Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns
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Abstract
As organisms age, cells accumulate genetic and epigenetic errors that eventually lead to impaired organ function or catastrophic transformation such as cancer. Because aging reflects a stochastic process of increasing disorder, cells in an organ will be individually affected in different ways, thus rendering bulk analyses of postmitotic adult cells difficult to interpret. Here, we directly measure the effects of aging in human tissue by performing single-cell transcriptome analysis of 2,544 human pancreas cells from eight donors spanning six decades of life. We find that islet endocrine cells from older donors display increased levels of transcriptional noise and potential fate drift. By determining the mutational history of individual cells, we uncover a novel mutational signature in healthy aging endocrine cells. Our results demonstrate the feasibility of using single-cell RNA sequencing (RNA-seq) data from primary cells to derive insights into genetic and transcriptional processes that operate on aging human tissue.
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Alternative measures of cell-cell heterogeneity using heuristically selected subsets of genes were highly correlated with the primary method.
“We also considered alternative measures of cell–cell heterogeneity using heuristically selected subsets of genes as in previous reports (Enge et al. 2017; Angelidis et al. 2019) and found that results were highly correlated with our primary method (Supplemental Fig. S12; Supplemental Methods).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Enge et al. 2017
Two data sets of the human cell atlas were used: a Drop-seq data set measured in lung tissue52 and a Smart-seq2 data set measured in pancreas tissue51.
“Two data sets of the human cell atlas were used for that, a Drop-seq data set measured in lung tissue52 and a Smart-seq2 data set measured in pancreas tissue51.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [51]
Single cell RNA sequencing studies show greater cell-to-cell variability in gene expression in human T cells and pancreatic cells, as well as differences in cellular mutation rates with age.
“These observations are supported by a rapidly growing number of single cell RNA sequencing studies showing greater cell-to-cell variability in gene expression in human T cells and pancreatic cells, as well as differences in cellular mutation rates with age.47,48”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [48]
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Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
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 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982 · see citing works
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063 · see citing works
Signatures of mutational processes in human cancer
Initiative, A. P. C. G., Alexandrov, L. B., ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, PedBrain, I., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., et al. (2013) · Nature · doi:10.1038/nature12477 · Cited by 9,660 · see citing works
Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy‐Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al. (2013) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1110s43 · Cited by 6,687 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works
Landscape of somatic mutations in 560 breast cancer whole-genome sequences
Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Alexandrov, L. B., Martin, S., Wedge, D. C., et al. (2016) · Nature · doi:10.1038/nature17676 · Cited by 2,218 · see citing works
A flexible R package for nonnegative matrix factorization
Gaujoux, R., & Seoighe, C. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-367 · Cited by 1,554 · see citing works
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550 · see citing works
A survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401 · see citing works
Self-citationSingle-cell genome sequencing: current state of the science
Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · see citing works
Self-citationA Single-Cell Transcriptome Atlas of the Human Pancreas
Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,288 · see citing works
HTSeq – A Python framework to work with high-throughput sequencing dataPreprint
Anders, S., Pyl, P. T., & Huber, W. K. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/002824 · Cited by 1,286 · see citing works
