The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Open accessAbstract
Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2'-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recent advances in single-nucleus RNA sequencing (snRNA-Seq) have extended transcriptomic analysis to frozen or archived post-mortem brain tissues.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [165]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This technique has enabled detailed profiling of vulnerable neuronal subtypes and glial populations.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [165]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“By isolating nuclei rather than whole cells, snRNA-Seq retains transcriptional fidelity while avoiding cell dissociation artifacts155,165.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [165]
In the citing paper
“However, these methods either rely on sorting nuclei by FACS into plates (96 or 384 wells)3,5 or on C1 microfluidics4, neither of which are scalable, precluding profiling tens of thousands of nuclei”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“the DroNc-seq sub-clusters matched with a nearly one-to-one mapping to sub-clusters defined by sNuc-Seq3 on the hippocampus, and with a satisfactory match to sets of fine-resolution sub-clusters defined by scRNA-seq of a distinct brain region, the visual cortex17”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“we found good congruence between our GABAergic sub-clusters and those previously defined3,4,17 in mouse and human”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“reflecting the enrichment of nascent transcripts in the nucleus3,13–16”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“the cell types and their signatures from DroNc-seq are comparable to those obtained previously with sNuc-Seq of mouse hippocampus3 and scRNA-seq of the visual cortex17”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“The cell-type specific gene signatures we determined for each human cell-type cluster (Fig. 2d, Supplementary Table 8) agreed well with previously defined signatures in mouse hippocampus3 and cortex17”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“To address this challenge, we3 and others4–6 developed single nucleus RNA-seq (snRNA-seq) for analysis of RNA in single nuclei from fresh, frozen or lightly fixed tissues.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [3]
In the citing paper
“Differentiation trajectories of newborn neurons and oligodendrocytes in adults can also be reconstructed (Habib et al., 2016; Marques et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Habib et al., 2016
In the citing paper
“Continuous trajectories occur during brain development (Nowakowski et al., 2017) and in differentiating cell populations in the adult, such as oligodendrocyte lineage cells (Marques et al., 2016) or newborn granule cells in the dentate gyrus of the hippocampus (Habib et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Habib et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
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
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
(2008) · Journal of Machine Learning Research · Cited by 33,051
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. (2011) · Nature Biotechnology · doi:10.1038/nbt.1754 · Cited by 15,390
Flagged first, then by citations
Research works printed but not linked: 1
- P. N. Yianilos Data structures and algorithms for nearest neighbor search in general metric spaces in SODA '93: Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (Society for Industrial and Applied Mathematics Philadelphia PA 1993) pp. 311–321.
Cited by
Sorted by citations
Items 101–200 of 612
The multilayered transcriptional architecture of glioblastoma ecosystems
Nomura, M., Spitzer, A., Johnson, K. C., Garofano, L., Nehar-Belaid, D., Darnell, N. G., Greenwald, A., Bussema, L., Oh, Y. T., Varn, F. S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02167-5 · Cited by 151
Single-cell nascent RNA sequencing unveils coordinated global transcription
Mahat, D. B., Tippens, N. D., Martin-Rufino, J. D., Waterton, S. K., Fu, J., Blatt, S. E., & Sharp, P. A. (2024) · Nature · doi:10.1038/s41586-024-07517-7 · Cited by 131
