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Single-cell transcriptional diversity is a hallmark of developmental potential
Open accessPublished version of a preprint
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.
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“Pluripotency genes, by contrast, showed an arc-like pattern early in human embryogenesis, characterized by progressively increasing expression until the emergence of embryonic stem cells, followed by decreasing expression (fig. S2B, right).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
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In the paper
“Appearing in the top 1% of the ranked list (104 out of 18,711), this data-driven feature compared favorably to well-established stem cell signatures”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
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“For example, regardless of whether we examined cycling cells, non-cycling cells, or published data from the earliest stages of human embryogenesis prior to the upregulation of pluripotency factors (21), gene counts generally decreased with successive stages of differentiation (fig. S2, A and B, left).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
What the sources say
- Not checked yetcites [21] source
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In the paper
“We observed strong concordance between the number of accessible peaks and the mean number of detectably expressed genes per phenotype (fig. S3A).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, we demonstrate a simple, yet robust, determinant of developmental potential—the number of expressed genes per cell—and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data.”
- Also stated in: § Discussion · boosted
“In summary, we have shown that the number of expressed genes per cell is a hallmark of developmental potential.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The experimentally determined differentiation states in the training and validation datasets accurately reflect developmental potential.
- Rests on an assumption: The known phenotype labels used to average features and assess correlation are correct.
- Also stated in: § Discussion · boosted
None of the sources cited for this claim have been checked yet.
In the paper
“Indeed, genome-wide chromatin accessibility was observed to progressively decrease with differentiation of hESCs into paraxial mesoderm and lateral mesoderm lineages (Fig. 1E; fig. S3, A and B).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
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In the paper
“Notably, this relationship was also observed in other organisms, including Caenorhabditis elegans (Fig. 1D) and zebrafish (table S4), suggesting that it is a general feature of cellular ontogeny.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, when averaged according to known phenotypes and assessed across independent studies, the association between gene counts and differentiation was maintained (R2 = 0.89, P = 1.8 × 10−8) (Fig. 1C and materials and methods).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The known phenotype labels used to average features and assess correlation are correct.
In the paper
“Moreover, genes that were up-regulated in highly clonogenic normal LPs (39) were skewed toward genes predicted to mark less-differentiated cells (Fig. 4B).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Application to neoplastic disease
What the sources say
- Not checked yetcites [39] source
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In the paper
“Similar patterns were observed for lung adenocarcinoma cells jointly profiled by ATAC-seq and RNA-seq (sci-CAR) and for human hematopoiesis, supporting the generality of this result (fig. S3, C to E).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
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- It may state two separate claims as one. 13
- Probably not a substantial claim. 8
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In the paper
“For example, when applied to 4,442 bone marrow cells, Monocle 2 identified 23 possible “roots” from which to calculate pseudotime values (Fig. 3B, left). By contrast, CytoTRACE readily identified the correct root without user input (Fig. 3B, right, and fig. S16, A and B).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Stem-cell-related genes and hierarchies
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of expressed genes per cell generally showed consistent performance with respect to key technical parameters and was generally correlated with mRNA content (figs. S4 to S7 and supplementary text).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Development of CytoTRACE
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Although both cycling and quiescent hematopoietic stem cell (HSC) subpopulations (34) were correctly predicted to be less differentiated, only proliferative HSCs were significantly ranked above early progenitors (Fig. 3C).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Stem-cell-related genes and hierarchies
What the sources say
- Not checked yetcites [34] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“After removing low quality cells and applying principal component analysis to visualize the data, we confirmed three well-separated clusters of basal, LP, and ML cells, each with characteristic expression patterns of previously described lineage markers (Fig. 4A and fig. S17B).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Application to neoplastic disease
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“No obvious clustering was observed for tumor versus normal cell differences or by patient (Fig. 4A; fig. S18A).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Application to neoplastic disease
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Regardless of whether we integrated datasets profiled on different scRNA-seq platforms (Fig. 3A) or datasets containing developmentally distinct cell types (fig. S14), single-cell orderings predicted by CytoTRACE were accurate.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Performance evaluation across tissues, species, and platforms
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“In addition, GULP1 knockdown in PDXs (n=2) either inhibited tumor growth (TNBC sample) or fully abrogated tumor growth (ER+ sample) (Fig. 4, E and F).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Application to neoplastic disease
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“While gene expression-based models can potentially overcome these limitations (e.g., transcriptional entropy (11–13), pluripotency-associated gene sets (14), and machine learning strategies (15)), their utility across diverse developmental systems and single-cell sequencing technologies is still unclear.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
What the sources say
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In the paper
“However, inferring both the state and direction of differentiation is challenging.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories.”
