A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
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Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.
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Using bulk RNA-seq from intact kidneys and BSEQ-sc, approximately 1.51% of cells were estimated to be immune cells in Balb/c female mice and 4.84% in Balb/c male mice.
“we estimated that approximately 1.51% should correspond to immune cells in Balb/c female mice and 4.84% in Balb/c male mice.”
From Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows (2020) · cites this paper as [35]
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Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome Biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 13,133 · see citing works
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 12,116 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Self-citationCell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works
Generation of Functional Human Pancreatic β Cells In Vitro
Pagliuca, F. W., Millman, J. R., Gürtler, M., Segel, M., Van Dervort, A., Ryu, J. H., Peterson, Q. P., Greiner, D., & Melton, D. A. (2014) · Cell · doi:10.1016/j.cell.2014.09.040 · Cited by 2,025 · see citing works
Self-citationIn vivo reprogramming of adult pancreatic exocrine cells to β-cells
Zhou, Q., Brown, J., Kanarek, A., Rajagopal, J., & Melton, D. A. (2008) · Nature · doi:10.1038/nature07314 · Cited by 2,010 · see citing works
Self-citationReversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells
Rezania, A., Bruin, J. E., Arora, P., Rubin, A., Batushansky, I., Asadi, A., O'Dwyer, S., Quiskamp, N., Mojibian, M., Albrecht, T., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3033 · Cited by 1,575 · see citing works
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
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. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342 · see citing works
The unique cytoarchitecture of human pancreatic islets has implications for islet cell function
Cabrera, O., Berman, D. M., Kenyon, N. S., Ricordi, C., Berggren, P.-O., & Caicedo, A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510790103 · Cited by 1,312 · see citing works
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231 · see citing works
Self-citationCEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq
Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0938-8 · Cited by 1,125 · see citing works
Self-citationSingle-cell in situ RNA profiling by sequential hybridization
Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M., & Cai, L. (2014) · Nature Methods · doi:10.1038/nmeth.2892 · Cited by 985 · see citing works
Highly Multiplexed Subcellular RNA Sequencing in Situ
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., et al. (2014) · Science · doi:10.1126/science.1250212 · Cited by 973 · see citing works
Brissova, M., Fowler, M. J., Nicholson, W. E., Chu, A., Hirshberg, B., Harlan, D. M., & Powers, A. C. (2005) · Journal of Histochemistry & Cytochemistry · doi:10.1369/jhc.5c6684.2005 · Cited by 797 · see citing works
Selective β-Cell Loss and α-Cell Expansion in Patients with Type 2 Diabetes Mellitus in Korea
Yoon, K., Ko, S., Cho, J., Lee, J. M., Ahn, Y., Song, K. H., Yoo, S. J., Kang, M. I., Cha, B. Y., Lee, K. W., et al. (2003) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2002-020735 · Cited by 645 · see citing works
The role of gut hormones in glucose homeostasis
Drucker, D. J. (2007) · Journal of Clinical Investigation · doi:10.1172/jci30076 · Cited by 611 · see citing works
Generation and Regeneration of Cells of the Liver and Pancreas
Zaret, K., & Grompe, M. (2008) · Science · doi:10.1126/science.1161431 · Cited by 601 · see citing works
Controlled induction of human pancreatic progenitors produces functional beta‐like cells in vitro
Russ, H. A., Parent, A. V., Ringler, J. J., Hennings, T. G., Nair, G. G., Shveygert, M., Guo, T., Puri, S., Haataja, L., Cirulli, V., et al. (2015) · The EMBO Journal · doi:10.15252/embj.201591058 · Cited by 591 · see citing works
Human Pancreatic Digestive Enzymes
Whitcomb, D. C., & Lowe, M. E. (2007) · Digestive Diseases and Sciences · doi:10.1007/s10620-006-9589-z · Cited by 544 · see citing works
Negative regulation of myelination: Relevance for development, injury, and demyelinating disease
Jessen, K. R., & Mirsky, R. (2008) · Glia · doi:10.1002/glia.20761 · Cited by 515 · see citing works
Cell type–specific gene expression differences in complex tissues
Shen-Orr, S. S., Tibshirani, R. J., Khatri, P., Bodian, D. L., Staedtler, F., Perry, N. M., Hastie, T. J., Sarwal, M., Davis, M. M., & Butte, A. J. (2010) · Nature Methods · doi:10.1038/nmeth.1439 · Cited by 489 · see citing works
Self-citationFadista, J., Vikman, P. J., Ottosson-Laakso, E., Mollet, I. G., Esguerra, J. L. S., Taneera, J., Storm, P., Osmark, P., Ladenvall, C., Prasad, R. B., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402665111 · Cited by 470 · see citing works
Functional subdivision of islets of Langerhans and possible role of D cells.