Highly selective brain-to-gut communication via genetically defined vagus neurons
Tao, J., Campbell, J. N., Tsai, L. T.-Y., Wu, C., Liberles, S. D., & Lowell, B. B. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.004 · Cited by 132
Worldwide initiatives to advance brain research
Grillner, S. E., Ip, N. Y., Koch, C., Koroshetz, W. J., Okano, H., Polachek, M., Poo, M., & B, T. J. S. A. (2016) · Nature Neuroscience · doi:10.1038/nn.4371 · Cited by 136
Transcriptional trajectories of human kidney injury progression
Cippà, P. E., Sun, B., Liu, J., Chen, L., Naesens, M., & McMahon, A. P. (2018) · JCI Insight · doi:10.1172/jci.insight.123151 · Cited by 125
Cheng, M. L., Nakib, D., Perciani, C. T., & MacParland, S. A. (2021) · Clinical Science · doi:10.1042/cs20190654 · Cited by 130
Bhattarai, P., Coşacak, M. İ., Mashkaryan, V., Demir, S., Popova, S. D., Govindarajan, N., Brandt, K., Zhang, Y., Chang, W., Ampatzis, K., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000585 · Cited by 121
Single cell transcriptome profiling of the human alcohol-dependent brain
Brenner, E., Tiwari, G. R., Kapoor, M., Liu, Y., Brock, A., & Mayfield, R. D. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa038 · Cited by 118
Single-nucleus transcriptomic landscape of primate hippocampal aging
Zhang, H., Li, J., Ren, J., Sun, S., Ma, S., Zhang, W., Yu, Y., Cai, Y., Yan, K., Li, W., et al. (2021) · Protein & Cell · doi:10.1007/s13238-021-00852-9 · Cited by 122
Understanding development and stem cells using single cell-based analyses of gene expression
Kumar, P., Tan, Y., & Cahan, P. (2017) · Development · doi:10.1242/dev.133058 · Cited by 117
A brain cell atlas integrating single-cell transcriptomes across human brain regions
Chen, X., Huang, Y., Huang, L., Huang, Z., Hao, Z.-Z., Xu, L., Xu, N., Li, Z., Mou, Y., Ye, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03150-z · Cited by 118
Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer
Gao, R., Kim, C. R., Sei, E., Foukakis, T., Crosetto, N., Chan, L., Srinivasan, M., Zhang, H., Meric‐Bernstam, F., & Navin, N. E. (2017) · Nature Communications · doi:10.1038/s41467-017-00244-w · Cited by 118
NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport
Moriel, N., Senel, E., Friedman, N., Rajewsky, N., Karaiskos, N., & Nitzan, M. (2021) · Nature Protocols · doi:10.1038/s41596-021-00573-7 · Cited by 119
Toward a Common Coordinate Framework for the Human Body
Rood, J., Stuart, T., Ghazanfar, S., Biancalani, T., Fisher, E., Butler, A., Hupalowska, A., Gaffney, L., Mauck, W. M., Eraslan, G., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.019 · Cited by 117
Self-citationA new era for plant science: spatial single-cell transcriptomics
Giacomello, S. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102041 · Cited by 115
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 114
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112
Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila
McLaughlin, C. N., Brbić, M., Xie, Q., Li, T., Horns, F., Kolluru, S. S., Kebschull, J. M., Vacek, D., Xie, A., Li, J., et al. (2021) · eLife · doi:10.7554/elife.63856 · Cited by 115
The development and evolution of inhibitory neurons in primate cerebrum
Schmitz, M. T., Sandoval, K., Chen, C. P., Mostajo-Radji, M. A., Seeley, W. W., Nowakowski, T. J., Ye, C. J., Paredes, M. F., & Pollen, A. A. (2022) · Nature · doi:10.1038/s41586-022-04510-w · Cited by 111
FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants
Long, Y., Liu, Z., Jia, J., Mo, W., Fang, L., Lu, D., Liu, B., Zhang, H., Chen, W., & Zhai, J. (2021) · Genome biology · doi:10.1186/s13059-021-02288-0 · Cited by 110
Tissue morphology influences the temporal program of human brain organoid development
Chiaradia, I., Imaz-Rosshandler, I., Nilges, B. S., Boulanger, J., Pellegrini, L., Das, R., Kashikar, N. D., & Lancaster, M. A. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.09.003 · Cited by 106
Chromatin organization in the female mouse brain fluctuates across the oestrous cycle
Jarić, I., Rocks, D., Greally, J. M., Suzuki, M., & Kundaković, M. (2019) · Nature Communications · doi:10.1038/s41467-019-10704-0 · Cited by 102
