The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 2 of the 8 sources its claims cite were checked for support.
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
Open accessAbstract
Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.
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Claims the paper makes
44 Own result · 26 Own method · 20 Own interpretation · 5 No full text of the sources · 5 No basis given · 1 Supported · 1 Partly supported · 1 Sources not found · accuracy not yet measured
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In the paper
“Cluster co-membership alone, however, is insufficient to conclude that two cell populations from different organs represent the same or similar cell types; at any given resolution, unbiased clustering that groups related cells may also group unrelated cells21.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Partly supportedcites [21] Freytag, S., Tian, L., Lonnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res 7, 1297 (2018). sourceExperimental — accuracy not yet measured
The Results (Evaluation of clustering tools, Silver standard data; page unavailable) report that most clusters contained mixtures of different inferred cell types, supporting the caution that co-membership alone does not establish cell-type identity. The paper evaluates PBMC datasets, however, and does not test populations from different organs or establish that such populations were grouped together. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
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In the paper
“transcriptional heterogeneity of brain endothelial cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“Clusters 1–3 contain mostly single positive T cells (Cd4+ or Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“a cell population expressing Chodl in limb muscle”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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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
“Therefore, to determine which clusters are composed of related or unrelated cell types, we computed a heterogeneity score for each cluster (Methods), and found low scores for the biologically sensible clusters discussed above (Extended Data Fig. 9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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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
“To understand which TFs were most informative for specifying cell types, we performed variable selection from random forests (Methods) and determined that 136 TFs are needed to simultaneously define all cell types across all organs (Fig. 5f, Supplementary Table 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plainstates an association- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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 compendium is comprised of single-cell transcriptomic data from 100,605 cells isolated from 20 organs from 3 female and 4 male, C57BL/6JN, 3-month-old mice (10–15 weeks), analogous to 20-year-old humans (Fig. 1).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The resulting dendrogram closely resembles the dendrogram produced using all expressed genes, indicating that TFs can be used to reconstruct known cell ontology relationships between bulk populations (entanglement = 0.11; Extended Data Fig. 10a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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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
“Nonetheless, microfluidic droplets identified cell types missed by FACS in both organs, for example kidney mesangial cells, and splenic dendritic and natural killer cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“the expression of MHC class II genes by adult mouse T cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 27
- It may state two separate claims as one. 45
Every source the paper cites for this claim was checked and supports it.
In the paper
“For each cluster, we provide annotations in the controlled vocabulary of a cell ontology19 to facilitate inter-experiment comparisons.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
What the sources say
- Supportedcites [19] Bakken, T. et al. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 18 (Suppl 17), 559 (2017). source
The Background section describes the Cell Ontology as a standard reference nomenclature needed to compare experimental results about cell types. It also says the ontology is used as a reference annotation vocabulary. The citing authors’ statement that they annotated each cluster describes their own work; the citation supports their choice of vocabulary and its purpose. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
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
“Single-cell transcriptomes were sequenced to an average depth of 814,488 reads per cell (FACS) and 7,709 unique molecular identifiers (UMIs) per cell (microfluidic droplet).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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It 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 bladder, liver, lung, mammary gland, trachea, tongue, and spleen show nearly twice as many genes detected per cell with FACS compared to microfluidic droplet, whereas heart and marrow show comparable numbers (Extended Data Fig. 5a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plainstates an association- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Their data contains four times as many cells and included several sample types not present in our data, such as neonatal and fetal organs, cell lines, and young adult ovary, peripheral blood, placenta, prostate, small intestine, stomach, testis, and uterus.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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It 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
“We then determined the TF sets distinguishing each individual cell type from all other cells. These sets vary substantially in size (from 2 to 813 TFs) and are not necessarily unique to each cell type (Fig. 5g–i; Supplementary Table 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“However, our FACS data contains four times as many genes per cell, and we analyzed several organs not present in the Han et al. data set, such as aorta, four brain regions, diaphragm, four fat types, four adult heart chambers, adult skin, tongue, and trachea.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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It 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
“while Cluster 2 contains mostly non-thymic T cells expressing the high affinity IL2 receptor (Il2ra, Il2rb), suggesting they are activated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It 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
“Cluster 0 comprises thymic cells undergoing VDJ recombination characterized by RAG (Rag1, Rag2) and TdT (Dntt) expression, and includes uncommitted double positive T-cells (Cd4+, Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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It 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
“Cluster 2 contains B cells from fat, limb muscle, lung, spleen, marrow, and liver, but also cells annotated as leukocytes and lymphocytes from the thymus, heart, and limb muscle.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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It 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
“As expected, cells from different organs often mixed, with 25 of 54 clusters containing (at least five) cells from distinct organs (Fig. 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plainstates an association- Asserted: passes — The authors put it forward as true.
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It 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 contrast, when we repeated the analysis using cell surface markers, RNA splicing factors, or the two groups combined (equivalent to a random set of genes), the entanglement was 0.22, 0.25 and 0.34, respectively, suggesting that none of these molecular classes define cell type to the extent that transcription factors do.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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It 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
“Finally, Cluster 1 also represents mature T cells, but primarily splenic.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plainstates an association- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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
“This seems only to occur for populations smaller than ~30 cells, which comprise under 4% of the overall dataset, and represents a lower limit of sensitivity in the current release of data interpretation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It 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
“Comparing methods shows organ-specific differences in the number of cells analyzed (Fig. 1b, c), reads per cell (Extended Data Fig. 3a, c) and genes per cell (Extended Data Fig. 3b, d).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plainstates an association- Asserted: passes — The authors put it forward as true.
