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Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Open accessAbstract
The mammalian lung is a highly branched network in which the distal regions of the bronchial tree transform during development into a densely packed honeycomb of alveolar air sacs that mediate gas exchange. Although this transformation has been studied by marker expression analysis and fate-mapping, the mechanisms that control the progression of lung progenitors along distinct lineages into mature alveolar cell types are still incompletely known, in part because of the limited number of lineage markers and the effects of ensemble averaging in conventional transcriptome analysis experiments on cell populations. Here we show that single-cell transcriptome analysis circumvents these problems and enables direct measurement of the various cell types and hierarchies in the developing lung. We used microfluidic single-cell RNA sequencing (RNA-seq) on 198 individual cells at four different stages encompassing alveolar differentiation to measure the transcriptional states which define the developmental and cellular hierarchy of the distal mouse lung epithelium. We empirically classified cells into distinct groups by using an unbiased genome-wide approach that did not require a priori knowledge of the underlying cell types or the previous purification of cell populations. The results confirmed the basic outlines of the classical model of epithelial cell-type diversity in the distal lung and led to the discovery of many previously unknown cell-type markers, including transcriptional regulators that discriminate between the different populations. We reconstructed the molecular steps during maturation of bipotential progenitors along both alveolar lineages and elucidated the full life cycle of the alveolar type 2 cell lineage. This single-cell genomics approach is applicable to any developing or mature tissue to robustly delineate molecularly distinct cell types, define progenitors and lineage hierarchies, and identify lineage-specific regulatory factors.
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Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Quality control and preprocessing of metagenomic datasets
Schmieder, R., & Edwards, R. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr026 · Cited by 5,119
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
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Fank1 and Jazf1 promote multiciliated cell differentiation in the mouse airway epithelium
Johnson, J.-A., Watson, J. K., Nikolić, M., & Rawlins, E. L. (2018) · Biology Open · doi:10.1242/bio.033944 · Cited by 19
Transcriptional regulation of cell shape during organ morphogenesis
Sivakumar, A., & Kurpios, N. A. (2018) · The Journal of Cell Biology · doi:10.1083/jcb.201612115 · Cited by 19
Campbell, K. R., & Yau, C. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/026872 · Cited by 20
Clinical application of single‐cell RNA sequencing in disease and therapy
Nadukkandy, A. S., Kalaiselvan, S., Lin, L., & Luo, Y. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70512 · Cited by 21
Pang, L., Hu, J. R., Li, F., Yuan, H., Yan, M., Liao, G., Xu, L., Pang, B., Ping, Y., Xiao, Y., et al. (2019) · Cancers · doi:10.3390/cancers11122025 · Cited by 18
Cell-by-Cell: Unlocking Lung Cancer Pathogenesis
Sinjab, A. F., Rahal, Z., & Kadara, H. N. (2022) · Cancers · doi:10.3390/cancers14143424 · Cited by 19
Li, X., Hu, Z., Yu, X., Zhang, C., Ma, B., He, L., Wei, C., & Wu, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-03829-z · Cited by 18
Transcriptomic Characterization of the Human Cell Cycle in Individual Unsynchronized Cells
Karlsson, J. W., Kroneis, T., Jonasson, E., Larsson, E., & Ståhlberg, A. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.10.011 · Cited by 18
The Xenopus animal cap transcriptome: building a mucociliary epithelium
Angerilli, A., Smialowski, P., & Rupp, R. A. W. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky771 · Cited by 18
Hao, J., Cao, W., Huang, J. J., Zou, X., & Han, Z. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1016 · Cited by 19
Quality Control of Single-Cell RNA-seq
