The DOI matches its Crossref record; 145 of the 145 references it cites were found in a registry or an index; none of the 25 sources its claims cite is checked for support yet.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 25 cited sources checked · 0 supported · 0 flagged
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
“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
Strength:plain· not yet calibratedCausal:direct· 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
“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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“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
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
“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
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
“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
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
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 13
- Probably not a substantial claim. 8
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
“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.
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
“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.
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
“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.
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
“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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
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.
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
“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.”
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
“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).
Cited by
Sorted by citations
Items 901–998 of 998
Yu, J., Zhou, S., Shen, Z., Shi, A., Guo, H., He, J., Qin, B., & Wang, X. (2025) · Journal of Environmental Sciences · doi:10.1016/j.jes.2025.06.034 · Cited by 2
Refining cell subpopulation identification and cell fate mapping using cell energy
Long, C., Li, H., Li, Z., Zhang, J., Xi, Q., & Zuo, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128999 · Cited by 3
Ge, J., Cai, J., Zhang, G., Li, D., & Tao, L. (2025) · Immunologic Research · doi:10.1007/s12026-025-09666-x · Cited by 4
Wang, Z., Zhang, J., Chen, H., Zhang, X., Zhang, K., Zhang, F., Xie, Y., Ma, H., Pan, L., Zhang, Q., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1650726 · Cited by 2
Tan, Z., Xin, H., Chen, J., Lei, M., Tu, G., & Tang, L. (2025) · Genes & Diseases · doi:10.1016/j.gendis.2025.101799 · Cited by 2
Chen, Y., Tang, X. H., Zhu, L.-R., Wang, Y., Li, G., & Yang, W. (2025) · Cancer Cell International · doi:10.1186/s12935-025-03942-4 · Cited by 2
Lu, F., Chen, S., Guo, H., Li, Q., Wu, L., Pan, Y., Wu, Y., Shu, H., Liu, S., Zhang, B., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1622395 · Cited by 2
Jiang, L., Fu, L., Xiong, S., Cao, G., Mei, Y., Wu, Y., Chen, J., & Lv, Y. (2025) · Biocell · doi:10.32604/biocell.2025.068507 · Cited by 2
Huang, H., Shi, Y., Shen, B., Zhang, Z., Yue, J., Lü, M., & Chen, Q. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.09.040 · Cited by 2
Liu, Y., Fan, L., Ye, F., Zhao, Y., Zhu, Y., Yang, Y., Xu, F., Gu, Y., & Guan, X. (2025) · British Journal of Cancer · doi:10.1038/s41416-025-03202-2 · Cited by 3
Yin, X., Li, X., Mi, L., Hou, J., & Yin, F. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1672587 · Cited by 2
Li, Z., Li, H., Qiao, W., Yu, S., Fan, B., Yang, M., Zhou, L., Qiu, F., Wu, Z., & Wang, J. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003601 · Cited by 3
Quantifying human pluripotent stem cell attributes with population balance modeling
Brown, A., Stoukides, D. M., & Tzanakakis, E. S. (2025) · Stem Cell Research & Therapy · doi:10.1186/s13287-025-04668-w · Cited by 2