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It 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
“Cluster 4 contains predominantly proliferating thymic T cells which may represent pre-T cells expanding following VDJ recombination.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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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
“Furthermore, with both methods the most abundant cell types analyzed are epithelial cells and leukocytes, although FACS captured a larger diversity of cell types (Extended Data Fig. 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plainstates an association- Asserted: passes — The authors put it forward as true.
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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 those cell types captured by both methods, the proportion of each cell type is equivalent (Pearson correlation coefficient: spleen = 0.99, kidney = 0.99).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“For example, the median number of genes detected in bladder is ~4,900 (FACS), 2,900 (droplet), and 900 (microwell-seq), while in kidney it is ~1,400 (FACS), 1,900 (droplet), and 500 (microwell-seq).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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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, clusters 3 and 48 each contain endothelial cells from five or more organs, and clusters 1 and 24 contain mesenchymal and stromal cells from four or more organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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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
“We clearly observed these markers in several organs including limb muscle (Extended Data Fig. 11), but many showed little evidence of cellular activation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
boosted- Asserted: passes — The authors put it forward as true.
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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
“Dotted horizontal line demarcates the median saturation (~ 0.9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
hedgedn = 55,032- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
“Fraction of all detected genes (defined as > 0 reads or UMIs) for each cell, across all organs, detected at increasing read or UMI thresholds for FACS (left; n= 44,949 individual cells), microfluidic droplet (middle; n= 55,656 individual cells), and microwell-Seq (right; n= 28,372 individual cells).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
plainn = 44,949- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The number of genes detected (threshold of > 0 reads or UMIs per cell) by FACS (red; n= 21,105 individual cells), microfluidic droplets (green; n = 55,032 individual cells), and microwell-Seq (blue; n= 25,891 individual cells) (Han et al.).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
What the sources say
- Not matchedcites Han et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainn = 21,105- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
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
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plainstates an association- Asserted: passes — The authors put it forward as true.
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It 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
“Such gene expression profiles largely correlate (Pearson correlation coefficient: 0.74 – 0.90), suggesting that while biases between methods exist, both accurately recapitulate average cell type gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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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
“All organs used a standard annotation method; an example using liver is contained in the Organ Annotation Vignette.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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Probably 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
“This release allows for the exact replication of all results, facilitates in-depth analyses not completed here, and provides a comparative framework for future studies using the large variety of murine disease models.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plainstates a cause- Asserted: passes — The authors put it forward as true.
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It 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
“In fact, for nearly all reprogramming protocols the TFs used also specified the targeted cell type in our data (Supplementary Table 5), suggesting that our data can inform novel reprogramming schemes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Recently, Han et al.20 published a complementary scRNA-seq study across mouse organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably 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
“Many of these cell types have not previously been obtained in pure populations, and our data provide a wealth of new information on their characteristic gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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 is partially explained by cellular abundance and sampling depth (12,333 microfluidic droplet cells vs. 2,216 FACS cells, Supplementary Table 1), and possibly from cell capture and lysis biases between methods.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“Therefore, the dissociation- related satellite cell markers are not universal, and organs likely display unique dissociation-related expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“While these data are by no means a complete representation of all mouse organs and cell types, they provide a first draft attempt to create an organism-wide representation of cellular diversity.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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 enabled the direct comparison of cell type composition between organs, and the comparison of shared cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“The fact that most cells of similar cell types cluster together across organs and biological replicates shows that batch effects are not the main source of variance in the dataset.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“3) We annotated cell types using standard cell ontology terms, thereby enabling cross-organ and cross-experiment analyses”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“While this is known in human T cells, MHC class II was previously thought restricted to professional antigen presenting cells in mice22.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Not checked — no full text of the sourcecites [22] Holling, T. M., Schooten, E. & van Den Elsen, P. J. Function and regulation of MHC class II molecules in T-lymphocytes: of mice and men. Hum. Immunol. 65, 282–290 (2004). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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
“1) All data, analysis, and code is freely available”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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 is a predictable artifact of the annotation scheme: since entire clusters, rather than individual cells, were annotated in each organ, a sufficiently rare cell type that was algorithmically grouped with a more populous cell type will be mis-annotated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
boostedstates a causeSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“All data, protocols, analysis scripts, and an interactive data browser are publicly shared (http://tabula-muris.ds.czbiohub.org/ ).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“Importantly, the presence of such gene expression changes does not prevent cell type identification or the comparison of cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“As the nomenclature is not identical, the analysis was performed between cell types that could be matched with a certain degree of confidence between the three methods (Supplementary Table 2).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Differential expression overlap analysis
