
Lee, C. M. et al. Chitinase 1 regulates pulmonary fibrosis by modulating TGF-β/SMAD7 pathway via TGFBRAP1 and FOXO3. Life Sci. Alliance 2, e201900350 (2019).
pubmed: 31085559
pmcid: 6516052
doi: 10.26508/lsa.201900350
Lee, C. G. et al. Chitinase 1 is a biomarker for and therapeutic target in scleroderma-associated interstitial lung disease that augments TGF-β1 signaling. J. Immunol. 189, 2635–2644 (2012).
pubmed: 22826322
doi: 10.4049/jimmunol.1201115
Joshi, H. et al. L-plastin enhances NLRP3 inflammasome assembly and bleomycin-induced lung fibrosis. Cell Rep. 38, 110507 (2022).
pubmed: 35294888
pmcid: 8998782
doi: 10.1016/j.celrep.2022.110507
Sklepkiewicz, P. Inhibition of CHIT1 as a novel therapeutic approach in idiopathic pulmonary fibrosis. Eur. J. Pharmacol. 919, 174792 (2022).
pubmed: 35122869
doi: 10.1016/j.ejphar.2022.174792
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Duong, E., Mazutis, L., Masilionis, I. & Chaudhary, O. Frozen lung tissue dissociation for single-nucleus RNA & chromatin assays. protocols.io https://doi.org/10.17504/protocols.io.bh26j8he (2020).
Urata, S. et al. 10X Genomics single-nucleus RNA-sequencing for transcriptomic profiling of adult human tissues. protocols.io https://www.protocols.io/view/10x-genomics-single-nucleus-rna-sequencing-for-tra-86khzcw (2019).
Gayoso, A. & Shor, J. JonathanShor/DoubletDetection: doubletdetection v3.0. Zenodo https://github.com/JonathanShor/DoubletDetection/tree/dev-v2.4 (2020).
Heijink, I. H. et al. Down-regulation of E-cadherin in human bronchial epithelial cells leads to epidermal growth factor receptor-dependent Th2 cell-promoting activity. J. Immunol. 178, 7678–7685 (2007).
pubmed: 17548604
doi: 10.4049/jimmunol.178.12.7678
Berg, M. et al. FastCAR: Fast Correction for Ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets. Preprint at bioRxiv https://doi.org/10.1101/2022.07.19.50059 (2022).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Zaragosi, L.-E. & Barbry, P. Cell dissociation from nasal and bronchial brushings with cold-active protease for single-cell RNA-seq. protocols.io https://www.protocols.io/view/cell-dissociation-from-nasal-and-bronchial-brushin-qubdwsn (2019).
Heaton, H. et al. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. Methods 17, 615–620 (2020).
pubmed: 32366989
doi: 10.1038/s41592-020-0820-1
Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).
pubmed: 28192419
pmcid: 5376227
doi: 10.1038/nmeth.4179
Butler, A. Hoffman, P., Smibert, P., Papalexi, E. & Sat, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
Morales, J. et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol. 19, 21 (2018).
pubmed: 29448949
pmcid: 5815218
doi: 10.1186/s13059-018-1396-2
10x Genomics. Build notes for reference packages. https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build#grch38_1.2 (2016).
Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
pubmed: 27122128
doi: 10.1186/s13059-016-0947-7
Weibel, E. R. Morphometry of the Human Lung (Springer, 1963).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2021).
pubmed: 34949812
pmcid: 8748196
doi: 10.1038/s41592-021-01336-8
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
pubmed: 28091601
pmcid: 5241818
doi: 10.1038/ncomms14049
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
pubmed: 30573817
doi: 10.1038/s41592-018-0254-1
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2018).
Garreta, R. & Moncecchi, G. Learning scikit-learn: Machine Learning in Python (Packt Publishing, 2013).
Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).
pubmed: 33257685
pmcid: 7705760
doi: 10.1038/s41467-020-19894-4
Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).
pubmed: 34584091
pmcid: 8479118
doi: 10.1038/s41467-021-25960-2
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
pubmed: 24485249
pmcid: 4053721
doi: 10.1186/gb-2014-15-2-r29
Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).
pubmed: 22638577
pmcid: 3458527
doi: 10.1093/nar/gks461
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
doi: 10.1038/75556
Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 49, D325–D334 (2021).
doi: 10.1093/nar/gkaa1113
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
pmcid: 1239896
doi: 10.1073/pnas.0506580102
Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259 (2018).
pubmed: 29344893
pmcid: 5895181
doi: 10.1007/978-1-4939-7493-1_12
Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40, 121–130 (2022).
pubmed: 34462589
doi: 10.1038/s41587-021-01001-7
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
pubmed: 25867923
pmcid: 4430369
doi: 10.1038/nbt.3192
Allen, R. J. et al. Genome-wide association study of susceptibility to idiopathic pulmonary fibrosis. Am. J. Respir. Crit. Care Med. 201, 564–574 (2020).
pubmed: 31710517
pmcid: 7047454
doi: 10.1164/rccm.201905-1017OC
Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).
pubmed: 29662059
pmcid: 5902628
doi: 10.1038/s41467-018-03819-3
Domínguez Conde, C. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).
pubmed: 35549406
pmcid: 7612735
doi: 10.1126/science.abl5197