- Also stated in: § Abstract · hedged
“Single-cell RNA-sequencing (scRNA-seq) has emerged as a promising approach to study cellular differentiation trajectories at high resolution in primary tissue specimens (5).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
What the sources say
- Not checked yetcites [5] source
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Also stated in: § Abstract · hedged
In the paper
“These requirements can be challenging to satisfy in certain contexts such as human cancer development (10).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
What the sources say
- Not checked yetcites [10] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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In the paper
“To validate our findings, we assembled an expanded compendium of 33 additional scRNA-seq datasets from 26 studies (fig. S10A, table S1, and materials and methods).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Performance evaluation across tissues, species, and platforms
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“In this context, differentiating cells descend toward stable regions of the network (attractor states), characterized by restricted gene expression, whereas metastable cells broadly sample the network, maintaining higher differentiation potential (47).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Discussion
What the sources say
- Not checked yetcites [47] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Our results are therefore consistent with a model in which less mature cells maintain looser chromatin to permit wider sampling of the transcriptome, while more differentiated cells generally restrict chromatin accessibility and transcriptional diversity as they specialize (Fig. 1E and fig. S3) (46).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Discussion
What the sources say
- Not checked yetcites [46] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These datasets were selected to prioritize commonly used benchmarking datasets from earlier studies and to ensure a broad sampling of developmental states from the mammalian zygote to terminally differentiated cells (table S1).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > RNA-based correlates of single-cell differentiation states
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The experimentally determined differentiation states in the training and validation datasets accurately reflect developmental potential.
In the paper
“Theoretically, this model can be represented by “attractor states” within a genome-wide gene regulatory network (47).”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Discussion
What the sources say
- Not checked yetcites [47] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since these cells could not be identified by gene counts or RNA content alone, this analysis confirmed the utility of CytoTRACE and demonstrates an approach for elucidating tissue-specific stem cells from scRNA-seq data.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Results > Stem-cell-related genes and hierarchies
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.”
§ Single-cell transcriptional diversity is a hallmark of developmental potential > Abstract
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
The experimentally determined differentiation states in the training and validation datasets accurately reflect developmental potential.
“nine gold standard scRNA-seq datasets with experimentally-confirmed differentiation trajectories”
“52 experimentally determined developmental trajectories”
The paper relies on this without support.
The known phenotype labels used to average features and assess correlation are correct.
“when averaged according to known phenotypes and assessed across independent studies”
“compare each RNA-based feature, averaged by phenotype, against known differentiation states”
The paper relies on this without support.
The differentiation states and tumor-initiating properties of LP subsets remain incompletely understood.
“However, the differentiation states and tumor-initiating properties of LP subsets remain incompletely understood.”
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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
“It is based on the observation that less mature cells maintain looser chromatin to permit wider sampling of the transcriptome, whereas more differentiated cells generally restrict chromatin accessibility and transcriptional diversity as they specialize.”
From Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues (2023) · cites this paper as [35]
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
“CytoTRACE score was calculated from a set of 19,000 annotated genes to determine the differentiation status of cells.”
From Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues (2023) · cites this paper as [35]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“CytoTRACE leverages gene counting and expression to reconstruct cell trajectories, enabling the prediction of relative differentiation states of single cells based on single-cell RNA expression data, without being constrained by specific time scales or the presence of continuous developmental processes in the data [28].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [28]
The cited source says what the claim attributes to it.
In the citing paper
“Consequently, the number of expressed genes per cell can be considered a hallmark of the developmental potential of a cell.”
From Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues (2023) · cites this paper as [35]
The cited source says what the claim attributes to it.