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Littermates were not recorded or tracked, and mice were housed at the VA VMU for no longer than 2 weeks before euthanasia.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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
“Sample wells were then selected if their cDNA concentrations were at least one standard deviation greater than the mean concentration of the blanks.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Sorters were calibrated using FACS buffer every day before collecting any cells, and also after every 8 sorted plates.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > FACS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“A similar analysis was done globally for all FACS processed cells and for all microfluidic droplet processed cells to produce an unbiased clustering.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“Cells with fewer than 500 detected genes were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“While it is not an exhaustive characterization of all mouse organs, it does provide a rich dataset of the most highly studied organs in biology.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“This Tabula Muris, or “Mouse Atlas”, has many uses, including the discovery of new putative cell types, the discovery of novel gene expression in known cell types, and the ability to compare cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It 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
“Cells with fewer than 50,000 reads (FACS) or 1000 UMI (microfluidic droplet) were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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
“No sample size choice was performed prior to the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Cells were lexicographically sorted by cell ID to ensure reproducibility.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Cell types were assigned to each cluster using the abundance of known marker genes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“When clusters appeared to be mixtures of cell types, they were refined either by increasing the resolution parameter for clustering or subsetting the data and rerunning steps 3–7.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“All animal care and procedures were carried out in accordance with institutional guidelines approved by the VA Palo Alto Committee on Animal Research.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Sample plates were selected for downstream processing if the mean concentration of blanks (ERCC-containing, non-cell wells) was greater than 0 ng/μl, and, after linear regression of the values obtained from the Quant-iT dsDNA standard curve, the R2 value was greater than 0.98.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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
“Four 10–15 week old male and four virgin female C57BL/6JN (derived from C57BL/6J in 2016) mice were shipped from the National Institute on Aging colony at Charles River to the Veterinary Medical Unit (VMU) at the VA Palo Alto (VA).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“Tabula Muris provides a framework and description of many of the most populous and important cell populations within the mouse, and represents a foundation for future studies across a multitude of diverse physiological disciplines.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“Randomization and blinding were not performed – the authors were aware of all data and metadata related variables during the entire course of the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It 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
“7) Full-length transcript data makes possible transcription factor, splice variant, and sequence variant analyses.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
It was unknown whether single-cell data from different animals, labs, and techniques could be synthesized into a general resource for biology.
“It therefore remains unknown whether these data can be synthesized as a more general resource for biology.”
The paper relies on this without support.
Comparing data collected from different animals by independent labs with varying experimental techniques is challenging.
“However, while these reports provide valuable characterization of individual organs, it is challenging to compare data collected from different animals by independent labs with varying experimental techniques.”
The paper relies on this without support.
Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.
“Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.”
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 cited source says what the claim attributes to it.
In the citing paper
“A Tabula Muris compendium of single-cell RNA-Seq transcriptome data from 20 mouse organs and tissues (czbiohub.org) (14) revealed that ASGR1 transcripts are exclusively expressed in liver, specifically in hepatocytes (Fig. 1C).”
From Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR cleaved by furin (2021) · cites this paper as [14]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Ageing is associated with increased activation of the NLRP3 inflammasome45,107, which is primarily expressed by myeloid immune cells107,108, and a subsequent increase in the expression of IL-1β, which is a major cytokine implicated in promoting ageing-related disease107.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
Experimental — accuracy not yet measuredIn the citing paper
“CD38 expression was specific to adipose-tissue-resident macrophages (CD68+CD64+F4/80+CD11b+CD11c−), not the non-resident population (CD68+CD64+F4/80+CD11b+CD11c+) (Fig. 4h)”
From Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages (2020) · cites this paper as [36]
In the citing paper
“Multiple cells in the brain, including microglia, astrocytes, neurons and endothelial cells, express CD38 (REFS108,160), and treating microglia and astrocytes with inflammatory cytokines induces CD38 expression163,164.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
In the citing paper
“Transcription factors (TFs) have been shown to be the best molecular feature for cell type definition (Schaum et al., 2018).”
From Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei (2019) · cites this paper as Schaum et al., 2018
In the citing paper
“Furthermore, it has been shown that commonly used cell surface markers are limited in their ability to define cell identities (Tabula Muris Consortium et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tabula Muris Consortium et al, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
“liver, lung, mammary gland, pancreas, skin, spleen, thymus, tongue, and trachea from the same mouse were immediately processed into single cell suspensions.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“a) Barplot showing the heterogeneity score for each cluster containing multiple cell types. b-f) Heatmaps showing the average between-cell-type distances within select clusters”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“for clusters a) 1, b) 2, c) 3, d) 24, e) 48, f) 53.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Calculation of dissociation scores
“For each organ, principal component analysis was performed on a subset of 140 dissociation-related genes23.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > References
“23. Reichardt J & Bornholdt S Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys 74, 016110 (2006).”
Citations
Cites
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Gene selection and classification of microarray data using random forest
Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 3,013
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3881 · Cited by 2,136
Dynamic Pattern Formation in a Vesicle-Generating Microfluidic Device
Thorsen, T., Roberts, R. W., Arnold, F. H., & Quake, S. R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.4163 · Cited by 2,052
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Research works printed but not linked: 1
- Kassambara, A. Practical guide to cluster analysis in R: unsupervised machine learning 1st edn (CreateSpace, North Charleston, 2017).