The subiculum is a patchwork of discrete subregions
Cembrowski, M. S., Wang, L., Lemire, A. L., Copeland, M., DiLisio, S. F., Clements, J., & Spruston, N. (2018) · eLife · doi:10.7554/elife.37701 · Cited by 98
Patch-seq: Past, Present, and Future
Lipovsek, M., Bardy, C., Cadwell, C. R., Hadley, K., Kobak, D., & Tripathy, S. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1653-20.2020 · Cited by 104
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97
Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration
Lust, K., Maynard, A., Gomes, T., Fleck, J. S., Camp, J. G., Tanaka, E. M., & Treutlein, B. (2022) · Science · doi:10.1126/science.abp9262 · Cited by 99
Advances in single-cell RNA sequencing and its applications in cancer research
Zhu, S., Qing, T., Zheng, Y., Li, J., & Shi, L. M. (2017) · Oncotarget · doi:10.18632/oncotarget.17893 · Cited by 98
Jaffe, A. E., Hoeppner, D. J., Saito, T., Blanpain, L. T., Ukaigwe, J., Burke, E. E., Collado‐Torres, L., Tao, R., Tajinda, K., Maynard, K. R., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0604-z · Cited by 96
Spitzer, A., Johnson, K. C., Nomura, M., Garofano, L., Nehar-Belaid, D., Darnell, N. G., Greenwald, A., Bussema, L., Oh, Y. T., Varn, F. S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02168-4 · Cited by 107
Alvarez, M., Rahmani, E., Jew, B., Garske, K. M., Miao, Z., Benhammou, J. N., Ye, C. J., Pisegna, J. R., Pietiläinen, K. H., Halperin, E., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-67513-5 · Cited by 93
Farris, S., Ward, J. M., Carstens, K. E., Samadi, M., Wang, Y., & Dudek, S. M. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.093 · Cited by 94
Adult neurogenesis, human after all (again): Classic, optimized, and future approaches
Lucassen, P. J., Fitzsimons, C. P., Salta, E., & Maletić‐Savatić, M. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.112458 · Cited by 102
Park, H., Cho, B., Kim, H., Saito, T., Saido, T. C., Won, K.-J., & Kim, J. (2023) · Nature Communications · doi:10.1038/s41467-023-36519-8 · Cited by 92
Structure-preserving visualisation of high dimensional single-cell datasets
Szubert, B., Cole, J. E., Monaco, C. G., & Drozdov, I. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-45301-0 · Cited by 95
Species and cell-type properties of classically defined human and rodent neurons and glia
Xu, X., Stoyanova, E. I., Patriotis, A. L., Xing, J., Mash, D. C., & Heintz, N. (2018) · eLife · doi:10.7554/elife.37551 · Cited by 90
Single cell approaches to address adipose tissue stromal cell heterogeneity
Rondini, E. A., & Granneman, J. G. (2020) · Biochemical Journal · doi:10.1042/bcj20190467 · Cited by 90
Liu, W., Venugopal, S., Majid, S., Ahn, I. S., Diamante, G., Hong, J. S., Yang, X., & Chandler, S. H. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104877 · Cited by 88
Microfluidics for Neuronal Cell and Circuit Engineering
Habibey, R., Arias, J. E. R., Striebel, J., & Busskamp, V. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00212 · Cited by 88
Hartl, C., Ramaswami, G., Pembroke, W. G., Muller, S., Pintacuda, G., Saha, A., Parsana, P., Battle, A. J., Lage, K., & Geschwind, D. H. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00887-5 · Cited by 86
Neumann, M., Xu, X., Smaczniak, C., Schumacher, J., Yan, W., Blüthgen, N., Greb, T., Jönsson, H., Traas, J. A., Kaufmann, K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30177-y · Cited by 86
Single cell transcriptomics in neuroscience: cell classification and beyond
Tasic, B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.021 · Cited by 83
Single-cell genomics to guide human stem cell and tissue engineering
Camp, J. G., Wollny, D., & Treutlein, B. (2018) · Nature Methods · doi:10.1038/s41592-018-0113-0 · Cited by 83
Placing RNA in context and space – methods for spatially resolved transcriptomics
Strell, C., Hilscher, M. M., Laxman, N., Svedlund, J., Wu, C., Yokota, C., & Nilsson, M. F. (2018) · FEBS Journal · doi:10.1111/febs.14435 · Cited by 83
The Human Cell Atlas White PaperPreprint
Regev, A., Teichmann, S. A., Rozenblatt–Rosen, O., Stubbington, M. J. T., Ardlie, K., Amit, I., Arlotta, P., Bader, G. D., Benoist, C. O., Biton, M., et al. (2018) · arXiv (Cornell University) · doi:10.17863/cam.40032 · Cited by 88