Deciphering Signatures of Mutational Processes Operative in Human Cancer
Alexandrov, L. B., Nik‐Zainal, S., Wedge, D. C., Campbell, P. J., & Stratton, M. R. (2013) · Cell Reports · doi:10.1016/j.celrep.2012.12.008 · Cited by 1,277 · see citing works
Tiedge, M., Lortz, S., Drinkgern, J., & Lenzen, S. (1997) · Diabetes · doi:10.2337/diab.46.11.1733 · Cited by 1,211 · see citing works
The heat shock protein 70 family: Highly homologous proteins with overlapping and distinct functions
Daugaard, M., Rohde, M., & Jäättelä, M. (2007) · FEBS Letters · doi:10.1016/j.febslet.2007.05.039 · Cited by 1,161 · see citing works
Clock-like mutational processes in human somatic cells
Alexandrov, L. B., Jones, P. H., Wedge, D. C., Sale, J. E., Campbell, P. J., Nik‐Zainal, S., & Stratton, M. R. (2015) · Nature Genetics · doi:10.1038/ng.3441 · Cited by 1,038 · see citing works
Tissue-specific mutation accumulation in human adult stem cells during life
Blokzijl, F., de Ligt, J., Jager, M., Sasselli, V., Roerink, S., Sasaki, N., Huch, M., Boymans, S., Kuijk, E., Prins, P., et al. (2016) · Nature · doi:10.1038/nature19768 · Cited by 935 · see citing works
In situ sequencing for RNA analysis in preserved tissue and cells
Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013) · Nature Methods · doi:10.1038/nmeth.2563 · Cited by 934 · see citing works
Self-citationHiguchi, M., Single, F. N., Köhler, M. G., Sommer, B., Sprengel, R., & SEEBURG, P. H. (1993) · Cell · doi:10.1016/0092-8674(93)90622-w · Cited by 662 · see citing works
Increased cell-to-cell variation in gene expression in ageing mouse heart
Bahar, R., Hartmann, C. H., Rodriguez, K. A., Denny, A. D., Busuttil, R. A., Dollé, M. E. T., Calder, R. B., Chisholm, G. B., Pollock, B. H., Klein, C. A., et al. (2006) · Nature · doi:10.1038/nature04844 · Cited by 603 · see citing works
Chang, A. M., & Halter, J. B. (2003) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00366.2002 · Cited by 585 · see citing works
Somatic mutation in single human neurons tracks developmental and transcriptional history
Lodato, M. A., Woodworth, M. B., Lee, S., Evrony, G. D., Mehta, B. K., Karger, A., Lee, S., Chittenden, T. W., D’Gama, A. M., Cai, X., et al. (2015) · Science · doi:10.1126/science.aab1785 · Cited by 570 · see citing works
Single-Cell Transcriptomics of the Human Endocrine Pancreas
Wang, Y. J., Schug, J., Won, K., Liu, C., Naji, A., Avrahami, D., Golson, M. L., & Kaestner, K. H. (2016) · Diabetes · doi:10.2337/db16-0405 · Cited by 434 · see citing works
Blodgett, 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
Self-citationOxidized, deaminated cytosines are a source of C → T transitions in vivo
Kreutzer, D. A., & Essigmann, J. M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.7.3578 · Cited by 311 · see citing works
Moriya, M., Ou, C., Bodepudi, V., Johnson, F., Takeshita, M., & Grollman, A. P. (1991) · Mutation Research/DNA Repair · doi:10.1016/0921-8777(91)90067-y · Cited by 310 · see citing works
PDGF signalling controls age-dependent proliferation in pancreatic β-cells
Chen, H., Gu, X., Liu, Y., Wang, J., Wirt, S. E., Bottino, R., Schorle, H., Sage, J., & Kim, S. K. (2011) · Nature · doi:10.1038/nature10502 · Cited by 278 · see citing works
Self-citationNik-Zainal, S., Wedge, D. C., Alexandrov, L. B., Petljak, M., Butler, A. P., Bolli, N., Davies, H. R., Knappskog, S., Martin, S., Papaemmanuil, E., et al. (2014) · Nature Genetics · doi:10.1038/ng.2955 · Cited by 277 · 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
DNA instability in postmitotic neurons
Gonitel, R., Moffitt, H., Sathasivam, K., Woodman, B., Detloff, P., Faull, R., & Bates, G. (2008) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0800048105 · Cited by 225 · see citing works
Age-Dependent Pancreatic Gene Regulation Reveals Mechanisms Governing Human β Cell Function
Arda, H. E., Li, L., Tsai, J., Torre, E. A., Rosli, Y., Peiris, H., Spitale, R. C., Dai, C., Gu, X., Qu, K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.04.002 · Cited by 222 · see citing works
Self-citationThe Free Radical Theory of Aging: Effect of Age on Serum Copper Levels
Harman, D. (1965) · Journal of Gerontology · doi:10.1093/geronj/20.2.151 · Cited by 185 · see citing works
Possible cause of G·C→C·G transversion mutation by guanine oxidation product, imidazolone
Kino, K., & Sugiyama, H. (2001) · Chemistry & Biology · doi:10.1016/s1074-5521(01)00019-9 · Cited by 181 · see citing works
