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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- Findingown data
Pluripotency genes showed an arc-like pattern early in human embryogenesis, increasing until the emergence of embryonic stem cells and then decreasing.
“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).”
Quote in the paperNot a substantial claim - Findingown data
Gene counts appeared in the top 1% of the ranked list (104 out of 18,711) in the screen of RNA-based correlates.
“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”
Quote in the paperSubstantial claim - Findingown data
Gene counts generally decreased with successive stages of differentiation regardless of whether cycling cells, non-cycling cells, or early human embryogenesis prior to pluripotency factor upregulation were examined.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21] source
Not checked in the free run.
- Findingown data
There was strong concordance between the number of accessible peaks and the mean number of detectably expressed genes per phenotype.
“We observed strong concordance between the number of accessible peaks and the mean number of detectably expressed genes per phenotype (fig. S3A).”
Quote in the paperNot a substantial claim - Findingown data
The number of detectably expressed genes per cell ('gene counts') is a robust determinant of developmental potential.
“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.”
Quote in the paperSubstantial claim- 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
- Findingcitation
Genome-wide chromatin accessibility progressively decreased with differentiation of hESCs into paraxial mesoderm and lateral mesoderm lineages.
“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).”
Quote in the paperSubstantial claim - Findingown data
The relationship between gene counts and differentiation was also observed in other organisms, including Caenorhabditis elegans and zebrafish.
“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.”
Quote in the paperSubstantial claim - Findingown data
The association between gene counts and differentiation was maintained across independent in vivo mouse lineage trajectories (R2 = 0.89, P = 1.8 × 10−8).
“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).”
Quote in the paperSubstantial claim- Rests on an assumption: The known phenotype labels used to average features and assess correlation are correct.
- Findingown data
Genes up-regulated in highly clonogenic normal LPs were skewed toward genes predicted to mark less-differentiated cells.
“Moreover, genes that were up-regulated in highly clonogenic normal LPs (39) were skewed toward genes predicted to mark less-differentiated cells (Fig. 4B).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [39] source
Not checked in the free run.
- Findingown data
Similar patterns of concordance between chromatin accessibility and gene counts were observed for lung adenocarcinoma cells jointly profiled by ATAC-seq and RNA-seq (sci-CAR) and for human hematopoiesis.
“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).”
Quote in the paperSubstantial claim - Findingown data
GULP1 emerged as the top candidate gene after filtering for genes more highly expressed in tumor LPs than MLs and expressed in <20% of tumor LPs.
“After applying this filter, GULP1 emerged as the top candidate gene (Fig. 4D, bottom right, and fig. S18C).”
Quote in the paperSubstantial claim - Findingown data
A distinct valley in RNA abundance coincided with elevated expression of Hoxb5, a marker of long-term or quiescent HSCs.
“By devising a simple approach to visualize inferred RNA content as a function of CytoTRACE (Fig. 3D, top), we observed a distinct valley in RNA abundance that coincided with elevated expression of Hoxb5, a marker of long-term or quiescent HSCs (35) (Fig. 3D, bottom).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [35] source
Not checked in the free run.
- Findingcitation
GCS is inherently insensitive to dropout events.
“GCS, like gene counts, is inherently insensitive to dropout events, is agnostic to prior knowledge of developmentally regulated genes, and is not solely attributable to multi-lineage priming (28) (fig. S9 and supplementary text) or a known molecular signature (e.g., pluripotency) (fig. S2B and table S5).”
Quote in the paperSubstantial claim - Findingreasoning
GCS is agnostic to prior knowledge of developmentally regulated genes.
“GCS, like gene counts, is inherently insensitive to dropout events, is agnostic to prior knowledge of developmentally regulated genes, and is not solely attributable to multi-lineage priming (28) (fig. S9 and supplementary text) or a known molecular signature (e.g., pluripotency) (fig. S2B and table S5).”
Quote in the paperSubstantial claim - Findingown data
The validation cohort consisted of 141,267 single cells spanning 266 phenotypes, nine biological systems, five species, and nine scRNA-seq platforms.