Sun, Q., Yang, L., Zhou, Z., Wu, N., Li, C., Hu, Q., & Cong, X. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1638083 · Cited by 2
Zhang, B., Zhang, J., Li, Z., Sheng, H., Li, H., Lu, Y., Liu, X., Xu, Z., Huang, Y., Zhu, C., et al. (2025) · Neuro-Oncology · doi:10.1093/neuonc/noaf249 · Cited by 3
Murray, N. P., & Aedo, S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262311346 · Cited by 4
Multi-omics analysis reveals different cholesterol metabolism subtypes in colorectal cancer
Sun, Y., Kong, D., Zhang, H., Fan, J., Lu, S., Yang, W., Xiang, R., Zhang, Q., Pei, W., Feng, L., et al. (2025) · Discover Oncology · doi:10.1007/s12672-025-03993-z · Cited by 2
Jiang, T., Guan, X., Wei, R., Lv, C., Zan, L. K., Zhang, Z., Jiao, S., Ma, J., Cheng, P., Zhao, J., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116625 · Cited by 2
Nguyen, D. T., Prieto, L. I., Zhang, C., Li, H., Khoór, A., Weems, C. R., Jamieson, L., Jeganathan, K. B., Babu, S. R., Murray, N. R., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116606 · Cited by 2
Xu, Z., Zhai, X., Liu, M., Zhang, Y., Cao, J., He, J., Li, H., Liang, R., Wang, W., Zhu, R., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-025-07630-9 · Cited by 2
Srinivasan, J., Moore, C. R., Calindi, A., Helm, B. R., Yang, Y., Moore, J., Selden, H. J., Heiser, C. N., Liu, Q., Lau, K. S., et al. (2026) · Nature Immunology · doi:10.1038/s41590-025-02371-9 · Cited by 6
Zhang, B., Yuan, X., Gao, K., You, B., Wang, Y., Meng, L., Chen, Z., Xie, S., Liu, Y., Dong, Z., et al. (2026) · Advanced Science · doi:10.1002/advs.202514982 · Cited by 2
Shen, B., Jiang, J., Zhang, X., Yi, C., Liu, J., Li, Z., & Ma, Y. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1710915 · Cited by 2
A multi-omics analysis of pancreatitis: bridging familial genetics and disease progression
Li, F., Huang, J.-X., Sun, W.-J., Zeng, J., Gan, K.-X., Gong, B., Ji, J.-M., Chen, J., Deng, Z.-H., & Xu, D.-L. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1707821 · Cited by 2
Wang, J., Zhu, Q., Bie, J., Han, Y., Liu, H., & Chen, C. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1722524 · Cited by 2
Qi, Y., Wang, F., Ren, W., Cai, C., Zhou, Y., Zhu, P., He, P., & Wang, Q. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1624883 · Cited by 2
Olig2 acts as an inducible barrier to in vivo astrocyte-to-neuron conversion
Lai, C., Hou, K., Li, W., Wang, J., Cai, X., Shi, Y., Yang, M., Yu, D., Lu, K., Li, W., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68869-4 · Cited by 5
Meningioma microenvironment harbors a rich immune landscape that evolves with biological state
Guo, X., Moynihan, Z. A., Driver, J., Patel, R. V., Bhave, V. M., Maury, E. A., Knelson, E. H., Guo, H., Lin, J., Coy, S., et al. (2026) · Neuro-Oncology · doi:10.1093/neuonc/noag022 · Cited by 3
Li, G., Yang, X., Huang, L., Zheng, Y., Xu, L., Zhang, Z., Li, J., Li, T., Zhang, Y., Yang, Y., et al. (2026) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-026-03660-3 · Cited by 2
Wang, Y., Ni, J., Feng, G., You, Y., You, K., Bao, W., Chen, T., Zhang, L., Cao, X., Wang, X., et al. (2026) · Pharmacological Research · doi:10.1016/j.phrs.2026.108143 · Cited by 2
Yin, H., Zhang, H., Zheng, S., Guo, R., Wen, J., Liu, M., Li, A., Zhao, M., Yuan, Y., & Gong, Y. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01355-8 · Cited by 4
Zhang, H., Liu, Y., Zhang, W., Guo, Y., Lin, A., Jin, Y., Lü, P., Fang, Y., Zhang, H., Qu, L., et al. (2026) · Drug Resistance Updates · doi:10.1016/j.drup.2026.101388 · Cited by 2
Miao, N., Lee, T., Chen, L., Zhang, H., Wang, J. H., Sun, J., Sun, J., Sha, Y., Huang, S., & Sun, T. (2026) · iScience · doi:10.1016/j.isci.2026.115399 · Cited by 3
Single-cell multiomics in clinical immunology: Revolutionizing immune profiling
Chowdhury, S., Khan, N., Dutta, R., Chakraborty, P., & Kar, K. (2026) · Clinical Immunology Communications · doi:10.1016/j.clicom.2026.03.002 · Cited by 2