Redox control of AP-1-like factors in yeast and beyond
Toone, W. M., Morgan, B. A., & Jones, N. (2001) · Oncogene · doi:10.1038/sj.onc.1204384 · Cited by 174 · 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
Age-Related Impairment of Pancreatic Beta-Cell Function: Pathophysiological and Cellular Mechanisms
Tata, V. D. (2014) · Frontiers in Endocrinology · doi:10.3389/fendo.2014.00138 · Cited by 144 · see citing works
Preferential Repair of Oxidized Base Damage in the Transcribed Genes of Mammalian Cells
Banerjee, D., Mandal, S. M., Das, A., Hegde, M. L., Das, S., Bhakat, K. K., Boldogh, I. R., Sarkar, P. S., MITRA, S. P., & Hazra, T. K. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.198796 · Cited by 143 · see citing works
Serum 8-Hydroxy-Guanine Levels Are Increased in Diabetic Patients
Shin, C. S., Moon, B. S., Park, K. S., Kim, S. Y., Park, S. J., Chung, M. H., & Lee, H. K. (2001) · Diabetes Care · doi:10.2337/diacare.24.4.733 · Cited by 133 · see citing works
Self-citationNeutral Substitutions Occur at a Faster Rate in Exons Than in Noncoding DNA in Primate GenomesPreprint
Subramanian, S., & Kumar, S. (2003) · Genome Research · doi:10.1101/gr.1152803 · Cited by 121 · see citing works
Genomic evidence for elevated mutation rates in highly expressed genes
Park, C., Qian, W., & Zhang, J. (2012) · EMBO Reports · doi:10.1038/embor.2012.165 · Cited by 120 · see citing works
Paneni, F., Osto, E., Costantino, S., Mateescu, B., Schumacher, S. B., Coppolino, G., Perna, E., Mocharla, P. S., Akhmedov, A. T., Kubant, R., et al. (2013) · Circulation · doi:10.1161/circulationaha.112.000826 · Cited by 111 · see citing works
UVR-induced G–C to C–G transversions from oxidative DNA damage
Kino, K., & Sugiyama, H. (2005) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/j.mrfmmm.2004.10.010 · Cited by 78 · see citing works
Single pancreatic beta cells co-express multiple islet hormone genes in mice
Katsuta, H., Akashi, T., Katsuta, R., Nagaya, M., Kim, D., Arinobu, Y., Hara, M., Bonner‐Weir, S., Sharma, A. J., Akashi, K., et al. (2009) · Diabetologia · doi:10.1007/s00125-009-1570-x · Cited by 66 · see citing works
Transcriptional instability is not a universal attribute of aging
Warren, L. A., Rossi, D. J., Schiebinger, G. R., Weissman, I. L., Kim, S. K., & Quake, S. R. (2007) · Aging Cell · doi:10.1111/j.1474-9726.2007.00337.x · Cited by 56 · see citing works
Self-citationImpact of genome instability on transcription regulation of aging and senescence
Vijg, J. (2004) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2004.07.004 · Cited by 38 · see citing works
Oligonucleotide gap-fill ligation for mutation detection and sequencing in situ
Mignardi, M., Mezger, A., Qian, X., Fleur, L. L., Botling, J., Larsson, C., & Nilsson, M. F. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv772 · Cited by 32 · see citing works
Self-citationYU, Y., Cai, Z., CUI, M., Nie, P., Sun, Z., Sun, S., Chu, S., Wang, X., Hu, L., Yi, J., et al. (2015) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2015.2375 · Cited by 26 · see citing works
Kamiya, H., Suzuki, A., Yamaguchi, Y., Handa, H., & Harashima, H. (2009) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2009.04.005 · Cited by 17 · see citing works
Mutational landscape and underlying mutational processes in chronic lymphocytic leukemia
Kasar, S. N., & Brown, J. R. (2016) · Molecular & Cellular Oncology · doi:10.1080/23723556.2016.1157667 · Cited by 12 · see citing works
High levels of transcription stimulate transversions at GC base pairs in yeast
Alexander, M. P., Begins, K. J., Crall, W. C., Holmes, M. P., & Lippert, M. J. (2012) · Environmental and Molecular Mutagenesis · doi:10.1002/em.21740 · Cited by 10 · see citing works
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Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · 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
A molecular cell atlas of the human lung from single-cell RNA sequencing
Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2922-4 · Cited by 1,657 · see citing works
Self-citationSingle 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
Diversity and biology of cancer-associated fibroblasts
Biffi, G., & Tuveson, D. A. (2021) · Physiological Reviews · doi:10.1152/physrev.00048.2019 · Cited by 1,265 · see citing works
Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00921-z · Cited by 1,011 · see citing works
A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · see citing works