Self-citationSingle-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 90
Continuous Variation within Cell Types of the Nervous System
Cembrowski, M. S., & Menon, V. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.02.010 · Cited by 85
Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data
Climente-González, H., Azencott, C., Samuel Kaski, A., & Yamada, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz333 · Cited by 82
STRT-seq-2i: dual-index 5ʹ single cell and nucleus RNA-seq on an addressable microwell array
Hochgerner, H., Lönnerberg, P., Hodge, R. D., Mikeš, J., Heskol, A., Hubschle, H., Lin, P., Picelli, S., Manno, G. L., Ratz, M., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16546-4 · Cited by 78
The State of the NIH BRAIN Initiative
Koroshetz, W. J., Gordon, J. A., Adams, A. B., Beckel-Mitchener, A. C., Churchill, J. D., Farber, G. K., Freund, M. P., Gnadt, J., Hsu, N. S., Langhals, N. B., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3174-17.2018 · Cited by 80
Erwin, S. R., Sun, W., Copeland, M., Lindo, S. E., Spruston, N., & Cembrowski, M. S. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107551 · Cited by 82
Saunders, R. A., Allen, W. E., Pan, X., Sandhu, J., Lu, J., Lau, T. K., Smolyar, K., Sullivan, Z. A., Dulac, C. G., Weissman, J. S., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.022 · Cited by 82
The Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 86
Self-citationSingle-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy
Richter, M. L., Deligiannis, I. K., Yin, K., Danese, A., Lleshi, E., Coupland, P., Vallejos, C. A., Matchett, K. P., Henderson, N. C., Colome-Tatche, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24543-5 · Cited by 75
Defining Cell Identity with Single‐Cell Omics
Mincarelli, L., Lister, A., Lipscombe, J., & Macaulay, I. C. (2018) · PROTEOMICS · doi:10.1002/pmic.201700312 · Cited by 78
Brain Transcriptome Databases: A User's Guide
Keil, J. M., Qalieh, A., & Kwan, K. Y. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1930-17.2018 · Cited by 79
Single-Cell Genomics and Epigenomics: Technologies and Applications in Plants
Luo, C. Y., Fernie, A. R., & Yan, J. (2020) · Trends in Plant Science · doi:10.1016/j.tplants.2020.04.016 · Cited by 74
Raabe, F. J., Slapakova, L., Rossner, M. J., Cantuti‐Castelvetri, L., Simons, M., Falkai, P., & Schmitt, A. (2019) · Cells · doi:10.3390/cells8121496 · Cited by 80
The promise of single-cell RNA sequencing for kidney disease investigation
Wu, H., & Humphreys, B. D. (2017) · Kidney International · doi:10.1016/j.kint.2017.06.033 · Cited by 73
Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data
Menon, V. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx044 · Cited by 73
Ultrafast immunostaining of organ-scale tissues for scalable proteomic phenotypingPreprint
Yun, D. H., Park, Y.-G., Cho, J. H., Kamentsky, L. D., Evans, N. B., Albanese, A., Xie, K., Swaney, J., Sohn, C. H., Tian, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/660373 · Cited by 75
Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing
Gupta, R. K., & Kuźnicki, J. (2020) · Cells · doi:10.3390/cells9081751 · Cited by 74
Dissecting human gliomas by single-cell RNA sequencing
Tirosh, I., & Suvà, M. L. (2017) · Neuro-Oncology · doi:10.1093/neuonc/nox126 · Cited by 72
Stress relief: emerging methods to mitigate dissociation-induced artefacts
Machado, L., Relaix, F., & Mourikis, P. (2021) · Trends in Cell Biology · doi:10.1016/j.tcb.2021.05.004 · Cited by 70
Mutant IDH inhibitors induce lineage differentiation in IDH-mutant oligodendroglioma
Spitzer, A., Gritsch, S., Nomura, M., Jucht, A., Fortin, J., Raviram, R., Weisman, H. R., Gonzalez Castro, L. N., Druck, N., Chanoch-Myers, R., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.03.008 · Cited by 69
Nault, R., Fader, K. A., Bhattacharya, S., & Zacharewski, T. R. (2020) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2020.07.012 · Cited by 71
Building a high-quality Human Cell Atlas