Cited by
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Items 2,201–2,300 of 2,952
Interpretable deep generative ensemble learning for single-cell omics with Hydra
Wagle, M. M., Liu, C., Liu, Z., Wang, Y., Kellis, M., Patrick, E., & Yang, P. (2026) · Molecular Systems Biology · doi:10.1038/s44320-026-00208-7 · Cited by 1
Hejazi, P., Mury, P., Thorin, É., & Lettre, G. (2025) · Physiological Reports · doi:10.14814/phy2.70537 · Cited by 1
“To annotate cell types, we obtained the skin canonical markers from two different databases: Tabula muris (Schaum et al., 2018) and immunesinglecell skin atlas (Li et al., 2022).”
The molecular components of the anti-inflammatory cholinergic pathway are extrasplenic
Merchant, W., Wyler, S. C., Chen, B., & Gautron, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331707 · Cited by 3
“Likewise, publicly available transcriptomics data show enrichment of β -2 adrenergic receptors, but not Chrna7 in the mouse, human, and non-human primate spleen as well as varied immune cell types [83–85].”
scAGC: Adaptive Graph Convolutional Network for Clustering Single-Cell RNA-seq Data
Lai, Q., Yang, C.-M., Xia, X., Huang, D., Zhou, Z.-F., & Wen, Z. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94892-3_3 · Cited by 1
CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data
Kim, D., Chen, C., Yu, L., Yang, J. Y. H., & Yang, P. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf528 · Cited by 1
“The Tabula Muris Atlas (a.k.a. Mouse Cell Atlas [MCA]) (The Tabula Muris Consortium et al. 2018) and Tabula Sapiens Atlas (a.k.a. Human Cell Atlas [HCA]) (The Tabula Sapiens Consortium 2022) each comprise two versions: one generated using Fluorescence-Activated Cell Sorting (FACS) and Smart-Seq2 sequencing protocols,…”
Assessing tissue-specific gene expression of essential genes from human and mouse
Zheng, H., Fard, A. T., & Mar, J. C. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf487 · Cited by 1
“Therefore, investigating the characteristics of essential genes at the cell type level with largescale single-cell atlases like Tabula Muris(TM)andTabulaSapiens(TS)iscrucial[13,14].”
Physical Modeling of Embryonic Transcriptomes Identifies Collective Modes of Gene Expression
Skinner, D. J., Lemaire, P., & Mani, M. (2025) · PRX Life · doi:10.1103/7gs5-w48q · Cited by 3
Multi-level expression profiling of cardiac and musculoskeletal Schwann cells in young and old mice
Haider, S., Stefanovska, D., Sassu, E., Domisch, C., Hoßfeld, M., Iaconianni, P., Perez‐Feliz, S., Schneider‐Warme, F., Kohl, P. K., Preißl, S., et al. (2025) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2025.10.001 · Cited by 1
scAFGCC: An augmentation-free graph contrastive clustering method for scRNA-seq data analysis
Tian, S., Wang, Y., Wang, Y., Ji, C., & Ni, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131698 · Cited by 1
Stabilized marker gene identification and functional annotation from single-cell transcriptomic data
Acharya, S., Kossinna, P., Zhang, Q., & Guo, J. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013574 · Cited by 2
“We performed a systematic benchmarking of scSCOPE and other state-of-the-art methods [15–20] across 9 scRNAseq datasets including 6 human PBMC (Peripheral Blood Mononuclear Cell) [33] datasets and 3 mouse immune cell datasets generated by different sequencing technologies [34–36].”
InfluentialHu, Y., Zhao, Z., & Lian, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-20316-y · Cited by 2
Yu, Z., Ye, Y., & Pan, J. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2025.10.056 · Cited by 2
Wang, H., Dong, H., & Du, P.-F. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf597 · Cited by 1
“Over the last few years, many efforts have been made in performing cell type annotations with unsupervised clustering algorithms [2, 7–9].”
Ning, X., Li, J., Huang, Y., Chen, H., Zhang, X., Mei, Z., Fan, L., Chen, L., Gao, L. P., Li, S., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05757 · Cited by 1
Distinct immune landscapes between murine and human periodontitis
Zhao, Y., Wang, Q., Lin, W., et al. (2025) · iScience · doi:10.1016/j.isci.2025.114073 · Cited by 1
CARINH, an Interferon-Induced LncRNA in Cancer and Inflammation
Gourvest, M., & Solingen, C. V. (2025) · Non-Coding RNA · doi:10.3390/ncrna11060079 · Cited by 1
Zhu, Z., Shi, L., Li, H., Wang, X., Gao, J., Cheng, L., & Zhang, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf633 · Cited by 1
Distinguishing subtypes of endothelial cells in the mouse aorta
He, L., Pietilä, R. S., Miao, Y., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2525755122 · Cited by 1
“2 illustrates our scRNA - seq reanalysis of the eight previous studies (3–10).”