Orci, L., & Unger, R. H. (1975) · The Lancet · doi:10.1016/s0140-6736(75)92078-4 · Cited by 442 · see citing works
Morán, I., Akerman, I., van de Bunt, M., Xie, R., Benazra, M., Nammo, T., Arnes, L., Nakić, N., García-Hurtado, J., Rodríguez-Seguí, S., et al. (2012) · Cell Metabolism · doi:10.1016/j.cmet.2012.08.010 · Cited by 422 · see citing works
The endoplasmic reticulum in pancreatic beta cells of type 2 diabetes patients
Marchetti, P., Bugliani, M., Lupi, R., Marselli, L., Masini, M., Boggi, U., Filipponi, F., Weir, G. C., Eizirik, D. L., & Cnop, M. (2007) · Diabetologia · doi:10.1007/s00125-007-0816-8 · Cited by 413 · see citing works
Activation of Pancreatic Stellate Cells in Human and Experimental Pancreatic Fibrosis
Haber, P., Keogh, G. W., Apte, M. V., et al. (1999) · American Journal Of Pathology · doi:10.1016/s0002-9440(10)65211-x · Cited by 401 · see citing works
Blum, B., Hrvatin, S., Schuetz, C., Bonal, C., Rezania, A., & Melton, D. (2012) · Nature Biotechnology · doi:10.1038/nbt.2141 · Cited by 349 · see citing works
Self-citationBlodgett, D. M., Nowosielska, A., Afik, S., Pechhold, S., Cura, A. J., Kennedy, N. J., Kim, S., Kucukural, A., Davis, R. J., Kent, S. C., et al. (2015) · Diabetes · doi:10.2337/db15-0039 · Cited by 330 · see citing works
The imprinted DLK1-MEG3 gene region on chromosome 14q32.2 alters susceptibility to type 1 diabetes
Wallace, C., Smyth, D. J., Maisuria-Armer, M., Walker, N. M., Todd, J. A., & Clayton, D. G. (2009) · Nature Genetics · doi:10.1038/ng.493 · Cited by 325 · see citing works
Rovira, M., Scott, S.-G., Liss, A., Jensen, J., Thayer, S., & Leach, S. (2009) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0912589107 · Cited by 313 · see citing works
Pancreas and beta-cell development: from the actual to the possible
Murtaugh, L. (2006) · Development · doi:10.1242/dev.02770 · Cited by 303 · see citing works
Benner, C., van der Meulen, T., Cacéres, E., Tigyi, K., Donaldson, C. J., & Huising, M. O. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-620 · Cited by 302 · see citing works
Nostro, M., Sarangi, F., Yang, C.-X., Holland, A. M., Elefanty, A., Stanley, E., Greiner, D. L., & Keller, G. (2015) · Stem Cell Reports · doi:10.1016/j.stemcr.2015.02.017 · Cited by 302 · see citing works
Computational deconvolution: extracting cell type-specific information from heterogeneous samples
Shen-Orr, S. S., & Gaujoux, R. (2013) · Current Opinion in Immunology · doi:10.1016/j.coi.2013.09.015 · Cited by 280 · see citing works
Self-citationThe Leptin-Deficient (ob/ob) Mouse
Drel, V. R., Mashtalir, N., Ilnytska, O., Shin, J., Li, F. R., Lyzogubov, V. V., & Obrosova, I. G. (2006) · Diabetes · doi:10.2337/db06-0885 · Cited by 265 · see citing works
MECHANISMS OF BICARBONATE SECRETION IN THE PANCREATIC DUCT
Steward, M. C., Ishiguro, H., & CASE, R. M. (2004) · Annual Review of Physiology · doi:10.1146/annurev.physiol.67.031103.153247 · Cited by 260 · see citing works
Urocortin3 mediates somatostatin-dependent negative feedback control of insulin secretion
van der Meulen, T., Donaldson, C. J., Cáceres, E., Hunter, A. E., Cowing-Zitron, C., Pound, L. D., Adams, M. W., Zembrzycki, A., Grove, K. L., & Huising, M. O. (2015) · Nature Medicine · doi:10.1038/nm.3872 · Cited by 256 · see citing works