Single-cell transcriptomic profiling of the aging mouse brain
Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0491-3 · Cited by 736 · see citing works
Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing
Maynard, A., McCoach, C. E., Rotow, J. K., Harris, L., Haderk, F., Kerr, D. L., Yu, E. A., Schenk, E. L., Tan, W., Zee, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.017 · Cited by 697 · see citing works
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637 · see citing works
Single-Cell Transcriptomic Atlas of Primate Ovarian Aging
Wang, S., Zheng, Y., Li, J., Yu, Y., Zhang, W., Song, M., Liu, Z., Min, Z., Hu, H., Jing, Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.009 · Cited by 592 · see citing works
Charting cellular identity during human in vitro β-cell differentiation
Veres, A., Faust, A. L., Bushnell, H. L., Engquist, E. N., Kenty, J. H.-R., Harb, G., Poh, Y.-C., Sintov, E., Gürtler, M., Pagliuca, F. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1168-5 · Cited by 552 · 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
The ageing epigenome and its rejuvenation
Zhang, W., Qu, J. T., Liu, G., & Belmonte, J. C. I. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0204-5 · Cited by 472 · see citing works
Wang, L., Peng, Y., Zhou, B., Song, J., Li, Z., Zhang, M., Guo, G., Wang, Y., Chen, X., Han, L., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-019-0446-7 · Cited by 411 · see citing works
Bao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 389 · see citing works
Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment
Ren, X., Zhang, L., Zhang, Y., Li, Z., Siemers, N. O., & Zhang, Z. (2021) · Annual Review of Immunology · doi:10.1146/annurev-immunol-110519-071134 · Cited by 369 · see citing works
Targeted Elimination of Senescent Beta Cells Prevents Type 1 Diabetes
Thompson, P. J., Shah, A., Ntranos, V., Van Gool, F., Atkinson, M., & Bhushan, A. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.01.021 · Cited by 353 · see citing works
Caloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging
Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., Ji, Q., Zou, Z., Wang, S., He, X., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.008 · Cited by 349 · see citing works
Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians
Hashimoto, K., Kouno, T., Ikawa, T., Hayatsu, N., Miyajima, Y., Yabukami, H., Terooatea, T., Sasaki, T., Suzuki, T., Valentine, M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907883116 · Cited by 343 · see citing works
Single-cell chromatin modification profiling reveals increased epigenetic variations with aging
Cheung, P., Vallania, F., Warsinske, H. C., Donato, M., Schaffert, S., Chang, S. E., Dvorak, M., Dekker, C. L., Davis, M. M., Utz, P. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.079 · Cited by 323 · see citing works
“Elevated single-cell transcriptional variability with age has been reported in several studies (Bahar et al., 2006; Enge et al., 2017; Martinez-Jimenez et al., 2017); however, the molecular mechanisms underlying the transcriptional variability remain unclear.”
Genetic mapping of cell type specificity for complex traits
Watanabe, K., Mirkov, M. U., de Leeuw, C., van den Heuvel, M. P., & Posthuma, D. (2019) · Nature Communications · doi:10.1038/s41467-019-11181-1 · Cited by 323 · see citing works
Revisiting the Complexity of GLP-1 Action from Sites of Synthesis to Receptor Activation
McLean, B. A., Wong, C. K., Campbell, J. E., Hodson, D. J., Trapp, S., & Drucker, D. J. (2021) · Endocrine Reviews · doi:10.1210/endrev/bnaa032 · Cited by 289 · see citing works
Danaher, P. J., Kim, Y., Nelson, B., Griswold, M., Yang, Z. G., Piazza, E., & Beechem, J. (2022) · Nature Communications · doi:10.1038/s41467-022-28020-5 · Cited by 293 · see citing works
Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes
Camuñas-Soler, J., Dai, X.-Q., Hang, Y., Bautista, A., Lyon, J. G., Suzuki, K., Kim, S. K., Quake, S. R., & MacDonald, P. E. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.04.005 · Cited by 282 · see citing works
Self-citationChallenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247 · see citing works
scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
Shao, X., Liao, J., Lu, X., Xue, R., Ai, N., & Fan, X. (2020) · iScience · doi:10.1016/j.isci.2020.100882 · Cited by 289 · see citing works
Cellular and epigenetic drivers of stem cell ageing
