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 601–612 of 612
Kif15 orchestrates neuronal–microglial communication via CX3CL1 to impede nerve regeneration
Wu, R., Zhang, W., Qian, X., Dong, Z., He, X., Zhang, S., Chen, T., Yang, L., Liu, M., & Liu, M. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113090
Miyata, S., Shimizu, S., & Ishino, Y. (2026) · Neurology International · doi:10.3390/neurolint18060108
Rong, W., Xu, J., Li, B., Zhang, X., Li, Y., Song, B., & Xu, Y. (2026) · The Journal of Headache and Pain · doi:10.1186/s10194-026-02447-3
Self-citationAdult neural stem cell pools in homeostasis and pathogenesis
Li, S., Shi, X., Sun, X., & Wu, Q. (2026) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2026.103685
Zhang, C., Li, S., Jiang, R., Duan, H., Li, C., Qi, E., Wu, M., Li, X., Feng, S., & Zhou, H. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014573
Blade-dependent molecular identity and neurogenic potential in the adult dentate gyrusPreprint
Bhat, U. A., Valbuena, S., Márquez-Galera, Á., Tirado-Melendro, P., Velotto, M., Navarrón, C. M., Martins, F., Doat, H., Maître, M., Rouglan, V., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.24.740228
Stephens, G. S., Alcantara‐Gonzalez, D., & Scharfman, H. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.28.741316
Lu, Y., Ren, H., Lv, Z., Zhang, T., Zhang, J., & Ren, L. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.143284
Qiao, Q., Katsumata, Y., Lai, H.-Y., Lin, K. Z., Claas, S. A., Gabitto, M. I., Morganti, J. M., Nelson, P. T., Mukherjee, S., & Fardo, D. W. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71810
scMustree: a multi-scale functional hierarchy for single-cell transcriptomic analysis
Xu, Y., Wang, S., Ding, L., Li, H., & Wang, J. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag672
DR-GEM enables self-supervised machine learning for single-cell embeddings and annotations
Yeh, C. Y., Sun, M., Jerby‐Arnon, L., & Jerby‐Arnon, L. (2026) · Nature Communications · doi:10.1038/s41467-026-77908-z
A Molecularly Defined Midbrain Circuit Mechanism for Sound‐Driven Defensive Behavior and Anxiety
Cao, J., Lai, X., Huo, L., Zheng, Z., Zhang, Q., Ye, Z., Wan, P., Ma, C., & Shang, C. (2026) · Advanced Science · doi:10.1002/advs.77983
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