“These datasets represent diverse developmental and differentiation processes and consist of 141,267 single cells spanning 266 phenotypes, nine biological systems, five species [including two whole organisms (29, 30)], and nine scRNA-seq platforms”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingown data
There was a significant enrichment of genes whose knockdown by RNAi led to decreased viability of tumor cells in PDX models of TNBC (Q = 0.002, GSEA) among genes ordered by correlation with CytoTRACE in malignant LPs.
“In this rank-ordered list, we observed a significant enrichment of genes whose knockdown by RNA interference (RNAi) led to decreased viability of tumor cells in patient-derived xenograft (PDX) models of TNBC (41) (Q = 0.002, gene set enrichment analysis) (Fig. 4C; fig. S18, B and C, and table S8).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [41] source
Not checked in the free run.
- Findingown data
CytoTRACE achieved higher accuracy overall than RNA velocity (median 74% versus 54%) on five datasets with continuous developmental processes.
“Although both methods performed similarly on an embryonic chromaffin dataset from the RNA velocity study (8), CytoTRACE achieved higher accuracy overall (median of 74% versus 54%, respectively; fig. S13C).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] source
Not checked in the free run.
- Findingown data
CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The experimentally determined differentiation states in the training and validation datasets accurately reflect developmental potential.
- Findingown data
CytoTRACE outperformed all evaluated RNA-based features in the validation cohort at the single-cell level.
“When assessed at the single-cell level, CytoTRACE outperformed all evaluated RNA-based features in the validation cohort (Fig. 2B)”
Quote in the paperSubstantial claim - Findingown data
A dataset-specific gene counts signature (GCS), formed by averaging expression levels of genes most highly correlated with gene counts in each dataset, became the top-performing measure in the screen, outranking every predefined gene set and computational tool assessed.
“Indeed, by simply averaging the expression levels of genes that were most highly correlated with gene counts in each dataset (materials and methods), the resulting dataset-specific gene counts signature (GCS) became the top-performing measure in the screen, outranking every predefined gene set and computational tool that we assessed (fig. S8, A to D).”
Quote in the paperSubstantial claim - Findingown data
CytoTRACE achieved a median rho of 0.72 versus 0.53 for the second-highest-ranking approach (P = 0.001).
“achieving a substantial gain in performance over the second highest-ranking approach (median rho = 0.72 versus 0.53 for the second-highest-ranking approach, P = 0.001) (Fig. 2C; fig. S10B; and table S2 and S4).”
Quote in the paperSubstantial claim - Findingcitation
Variability in gene counts between phenotypically identical single cells is not exclusively due to drop-out events, but also due to differential sampling of the transcriptome.
“Moreover, as has been previously shown (45), our results indicate that variability in gene counts between phenotypically identical single cells is not exclusively due to drop-out events, but also due to differential sampling of the transcriptome (fig. S4).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [45] source
Not checked in the free run.
- Findingcitation
GCS is not solely attributable to multi-lineage priming.
“GCS, like gene counts, is inherently insensitive to dropout events, is agnostic to prior knowledge of developmentally regulated genes, and is not solely attributable to multi-lineage priming (28) (fig. S9 and supplementary text) or a known molecular signature (e.g., pluripotency) (fig. S2B and table S5).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28] source
Not checked in the free run.
- Findingown data
GULP1 knockdown reduced proliferation of metastatic TNBC cell lines MDA-MB-231 and MDA-MB-468 as measured by a colorimetric assay for metabolic activity.
“We found that GULP1 knockdown reduced proliferation of both cell lines as measured by a colorimetric assay for metabolic activity (fig. S19E).”
Quote in the paperSubstantial claim - Findingown data
GCS is not solely attributable to a known molecular signature (e.g., pluripotency).
“GCS, like gene counts, is inherently insensitive to dropout events, is agnostic to prior knowledge of developmentally regulated genes, and is not solely attributable to multi-lineage priming (28) (fig. S9 and supplementary text) or a known molecular signature (e.g., pluripotency) (fig. S2B and table S5).”
Quote in the paperSubstantial claim - Findingown data
Markers of immature cells were readily prioritized in 86% of benchmarking datasets by rank-ordering genes based on correlation with CytoTRACE.