Jiang, P. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9057-3_1 · Cited by 18
STK11 is required for the normal program of ciliated cell differentiation in airways
Chu, Q., Yao, C., Qi, X., Stripp, B. R., & Tang, N. (2019) · Cell Discovery · doi:10.1038/s41421-019-0104-z · Cited by 20
PanoView: An iterative clustering method for single-cell RNA sequencing data
Hu, M.-W., Kim, D. W., Liu, S., Zack, D. J., Blackshaw, S., & Qian, J. X. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007040 · Cited by 19
Hadebe, S., & Brombacher, F. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02696 · Cited by 18
Single-cell genomic analysis of human cerebral organoids
Kanton, S., Treutlein, B., & Camp, J. G. (2020) · Methods in cell biology · doi:10.1016/bs.mcb.2020.03.013 · Cited by 18
Self-citationLongitudinal single-cell RNA-seq of hESCs-derived retinal organoids
Wang, S., Poli, S., Liang, X., & Peng, G.-H. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1836-7 · Cited by 19
Zhou, B., Stueve, T. R., Mihalakakos, E. A., Miao, L., Mullen, D. J., Wang, Y., Liu, Y., Luo, J., Tran, E., Siegmund, K. D., et al. (2021) · BMC Genomics · doi:10.1186/s12864-021-08152-6 · Cited by 18
Shi, Y. Y., Huang, X., Du, Z., & Tan, J. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022271 · Cited by 18
Khatri, A., Kraft, B. D., Tata, P. R., Randell, S. H., Piantadosi, C. A., & Pendergast, A. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816030116 · Cited by 18
Dissecting stem cell differentiation using single cell expression profiling
Moignard, V., & Göttgens, B. (2016) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2016.08.005 · Cited by 18
Zhang, J., Zhang, G., Li, Z., Qu, L., & Wen, C. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107876 · Cited by 17
Li, C., Peinado, N., Smith, S. M., Zhou, J., Gao, F., Kohbodi, G. A., Zhou, B., Thornton, M. E., Grubbs, B. H., Lee, M. K., et al. (2022) · Stem Cells · doi:10.1093/stmcls/sxac031 · Cited by 17
Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing
Dudchenko, O., Ordovás-Montañés, J., & Bingle, C. D. (2023) · Biochemical Journal · doi:10.1042/bcj20220572 · Cited by 18
Early-stage lung cancer is driven by a transitional cell state dependent on a KRAS-ITGA3-SRC axis
Moye, A. L., Dost, A. F. M., Ietswaart, R., Sengupta, S., Ya, V., Aluya, C., Fahey, C. G., Louie, S. M., Paschini, M., & Kim, C. F. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00113-5 · Cited by 18
Wang, Z., Jin, S., Liu, G., Zhang, X., Wang, N., Wu, D., Hu, Y., Zhang, C., Jiang, Q., Xu, L., et al. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1647-3 · Cited by 17
Quo vadis: tracing the fate of neural crest cells
Zurkirchen, L., & Sommer, L. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.07.001 · Cited by 17
Costa, F., Grün, D., & Backofen, R. (2018) · Nature Communications · doi:10.1038/s41467-018-05988-7 · Cited by 17
Generation of Distal Renal Segments Involves a Unique Population of Aqp2+ Progenitor Cells
Gao, C., Chen, L., Chen, E., Tsilosani, A., Xia, Y., & Zhang, W. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021030399 · Cited by 17
Isolation and Characterization of Single Cells from Zebrafish Embryos
Samsa, L. A., Fleming, N., Magness, S. T., Li, Q., & Liu, J. (2016) · Journal of Visualized Experiments · doi:10.3791/53877 · Cited by 21
Engels, A., Brady, P., Kammoun, M., Ferreiro, J. F., DeKoninck, P. L. J., Endo, M., Toelen, J., Vermeesch, J. R., & Deprest, J. A. (2016) · Disease Models & Mechanisms · doi:10.1242/dmm.021626 · Cited by 17
Nguyen, T. M., Jiménez, J., Elowsson, L., Müller, C., Westergren‐Thorsson, G., Deprest, J. A., & Toelen, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214793 · Cited by 17
Bi, W., Wang, R., Hou, S., Liu, W., Zhang, H., Xu, Z., Deng, J., Wang, Z., Zeng, L., & Jiang, J. (2025) · Burns & Trauma · doi:10.1093/burnst/tkaf050 · Cited by 17
Heterogeneity of Pulmonary Stem Cells
Lynch, T. J., Ievlev, V., & Parekh, K. R. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-24108-7_6 · Cited by 16
Kashima, M., Shida, Y., Yamashiro, T., Hirata, H., & Kurosaka, H. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.777299 · Cited by 17