Human Cell Atlas Standards and Technology Working Group, Rozenblatt–Rosen, O., Shin, J. W., Rood, J., Hupalowska, A., Regev, A., & Heyn, H. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00812-4 · Cited by 67
Self-citationDai, C., Jiang, Y., Yin, C., Su, R., Zeng, X., Zou, Q., Nakai, K., & Wei, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac317 · Cited by 67
Durán, R. C., González-Orozco, J. C., Velasco, I., & Wu, J. Q. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.884748 · Cited by 64
Ferrer, I. (2018) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2018.07.004 · Cited by 65
Portugal, C. C., Almeida, T. O., Socodato, R., & Relvas, J. B. (2021) · FEBS Journal · doi:10.1111/febs.16197 · Cited by 66
Kurki, S., Uvarov, P., Pospelov, A. S., Trontti, K., Huebner, A. K., Srinivasan, R., Watanabe, M., Hovatta, I., Hübner, C. A., Kaila, K., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac470 · Cited by 62
Deciphering Brain Complexity using Single-Cell Sequencing
Mu, Q., Chen, Y., & Wang, J. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.07.007 · Cited by 61
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
Geddes, T. A., Kim, T.-Y., Nan, L., Burchfield, J. G., Yang, J. Y. H., Tao, D., & Yang, P. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3179-5 · Cited by 60
An atlas of late prenatal human neurodevelopment resolved by single-nucleus transcriptomics
Ramos, S. I., Mussa, Z. M., Falk, E. N., Pai, B., Giotti, B., Allette, K., Cai, P., Dekio, F., Sebra, R., Beaumont, K. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34975-2 · Cited by 59
Mapping visual functions onto molecular cell types in the mouse superior colliculus
Liu, Y., Savier, E. L., DePiero, V. J., Chen, C., Schwalbe, D. C., Abraham‐Fan, R., Chen, H., Campbell, J. N., & Cang, J. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.036 · Cited by 62
Quantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 60
Molecularly defined and functionally distinct cholinergic subnetworks
Li, X., Yu, H., Zhang, B., Li, L., Chen, W., Yu, Q., Huang, X., Xiao, K., Wang, Y., Jing, W., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.08.025 · Cited by 58
Agrin-Lrp4-Ror2 signaling regulates adult hippocampal neurogenesis in mice
Zhang, H., Sathyamurthy, A., Liu, F., Li, L., Zhang, L., Dong, Z., Cui, W., Sun, X., Zhao, K., Wang, H., et al. (2019) · eLife · doi:10.7554/elife.45303 · Cited by 58
Abdelmoez, M. N., Iida, K., Oguchi, Y., Nishikii, H., Yokokawa, R., Kotera, H., Uemura, S., Santiago, J. G., & Shintaku, H. (2018) · Genome biology · doi:10.1186/s13059-018-1446-9 · Cited by 58
Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing
Matson, K. J. E., Sathyamurthy, A., Johnson, K. R., Kelly, M. C., Kelley, M. W., & Levine, A. J. (2018) · Journal of Visualized Experiments · doi:10.3791/58413 · Cited by 59
Single-Cell RNA Sequencing to Understand Host–Pathogen Interactions
Peñaranda, C., & Hung, D. T. (2019) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.8b00369 · Cited by 58
Single cell transcriptomics: moving towards multi-omics
Song, Y., Xu, X., Wang, W., Tian, T., Zhu, Z., & Yang, C. J. (2019) · The Analyst · doi:10.1039/c8an01852a · Cited by 56
Wang, J., & Hamill, O. P. (2021) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2004085 · Cited by 57
Andreadou, M., Ingelfinger, F., Feo, D. D., Cramer, T. L. M., Tuzlak, S., Friebel, E., Schreiner, B., Eede, P., Schneeberger, S., Geesdorf, M., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01435-z · Cited by 56
The brain parenchyma has a type I interferon response that can limit virus spread
Drokhlyansky, E., Aytürk, D. G., Soh, T. K., Chrenek, R., O'Loughlin, E. K., Madore, C., Butovsky, O., & Cepko, C. L. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618157114 · Cited by 55
Transcriptional Profiling of Somatostatin Interneurons in the Spinal Dorsal Horn
Chamessian, A. G., Young, M. N., Qadri, Y. J., Berta, T., Ji, R., & de Ven, T. V. (2018) · Scientific Reports · doi:10.1038/s41598-018-25110-7 · Cited by 54
Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq
Mylka, V., Matetovici, I., Poovathingal, S., Aerts, J., Vandamme, N., Seurinck, R., Verstaen, K., Hulselmans, G., Van den Hoecke, S., Scheyltjens, I., et al. (2022) · Genome Biology · doi:10.1186/s13059-022-02628-8 · Cited by 53