In the citing paper
“CytoTRACE has been used to predict the differentiation and developmental state of cells across tissues and species.35”
From Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues (2023) · cites this paper as [35]
Citations
Cites
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
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
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEMPreprint
Li, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.3997 · Cited by 10,154
Complex heatmaps reveal patterns and correlations in multidimensional genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw313 · Cited by 10,037
Flagged first, then by citations
Research works printed but not linked: 1
- T. Hastie R. Tibshirani J. H. Friedman The Elements of Statistical Learning: Data Mining Inference and Prediction (Springer ed. 2 2017).
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Sorted by citations
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Cheng, S., Li, Z., Gao, R., Xing, B., Gao, Y., Yang, Y., Qin, S., Zhang, L., Ouyang, H., Du, P., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.010 · Cited by 1,601
Liver tumour immune microenvironment subtypes and neutrophil heterogeneity
Xue, R., Zhang, Q., Cao, Q., Kong, R., Xiao, X., Liu, H., Feng, M., Wang, F., Cheng, J., Zhao, L., et al. (2022) · Nature · doi:10.1038/s41586-022-05400-x · Cited by 984
Pham, D., Tan, X., Balderson, B., Xu, J., Grice, L. F., Yoon, S., Willis, E. F., Tran, M., Lam, P. Y., Raghubar, A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43120-6 · Cited by 757
Jiang, Y., Trotsyuk, A. A., Niu, S., Henn, D., Chen, K., Shih, C., Larson, M. R., Mermin-Bunnell, A. M., Mittal, S., Lai, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01528-3 · Cited by 616
Spatially confined sub-tumor microenvironments in pancreatic cancer
Grünwald, B. T., Devisme, A., Andrieux, G., Vyas, F., Aliar, K., McCloskey, C. W., Macklin, A., Jang, G. H., Denroche, R., Romero, J. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.09.022 · Cited by 433
Neutrophil profiling illuminates anti-tumor antigen-presenting potency
Wu, Y. C., Ma, J., Yang, X., Fang, N., Zhang, T., Ji, S., Rao, D., Feng, H., Gao, K., Gu, X., et al. (2024) · Cell · doi:10.1016/j.cell.2024.02.005 · Cited by 453
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Ambrosi, T. H., Marecic, O., McArdle, A., Sinha, R., Gulati, G. S., Tong, X., Wang, Y., Steininger, H. M., Hoover, M. Y., Koepke, L. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03795-7 · Cited by 392
Self-citationShahan, R., Hsu, C., Nolan, T. M., Cole, B., Taylor, I. W., Greenstreet, L., Zhang, S., Afanassiev, A., Vlot, A. H. C., Schiebinger, G. R., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.01.008 · Cited by 394
Hildreth, A. D., Ma, F., Wong, Y. Y., Sun, R., Pellegrini, M. A., & O’Sullivan, T. E. (2021) · Nature Immunology · doi:10.1038/s41590-021-00922-4 · Cited by 377
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Steen, C. B., Luca, B. A., Esfahani, M. S., Azizi, A., Sworder, B. J., Nabet, B. Y., Kurtz, D. M., Liu, C. L., Khameneh, F., Advani, R. H., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.08.011 · Cited by 345
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Karras, P., Bordeu, I., Poźniak, J., Nowosad, A., Pazzi, C., Raemdonck, N. V., Landeloos, E., Herck, Y. V., Pedri, D., Bervoets, G., et al. (2022) · Nature · doi:10.1038/s41586-022-05242-7 · Cited by 261