Proton FLASH Exposure Preserves Gut Commensal Microbiomes and Spares Intestinal Stem Cells
Chugh, R. M., Bhanja, P., Schueddig, E., Setianegara, J., Lin, Y., Guida, K., Rehman, S., Krepel, S. A., Koestler, D. C., Chen, R. C., et al. (2026) · Advanced Science · doi:10.1002/advs.202519249 · Cited by 2
STEER: decoupling kinetics with Spatial-Temporal Explainable Expert model for RNA velocity inference
Liu, Z., Li, C., Wu, D., Zhai, W., & Ma, L. (2026) · National Science Review · doi:10.1093/nsr/nwag199 · Cited by 2
Single cell profiling reveals malignant states and immune landscapes in PCNSL and systemic DLBCL
Cai, F., Wang, X., Zhang, S., Li, Y., Wang, S., Zhu, J., Li, P., Huang, Z., Liu, W., Song, Z., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115563 · Cited by 2
Lai, Y., Lin, P., Zhang, K., Hong, W., Zheng, M., Wu, L., Chen, T., & Lin, F. (2026) · Cell Biology and Toxicology · doi:10.1007/s10565-026-10177-0 · Cited by 2
Zhang, Y., Liu, H., Xu, M., Xie, M., Deng, S., Ma, X., Zhao, L., He, F., Li, M., Long, R., et al. (2026) · Horticulture Research · doi:10.1093/hr/uhag117 · Cited by 2
Single‐Cell Virtual Perturbation Screening Identifies STAT3 as a Key Regulator of Dentinogenesis
Zhu, Y., Xu, H., Zhang, Z., Sun, S., Huang, Z., Gao, X., Pan, H., Huang, X., Liu, Y., Wang, X., et al. (2026) · Cell Proliferation · doi:10.1111/cpr.70203 · Cited by 2
Yu, K., Chen, J., Chu, Y.-Y., Nair, S. K., Crupi, E., Hasanov, E., Mei, Y., Han, X., Liu, Y., Liu, Y., et al. (2026) · Cancer Discovery · doi:10.1158/2159-8290.cd-26-0099 · Cited by 4
Chen, Z., Ren, X., Zhang, Y. M., Kang, Y., Yang, Y., Xu, Y., Zhou, Q., Liao, C., Zeng, Q., Liu, X., et al. (2026) · Cancer Discovery · doi:10.1158/2159-8290.cd-25-0820 · Cited by 2
Fei, Y., Liu, L., Ma, S., Jin, Y., Wang, S., Zhang, L., Yang, J., Liu, Y., Lu, M., Xue, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73383-8 · Cited by 2
Cao, Y., Duan, L., Lin, D. X., Liu, Y., 杨明子, Liu, Y., Xu, Y., Xu, Y., & 王连召 (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1873878 · Cited by 2
Gao, H., You, G., Huang, J., Lin, Y., Huang, J.-M., Liang, R., & Lin, C. (2026) · Journal of Neuroinflammation · doi:10.1186/s12974-026-03905-0 · Cited by 2
dos Santos, G. A., Castro, J. P., Guardia, G. D. A., dos Santos, F. F., Birbrair, A., & Galante, P. A. F. (2026) · Aging Cell · doi:10.1111/acel.70696 · Cited by 3
Wang, C., Chen, Y., Zhou, R., Yang, Y., & Fang, Y. (2023) · Current Medicinal Chemistry · doi:10.2174/0109298673278775231101064235 · Cited by 2
Zhu, X., Zhang, J., Zhang, Z., Yuan, H., Xie, A., Zhang, N., Wang, M., Jiang, M., Xiao, Y., Wang, H., et al. (2023) · Current Bioinformatics · doi:10.2174/0115748936268168231114103440 · Cited by 2
Qin, H., Yang, S., Feng, Z., Wu, S. J., Cai, T., Xie, Z., & Hu, H. (2024) · Environmental Toxicology · doi:10.1002/tox.24261 · Cited by 2
Ma, J., Zhang, L., Jin Wei, L. U., & Zhang, H. (2024) · Chinese Medical Sciences Journal · doi:10.24920/004354 · Cited by 2
Shi, J., Zhang, Y., Xu, L., & Wang, F. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1467682 · Cited by 2
Yao, J., Yao, P., Li, Y., He, K., Ma, X., Yang, Q., Jia, J., Chen, Z., Yu, S., Gu, S., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110946 · Cited by 2
Desterke, C., Francés, R., Monge, C., Marchio, A., Pineau, P., & Mata-Garrido, J. (2024) · Cells · doi:10.3390/cells13191634 · Cited by 2
IGFBP7+ subpopulation and IGFBP7 risk score in astrocytoma: insights from scRNA-Seq and bulk RNA-Seq
Zhao, L., Shao, W., Xiahou, Z., Ren, L., Liu, C., Song, Y., Xu, H. Y., Wang, Z., & Jin, X. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1434300 · Cited by 2
Zhang, S., Zhang, X., Xiahou, Z., Zuo, S., Xue, J., & Zhang, Y. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1470449 · Cited by 2
Jiang, C., Wu, W., Jiang, X., & Qian, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-74281-z · Cited by 2