Ermolaeva, M., Neri, F., Ori, A., & Rudolph, K. L. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0020-3 · Cited by 272 · see citing works
Diabetes, obesity, metabolism, and SARS-CoV-2 infection: the end of the beginning
Drucker, D. J. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.01.016 · Cited by 272 · see citing works
Mertens, J., Reid, D. A., Lau, S., Kim, Y., & Gage, F. H. (2018) · Annual Review of Genetics · doi:10.1146/annurev-genet-120417-031534 · Cited by 252 · see citing works
Hernando-Herraez, I., Evano, B., Stubbs, T., Commere, P.-H., Jan Bonder, M., Clark, S., Andrews, S., Tajbakhsh, S., & Reik, W. (2019) · Nature Communications · doi:10.1038/s41467-019-12293-4 · Cited by 236 · see citing works
Pathogenic Mechanisms of Somatic Mutation and Genome Mosaicism in Aging
Vijg, J., & Dong, X. (2020) · Cell · doi:10.1016/j.cell.2020.06.024 · Cited by 230 · see citing works
Zhang, L., Dong, X., Lee, M., Maslov, A. Y., Wang, T., & Vijg, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902510116 · Cited by 222 · see citing works
Human skeletal muscle aging atlas
Kedlian, V. R., Wang, Y., Liu, T., Chen, X., Bolt, L., Tudor, C., Shen, Z., Fasouli, E. S., Prigmore, E., Kleshchevnikov, V., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00613-3 · Cited by 218 · see citing works
Single-nucleus and in situ RNA-sequencing reveal cell topographies in the human pancreas
Tosti, L., Hang, Y., Debnath, O., Tiesmeyer, S., Trefzer, T., Steiger, K., Ten, F. W., Lukassen, S., Ballke, S., Kühl, A. A., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.11.010 · Cited by 228 · see citing works
Self-citationPeripheral immune system in aging and Alzheimer’s disease
Cao, W., & Zheng, H. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0284-2 · Cited by 214 · 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
A molecular cell atlas of the human lung from single cell RNA sequencingPreprint
Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/742320 · Cited by 216 · see citing works
Self-citationMurine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
Kimmel, J. C., Penland, L., Rubinstein, N. D., Hendrickson, D. G., Kelley, D. R., & Rosenthal, A. Z. (2019) · Genome Research · doi:10.1101/gr.253880.119 · Cited by 184 · see citing works
“The cell-type proportions that we recovered differ from the Tabula Muris (e.g., we recovered comparatively more immune cells in the kidney and lung), but are not outside expected ranges based on previous comparisons between scRNA-seq data sets (Park et al. 2018; The Tabula Muris Consortium 2018).”
BackgroundMethodResultInfluentialSingle-cell analyses of aging, inflammation and senescence
Uyar, B., Palmer, D. H., Kowald, A., Escobar, H. M., Barrantes, I., Möller, S., Akalin, A., & Fuellen, G. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101156 · Cited by 202 · 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
Co-transcriptional splicing regulates 3′ end cleavage during mammalian erythropoiesis
Reimer, K. A., Mimoso, C. A., Adelman, K., & Neugebauer, K. M. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.12.018 · Cited by 180 · see citing works
SARS-CoV-2 infection induces beta cell transdifferentiation
Tang, X., Uhl, S. A., Zhang, T., Xue, D., Li, B., Vandana, J. J., Acklin, J. A., Bonnycastle, L. L., Narisu, N., Erdos, M. R., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.05.015 · Cited by 192 · see citing works
Buckley, M. T., Sun, E., George, B. M., Liu, L. A., Schaum, N., Xu, L., Reyes, J. M., Goodell, M. A., Weissman, I. L., Wyss‐Coray, T., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00335-4 · Cited by 189 · see citing works
A comprehensive human embryo reference tool using single-cell RNA-sequencing data
Zhao, C., Reyes, Á. P., Schell, J. P., Weltner, J., Ortega, N. M., Zheng, Y., Björklund, Å. K., Baqué-Vidal, L., Sokka, J., Trokovic, R., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02493-2 · Cited by 177 · see citing works
Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche
Qadir, M. M. F., Álvarez-Cubela, S., Klein, D., van Dijk, J., Muñiz-Anquela, R., Moreno-Hernández, Y., Lanzoni, G., Sadiq, S. B., Navarro-Rubio, B., García, M. T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1918314117 · Cited by 176 · see citing works
The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives
Wang, S., Sun, S.-T., Zhang, X., Ding, H.-R., Yu, Y., He, J., Wang, M.-S., Yang, B., & Li, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032943 · Cited by 175 · see citing works