Single-cell transcriptome atlas of the leaf and root of rice seedlings
Wang, Y., Huan, Q., Chu, X., Li, K., & Qian, W. (2021) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2021.06.001 · Cited by 244
Multi-omic analysis reveals divergent molecular events in scarring and regenerative wound healing
Mascharak, S., desJardins-Park, H. E., Januszyk, M., Griffin, M., Chen, K., Davitt, M. F., Demeter, J., Henn, D., Bonham, C. A., Foster, D. S., et al. (2022) · Cell stem cell · doi:10.1016/j.stem.2021.12.011 · Cited by 250
Herring, C. A., Simmons, R. K., Freytag, S., Poppe, D., Moffet, J. J. D., Pflueger, J., Buckberry, S., Vargas-Landín, D. B., Clément, O., Goñi, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.039 · Cited by 242
CellRank 2: unified fate mapping in multiview single-cell data
Weiler, P., Lange, M., Klein, M., Pe’er, D., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02303-9 · Cited by 285
Improved reconstruction of single-cell developmental potential with CytoTRACE 2
Kang, M., Armenteros, J. J. A., Gulati, G. S., Gleyzer, R., Avagyan, S., Brown, E. L., Zhang, W., Usmani, A., Earland, N. J., Wu, Z., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02857-2 · Cited by 266
Self-citationCao, T., Zhang, W., Wang, Q., Wang, C., Ma, W., Zhang, C., Ge, M., Tian, M., Yu, J., Jiao, A., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.011 · Cited by 249
Lopez‐Anido, C., Vatén, A., Smoot, N. K., Sharma, N., Guo, V., Gong, Y., Gil, M. X. A., Weimer, A. K. K., & Bergmann, D. C. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.03.014 · Cited by 223
Hif-1a suppresses ROS-induced proliferation of cardiac fibroblasts following myocardial infarction
Janbandhu, V. C., Tallapragada, V. J., Patrick, R., Li, Y., Abeygunawardena, D., Humphreys, D. T., Martin, E. M. M. A., Ward, A. O., Contreras, O., Farbehi, N., et al. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.10.009 · Cited by 230
Foster, D. S., Januszyk, M., Delitto, D., Yost, K. E., Griffin, M., Guo, J. L., Guardino, N. J., Delitto, A. E., Chinta, M. S., Burcham, A. R., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.015 · Cited by 220
Advances and Opportunities in Single-Cell Transcriptomics for Plant Research
Seyfferth, C., Renema, J., Wendrich, J. R., Eekhout, T., Seurinck, R., Vandamme, N., Blob, B., Saeys, Y., Helariutta, Y., Birnbaum, K. D., et al. (2021) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-081720-010120 · Cited by 217
McKellar, D. W., Walter, L. D., Song, L. T., Mantri, M., Wang, M. F. Z., Vlaminck, I. D., & Cosgrove, B. D. (2021) · Communications Biology · doi:10.1038/s42003-021-02810-x · Cited by 224
Reprogramming roadmap reveals route to human induced trophoblast stem cells
Liu, X., Ouyang, J. F., Rossello, F. J., Tan, J. P., Davidson, K. C., Valdes, D. S., Schröder, J., Sun, Y., Chen, J., Knaupp, A. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2734-6 · Cited by 202
Integrated spatial multiomics reveals fibroblast fate during tissue repair
Foster, D. S., Januszyk, M., Yost, K. E., Chinta, M. S., Gulati, G. S., Nguyen, A. T., Burcham, A. R., Salhotra, A., Ransom, R. C., Henn, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110025118 · Cited by 197
Self-citationSingle-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 222
“Commonly used software includes Monocle [14], RNA velocity [26], Palantir [27], CytoTRACE [28], and others.”