“By rank-ordering genes on the basis of their correlation with CytoTRACE, markers of immature cells were readily prioritized in 86% of benchmarking datasets (fig. S15A).”
Quote in the paperSubstantial claim - Findingown data
CytoTRACE results were positively correlated with the direction of differentiation in 88% of datasets (P = 7 × 10−7, binomial test).
“In addition, CytoTRACE results were positively correlated with the direction of differentiation in 88% of datasets (P = 7 × 10–7, binomial test).”
Quote in the paperSubstantial claim - Findingown data
CytoTRACE, a two-step procedure that smooths GCS based on transcriptional covariance among single cells, outperformed GCS and the other RNA-based features evaluated.
“The resulting method, which we call CytoTRACE [for cellular (Cyto) Trajectory Reconstruction Analysis using gene Counts and Expression; https://cytotrace.stanford.edu], outperformed GCS and the other RNA-based features that we evaluated (fig. S8 and table S2).”
Quote in the paperSubstantial claim - Findingreasoning
CytoTRACE is a computational framework for predicting differentiation states from scRNA-seq data.
“and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data.”
Quote in the paperSubstantial claim - Findingown data
GCS was still moderately noisy in some datasets.
“Despite these characteristics, GCS was still moderately noisy in some datasets (e.g., Fig. 2A, center and fig. S8C).”
Quote in the paperSubstantial claim - Findingcitation
In some datasets, such as in vitro differentiation of hESCs into gastrulation layers, the number of expressed genes per cell exhibited considerable intra-phenotypic variation.
“However, in some datasets, such as that for in vitro differentiation of hESCs into the gastrulation layers (27), the number of expressed genes per cell exhibited considerable intra-phenotypic variation (Fig. 2A, left).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [27] source
Not checked in the free run.
- Findingown data
At a single-cell level, 412 predefined gene sets from the in silico screen outperformed gene counts.
“Indeed, when evaluated at a single-cell level, 412 predefined gene sets from our in silico screen outperformed gene counts (fig. S8A and table S2).”
Quote in the paperNot a substantial claim - Premisecitation
Existing computational methods for predicting lineage trajectories generally rely on a priori knowledge of the starting point (and thus direction) of the inferred biological process.
“Although a large number of computational methods for predicting lineage trajectories have been described, they generally rely upon (i) a priori knowledge of the starting point (and thus, direction) of the inferred biological process (6, 7)”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationown data
The relationship between gene counts and differentiation is a general feature of cellular ontogeny.
“suggesting that it is a general feature of cellular ontogeny.”
Quote in the paperSubstantial claim- 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.
- Premisenone given
With existing in silico approaches, it is difficult to distinguish quiescent (noncycling) adult stem cells that have long-term regenerative potential from more specialized cells.
“Moreover, with existing in silico approaches, it is difficult to distinguish quiescent (noncycling) adult stem cells that have long-term regenerative potential from more specialized cells.”
Quote in the paperSubstantial claim - Premisecitation
Existing computational methods for predicting lineage trajectories generally rely on the presence of intermediate cell states to reconstruct the trajectory.
“and (ii) the presence of intermediate cell states to reconstruct the trajectory (8, 9).”
Quote in the paperSubstantial claim0 of 2 sources checked - Study design and conductown data
CytoTRACE results were robust to the use of smoothing.
“These results were consistent with our findings for the training cohort (Fig. 2B and fig. S10D) and were robust to the use of smoothing (fig. S11).”
Quote in the paperNot a substantial claim - Interpretationown data
These data suggest a possible role for GULP1 in human breast cancer tumorigenesis.
“These data suggest a possible role for GULP1 in human breast cancer tumorigenesis.”
Quote in the paperSubstantial claim - Study design and conductnone given
The training cohort consisted of nine gold standard scRNA-seq datasets with experimentally-confirmed differentiation trajectories.
“To assess these RNA-based features, we compiled a training cohort consisting of nine gold standard scRNA-seq datasets with experimentally-confirmed differentiation trajectories.”
Quote in the paperSubstantial claim- Rests on an assumption: The experimentally determined differentiation states in the training and validation datasets accurately reflect developmental potential.
- May combine two claims. 13
- Classified as probably not a substantial claim. 8
May combine two claims.