Senescent cell turnover slows with age providing an explanation for the Gompertz law
Karin, O., Agrawal, A., Porat, Z., Krizhanovsky, V., & Alon, U. (2019) · Nature Communications · doi:10.1038/s41467-019-13192-4 · Cited by 151 · see citing works
Somatic Mutations in “Benign” Disease
Mustjoki, S. M., & Young, N. S. (2021) · New England Journal of Medicine · doi:10.1056/nejmra2101920 · Cited by 160 · see citing works
BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy
Meyer, D. H., & Schumacher, B. (2021) · Aging Cell · doi:10.1111/acel.13320 · Cited by 165 · see citing works
Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D
Viñuela, A., Varshney, A., van de Bunt, M., Prasad, R. B., Asplund, O., Bennett, A., Boehnke, M., Brown, A. A., Erdos, M. R., Fadista, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18581-8 · Cited by 152 · see citing works
A single-cell transcriptomic landscape of primate arterial aging
Zhang, W., Zhang, S., Yan, P., Ren, J., Song, M., Li, J., Lei, J., Pan, H., Wang, S., Ma, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15997-0 · Cited by 163 · see citing works
Human pluripotent stem-cell-derived islets ameliorate diabetes in non-human primates
Du, Y., Liang, Z., Wang, S., Sun, D., Wang, X., Liew, S. Y., Lu, S., Wu, S., Jiang, Y., Wang, Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01645-7 · Cited by 155 · see citing works
Luo, O. J., Lei, W., Zhu, G., Ren, Z., Xu, Y., Xiao, C., Zhang, H., Cai, J., Luo, Z., Gao, L., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00198-9 · Cited by 151 · see citing works
Epigenetics, DNA damage, and aging
Soto-Palma, C., Niedernhofer, L., Faulk, C. D., & Dong, X. (2022) · Journal of Clinical Investigation · doi:10.1172/jci158446 · Cited by 112 · see citing works
Molecular hallmarks of heterochronic parabiosis at single-cell resolution
Pálovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Kern, F., Bonanno, L., Calcuttawala, K., Webber, J., et al. (2022) · Nature · doi:10.1038/s41586-022-04461-2 · Cited by 153 · see citing works
Self-citationDefining the age-dependent and tissue-specific circadian transcriptome in male mice
Wolff, C. A., Gutierrez‐Monreal, M. A., Meng, L., Zhang, X., Douma, L. G., Costello, H. M., Douglas, C. M., Ebrahimi, E., Pham, A. T., Oliveira, A. C. D. S., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2022.111982 · Cited by 153 · see citing works
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
Cao, Z., Lin, W., Shen, L., Yang, D.-C., & Gao, G. (2020) · Nature Communications · doi:10.1038/s41467-020-17281-7 · Cited by 148 · see citing works
Carpenter, E. S., Elhossiny, A. M., Kadiyala, P., Li, J., McGue, J. J., Griffith, B. D., Zhang, Y., Edwards, J., Nelson, S., Lima, F., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0013 · Cited by 152 · see citing works
Evaluating measures of association for single-cell transcriptomics
Skinnider, M. A., Squair, J. W., & Foster, L. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0372-4 · Cited by 145 · see citing works
Liu, F., Zhang, Y., Zhang, L., Li, Z., Fang, Q., Gao, R., & Zhang, Z. (2019) · Genome biology · doi:10.1186/s13059-019-1863-4 · Cited by 144 · see citing works
WebCSEA: web-based cell-type-specific enrichment analysis of genes
Dai, Y., Hu, R., Liu, A., Cho, K. S., Manuel, A. M., Li, X., Dong, X., Jia, P., & Zhao, Z. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac392 · Cited by 138 · see citing works
Methylation across the central dogma in health and diseases: new therapeutic strategies
Liu, R., Zhao, E., Yu, H., Yuan, C., Abbas, M. N., & Cui, H. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01528-y · Cited by 147 · see citing works
Combinatorial expression of GPCR isoforms affects signalling and drug responses
Martí-Solano, M., Crilly, S. E., Malinverni, D., Munk, C., Harris, M., Pearce, A., Quon, T., Mackenzie, A. E., Wang, X., Peng, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2888-2 · Cited by 135 · see citing works
Strategies of neutrophil diversification
Palomino‐Segura, M., Sicilia, J., Ballesteros, I., & Hidalgo, A. (2023) · Nature Immunology · doi:10.1038/s41590-023-01452-x · Cited by 136 · see citing works
Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes
Fang, Z., Weng, C., Li, H., Tao, R., Mai, W., Liu, X., Lu, L., Lai, S., Duan, Q., Álvarez‐González, C. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.043 · Cited by 124 · see citing works
Single-cell omics in ageing: a young and growing field