BackgroundMethodHigh-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE
Vahid, M. R., Brown, E. L., Steen, C. B., Zhang, W., Jeon, H. S., Kang, M., Gentles, A. J., & Newman, A. M. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01697-9 · Cited by 187
Self-citationDistinct molecular profiles of skull bone marrow in health and neurological disorders
Kolabas, Z. I., Kuemmerle, L. B., Perneczky, R., Förstera, B., Ulukaya, S., Ali, M., Kapoor, S., Bartos, L. M., Büttner, M., Çalışkan, Ö. Ş., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.009 · Cited by 196
The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution
Dorrity, M. W., Alexandre, C. M., Hamm, M. O., Vigil, A.-L. B. G., Fields, S., Queitsch, C., & Cuperus, J. T. (2021) · Nature Communications · doi:10.1038/s41467-021-23675-y · Cited by 196
An atlas of epithelial cell states and plasticity in lung adenocarcinoma
Han, G., Sinjab, A. F., Rahal, Z., Lynch, A. M., Treekitkarnmongkol, W., Liu, Y., Serrano, A. G., Feng, J., Liang, K., Khan, K. B., et al. (2024) · Nature · doi:10.1038/s41586-024-07113-9 · Cited by 185
Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root
Nolan, T. M., Vukašinović, N., Hsu, C., Zhang, J., Vanhoutte, I., Shahan, R., Taylor, I. W., Greenstreet, L., Heitz, M., Afanassiev, A., et al. (2023) · Science · doi:10.1126/science.adf4721 · Cited by 188
Tumour vasculature at single-cell resolution
Pan, X., Li, X., Dong, L., Liu, T., Zhang, M., Zhang, L., Zhang, X., Huang, L., Shi, W., Sun, H., et al. (2024) · Nature · doi:10.1038/s41586-024-07698-1 · Cited by 199
Hua, Y., Vella, G., Rambow, F., Allen, E., Antoranz, A., Duhamel, M., Takeda, A., Jalkanen, S., Junius, S., Smeets, A., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.11.002 · Cited by 176
Liu, Z., Kong, X., Long, Y., Liu, S., Zhang, H., Jia, J., Cui, W., Zhang, Z., Song, X., Qiu, L., et al. (2023) · Nature Plants · doi:10.1038/s41477-023-01387-z · Cited by 176
Unraveling Hematopoiesis through the Lens of Genomics
Liggett, L. A., & Sankaran, V. G. (2020) · Cell · doi:10.1016/j.cell.2020.08.030 · Cited by 163
The Single-Cell Immunogenomic Landscape of B and Plasma Cells in Early-Stage Lung Adenocarcinoma
Hao, D., Han, G., Sinjab, A. F., Gomez-Bolanos, L. I., Lazcano, R., Serrano, A. G., Hernandez, S. D., Dai, E., Cao, X., Hu, J., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1658 · Cited by 161
Failure of human rhombic lip differentiation underlies medulloblastoma formation
Hendrikse, L. D., Haldipur, P., Saulnier, O., Millman, J., Sjoboen, A. H., Erickson, A. W., Ong, W., Gordon, V. D., Coudière-Morrison, L., Mercier, A. L., et al. (2022) · Nature · doi:10.1038/s41586-022-05215-w · Cited by 167
Wang, Z., Wu, Z., Wang, H., Feng, R., Wang, G., Li, M., Wang, S.-Y., Chen, X., Su, Y., Wang, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.019 · Cited by 158
Mapping cells through time and space with moscot
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Saber, L., Jing, C., et al. (2025) · Nature · doi:10.1038/s41586-024-08453-2 · Cited by 235
Tu, J., Li, W., Yang, S., Yang, P., Qi, Y., Wang, S., Lai, K., Bai, X., Wu, C., Ding, W., et al. (2021) · Advanced Science · doi:10.1002/advs.202103631 · Cited by 152
High-throughput single-сell sequencing in cancer research
Jia, Q., Chu, H., Jin, Z., Long, H., & Zhu, B. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00990-4 · Cited by 157
Zhang, X., Powell, K. A., & Li, L. (2020) · Cancers · doi:10.3390/cancers12123765 · Cited by 163
Single-cell transcriptomic profiling reveals the tumor heterogeneity of small-cell lung cancer
Tian, Y., Li, Q., Yang, Z., Zhang, S., Xu, J., Wang, Z., Bai, H., Duan, J., Zheng, B. J., Li, W., et al. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01150-4 · Cited by 153