Dada, L. A., Welch, L. C., Magnani, N., Ren, Z., Han, H., Brazee, P. L., Celli, D. A., Flozak, A. S., Weng, A., Maciel, M., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.159331 · Cited by 16
Aging represses oncogenic KRAS-driven lung tumorigenesis and alters tumor suppression
Shuldiner, E. G., Karmakar, S., Tsai, M. K., Hebert, J. D., Tang, Y. J., Andrejka, L., Robertson, M. R., Wang, M., Detrick, C. R., Cai, H., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00986-z · Cited by 17
The niche in single‐cell technologies
Donati, G. (2015) · Immunology and Cell Biology · doi:10.1038/icb.2015.107 · Cited by 16
A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data
Zhu, X., Li, H., Xu, Y., Guo, L., Wu, F., Duan, G., & Wang, J. (2019) · Genes · doi:10.3390/genes10020098 · Cited by 17
Nakajima, K., Ono, M., Radović, U., Dizdarević, S., Tomizawa, S.-I., Kuroha, K., Naganatsu, G., Hoshi, I., Matsunaga, R., Shirakawa, T., et al. (2019) · Disease Models & Mechanisms · doi:10.1242/dmm.040139 · Cited by 16
Jones, M. R., Lingampally, A., Wu, J., Sedighi, J., Ahmadvand, N., Wilhelm, J., Vazquez‐Armendariz, A. I., Herold, S., Chen, C., Zhang, J., et al. (2020) · Cells · doi:10.3390/cells9051274 · Cited by 16
Analysis of time-series regulatory networks
Ding, J., & Bar‐Joseph, Z. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.005 · Cited by 16
Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework
Wang, C., Gao, Y.-L., Liu, J., Kong, X.-Z., & Zheng, C.-H. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3029187 · Cited by 16
Yang, B., Bao, W., Chen, B., & Song, D. (2022) · BioData Mining · doi:10.1186/s13040-022-00297-8 · Cited by 16
Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review
Rochigneux, P., Garcia, A. J., Chanez, B., Madroszyk, A., Olive, D., & Garon, E. B. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01036 · Cited by 17
An ILC2-chitinase circuit restores lung homeostasis after epithelial injury
Jung, H., Kim, D.-H., Díaz, R. E., White, J. M., Rucknagel, S., Mosby, L., Wang, Y., Reddy, S., Winkler, E. S., Hassan, A. O., et al. (2024) · Science Immunology · doi:10.1126/sciimmunol.adl2986 · Cited by 15
Persistent lineage plasticity driving lung cancer development and progression
Meng, F., Li, J., Xia, Z., Wang, Q., Sun, Q., Wang, S., Xu, L., & Yin, R. H. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70458 · Cited by 17
Lung development: AT1 and AT2 property
Chen, Y., Dong, Y., & Du, X. (2020) · Biocell · doi:10.32604/biocell.2020.08041 · Cited by 15
Yan, X., Zheng, R., Chen, J., & Li, M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad505 · Cited by 15
OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data
Leng, N., Choi, J., Chu, L., Thomson, J. A., Kendziorski, C. M., & Stewart, R. M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw004 · Cited by 15
Emergence of Bias During the Synthesis and Amplification of cDNA for scRNA-seq
Luo, Q., & Zhang, H. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_12 · Cited by 15
scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization
Feng, Z., Ren, X., Fang, Y., Yin, Y., Huang, C., Yimin, Z., & Wang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz936 · Cited by 15
Goldblatt, D. L., Flores, J. R., Ha, G. V., Jaramillo, A. M., Tkachman, S., Kirkpatrick, C. T., Wali, S., Hernandez, B., Ost, D. E., Scott, B. L., et al. (2020) · British Journal of Pharmacology · doi:10.1111/bph.14977 · Cited by 15
Lieberman, B., Kusi, M., Hung, C., Chou, C., Ning, H., Ho, Y., Taverna, J. A., Huang, T. H. ‐., & Chen, C. (2020) · Journal of Translational Genetics and Genomics · doi:10.20517/jtgg.2020.51 · Cited by 15
Advanced single-cell technologies to guide the development of bioengineered lungs
Wanczyk, H., Jensen, T., Weiss, D. J., & Finck, C. M. (2021) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00089.2021 · Cited by 16
Improving Single-Cell RNA-seq Clustering by Integrating Pathways
Zhang, C., Gao, L., Wang, B., & Gao, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab147 · Cited by 15
Shiga, M., Seno, S., Onizuka, M., & Matsuda, H. (2021) · PeerJ · doi:10.7717/peerj.12087 · Cited by 15
SLRRSC: Single-Cell Type Recognition Method Based on Similarity and Graph Regularization Constraints