Findingown dataCytoTRACE identified the correct root in 4,442 bone marrow cells without user input, whereas Monocle 2 identified 23 possible roots.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe number of expressed genes per cell generally showed consistent performance with respect to key technical parameters and was generally correlated with mRNA content.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the mouse hematopoietic system, both cycling and quiescent HSC subpopulations were correctly predicted to be less differentiated, but only proliferative HSCs were significantly ranked above early progenitors.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] source
Not checked in the free run.
May combine two claims.
Findingown datascRNA-seq profiling of breast tumor epithelial cells and adjacent normal epithelial cells from eight patients with TNBC or ER+ breast cancer confirmed three well-separated clusters of basal, LP, and ML cells, each with characteristic expression patterns of previously described lineage markers.
“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).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNo obvious clustering was observed for tumor versus normal cell differences or by patient.
“No obvious clustering was observed for tumor versus normal cell differences or by patient (Fig. 4A; fig. S18A).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCytoTRACE can be applied across independent scRNA-seq datasets unified by batch correction, and single-cell orderings predicted by CytoTRACE were accurate.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataGULP1 knockdown in PDXs (n=2) either inhibited tumor growth (TNBC sample) or fully abrogated tumor growth (ER+ sample).
“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).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Premisenone givenThe utility of gene expression-based models (e.g., transcriptional entropy, pluripotency-associated gene sets, and machine learning strategies) across diverse developmental systems and single-cell sequencing technologies is still unclear.
“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.”
Quote in the paperSubstantial claim0 of 5 sources checkedProbably not a substantial claim: may not pass S4 (based).
Premisenone givenInferring both the state and direction of differentiation from scRNA-seq data is challenging.
“However, inferring both the state and direction of differentiation is challenging.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationSingle-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] source
Not checked in the free run.
- Also stated in: § Abstract · hedged
Classified as probably not a substantial claim.
PremisecitationThese requirements can be challenging to satisfy in certain contexts such as human cancer development.
“These requirements can be challenging to satisfy in certain contexts such as human cancer development (10).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductown dataThe validation cohort consisted of 33 additional scRNA-seq datasets from 26 studies representing diverse developmental and differentiation processes.
“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).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationcitationDifferentiating 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.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [47] source
Not checked in the free run.
May combine two claims.
InterpretationcitationLess 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.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [46] source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenThe training cohort was 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.
“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).”
Quote in the paperSubstantial claim- Rests on an assumption: The experimentally determined differentiation states in the training and validation datasets accurately reflect developmental potential.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationThe model of less mature cells maintaining looser chromatin can be represented by 'attractor states' within a genome-wide gene regulatory network.
“Theoretically, this model can be represented by “attractor states” within a genome-wide gene regulatory network (47).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [47] source
Not checked in the free run.
May combine two claims.
Interpretationown dataCytoTRACE and the RNA content visualization approach can identify quiescent stem cells that could not be identified by gene counts or RNA content alone.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataCytoTRACE facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis.
“Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThis study establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.
“This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.”
Quote in the paperSubstantial claim
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?
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.
“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]
The Discussion describes nearly the same relationship between cell maturity, chromatin accessibility, and transcriptome sampling, but calls it a model consistent with the results rather than an established observation. The Results report decreasing chromatin accessibility and concordant gene counts during differentiation; they do not establish that looser chromatin permits wider sampling. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCytoTRACE score was calculated from a set of 19,000 annotated genes to determine the differentiation status of cells.
“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]
The Results sections “RNA-based correlates of single-cell differentiation states” and “Development of CytoTRACE” support using CytoTRACE to infer differentiation status. But the approximately 19,000 items were candidate correlates screened—including gene sets—not a set of annotated genes used to calculate each CytoTRACE score. Our copy provides no page numbers for these passages. (LLM, conf=0.91)
Experimental — accuracy not yet measuredCytoTRACE 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.
“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 number of expressed genes per cell can be considered a hallmark of the developmental potential of a cell.
“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]
CytoTRACE has been used to predict the differentiation and developmental state of cells across tissues and species.
“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]
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- 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 · 2019: 1
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-11.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.
Statistics and arithmetic are recomputed, not judged.
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