He, X., Memczak, S., Qu, J. T., Belmonte, J. C. I., & Liu, G. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0196-7 · Cited by 128 · see citing works
The somatic mutation landscape of the human body
García-Nieto, P. E., Morrison, A. J., & Fraser, H. B. (2019) · Genome biology · doi:10.1186/s13059-019-1919-5 · Cited by 122 · see citing works
Hallmarks and detection techniques of cellular senescence and cellular ageing in immune cells
Zhou, D., Borsa, M., & Simon, A. K. (2021) · Aging Cell · doi:10.1111/acel.13316 · Cited by 129 · see citing works
The cycling and aging mouse female reproductive tract at single-cell resolution
Winkler, I., Tolkachov, A., Lammers, F., Lacour, P., Daugėlaitė, K., Schneider, N. G., Koch, M.-L., Panten, J., Grünschläger, F., Poth, T., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.021 · Cited by 124 · see citing works
Espinet, E., Gu, Z., Imbusch, C. D., Giese, N. A., Büscher, M., Safavi, M., Weisenburger, S., Klein, C., Vogel, V., Falcone, M., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1202 · Cited by 123 · see citing works
Self-citationComprehensive evaluation of deconvolution methods for human brain gene expression
Sutton, G. J., Poppe, D., Simmons, R. K., Walsh, K., Nawaz, U., Lister, R., Gagnon-Bartsch, J. A., & Voineagu, I. (2022) · Nature Communications · doi:10.1038/s41467-022-28655-4 · Cited by 117 · see citing works
From DNA damage to mutations: All roads lead to aging
Vijg, J. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101316 · Cited by 125 · see citing works
Brazhnik, K., Sun, S., Alani, O., Kinkhabwala, M., Wolkoff, A. W., Maslov, A. Y., Dong, X., & Vijg, J. (2020) · Science Advances · doi:10.1126/sciadv.aax2659 · Cited by 114 · see citing works
Asynchronous, contagious and digital aging
Rando, T. A., & Wyss-Coray, T. (2021) · Nature Aging · doi:10.1038/s43587-020-00015-1 · Cited by 121 · see citing works
Navigating the Depths and Avoiding the Shallows of Pancreatic Islet Cell Transcriptomes
Mawla, A. M., & Huising, M. O. (2019) · Diabetes · doi:10.2337/dbi18-0019 · Cited by 117 · see citing works
Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues
Birnbaum, K. D. (2018) · Annual Review of Genetics · doi:10.1146/annurev-genet-120417-031247 · Cited by 113 · see citing works
Label-free Mass Cytometry for Unveiling Cellular Metabolic Heterogeneity
Yao, H., Zhao, H., Zhao, X., Pan, X., Feng, J., Xu, F., Zhang, S., & Zhang, X. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b01419 · Cited by 117 · see citing works
Single-Cell RNA-Seq of the Pancreatic Islets––a Promise Not yet Fulfilled?
Wang, Y. J., & Kaestner, K. H. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.11.016 · Cited by 111 · see citing works
Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors
Krentz, N. A. J., Lee, M. Y. Y., Xu, E. E., Sproul, S. L. J., Maslova, A., Sasaki, S., & Lynn, F. C. (2018) · Stem Cell Reports · doi:10.1016/j.stemcr.2018.11.008 · Cited by 114 · see citing works
Breunig, M., Merkle, J., Wagner, M., Melzer, M. K., Barth, T. F. E., Engleitner, T., Krumm, J., Wiedenmann, S., Cohrs, C. M., Perkhofer, L., et al. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.03.005 · Cited by 110 · see citing works
Individual cell types in C. elegans age differently and activate distinct cell-protective responses
Roux, A. E., Yuan, H., Podshivalova, K., Hendrickson, D. G., Kerr, R. A., Kenyon, C. J., & Kelley, D. R. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112902 · Cited by 112 · see citing works
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
Inflammation and Alzheimer’s Disease: Mechanisms and Therapeutic Implications by Natural Products
Rather, M. A., Khan, A., Alshahrani, S., Rashid, H., Qadri, M. M., Rashid, S., Alsaffar, R. M., Kamal, M. A., & Rehman, M. U. (2021) · Mediators of Inflammation · doi:10.1155/2021/9982954 · Cited by 112 · see citing works
Clonal hematopoiesis, somatic mosaicism, and age-associated disease
Evans, M. A., & Walsh, K. (2022) · Physiological Reviews · doi:10.1152/physrev.00004.2022 · Cited by 105 · see citing works
AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution
Aliee, H., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.006 · Cited by 103 · see citing works
Principles of the Molecular and Cellular Mechanisms of Aging
da Silva, P. F. L., & Schumacher, B. (2021) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.11.018 · Cited by 99 · see citing works
Xiong, L., Tian, K., Li, Y., Ning, W., Gao, X., & Zhang, Q. C. (2022) · Nature Communications · doi:10.1038/s41467-022-33758-z · Cited by 98 · see citing works