Single‐cell transcriptomes reveal characteristic features of human pancreatic islet cell types
Li, J., Klughammer, J., Farlik, M., Penz, T., Spittler, A., Barbieux, C., Berishvili, E., Bock, C., & Kubicek, S. (2015) · EMBO Reports · doi:10.15252/embr.201540946 · Cited by 254 · see citing works
Molecular mechanisms regulating myelination in the peripheral nervous system
Pereira, J. A., Lebrun-Julien, F., & Suter, U. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.11.006 · Cited by 253 · see citing works
Morioka, T., Asilmaz, E., Hu, J., Dishinger, J. F., Kurpad, A. J., Elias, C. F., Li, H., Elmquist, J. K., Kennedy, R. T., & Kulkarni, R. (2007) · Journal of Clinical Investigation · doi:10.1172/jci30910 · Cited by 247 · see citing works
Xie, R., Everett, L., Lim, H., Patel, N., Schug, J., Kroon, E. J., Kelly, O. G., Wang, A., D'Amour, K., Robins, A., et al. (2013) · Cell Stem Cell · doi:10.1016/j.stem.2012.11.023 · Cited by 240 · see citing works
NATIVE KIDNEYS IN POST-TRANSPLANTATION HYPERTENSION
de Strihou, C. V. Y. (1985) · The Lancet · doi:10.1016/s0140-6736(85)90769-x · Cited by 229 · see citing works
Transcriptomes of the major human pancreatic cell types
Dorrell, C., Schug, J., Lin, C., Canaday, P. S., Fox, A. J., Smirnova, O., Bonnah, R. A., Streeter, P. R., Stoeckert, C. J., Kaestner, K. H., et al. (2011) · Diabetologia · doi:10.1007/s00125-011-2283-5 · Cited by 218 · see citing works
Insulin demand regulates β cell number via the unfolded protein response
Sharma, R. B., O’Donnell, A. C., Stamateris, R. E., Ha, B., McCloskey, K. M., Reynolds, P. R., Arvan, P., & Alonso, L. C. (2015) · Journal of Clinical Investigation · doi:10.1172/jci79264 · Cited by 202 · see citing works
Johnson, K. H., O’Brien, T. D., Hayden, D. W., Jordan, K., Ghobrial, H. K. G., Mahoney, W. C., & Westermark, P. (1988) · PubMed Central · Cited by 195 · see citing works
Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome
Nica, A. C., Ongen, H., Irminger, J.-C., Bosco, D., Berney, T., Antonarakis, S. E., Halban, P. A., & Dermitzakis, E. T. (2013) · Genome Research · doi:10.1101/gr.150706.112 · Cited by 189 · see citing works
Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells
Xin, Y., Kim, J., Ni, M., Wei, Y. Z., Okamoto, H., Lee, J., Adler, C., Cavino, K., Murphy, A. J., Yancopoulos, G. D., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602306113 · Cited by 168 · see citing works
CFTR Functions as a Bicarbonate Channel in Pancreatic Duct Cells
Ishiguro, H., Steward, M. C., Naruse, S., Ko, S. B. H., Goto, H., CASE, R. M., Kondo, T., & Yamamoto, A. (2009) · The Journal of General Physiology · doi:10.1085/jgp.200810122 · Cited by 133 · see citing works
Stone, V. M., Dhayal, S., Brocklehurst, K. J., Lenaghan, C., Winzell, M. S., Hammar, M., Xu, X., Smith, D. M., & Morgan, N. G. (2014) · Diabetologia · doi:10.1007/s00125-014-3213-0 · Cited by 128 · see citing works
Buchholz, M., Kestler, H. A., Holzmann, K., Ellenrieder, V., Schneiderhan, W., Siech, M., Adler, G., Bachem, M. G., & Gress, T. M. (2005) · Journal of Molecular Medicine · doi:10.1007/s00109-005-0680-2 · Cited by 122 · see citing works
The P-domain or trefoil motif: a role in renewal and pathology of mucous epithelia?