The hologenome in brain health and disease
Dooling, S. W., & Costa‐Mattioli, M. (2025) · Neuron · doi:10.1016/j.neuron.2025.10.039 · Cited by 2
Loss of MFAP5 and Its Effect on Skin Homeostasis and Wound Healing
Han, C., Yuan, H., Koh, T. J., Chen, L., Mecham, R. P., & DiPietro, L. A. (2025) · The FASEB Journal · doi:10.1096/fj.202501770r · Cited by 1
De, B., Liu, Y., Lu, L., Meng, F., Liu, Y., Jia, T., Chen, Y., Liu, C., Wang, S., Li, T., et al. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1697432 · Cited by 1
Liu, Y., Zhang, Z., Qui, M., Wang, S., Salim, F. D., Shen, J., Chen, T., Razzak, I., Li, F., & Bian, J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag330 · Cited by 1
Ma, Q., Wang, L., & Li, W. (2025) · mSystems · doi:10.1128/msystems.00223-25 · Cited by 1
Kowal, T., Wang, S. S.-M., Cheng, M., Liu, R., Blencowe, M., & Yang, X. (2025) · Toxicological Sciences · doi:10.1093/toxsci/kfaf171 · Cited by 2
ARAP2 regulates responses to interferon-gamma by restricting SOCS1
Keating, N., Doggett, K., Bidgood, G. M., Meza Guzman, L. G., Dagley, L. F., Li, K., Williams, B. E., Gabrielyan, A., Alvarado, C., Broomfield, B. J., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116667 · Cited by 1
Nimkulrat, S. D., Wagner, M. R., Waters, B. J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-32403-1 · Cited by 1
Adhikari, S., Carrier, C., Joshi, P. R., & Baral, P. (2025) · Nature Communications · doi:10.1038/s41467-025-66401-8 · Cited by 2
Janton, C., Nichols, A., & Hadjiargyrou, M. (2025) · JBMR Plus · doi:10.1093/jbmrpl/ziaf193 · Cited by 1
Robust subspace structure discovery for cell type identification in scRNA-seq data
Zhou, X., Wei, X., Liu, C., Shen, W., Xuan, P., Wu, S., & Wong, H. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06317-8 · Cited by 1
Fréreux, C., Karam, J. A. Q., Howley, B. V., Granger, B., Chakraborty, P., Vaena, S. G., Romeo, M. J., Dalton, A. C., Mohanty, B. K., Mehrotra, S., et al. (2026) · Communications Biology · doi:10.1038/s42003-025-09456-z · Cited by 2
“The Tabula Muris mammary gland dataset was used as a reference 78 .”
scMFE: A multi-view fusion enhanced graph contrastive learning method for scRNA-seq data clustering
Bai, Y., Shu, Z., Tan, K., Zhang, Y., & Yu, Z. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132662 · Cited by 2
Altendorfer, S., Walker, S. J., & Daub, C. O. (2026) · International Journal of Cancer · doi:10.1002/ijc.70733 · Cited by 2
Akins, K. A., Arkatkar, A., Flinn, M. A., Sandor, N., Purdy, A. L., Brusacoram, A., Swift, S. K., Buday, S. L., Choudhury, P., Cui, M., et al. (2026) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2025.101455 · Cited by 3
FAM83H Regulates Postnatal T Cell Development Through Thymic Stroma Organization
Ogan, B. M., Forstlova, V., Dowling, L. J., et al. (2026) · European Journal of Immunology · doi:10.1002/eji.70126 · Cited by 1
“Fam83h is enriched in epithelial cells of the gastrointestinal tract, skin, bladder, tongue, pancreas, and mammary gland (Figure 1A ) [ 26 ].”
Wang, S., Liu, S., Zhang, H., Sun, L., Tan, H., Shi, Y., Pan, L., Geng, M., Chen, M., Gao, B., et al. (2026) · Metabolism · doi:10.1016/j.metabol.2026.156509 · Cited by 2
MMP2 Sheds Glypican‐4 to Suppress Slit3‐Robo4 Signaling and Angiogenesis
Xiao, W., Orta, A. R., Yue, J., Song, X., Yin, H., & Wang, L. (2026) · Proteoglycan Research · doi:10.1002/pgr2.70044 · Cited by 1
Comparative Single‐Cell Transcriptomic Atlas Reveals the Genetic Regulation of Reproductive Traits
Zhao, B., Luo, H., Guan, D., Fu, X., Wang, F., Zhang, G., Li, S., Yang, H., & Zhang, Y. (2026) · Advanced Science · doi:10.1002/advs.202517633 · Cited by 1
Yan, S., Shang, J., Jiang, S., Zhang, X., Zhang, F., Sun, Y., & Liu, J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02735 · Cited by 1
Kaur, G., Lamb, T., Tjitropranoto, A., & Rahman, I. (2026) · eLife · doi:10.7554/elife.106380.4 · Cited by 1
Zhang, Z., Wang, Z., Zhu, L., Chen, T., Chen, R., Wu, Z., Chen, J., Zhang, H., Wang, M., Liu, J., et al. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.01.073 · Cited by 1
Welch, C. L., McEntagart, M., Moledina, S., Morgan, C. E., Swietlik, E. M., Hou, C., Qiao, L., Callejo, E. P., Craib, S., Smedley, D. P., et al. (2026) · Journal of Medical Genetics · doi:10.1136/jmg-2025-111027 · Cited by 1
Chen, K. L., Duemmel, S., Christof, M., Wang, G. C., Kenney, H. M., Nuzzo, M., Xu, Q., Korman, B. D., & Rahimi, H. (2026) · PLoS ONE · doi:10.1371/journal.pone.0340677 · Cited by 1
Paola, M. D., Reitz, C. J., Kuzmanov, U., Jia, K., & Gramolini, A. O. (2026) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2026.101527 · Cited by 2
Tanaka, P. P., Cotta-de-Almeida, V., Donadi, E. A., Westerberg, L. S., & Passos, G. A. S. (2026) · Human Molecular Genetics · doi:10.1093/hmg/ddag004 · Cited by 1
umite: fast quantification of Smart-seq3 libraries with improved UMI retrieval
Foerster, L. C., Frigoli, E., Sun, X., et al. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag075 · Cited by 1
“The advent of single-cell RNA-sequencing (scRNA-seq) technologies has had a transformative effect on molecular biology, enabling the rapid, high-throughput profiling of entire tissues and organs (Regev et al. 2017, The Tabula Muris Consortium et al. 2018, Cao et al. 2019).”