Zhang, N.-N., Liu, J., Zheng, C.-H., & Wang, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3148286 · Cited by 16
Su, E. Y., Spangler, A., Bian, Q., Kasamoto, J. Y., & Cahan, P. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2021.12.018 · Cited by 16
SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising
Liu, J., Pan, Y., Ruan, Z., & Guo, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac398 · Cited by 16
Single-cell analysis reveals key roles for Bcl11a in regulating stem cell fate decisions
Powers, A. N., & Satija, R. (2015) · Genome biology · doi:10.1186/s13059-015-0778-y · Cited by 15
Single-cell genomic profiling of acute myeloid leukemia for clinical use: A pilot study
Yan, B., Hu, Y., Ban, K. H.-K., Tiang, Z., Ng, C., Lee, J., Tan, W. L. W., Chiu, L., Tan, T. W., Seah, E., et al. (2017) · Oncology Letters · doi:10.3892/ol.2017.5669 · Cited by 16
Gangalum, R. K., Kim, D., Kashyap, R. K., Mangul, S., Zhou, X., Elashoff, D. A., & Bhat, S. P. (2018) · iScience · doi:10.1016/j.isci.2018.11.024 · Cited by 16
Single‐cell technologies in reproductive immunology
Vázquez, J., Ong, I. M., & Stanic, A. K. (2019) · American Journal of Reproductive Immunology · doi:10.1111/aji.13157 · Cited by 15
Hydroxypropyl Cyclodextrin Improves Amiodarone-induced Aberrant Lipid Homeostasis of Alveolar Cells
Kanagaki, S., Suezawa, T., Moriguchi, K., Nakao, K., Toyomoto, M., Yamamoto, Y., Murakami, K., Hagiwara, M., & Gotoh, S. (2021) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2020-0119oc · Cited by 15
scPred: Cell type prediction at single-cell resolutionPreprint
Alquicira-Hernández, J., Sathe, A., Ji, H. P., Nguyen, Q. H., & Powell, J. E. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/369538 · Cited by 15
Lynn, A. Y., Shin, K., Eaton, D. A., Rose, M., Zhang, X., Ene, M., Grundler, J., Deschenes, E., Rivero, R., Bracaglia, L. G., et al. (2025) · Biomaterials · doi:10.1016/j.biomaterials.2025.123238 · Cited by 14
Jones, M. R., Lingampally, A., Ahmadvand, N., Chong, L., Wu, J., Wilhem, J., Vazquez‐Armendariz, A. I., Ansari, M., Herold, S., Ornitz, D. M., et al. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04626-2 · Cited by 14
scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies
Baran, Y., & Doğan, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106634 · Cited by 14
Lettieri, S., Bertuccio, F. R., Frate, L. D., Perrotta, F., Corsico, A. G., & Stella, G. M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms25010547 · Cited by 14
A computational strategy for predicting lineage specifiers in stem cell subpopulations
Okawa, S., & del Sol, A. (2015) · Stem Cell Research · doi:10.1016/j.scr.2015.08.006 · Cited by 14
COOK, D. J., Achanta, S., Hoek, J. B., Ogunnaike, B. A., & Vadigepalli, R. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0605-7 · Cited by 15
Cell fate analysis in fetal mouse lung reveals distinct pathways for TI and TII cell development
Gonzalez, R. F., Leaffer, D., Chapin, C. J., Gillespie, A. M., Eckalbar, W. L., & Dobbs, L. G. (2019) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00503.2018 · Cited by 14
Single‐cell sequencing of miRNAs: A modified technology
Chakraborty, C., Bhattacharya, M., & Agoramoorthy, G. (2020) · Cell Biology International · doi:10.1002/cbin.11376 · Cited by 14
Single-Cell Sequencing Applications in the Inner Ear
Wu, M., Xia, M., Li, W., & Li, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.637779 · Cited by 14
Wang, Z., Liu, B., Chen, S., Ma, S., Xue, L., & Zhao, H. (2021) · INFORMS Journal on Optimization · doi:10.1287/ijoo.2021.0064 · Cited by 14
A novel method for single-cell data imputation using subspace regression
Tran, D., Tran, B., Nguyen, H., & Nguyen, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-06500-4 · Cited by 14
acorde unravels functionally interpretable networks of isoform co-usage from single cell data
Arzalluz-Luque, Á., Salguero, P., Tarazona, S., & Conesa, A. (2022) · Nature Communications · doi:10.1038/s41467-022-29497-w · Cited by 15
Li, Z., Tang, C., Zheng, X., Li, Z., Zhang, W., & Cao, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.046 · Cited by 14