Wiedenmann, S., Breunig, M., Merkle, J., von Toerne, C., Georgiev, T., Moussus, M., Schulte, L.-A., Seufferlein, T. T. W., Sterr, M., Lickert, H., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00757-2 · Cited by 97 · see citing works
Avrahami, D., Wang, Y. J., Schug, J., Feleke, E., Gao, L., Liu, C., Naji, A., Gläser, B., & Kaestner, K. H. (2020) · Molecular Metabolism · doi:10.1016/j.molmet.2020.101057 · Cited by 93 · see citing works
Evolution and antiviral activity of a human protein of retroviral origin
Frank, J. A., Singh, M., Cullen, H. B., Kirou, R. A., Benkaddour-Boumzaouad, M., Cortés, J. L., Pérez, J. G., Coyne, C. B., & Feschotte, C. (2022) · Science · doi:10.1126/science.abq7871 · Cited by 96 · see citing works
The Future of Islet Transplantation Is Now
Bottino, R., Knoll, M. F., Knoll, C. A., Bertera, S., & Trucco, M. M. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00202 · Cited by 93 · see citing works
Self-citationSHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projectionPreprint
Wan, S., Kim, J., & Won, K. (2020) · Genome Research · doi:10.1101/gr.254557.119 · Cited by 91 · see citing works
Self-assembling manifolds in single-cell RNA sequencing data
Tarashansky, A. J., Xue, Y., Li, P., Quake, S. R., & Wang, B. (2019) · eLife · doi:10.7554/elife.48994 · Cited by 80 · see citing works
Self-citationDelineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas
Hrovatin, K., Bastidas-Ponce, A., Bakhti, M., Zappia, L., Büttner, M., Salinno, C., Sterr, M., Böttcher, A., Migliorini, A., Lickert, H., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00876-x · Cited by 82 · see citing works
β cell aging and age-related diabetes
Zhu, M., Liu, X., Liu, W., Lu, Y., Cheng, J., & Chen, Y. (2021) · Aging · doi:10.18632/aging.202593 · Cited by 93 · see citing works
Generation of human islet cell type-specific identity genesets
van Gurp, L., Fodoulian, L., Oropeza, D., Furuyama, K., Bru-Tarí, E., Vu, A. N., Kaddis, J. S., Rodríguez, I., Thorel, F., & Herrera, P. L. (2022) · Nature Communications · doi:10.1038/s41467-022-29588-8 · Cited by 80 · see citing works
Cellular barcoding to decipher clonal dynamics in disease
Sankaran, V. G., Weissman, J. S., & Zon, L. I. (2022) · Science · doi:10.1126/science.abm5874 · Cited by 81 · see citing works
Shrestha, S., Erikson, G. A., Lyon, J., Spigelman, A., Bautista, A., Fox, J. E. M., dos Santos, C., Shokhirev, M., Cartailler, J.-P., Hetzer, M., et al. (2022) · Science Advances · doi:10.1126/sciadv.abo3932 · Cited by 75 · see citing works
Aevermann, B. D., Zhang, Y., Novotny, M. A., Keshk, M., Bakken, T. E., Miller, J. A., Hodge, R. D., Lelieveldt, B. P. F., Lein, E. S., & Scheuermann, R. H. (2021) · Genome Research · doi:10.1101/gr.275569.121 · Cited by 77 · see citing works
- Authors
- Enge, M., Arda, H. E., Mignardi, M., Beausang, J., Bottino, R., Kim, S. K., & Quake, S. R.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Uppsala University (SE)
- Allegheny Health Network (US)
- Corresponding author
- Quake, Stephen R.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 171
- Issue
- 2
- Pages
- 321-330.e14
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6047899
- OpenAlex
- W2758815122
- Cited by
- 568
- References
- 56
- Citations by year
- 2026: 40 · 2025: 51 · 2024: 69 · 2023: 78 · 2022: 77 · 2021: 82 · 2020: 79 · 2019: 46 · 2018: 42 · 2017: 3 · 2015: 1
- FWCI
- 18.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- California Institute for Regenerative Medicine (GC1R-06673)
- Center of Excellence for Stem Cell Genomics and NIH (U01-HL099999, U01-HL099995)
- NIH (UC4DK104211, DK10261201, P30DK116074-01)
- Helmsley Charitable Trust
- H.L. Snyder Foundation
- Elser Foundation
- JDRF (3-APF-2016-172-A-N)
- Wallenberg Research link at Stanford University (2013.0391)
- NIDDK (5T32DK007217-39)
- Swedish Research Council (2015-00599)
- 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 › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- human aging, pancreatic islets, endocrine cells, somatic mutations, mutational signatures, transcriptional noise, single-cell transcriptomics, tissue aging, cellular heterogeneity
- MeSH terms
- Adult, Aging, Child, Child, Preschool, Humans, Infant, Middle Aged, Mutation, Pancreas, Transcription, Genetic, Cellular Senescence, Sequence Analysis, RNA, Polymorphism, Single Nucleotide, Single-Cell Analysis
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