Chromatin accessibility landscapes define stromal cell identities across tissues
Nooranikhojasteh, A., Tavallaee, G., Khuu, N., Shen, S. Y., Ouladan, S., Raman, A., & Orouji, E. (2026) · Communications Biology · doi:10.1038/s42003-026-09720-w · Cited by 2
“The annotations were further re fi ned by integrating the scATAC-seq data with reference datasets from TabulaMurisandPanglaoDB 22,23 .”
Kim, E. S., Lin, M., Lu, C.-H., Casero, D., Aguirre, B., Brown, J., Olagoke, O. C., Martin, C. R., Wadehra, M., Calkins, K. L., et al. (2026) · Nutrients · doi:10.3390/nu18050749 · Cited by 1
“Estimates of cell type proportions for all TPM-normalized samples were obtained using the Gene Expression De-convolution Interactive Tool (GEDIT [30]) with signatures from the Tabula Muris Reference database [31].”
Lin, Z., Wei, J., He, X., Chen, Y., & Honghai, Z. H. O. U. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.108982 · Cited by 1
SEAL: Semantic-Aware Contrastive Learning for scRNA-Seq Clustering
Ye, Y., Sun, J., Peng, L., Yang, D., Xian, J., Shen, W., & Liu, C. (2026) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2026.3669981 · Cited by 1
A modular platform for Sterically Masked Activated Cytokines (SMACks)
Morgenstern, T. J., Ota, N., Lin, Z., Lechermann, L. M., Gill, H. S., Ogasawara, A. K., Williams, S., Marroquin, K. A., Scherl, A., Davies, C., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2513720123 · Cited by 1
Yan, X., Han, F., Wu, Y., & Tian, Z. (2026) · BMC Biology · doi:10.1186/s12915-026-02590-y · Cited by 1
Golomb, R., Yoles, M., Fishilevich, S., Cohen, B., Peled, S. S., Dahary, D., Gokhman, D., & Pilpel, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-71279-1 · Cited by 3
“Mouse single-cell RNA-seq data were obtained from Tabula Muris database 85 .”
Raposo, M. A., Mourabit, H. E., Gao, W., Minini, M., Morichon, R., Fouassier, L., Wendum, D., Saad, M. J. A., dos Santos, A., Gautheron, J., et al. (2026) · JHEP Reports · doi:10.1016/j.jhepr.2026.101819 · Cited by 3
Kottom, T. J., Pellegrino, M. R., Achilonu, C. C., Stelzig, K. E., & LIMPER, A. H. (2026) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiag228 · Cited by 1
Tang, S., Yao, X., Wang, S., Zhu, F., Zeng, W., Lai, D., Meng, X., Zhang, X., Zhu, Z., Zhang, T., et al. (2026) · Inflammation Research · doi:10.1007/s00011-026-02257-y · Cited by 1
“Pivotal to this approach was the Tabula Muris dataset [40], comprising single-cell RNA-seq data from approximately 100,000 cells across 20 murine organs and tissues.”
Multi-region proteomic mapping identifies FTL1 and SERPINA3K as protective factors in cardiac aging
Huang, J., Sun, X., Liu, H., Li, K. E. X. I. N., Liu, X., Bai, Y., Dong, Z., Wu, X., Liu, X., Jia, L., et al. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-08882-z · Cited by 2
Park, S. S., Heo, J., Kang, H. Y., & Park, T. J. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103185 · Cited by 1
IL-22 promotes genesis of small intestinal secretory cells that protect against cholera in mice
Suzuki, M., Hasegawa, Y., Zhang, H., Liang, Z., Tie, G., Shivdasani, R. A., & Waldor, M. K. (2026) · Nature Microbiology · doi:10.1038/s41564-026-02375-7 · Cited by 1
Zhao, R., Han, M., Zhang, B., Mm, M., Fan, K., Li, J., Sun, J., & Zhou, X. (2026) · Life · doi:10.3390/life16060950 · Cited by 1
“The baseline cell-type dataset was derived from the Tabula Muris reference [20].”
Stewart, C. A., Alar, C., Gozza, G. A., Bargsted, L., Kaklamanou, I., Polito, M. P., Bergamini, G., Tagliazucchi, L., Alessandrini, A., Costi, M. P., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73979-0 · Cited by 5
“In the entire atlas ( n = 32 animals, n =22.412 CTRL cells and n =13.904 EXP cells) we uncovered all the different cell types constituting the mouse back skin 23 – 25 (Fig.”