Dib, L. H., Koneva, L. A., Edsfeldt, A., Zurke, Y.-X., Sun, J., Nitulescu, M., Attar, M., Lutgens, E., Schmidt, S., Lindholm, M., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00295-x · Cited by 157
Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators
Tao, W., Yu, Z., & Han, J.-D. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.03.009 · Cited by 147
Xue, M., Zhu, Y., Jiang, Y., Han, L., Shi, M., Su, R. J., Wang, L., Xiong, C., Wang, C., Wang, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40314-w · Cited by 153
Bipotent transitional liver progenitor cells contribute to liver regeneration
Pu, W., Zhu, H., Zhang, M., Pikiolek, M., Ercan, C., Li, J., Huang, X., Han, X., Zhang, Z., Lv, Z., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01335-9 · Cited by 138
Liu, Y., Dai, Y., & Wang, L. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.12.009 · Cited by 153
Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics
Lu, L., Wang, J. R., Henderson, Y. C., Bai, S., Yang, J., Hu, M., Shiau, C.-K., Pan, T., Yan, Y., Tran, T. M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci169653 · Cited by 144
An exhausted-like microglial population accumulates in aged and APOE4 genotype Alzheimer’s brains
Millet, A., Ledo, J. H., & Tavazoie, S. F. (2024) · Immunity · doi:10.1016/j.immuni.2023.12.001 · Cited by 132
Defining human mesenchymal and epithelial heterogeneity in response to oral inflammatory disease
Caetano, A. J., Yianni, V., Volponi, A. A., Booth, V., D’Agostino, E. M., & Sharpe, P. T. (2021) · eLife · doi:10.7554/elife.62810 · Cited by 135
Taavitsainen, S., Engedal, N., Cao, S., Handle, F., Erickson, A. M., Preković, S., Wetterskog, D., Tolonen, T. T., Vuorinen, E. M., Kiviaho, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25624-1 · Cited by 123
Tang, Z., Bai, Y., Fang, Q., Yuan, Y., Zeng, Q., Chen, S., Xu, T., Chen, J., Tan, L., Wang, C., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.011 · Cited by 138
Quah, H. S., Cao, E. Y., Suteja, L., Li, C. H., Leong, H. S., Chong, F. T., Gupta, S., Arcinas, C., Ouyang, J. F., Ang, V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37379-y · Cited by 126
Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures
Zhang, M. J., Pisco, A. O., Darmanis, S., & Zou, J. (2021) · eLife · doi:10.7554/elife.62293 · Cited by 121
Statistical mechanics meets single-cell biology
Teschendorff, A. E., & Feinberg, A. P. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00341-z · Cited by 127
Single-cell transcriptomics identifies conserved regulators of neuroglandular lineages
Steger, J., Cole, A. G., Denner, A., Lebedeva, T. S., Genikhovich, G., Ries, A., Reischl, R., Taudes, E., Lassnig, M., & Technau, U. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111370 · Cited by 114
Li, R., Liu, X., Huang, X., Zhang, D., Chen, Z., Zhang, J., Bai, R., Zhang, S., Zhao, H., Xu, Z., et al. (2023) · Gut · doi:10.1136/gutjnl-2023-330243 · Cited by 138
Elucidating the fundamental fibrotic processes driving abdominal adhesion formation
Foster, D. S., Marshall, C. D., Gulati, G. S., Chinta, M. S., Nguyen, A. T., Salhotra, A., Jones, R. E., Burcham, A. R., Lerbs, T., Cui, L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17883-1 · Cited by 118
Self-citationLi, D., Kim, S., Li, J., Gao, Q., Choi, J., Bian, F., Hu, J., Zhang, Y., Li, J., Lu, R., et al. (2020) · The Ocular Surface · doi:10.1016/j.jtos.2020.12.004 · Cited by 119
Schwann cell precursors represent a neural crest‐like state with biased multipotency
Kastriti, M. E., Faure, L., Ahsen, D. V., Bouderlique, T., Boström, J., Solovieva, T., Jackson, C. R., Bronner-Fraser, M. E., Meijer, D., Hadjab, S., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021108780 · Cited by 125
Gasdermin D-mediated metabolic crosstalk promotes tissue repair
Chi, Z., Chen, S., Yang, D., Cui, W., Lu, Y., Wang, Z., Li, M., Yu, W., Zhang, J., Jiang, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-08022-7 · Cited by 116