Hoffmann, W., & Hauser, F. (1993) · Trends in Biochemical Sciences · doi:10.1016/0968-0004(93)90170-r · Cited by 116 · see citing works
Li, W., Cavelti‐Weder, C., Zhang, Y., Clement, K., Donovan, S., González, G., Zhu, J., Stemann, M., Xu, K., Hashimoto, T., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3082 · Cited by 110 · see citing works
Genes controlling pancreas ontogeny.
Bonal, C., & Herrera, P. (2008) · International Journal of Developmental Biology · doi:10.1387/ijdb.072444cb · Cited by 100 · see citing works
van der Meulen, T., Xie, R., Kelly, O. G., Vale, W., Sander, M., & Huising, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052181 · Cited by 98 · see citing works
The Endocrine Pancreas: insights into development, differentiation and diabetes
Mastracci, T. L., & Sussel, L. (2012) · Wiley Interdisciplinary Reviews: Developmental Biology · doi:10.1002/wdev.44 · Cited by 94 · see citing works
The microanatomy of human islets of Langerhans, with special reference to somatostatin (D-) cells.
Grube, D., & Reinhild, B. O. H. N. (1983) · Archivum histologicum japonicum · doi:10.1679/aohc.46.327 · Cited by 77 · see citing works
A Human Islet Cell Culture System for High-Throughput Screening
Walpita, D., Hasaka, T. P., Spoonamore, J. E., Vetere, A., Takane, K. K., Fomina‐Yadlin, D., Fiaschi‐Taesch, N., Shamji, A. F., Clemons, P. A., Stewart, A. F., et al. (2011) · SLAS DISCOVERY · doi:10.1177/1087057111430253 · Cited by 58 · see citing works
Self-citationHolliday, N. D., Watson, S., & Brown, A. (2012) · Frontiers in Endocrinology · doi:10.3389/fendo.2011.00112 · Cited by 52 · see citing works
Levetan, C. S., & Pierce, S. M. (2012) · Endocrine Practice · doi:10.4158/ep12138.ra · Cited by 50 · see citing works
Neural stem/progenitor cell properties of glial cells in the adult mouse auditory nerve
Lang, H., Xing, Y., Brown, L. N., et al. (2015) · Scientific Reports · doi:10.1038/srep13383 · Cited by 45 · see citing works
Fast and accurate approximate inference of transcript expression from RNA-seq data
Hensman, J. J., Papastamoulis, P., Glaus, P., Honkela, A., & Rattray, M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv483 · Cited by 32 · see citing works
Molecular Regulation of Pancreas Development in Zebrafish
Kimmel, R. A., & Meyer, D. (2010) · Methods in cell biology · doi:10.1016/b978-0-12-384892-5.00010-4 · Cited by 29 · see citing works
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Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
Extrapulmonary manifestations of COVID-19
Gupta, A., Madhavan, M. V., Sehgal, K., Nair, N., Mahajan, S., Sehrawat, T. S., Bikdeli, B., Ahluwalia, N., Ausiello, J. C., Wan, E. Y., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0968-3 · Cited by 3,185 · see citing works
HCA Lung Biological Network, Sungnak, W., Huang, N., Bécavin, C., Berg, M., Queen, R., Litvinukova, M., Talavera-López, C., Maatz, H., Reichart, D., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0868-6 · Cited by 2,501 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Elyada, E., Bolisetty, M., Laise, P., Flynn, W. F., Courtois, E. T., Burkhart, R. A., Teinor, J. A., Belleau, P., Biffi, G., Lucito, M. S., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0094 · Cited by 2,241 · see citing works
Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations
MacParland, S. A., Liu, J. C., Ma, X.-Z., Innes, B. T., Bartczak, A. M., Gage, B. K., Manuel, J., Khuu, N., Echeverri, J., Linares, I., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06318-7 · Cited by 1,552 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
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Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,383 · see citing works
“The reference set consists of Braon[79] and Muraro[80] 804 datasets, and the query set consists of Xin[81], Segerstolpe[82], and Lawlor[83] datasets.”
MethodMultiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · see citing works
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
Construction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087 · see citing works
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Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628 · see citing works
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · see citing works
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury
Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C., & Humphreys, B. D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005477117 · Cited by 650 · see citing works
Large-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628 · see citing works
Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Barkley, D., Moncada, R., Pour, M., Liberman, D. A., Dryg, I., Werba, G., Wang, W., Baron, M., Rao, A., Xia, B., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01141-9 · Cited by 532 · see citing works
Self-citationStratification of pancreatic ductal adenocarcinomas based on tumor and microenvironment features
Puleo, F., Nicolle, R., Blum, Y., Cros, J., Marisa, L., Demetter, P., Quertinmont, E., Svrcek, M., Elarouci, N., Iovanna, J., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.08.033 · Cited by 563 · see citing works
Benchmarking of cell type deconvolution pipelines for transcriptomics data
Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., & De Preter, K. (2020) · Nature Communications · doi:10.1038/s41467-020-19015-1 · Cited by 446 · see citing works
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497 · see citing works
Self-citationComputational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program
Geirsdottir, L., David, E., Keren-Shaul, H., Weiner, A., Bohlen, S. C., Neuber, J., Balic, A., Giladi, A., Sheban, F., Dutertre, C.-A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.010 · Cited by 461 · see citing works
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018) · Nature Communications · doi:10.1038/s41467-018-03282-0 · Cited by 365 · see citing works
Balboa, D., Barsby, T., Lithovius, V., Saarimäki-Vire, J., Omar-Hmeadi, M., Dyachok, O., Montaser, H., Lund, P.-E., Yang, M., Ibrahim, H., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01219-z · Cited by 433 · see citing works
Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR
Zemmour, D., Zilionis, R., Kiner, E., Klein, A. M., Mathis, D., & Benoist, C. (2018) · Nature Immunology · doi:10.1038/s41590-018-0051-0 · Cited by 384 · see citing works
Self-citationAcquisition of Dynamic Function in Human Stem Cell-Derived β Cells
Velazco-Cruz, L., Song, J., Maxwell, K. G., Goedegebuure, M. M., Augsornworawat, P., Hogrebe, N. J., & Millman, J. R. (2019) · Stem Cell Reports · doi:10.1016/j.stemcr.2018.12.012 · Cited by 399 · see citing works
Joint analysis of heterogeneous single-cell RNA-seq dataset collections
Barkas, N., Petukhov, V., Nikolaeva, D., Lozinsky, Y., Demharter, S., Khodosevich, K., & Kharchenko, P. V. (2019) · Nature Methods · doi:10.1038/s41592-019-0466-z · Cited by 292 · see citing works
Building consensus on definition and nomenclature of hepatic, pancreatic, and biliary organoids
Marsee, A., Roos, F. J. M., Verstegen, M. M. A., Marsee, A., Roos, F., Verstegen, M., Clevers, H., Vallier, L., Takebe, T., Huch, M., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.04.005 · Cited by 310 · see citing works
The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease
Schiller, H. B., Montoro, D. T., Simon, L. M., Rawlins, E. L., Meyer, K. B., Strunz, M., Vieira Braga, F. A., Timens, W., Koppelman, G. H., Budinger, G. R. S., et al. (2019) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2018-0416tr · Cited by 245 · see citing works
Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253 · see citing works
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works
Xin, Y., Dominguez Gutierrez, G., Okamoto, H., Kim, J., Lee, A.-H., Adler, C., Ni, M., Yancopoulos, G. D., Murphy, A. J., & Gromada, J. (2018) · Diabetes · doi:10.2337/db18-0365 · Cited by 191 · see citing works
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · see citing works
Augsornworawat, P., Maxwell, K. G., Velazco-Cruz, L., & Millman, J. R. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108067 · Cited by 200 · see citing works