InfluentialMemory regulatory T cells reprogram into protective TFH cell-like effectors in recurrent malaria
Charles-Chess, N. A. E., Ruberto, A. A., Bowers, C., Schmidt-Silva, C., Obeng-Adjei, N., Hansen, M. R., Prasad, D. B., Rosenberg, A., Traoré, B., Klonowski, K. D., et al. (2026) · Nature Immunology · doi:10.1038/s41590-026-02537-z · Cited by 1
“…define the cellular identity of xT reg cells, we performed unbiased computational alignment using Azimuth and Panglao databases for reference-based mapping and marker-based validation against the Tabula Muris reference atlas, identifying their closest canonical cell-type counterparts 44 .”
YIF1A activates mTORC1 signaling to promote cellular senescence
Zhang, X., Liu, L., Shang, M., Hu, B., Zhu, S., Wang, X., Jieying, L., Han, Y., Wei, X., Cao, Q., et al. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-09034-z · Cited by 1
DADARWAL, D., & Carrasco, R. A. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1863960 · Cited by 2
Hu, D., Zhang, F., Wang, Z., Jing, Y., Yee, P. L., Lu, T., Zhao, Y. Q., Cui, X., Yuan, S., & Wang, C. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10709-1 · Cited by 1
Faust, T. E., Saito, A., Ishikawa, S., Yang, K., Xin, W., Namkung, H., Agarwal, A., Ramos, A., Lee, B. J., Hayes, L. N., et al. (2026) · Science Advances · doi:10.1126/sciadv.aef6777 · Cited by 4
Wang, R., Li, Y., & Wang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1899588 · Cited by 1
Padvitski, T., Avila, P. U., Chen, H., Özel, C., Braun, F., Kroll, M.-K., Vossen, C., Wienand, P., Butt, L., Unnersjö‐Jess, D., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101337 · Cited by 2
de Groot, D. H., Leonardo, S. X. M., Pachkov, M. V., & van Nimwegen, E. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03220-2 · Cited by 5
Rumyantseva, A., Tippetts, T. S., Wilhalm, A., Carter, W., Moedas, M. F., Rosenberger, F. A., Moore, D., Végvári, Á., Hinze, Y., Muellner-Wong, L., et al. (2026) · Science Advances · doi:10.1126/sciadv.aeg8792 · Cited by 3
Single-Cell Explorations Are Underway, and Spatial Is the Next Frontier
Hindson, B. (2019) · Genetic Engineering & Biotechnology News · doi:10.1089/gen.39.07.05 · Cited by 1
Yu, W., Ishan, M., Wang, Z., et al. (2021) · Journal of Visualized Experiments · doi:10.3791/62163 · Cited by 1
Warman‐Chardon, J., Jasmin, B. J., Kothary, R., & Parks, R. J. (2021) · Journal of Neuromuscular Diseases · doi:10.3233/jnd-219001 · Cited by 1
Weldy, C. S., & Ashley, E. A. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003430 · Cited by 1
RNA-Scoop: interactive visualization of transcripts in single-cell transcriptomes
Stephenson, M., Nip, K. M., Hafezqorani, S., Gagalova, K., Yang, C., Warren, R. L., & Birol, İ. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab105 · Cited by 1
Rudnicki, M., Pislaru, A., & Haas, T. L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822546-2.00001-0 · Cited by 1
Role of macrophages in prevention and control of diseases and their upregulation through cowpathy
Chauhan, R. S., & Chauhan, D. (2022) · International Journal of Cow Science · doi:10.18231/j.ijcs.2022.004 · Cited by 1
Genetic insights into biological mechanisms governing human ovarian ageing
Ruth, K. S., Day, F. R., Hussain, J., Martínez-Marchal, A., Aiken, C. E., Azad, A., & Thompson, D. J. (2022) · Yearbook of pediatric endocrinology · doi:10.1530/ey.19.15.9 · Cited by 1
Laboratory skills for immunologists: utility and limitations with emphasis on allergy research
Patkar, R., Wai, C. Y. Y., Leung, N. Y. H., Nkamba, I., Shu, S. A., & Leung, P. S. C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-95061-9.00006-0 · Cited by 2
Yang, S., Lan, T., Wei, R., Zhang, L., Lin, L., Du, H., Huang, Y., Zhang, G., HUANG, S. H., Shi, M., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01691-2 · Cited by 1
Goodale, B. C. (2024) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2024.100461 · Cited by 2
Li, F., & Luo, Q. (2024) · Chinese Journal of Chromatography · doi:10.3724/sp.j.1123.2023.11005 · Cited by 1
Li, R., Liu, S., Yeo, K. K. L., Edwards, S., Li, M. Y., Santos, R., Rad, S. K., Wu, F., Maddern, G. J., Young, J. P., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.7352
Erin, N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18653-0.00027-7 · Cited by 1
STGAT: Graph attention networks for deconvolving spatial transcriptomics data
Li, W., Zhang, H., Wang, L., Wang, P., & Yu, K. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108431 · Cited by 1
Detecting expressed genes in cell populations at the single-cell level with scGeneXpress
Jung, S., Barlier, C., Perez, A. G., & del Sol, A. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae494 · 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
“We performed lineage tracing analysis of a hematopoietic cell differentiation dataset from Weinreb et al. in which undifferentiated cells are allowed to progress towards one of several terminal states and heritable DNA barcodes identify cells across three time points which share a common ancestor…”
MethodLoss of GATAD1 in cardiomyocyte does not cause cardiomyopathy in mice
Pang, J., Zhu, S., Shyy, M., et al. (2024) · Journal of Molecular Histology · doi:10.1007/s10735-024-10297-z · Cited by 1
“Published single-cell RNA sequencing data (Tabula Muris et al. 2018) revealed that Gatad1 is ubiquitously expressed in all tested organs (Figure.”