A multi-omic atlas of human embryonic skeletal development
To, K., Fei, L., Pett, J. P., Roberts, K., Blain, R., Polański, K., Li, T., Yayon, N., He, P., Xu, C., et al. (2024) · Nature · doi:10.1038/s41586-024-08189-z · Cited by 123
Franken, A., Bila, M., Mechels, A., Kint, S., Dessel, J. V., Pomella, V., Vanuytven, S., Philips, G., Bricard, O., Xiong, J., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.02.007 · Cited by 128
Yang, J., Vamvini, M. T., Nigro, P., Ho, L., Galani, K., Alvarez, M., Tanigawa, Y., Renfro, A., Carbone, N. P., Laakso, M., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.004 · Cited by 110
Heiser, C. N., Simmons, A. J., Revetta, F. L., McKinley, E. T., Ramirez-Solano, M. A., Wang, J., Kaur, H., Shao, J., Ayers, G. D., Wang, Y., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.006 · Cited by 112
Zhao, Z., Ding, Y., Tran, L. J., Chai, G., & Li, L. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1196892 · Cited by 111
Disrupting biological sensors of force promotes tissue regeneration in large organisms
Chen, K., Kwon, S. H., Henn, D., Kuehlmann, B. A., Tevlin, R., Bonham, C. A., Griffin, M., Trotsyuk, A. A., Borrelli, M. R., Noishiki, C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25410-z · Cited by 110
Li, K., Du, Y., Cai, Y., Liu, W., Lv, Y., Huang, B., Zhang, L., Wang, Z., Liu, P., Sun, Q., et al. (2022) · Leukemia · doi:10.1038/s41375-022-01789-6 · Cited by 108
Nian, Z., Dou, Y., Shen, Y., Liu, J., Du, X., Jiang, Y., Zhou, Y., Fu, B., Sun, R., Zheng, X., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.08.015 · Cited by 112
Fan, G., Xie, T., Li, L., Tang, L., Han, X., & Shi, Y. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00564-3 · Cited by 106
Isakova, A., Neff, N. F., & Quake, S. R. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113568118 · Cited by 104
Robotic search for optimal cell culture in regenerative medicine
Kanda, G. N., Tsuzuki, T., Terada, M., Sakai, N., Motozawa, N., Masuda, T., Nishida, M., Watanabe, C. T., Higashi, T., Horiguchi, S. A., et al. (2022) · eLife · doi:10.7554/elife.77007 · Cited by 102
p53 governs an AT1 differentiation programme in lung cancer suppression
Kaiser, A. M., Gatto, A., Hanson, K. J., Zhao, R. L., Raj, N., Ozawa, M. G., Seoane, J. A., Bieging-Rolett, K. T., Wang, M., Li, I., et al. (2023) · Nature · doi:10.1038/s41586-023-06253-8 · Cited by 100
Self-citationCancer-selective metabolic vulnerabilities in MYC-amplified medulloblastoma
Gwynne, W. D., Suk, Y., Custers, S., Mikolajewicz, N., Chan, J. K., Zador, Z. E., Chafe, S. C., Zhai, K., Escudero, L., Zhang, C., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.009 · Cited by 95
Malagola, E., Vasciaveo, A., Ochiai, Y., Kim, W., Zheng, B., Zanella, L., Wang, A. L. E., Middelhoff, M. A., Nienhüser, H., Deng, L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.05.004 · Cited by 102
Zheng, S., Zou, Y., Tang, Y., Yang, A., Liang, J.-Y., Wu, L., Tian, W., Xiao, W., Xie, X., Yang, L., et al. (2022) · OncoImmunology · doi:10.1080/2162402x.2021.2020984 · Cited by 92
Single-Cell RNA Sequencing for Plant Research: Insights and Possible Benefits
Bawa, G., Liu, Z., Yu, X., Qin, A., & Sun, X. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094497 · Cited by 96
Distinct ontogenetic lineages dictate cDC2 heterogeneity
Minutti, C. M., Piot, C., da Costa, M. P., Chakravarty, P., Rogers, N. C., Encabo, H. H., Cardoso, A., Loong, J. H. C., Bessou, G., Mionnet, C., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01745-9 · Cited by 95
Ma, F., Tsou, P., Gharaee‐Kermani, M., Plazyo, O., Xing, X., Kirma, J., Wasikowski, R., Hile, G. A., Harms, P. W., Jiang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44645-6 · Cited by 85
Li, Q. (2023) · Genome biology · doi:10.1186/s13059-023-02988-9 · Cited by 103
Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation
Bac, J., Mirkes, E. M., Gorban, A. N., Tyukin, I., & Zinovyev, A. Y. (2021) · Entropy · doi:10.3390/e23101368 · Cited by 87
Cui, W., Chen, S., Hu, T., Zhou, T., Qiu, C., Jiang, L., Cheng, X., Ji, J., Yao, K., & Han, H. (2024) · ACS Nano · doi:10.1021/acsnano.3c11514 · Cited by 86
Prrx1 Fibroblasts Represent a Pro-fibrotic Lineage in the Mouse Ventral Dermis
Leavitt, T., Hu, M. S.-M., Borrelli, M. R., Januszyk, M., Garcia, J. T., Ransom, R. C., Mascharak, S., desJardins-Park, H. E., Litzenburger, U. M., Walmsley, G. G., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108356 · Cited by 84
Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics
Subramanian, A., Alperovich, M., Yang, Y., & Li, B. (2022) · Genome biology · doi:10.1186/s13059-022-02820-w · Cited by 93
Cell type diversity in a developing octopus brain
Styfhals, R., Zolotarov, G., Hulselmans, G. J., Spanier, K. I., Poovathingal, S. K., Elagoz, A. M., Winter, S. D., Deryckere, A., Rajewsky, N., Ponte, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35198-1 · Cited by 76
Chiaro, P. D., Nacci, L., Arco, F., Brandini, S., Polletti, S., Palamidessi, A., Donati, B., Soriani, C., Gualdrini, F., Frigè, G., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.02.017 · Cited by 80
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
Cao, S., Wang, J. R., Ji, S., Peng, Y., Dai, Y., Guo, S., Montierth, M. D., Shen, J. P., Zhao, X. H., Chen, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01342-x · Cited by 81
Machine learning reveals diverse cell death patterns in lung adenocarcinoma prognosis and therapy
Wang, S., Wang, R., Hu, D., Zhang, C.-X., Cao, P., & Huang, J. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00538-5 · Cited by 79
Wang, Y., Liu, B., Min, Q., Yang, X., Yan, S., Ma, Y., Li, S., Fan, J., Wang, Y., Dong, B., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00591-7 · Cited by 82
Metadata
- Authors
- Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., Zabala, M., Scheeren, F. A., Lobo, N. A., Qian, D., Yu, F. B., Dirbas, F. M., Clarke, M. F., & Newman, A. M.
- Affiliations
- Institute for Stem Cell Biology and Regenerative Medicine (US)
- Stanford University (US)
- Westlake University (CN)
- Leiden University Medical Center (NL)
- Chan Zuckerberg Biohub San Francisco (US)
- Stanford Cancer Institute (US)
- Stanford Medicine (US)
- Corresponding author
- Newman, Aaron M.
- ORCID
- Venue
- Science
- Published
- 2020-01-24
- Volume
- 367
- Issue
- 6476
- Pages
- 405-411
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7694873
- OpenAlex
- W3002770500
- Semantic Scholar
- 7ca2a15b28320291edc8797c1bb6dd6e8fa9b6c8
- Cited by
- 1,571
- References
- 144
- Citations by year
- 2026: 360 · 2025: 386 · 2024: 346 · 2023: 224 · 2022: 135 · 2021: 88 · 2020: 31
- FWCI
- 40.23
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- National Science Foundation (DGE-1656518)
- U.S. Department of Defense (W81XWH-11-1-0287, W81XWH-13-1-0281, W81XWH-12-1-0020)
- National Cancer Institute (R00CA187192-03, 5R01CA100225-09, CA09302)
- Stanford Bio-X Bowes Graduate Student Fellowship
- Stanford Medical Science Training Program (5T32GM007365-38)
- Virginia and D.K. Ludwig Fund for Cancer Research
- Stinehart-Reed foundation
- Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program
- Breast Cancer Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- single-cell RNA sequencing, transcriptional diversity, developmental potential, quiescent stem cells, breast tumorigenesis, cellular hierarchy
- MeSH terms
- RNA-Seq, Animals, Base Sequence, Cell Differentiation, Humans, Neoplasms, Transcription, Genetic, Genetic Variation, RNA, Small Cytoplasmic, Mice, Single-Cell Analysis
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.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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