Tumor microbiome links cellular programs and immunity in pancreatic cancer
Ghaddar, B., Biswas, A., Harris, C., Omary, M. B., Carpizo, D. R., Blaser, M. J., & De, S. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.009 · Cited by 181 · see citing works
Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158 · see citing works
Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity
Payen, V. L., Lavergne, A., Alevra Sarika, N., Colonval, M., Karim, L., Deckers, M., Najimi, M., Coppieters, W., Charloteaux, B., Sokal, E. M., et al. (2021) · JHEP Reports · doi:10.1016/j.jhepr.2021.100278 · Cited by 160 · see citing works
Comparison of Transformations for Single-Cell RNA-Seq Data
Ahlmann-Eltze, C., & Huber, W. (2021) · doi:10.1101/2021.06.24.449781 · Cited by 148 · see citing works
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145 · see citing works
The α-cell in diabetes mellitus
Gromada, J., Chabosseau, P., & Rutter, G. A. (2018) · Nature Reviews Endocrinology · doi:10.1038/s41574-018-0097-y · Cited by 143 · see citing works
Impact of similarity metrics on single-cell RNA-seq data clustering
Kim, T., Chen, I. R., Lin, Y., Wang, A. Y.-Y., Yang, J. Y. H., & Yang, P. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby076 · Cited by 138 · see citing works
Predictability of human differential gene expression
Crow, M., Lim, N., Ballouz, S., Pavlidis, P., & Gillis, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802973116 · Cited by 117 · see citing works
clustifyr: an R package for automated single-cell RNA sequencing cluster classification
Fu, R., Gillen, A. E., Sheridan, R. M., Tian, C., Daya, M., Hao, Y., Hesselberth, J. R., & Riemondy, K. A. (2020) · F1000Research · doi:10.12688/f1000research.22969.2 · Cited by 135 · see citing works
Xu, P., Wang, M., Song, W.-M., Wang, Q., Yuan, G.-C., Sudmant, P. H., Zare, H., Tu, Z., Orr, M. E., & Zhang, B. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00507-7 · Cited by 114 · see citing works
Yeo, G. H. T., Saksena, S. D., & Gifford, D. K. (2021) · Nature Communications · doi:10.1038/s41467-021-23518-w · Cited by 103 · see citing works
Large Scale Foundation Model on Single-cell Transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639 · see citing works
Zhu, T., Liu, J., Beck, S., Pan, S., Capper, D., Lechner, M., Thirlwell, C., Breeze, C. E., & Teschendorff, A. E. (2022) · Nature Methods · doi:10.1038/s41592-022-01412-7 · Cited by 103 · see citing works
Evaluating stably expressed genes in single cells
Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D. M., Speed, T., Yang, J. Y. H., & Yang, P. (2019) · GigaScience · doi:10.1093/gigascience/giz106 · Cited by 84 · see citing works
Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis
Wang, Z.-Y., Keogh, A., Waldt, A., Cuttat, R., Neri, M., Zhu, S., Schuierer, S., Ruchti, A., Crochemore, C., Knehr, J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98806-y · Cited by 103 · see citing works
Zero-shot evaluation reveals limitations of single-cell foundation models
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2025) · Genome Biology · doi:10.1186/s13059-025-03574-x · Cited by 109 · see citing works
Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench
Chazarra-Gil, R., van Dongen, S., Kiselev, V. Y., & Hemberg, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab004 · Cited by 93 · see citing works
Gerace, D., Zhou, Q., Kenty, J. H.-R., Veres, A., Sintov, E., Wang, X., Boulanger, K. R., Li, H., & Melton, D. A. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100879 · Cited by 88 · see citing works
Self-citationIntegrated analysis of single cell transcriptomic data across conditions, technologies, and species
Butler, A., & Satija, R. (2017) · doi:10.1101/164889 · Cited by 13,200 · see citing works
Weng, C., Xi, J., Li, H., Cui, J., Gu, A., Lai, S., Leskov, K., Ke, L., Jin, F., & Li, Y. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00314-2 · Cited by 65 · see citing works
Wang, L., Nie, R., Yu, Z., Xin, R., Zheng, C., Zhang, Z., Zhang, J., & Cai, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00244-4 · Cited by 62 · see citing works
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H., & Liò, P. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab804 · Cited by 44 · see citing works