Cell cycle expression heterogeneity predicts degree of differentiation
Noller, K., & Cahan, P. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae536 · Cited by 2
Jin, X., Tian, Y., Zhu, H., Sun, Y., & Zhang, Z. (2024) · Discover Oncology · doi:10.1007/s12672-024-01614-9 · Cited by 3
Green, E. H., Kotrannavar, S., Rutherford, M. E., Lunnemann, H. M., Kaur, H., Heiser, C. N., Ding, H., Simmons, A. J., Liu, X., Lacy, D. B., et al. (2025) · The Journal of Pathology · doi:10.1002/path.6406 · Cited by 3
Wei, S., Lu, Y., Wang, P., Li, Q., Shuai, J., Zhao, Q., Lin, H., & Peng, Y. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06263-2 · Cited by 2
Zhao, G., Li, Y., Li, H., Bao, M., Lubamba, G. P., Wang, G., Han, B. R., Tang, Y., Li, T., & Li, C. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.027 · Cited by 2
Lei, J. (2025) · Lecture notes on mathematical modelling in the life sciences · doi:10.1007/978-3-031-82396-1_4 · Cited by 2
He, Z., Cao, J., Wang, X., Yang, S., Gao, H., Yu, Y., Di, Z., & Peng, C. (2025) · Hepatology Communications · doi:10.1097/hc9.0000000000000697 · Cited by 2
NSD2 and miRNAs as Key Regulators of Melanoma Response to Romidepsin and Interferon‐α2b Treatment
Santis, A. D., Santis, L. D., Rossi, F., Gasparini, S., Licursi, V., Amico, V. A., Capone, I., Fragale, A., D’Atri, S., Gabriele, L., et al. (2025) · Cancer Medicine · doi:10.1002/cam4.70917 · Cited by 2
Disruption of Morrbid alleviates autoinflammatory osteomyelitis in Pstpip2 -deficient mice
Huo, Q., Ding, J., Zhou, H., Wang, Y., Wang, S., He, H., Cai, L. C., Liu, J., Dong, G., & Cai, Z. (2025) · Disease Models & Mechanisms · doi:10.1242/dmm.052176 · Cited by 2
Wang, L., Chang, Z., Chen, S., Yang, J.-W., Wang, K.-Y., Li, K., Wan, S., Liu, S. H., & Li, Y. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1669822 · Cited by 2
CCDC137 knockdown suppresses bladder cancer progression by downregulating SCD
Zhang, H., Huang, W., Cai, Z., Mou, S., Liu, Y., Yan, B., Jiang, W., Liu, Y., Mei, Q., Liao, X., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07033-w · Cited by 3
Yi, M., Feng, X., Guan, Q., Liu, Y., Liu, Y., & Su, Z. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07086-x · Cited by 2
Mathematical Framework for Hierarchical Cell Identity Control by Collective Enhancer Dynamics
Grishechkin, A., Mukherjee, A., & Karin, O. (2025) · PRX Life · doi:10.1103/dkmq-h4d9 · Cited by 5
Lai, H., Wen, X., Peng, Y., & Zhang, L. (2023) · Current Stem Cell Research & Therapy · doi:10.2174/1574888x18666230821104844 · Cited by 2
Abedini‐Nassab, R., Adibi, E., & Ahmadiasl, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-74761-2 · Cited by 2
LSD induces increased signalling entropy in rats’ prefrontal cortexPreprint
Savino, A., & Nichols, C. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.23.449556 · Cited by 2
Billoud, B., Saint‐Marcoux, D., Chenivesse, S., Duchêne, C., Noûs, C., Langdale, J. A., & Charrier, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.21.457218 · Cited by 2
Emergence of an adaptive epigenetic cell state in human bladder urothelial carcinoma evolutionPreprint
Xiao, Y., Wan, J., Qian, K., Wu, K., Wang, G., Jiang, W. I., Cao, R., Ju, L., Zhao, Y., Zheng, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.30.466556 · Cited by 2
Self-citationYu, H., Ai, N., Peng, P., Ke, Y., Chen, X., Li, Y., Zhao, T., Jiang, S., Liu, J., & Jiang, L. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.02.466852 · Cited by 2
Hua, Y.-C., Vella, G., Rambow, F., Allen, E., Martinez, A. A., Duhamel, M., Junius, S., Smeets, A., Nittner, D., Laoui, D., et al. (2021) · bioRxiv · doi:10.1101/2021.12.24.474088 · Cited by 3
A Multi-Omics Approach to Visualize Early Neuronal Differentiation in 4DPreprint
SAMARA, A. A., Spildrejorde, M., Sharma, A., Falck, M., Leithaug, M., Modafferi, S., Bjørnstad, P. M., Acharya, G., Gervin, K., Lyle, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.01.478732 · Cited by 2