Prompsy, P., Saichi, M., Raimundo, F., & Vallot, C. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae174 · Cited by 1
scGO: interpretable deep neural network for cell status annotation and disease diagnosis
Wu, Y., Xu, P., Wang, L., Liu, S., Hou, Y., Lu, H. J., Hu, P., Li, X., & Yu, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf018 · Cited by 1
20 years of stemness: From stem cells to hypertranscription and back
Kim, Y., & Ramalho‐Santos, M. (2025) · Stem Cell Reports · doi:10.1016/j.stemcr.2025.102406 · Cited by 1
Chen, J., & Chen, X. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf004 · Cited by 1
Wang, Y., & Du, P.-F. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1553352 · Cited by 3
“The mouse brain dataset comes from the AMB dataset (Tasic et al., 2018), while the comprehensive mouse cell atlas is the Tabula Muris (TM) dataset (Tabula Muris Consortium, 2018).”
A single cell atlas of the mouse seminal vesicle
Sun, F., Desevin, K., Fu, Y., et al. (2025) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkaf045 · Cited by 1
“…have explored gene regulation (Li et al. 2018; Skerrett-Byrne, Nixon, et al. 2021) and proteomics (Skerrett-Byrne, Trigg, et al. 2021; Correa et al. 2023) of the seminal vesicle tissue, along with proteomics of seminal vesicle fluid (Dean et al. 2009; Chang et al. 2010; McKee et al. 2013;…”
Yu, Z., Zhang, S., Bogomolovas, J., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08012-z · Cited by 1
Kolonay, D., Grueter, C. E., & Baskin, K. K. (2025) · Journal of Molecular and Cellular Cardiology Plus · doi:10.1016/j.jmccpl.2025.100456 · Cited by 3
Li, R., Qu, R., Parisi, F., Strino, F., Lam, H., Stanley, J. S., Cheng, X., Myung, P. S., & Kluger, Y. (2025) · Communications Biology · doi:10.1038/s42003-025-08485-y · Cited by 1
“17 – 20 and Supplementary Data 5).”
InfluentialChen, Z., Duan, M., Wang, X., & Liu, B. (2025) · Advanced Science · doi:10.1002/advs.202507846 · Cited by 1
de Sande, A. H., Turunen, T., Bouvy‐Liivrand, M., Örd, T., Palani, S., Lahnalampi, M., Tundidor-Centeno, C., Liljenbäck, H., Virta, J., Niskanen, H., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01530-9 · Cited by 3
“The single-cell datasets from spleen of 3-month-old mice ( n = 1, male and female) produced by the TMS [12, 13, 47] and from human spleen by Tabula Sapiens [48, 49] consortia (refer to Additional file 2: Table S1B and C for sample codes) together with mature miRNA RT-PCR data collected from mouse…”
Quiles, K., Shao, F., Johnson, W. E., & Chen, F. F. (2026) · Scientific Reports · doi:10.1038/s41598-025-34926-z · Cited by 1
Geometric-k-means: a bound free approach to fast and eco-friendly k-means
Sharma, P., Malec, M., Kurban, H., Külekci, M. O., & Dalkilic, M. M. (2026) · Machine Learning · doi:10.1007/s10994-025-06891-1 · Cited by 1
Metadata
- Authors
- The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators
- Venue
- Nature
- Published
- 2018-10
- Volume
- 562
- Issue
- 7727
- Pages
- 367-372
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6642641
- OpenAlex
- W2894687190
- Cited by
- 2,842
- References
- 32
- Citations by year
- 2026: 329 · 2025: 382 · 2024: 384 · 2023: 379 · 2022: 391 · 2021: 412 · 2020: 380 · 2019: 167 · 2018: 14
- FWCI
- 77.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Jagiellonian University Repository (Jagiellonian University) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Veterans Affairs (I01 BX002324, I01RX001222)
- National Institute on Aging (DP1AG053015, P01AG036695)
- National Heart, Lung, and Blood Institute (R00HL127267)
- National Cancer Institute (R01CA157877)
- National Institute on Deafness and Other Communication Disorders (R01DC016892)
- National Institute of Diabetes and Digestive and Kidney Diseases (K08DK101603, P30DK026743, P30DK116074)
- U.S. National Library of Medicine (DP1LM012179)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- Tabula Muris, mouse organs, single-cell transcriptomics, cell types, gene expression, cell atlas, T cells, endothelial cells, fluorescence-activated cell sorting, Mus musculus
- MeSH terms
- Animals, Cell Biology, Female, Flow Cytometry, Male, Mice, Inbred C57BL, Organ Specificity, T-Lymphocytes, Transcription Factors, Gene Expression Profiling, Endothelial Cells, Microfluidics, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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.
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