Characterization of stem-cell-derived islets during differentiation and after implantation
Cuesta-Gomez, N., Verhoeff, K., Jasra, I. T., Pawlick, R., Dadheech, N., & Shapiro, A. M. J. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111238 · Cited by 41 · see citing works
Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems
Herring, C. A., Chen, B., McKinley, E. T., & Lau, K. S. (2018) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2018.01.023 · Cited by 35 · see citing works
Putman, M. S., Norris, A. W., Hull, R. L., Rickels, M. R., Sussel, L., Blackman, S. M., Chan, C. L., Ode, K. L., Daley, T., Stecenko, A. A., et al. (2023) · Diabetes Care · doi:10.2337/dc23-0380 · Cited by 35 · see citing works
Sfaira accelerates data and model reuse in single cell genomics
Fischer, D. S., Dony, L., König, M., Moeed, A., Zappia, L., Heumos, L., Tritschler, S., Holmberg, O., Aliee, H., & Theis, F. J. (2021) · Genome Biology · doi:10.1186/s13059-021-02452-6 · Cited by 33 · see citing works
Chiou, J., Zeng, C., Cheng, Z., Han, J. Y., Schlichting, M., Huang, S., Wang, J., Sui, Y., Deogaygay, A., Okino, M.-L., et al. (2019) · doi:10.1101/693671 · Cited by 189 · see citing works
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis
Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y., & Zhao, H. (2023) · doi:10.1101/2023.12.07.569910 · Cited by 30 · see citing works
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21 · see citing works
Nakamura, A., Wong, Y. F., Venturato, A., Michaut, M., Venkateswaran, S., Santra, M., Gonçalves, C., Larsen, M., Leuschner, M., Kim, Y. H., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.03.013 · Cited by 20 · see citing works
Xie, X., Wu, C., Ma, C., Gao, D., Su, W., Huang, J., Deng, K., Yan, D., & Lin, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124702 · Cited by 17 · see citing works
Expression quantitative trait locus studies in the era of single-cell omics
Luo, J., Wu, X., Cheng, Y., Chen, G., Wang, J., & Song, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1182579 · Cited by 11 · see citing works
Xie, X., Wu, C., Yang, Y., Su, W., Dao, F., Huang, J., Shi, Z., Lyu, H., & Lin, H. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02865-8 · Cited by 10 · see citing works
Tracing unknown tumor origins with a biological-pathway-based transformer model
Xie, J., Chen, Y., Luo, S., Yang, W., Lin, Y., Wang, L., Ding, X., Tong, M., & Yu, R. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100797 · Cited by 9 · see citing works
TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration
Melzer, M. K., Schirge, S., Gout, J., Arnold, F., Srinivasan, D., Burtscher, I., Allgöwer, C., Mulaw, M., Zengerling, F., Günes, C., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01553-x · Cited by 5 · see citing works
“The expression of TBX3 in adult pancreata of four human donors was interrogated in a published dataset: GSE84133 [41].”
BackgroundMethodInfluential
- Authors
- Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., Melton, D. A., & Yanai, I.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Harvard University (US)
- Broad Institute (US)
- Corresponding author
- Klein, Allon M.
- Melton, Douglas A.
- Yanai, Itai
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 3
- Issue
- 4
- Pages
- 346-360.e4
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC5228327
- OpenAlex
- W2523620612
- Cited by
- 1,508
- References
- 67
- Citations by year
- 2026: 99 · 2025: 174 · 2024: 219 · 2023: 205 · 2022: 175 · 2021: 236 · 2020: 174 · 2019: 107 · 2018: 74 · 2017: 41 · 2016: 2 · 2015: 1 · 2014: 1
- FWCI
- 38.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (T32GM080177)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- human pancreas, mouse pancreas, pancreatic cell types, single-cell transcriptomics, pancreatic islets, beta-cell heterogeneity, endoplasmic reticulum stress, pancreatic ductal cells, pancreatic stellate cells, cell type deconvolution, cell type-specific transcription factors
- MeSH terms
- Animals, Cell Differentiation, Humans, Islets of Langerhans, Pancreas, Transcription Factors, Gene Expression Regulation, Developmental, Gene Expression Profiling, Pancreas, Exocrine, Mice, Single-Cell Analysis, Transcriptome
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