Du, W. J., Wang, J., Kuo, T., Wang, L., McKimpson, W. M., Son, J., Watanabe, H., Kitamoto, T., Lee, Y., Ratner, L. E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.21.484748 · Cited by 2
Farshidfar, F., Peng, C., Levovitz, C., Knight, J., Bacchiocchi, A., Su, J., Rhrissorrakrai, K., Yin, M., Sznol, M., Ariyan, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.20.440286 · Cited by 2
Self-citationZamboulis, D. E., Marr, N., Lenzi, L., Birch, H. L., Screen, H. R. C., Clegg, P., & Thorpe, C. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.04.522701 · Cited by 2
A single-cell atlas of mongolia sheep early embryoPreprint
he, T., Guo, W., Yang, G., Su, H. L., dou, A., chen, L., ma, T., Su, J., Liu, M., Le, S. B. G. R., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-1794514/v1 · Cited by 2
Liu, Q., Zhang, S., Wang, Q., Tian, X., Zhang, N., Li, L., Wang, S., Lv, B., & Jiang, S. (2023) · Research Square · doi:10.21203/rs.3.rs-2721118/v1 · Cited by 2
Malanchi, I., Blasio, S. D., Rizou, T., Gay, L., Rabas, N., Chakravarty, P., Snoeks, T. J. A., Karoutas, A., Rodrigues, F. S., Bridgeman, V. L., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3173228/v1 · Cited by 2
Healey, H. M., Penn, H. B., Small, C. M., Bassham, S., Goyal, V., Woods, M. A., & Cresko, W. A. (2024) · eLife · doi:10.7554/elife.97764.3 · Cited by 3
Arai, J., Iwata, Y., Tsubosaka, A., Kinoshita, H., Shinohara, S., Abe, S., Shiokawa, T., Oura, K., Suzuki, N., Hata, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.15.648856 · Cited by 2
Luo, J., Meng, Z., Liu, D., Hu, Y., Qiu, J., Chen, B., Wang, L., He, J., Yan, B., Meng, Y., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6671626/v1 · Cited by 2
Henríquez, J. A., Jihad, M., Jassim, A., Lloyd, E. G., Luo, W., Manansala, J. S., Harish, S., Teles, S. P., Cheng, P. S. W., Mucciolo, G., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703350 · Cited by 2
Discrete Diffusion for Single-Cell Gene Expression ModelingPreprint
Bhattacharya, S., Gensbigler, C., Karim, S., & Lees, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.19.705033 · Cited by 4
Gbp2 driving macrophages dynamics in murine heart transplant
Zhang, B., Ji, W., Wang, D., Chen, G., Xiong, W., & Qi, F. (2024) · Tissue and Cell · doi:10.1016/j.tice.2024.102695 · Cited by 1
Deng, M. (2024) · American Journal of Clinical and Experimental Urology · doi:10.62347/ascy2532 · Cited by 1
c-JUN: a chromatin repressor that limits mesoderm differentiation in human pluripotent stem cells
Zhang, R., Li, G., Zhang, Q., Wang, Z., Xiang, D., Zhang, X., Chen, J., Hutchins, A. P., Qin, D., Su, H., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf001 · Cited by 1
Wen, C., Tao, H., Chen, H., Pu, W., Yan, Q., Zou, Y., Su, S. S., Zhou, L., Peng, Y., Wang, G., et al. (2025) · Journal of Gastroenterology · doi:10.1007/s00535-025-02218-y · Cited by 1
Tao, L., Xia, J., Hu, D., Zhang, G., Gong, Y., & Jin, Y. (2025) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70411 · Cited by 1
Lv, P., Zhang, Y., Wu, W., Jiang, X., & Xiang, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-90121-0 · Cited by 1
Song, Z., Li, Y., Xu, S., Qian, S., Xu, W., Li, X., & Tian, F. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101245 · Cited by 1
Tian, J., Zhang, L., Zhuang, L., Lin, P., Zhang, S., Yan, Y., Yang, Y., Zhang, G., Li, H., & Lai, B. (2025) · Theranostics · doi:10.7150/thno.99526 · Cited by 1
Fang, G., Chen, J., Xi, T., Liu, Y., Wu, Y.-L., Wen, Y., & Tang, H.-X. (2025) · International Immunopharmacology · doi:10.1016/j.intimp.2025.114466
Liu, C., Ding, T., Zou, R., Zhang, A., Zhi, Z., & Wang, S. (2025) · BMC Cancer · doi:10.1186/s12885-025-14166-0 · Cited by 1
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 ›
Are you an author, or do you see a problem with this page? Request a review