Baurès, M., Lombardi, E. P., Martino, D. D., Zeitouni, W., Pacreau, E., Santos, L. D., Dariane, C., Boutillon, F., Guidotti, J., & Goffin, V. (2022) · Cancers · doi:10.3390/cancers14153775 · Cited by 16
Injury activated alveolar progenitors (IAAPs): the underdog of lung repair
Chong, L., Ahmadvand, N., Noori, A., Lv, Y., Chen, C., Bellusci, S., & Zhang, J. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04789-6 · Cited by 15
Future clinical implications emerging from recent genome-wide expression studies in asthma
Bérubé, J., & Bossé, Y. (2014) · Expert Review of Clinical Immunology · doi:10.1586/1744666x.2014.932249 · Cited by 14
Schissler, A. G., Li, Q., Chen, J. L., Kenost, C., Achour, I., Billheimer, D. D., Li, H., Piegorsch, W. W., & Lussier, Y. A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw248 · Cited by 14
Sheng, K., & Zong, C. C. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9240-9_5 · Cited by 14
Assessment of Single Cell RNA-Seq Normalization Methods
Ding, B., Zheng, L., & Wang, W. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.040683 · Cited by 14
A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering
Xu, Y., Li, H., Pan, Y. F., Luo, F., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2931582 · Cited by 14
Network-Based Single-Cell RNA-Seq Data Imputation Enhances Cell Type Identification
Zand, M., & Ruan, J. (2020) · Genes · doi:10.3390/genes11040377 · Cited by 14
SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data
Abugessaisa, I., Hasegawa, A., Noguchi, S., Cardon, M., Watanabe, K., Takahashi, M., Suzuki, H., Katayama, S., Kere, J., & Kasukawa, T. (2022) · iScience · doi:10.1016/j.isci.2022.103777 · Cited by 14
GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis
Helmy, M., Agrawal, R., Ali, J., Soudy, M., Bui, T. T., & Selvarajoo, K. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.693836 · Cited by 13
Ding, Q., Yang, W., Luo, M., Xu, C., Xu, Z., Pang, F., Cai, Y., Anashkina, A. A., Su, X., Chen, N., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac300 · Cited by 13
Heterochronic regulation of lung development via the Lin28‐Let‐7 pathway
Gulman, N. K., Armon, L., Shalit, T., & Urbach, A. (2019) · The FASEB Journal · doi:10.1096/fj.201802702r · Cited by 13
Inferring TF activation order in time series scRNA-Seq studies
Lin, C. H., Ding, J., & Bar‐Joseph, Z. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007644 · Cited by 13
Mapping a mammalian adult adrenal gland hierarchy across species by microwell-seq
Lai, S., Ma, L., Weigao, E., Ye, F., Chen, H., Han, X., & Guo, G. (2020) · Cell Regeneration · doi:10.1186/s13619-020-00042-8 · Cited by 14
Uncovering cellular networks in branching morphogenesis using single-cell transcriptomics
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Metadata
- Authors
- Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R.
- Affiliations
- Howard Hughes Medical Institute (US)
- Stanford Medicine (US)
- Corresponding author
- Desai, Tushar J.
- Krasnow, Mark A.
- Quake, Stephen R.
- ORCID
- Venue
- Nature
- Published
- 2014-04-13
- Volume
- 509
- Issue
- 7500
- Pages
- 371-375
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4145853
- OpenAlex
- W2068881679
- Cited by
- 1,342
- References
- 41
- Citations by year
- 2026: 25 · 2025: 52 · 2024: 65 · 2023: 97 · 2022: 113 · 2021: 120 · 2020: 134 · 2019: 146 · 2018: 161 · 2017: 155 · 2016: 137 · 2015: 106 · 2014: 25
- FWCI
- 57.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Heart, Lung, and Blood Institute (K08HL084095, U01HL099995, U01HL099999)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32HD007249)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Neonatal Respiratory Health Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- distal lung epithelium, lung development, alveolar progenitor cells, alveolar type II cells, alveolar type I cells, cell type markers, lineage hierarchy, alveolar differentiation, transcription factors, mouse lung
- MeSH terms
- Animals, Bronchi, Cell Differentiation, Epithelial Cells, Female, Genetic Markers, Lung, Mice, Inbred C57BL, Pulmonary Alveoli, Pulmonary Gas Exchange, Stem Cells, Genome, Sequence Analysis, RNA, Cell Lineage, Genomics, Mice, Single-Cell Analysis, Transcriptome
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