Molecular connectomics reveals a glucagon-like peptide 1-sensitive neural circuit for satiety
Webster, A. N., Becker, J. J., Li, C., Schwalbe, D. C., Kerspern, D., Karolczak, E. O., Bundon, C. B., Onoharigho, R., Crook, M. E., Jalil, M., et al. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01168-8 · Cited by 53
Self-citationHuman hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease
Disouky, A., Sanborn, M. A., Sabitha, K. R., MOSTAFA, M., Ayala, I. A., Bennett, D. A. A., Lu, Y., Zhou, Y. F., Keene, C. D., Weıntraub, S., et al. (2026) · Nature · doi:10.1038/s41586-026-10169-4 · Cited by 59
Lipiński, M., Muñoz‐Viana, R., del Blanco, B., Márquez-Galera, Á., Medrano-Relinque, J., Caramés, J. M., Szczepankiewicz, A. A., Fernández‐Albert, J., Navarrón, C. M., Olivares, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16246-0 · Cited by 55
Single‐cell RNA sequencing in Drosophila: Technologies and applications
Li, H. (2020) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.396 · Cited by 55
Cid, E., Márquez-Galera, Á., Valero, M., Gal, B., Medeiros, D. D. C., Navarrón, C. M., Ballesteros-Esteban, L., Reig‐Viader, R., Morales, A. V., Fernández‐Lamo, I., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109229 · Cited by 51
Cerebral cortical neuron diversity and development at single-cell resolution
Johnson, M. B., & Walsh, C. A. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.11.001 · Cited by 53
Plasticity and heterogeneity of thermogenic adipose tissue
Sun, W., Modica, S., Dong, H., & Wolfrum, C. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00417-4 · Cited by 52
Generating and Using Transcriptomically Based Retinal Cell Atlases
Shekhar, K., & Sanes, J. R. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-032621-075200 · Cited by 51
Tracking cell-type-specific temporal dynamics in human and mouse brains
Lu, Z., Zhang, M., Lee, J., Sziraki, A., Anderson, S., Zhang, Z., Xu, Z., Jiang, W., Ge, S., Nelson, P. T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.042 · Cited by 51
Deciphering Human Tumor Biology by Single-Cell Expression Profiling
Tirosh, I., & Suvà, M. L. (2018) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-030518-055609 · Cited by 51
Achieving Automated Organelle Biopsy on Small Single Cells Using a Cell Surgery Robotic System
Shakoor, A., Xie, M., Luo, T., Hou, J., Shen, Y., Mills, J. K., & Sun, D. B. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2885772 · Cited by 50
Lobentanzer, S., Hanin, G., Klein, J., & Soreq, H. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.017 · Cited by 50
Molecular characterization of circulating tumor cells—from bench to bedside
Li, Y., Wu, S., & Bai, F. (2017) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2017.09.013 · Cited by 49
Self-citation
Metadata
- Authors
- Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A.
- Affiliations
- Broad Institute (US)
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
- Corresponding author
- Habib, Naomi
- Li, Yinqing
- Zhang, Feng
- Regev, Aviv
- Venue
- Science
- Published
- 2016-08-26
- Volume
- 353
- Issue
- 6302
- Pages
- 925-928
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5480621
- OpenAlex
- W2486882743
- Cited by
- 601
- References
- 63
- Citations by year
- 2026: 30 · 2025: 51 · 2024: 71 · 2023: 59 · 2022: 63 · 2021: 75 · 2020: 67 · 2019: 58 · 2018: 73 · 2017: 46 · 2016: 7
- FWCI
- 31.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- Howard Hughes Medical Institute
- Helen Hay Whitney Foundation
- Human Frontier Science Program
- Klarman Cell Observatory at the Broad Institute
- National Institute of Mental Health (U01MH105960)
- NIMH (5DP1-MH100706, 1R01-MH110049)
- NSF
- New York Stem Cell
- Simons
- Paul G. Allen Family
- Vallee Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-nucleus RNA sequencing, adult neurogenesis, newborn neurons, cell proliferation, transcriptional dynamics, rare cell types
- MeSH terms
- Animals, Cell Division, Cell Nucleus, Deoxyuridine, Hippocampus, Isotope Labeling, Neurons, Spinal Cord, Transcription, Genetic, Sequence Analysis, RNA, Mice, Neurogenesis, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
