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
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
“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.
- Based: passes — The paper gives a reason why it holds.
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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- 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
“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
plaincorrelational- 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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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 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
hedgedcorrelational- 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
plaindirect- 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
“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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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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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- 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
“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
plaincorrelational- 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.
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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- Load-bearing: passes — 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
“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
hedgedcorrelational- 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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- 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 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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- 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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — 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
“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
hedgedcorrelational- 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
plaincorrelational- 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.
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
hedgedcorrelational- 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
plaincorrelational- 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 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
hedgedconditional- 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
“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
plaincorrelational- 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
“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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It may state two separate claims as one.
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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- 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
“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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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 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
plaincorrelational- 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
plaincorrelational- 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 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
hedgedcorrelational- 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
plaindirect- 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
hedgedcorrelational- 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
hedgedconditional- 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
plaindirect- 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
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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
boosteddirectSingle 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
hedgeddirect- 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
plaindirect- 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
plaindirect- 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
plaindirect- 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
plaindirect- 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
plaindirect- 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
plaindirect- 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
Sorted by citations
Items 2,001–2,100 of 2,952
Qiu, J., Peng, S., Qu, R., Wu, L., Xing, L., Zhang, L., & Sun, J. (2024) · Clinical Science · doi:10.1042/cs20241658 · Cited by 3
Chai, R. C. C., Lundberg, M., Freudenthal, B., Smith, J. T., Boughton, A. P., Zhang, Y., Flynn, K. A., Frysz, M., Corr, A. P., Khoo, W. H., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02640-9 · Cited by 4
“Gene expression across non-osseous tissues was interrogated using processed scRNA-seq data from the TabulaMuris 92 and TabulaSapi-ens 93 databases.”
BayesAge 2.0: a maximum likelihood algorithm to predict transcriptomic age
Mboning, L., Costa, E. K., Chen, J., Bouchard, L., & Pellegrini, M. A. (2025) · GeroScience · doi:10.1007/s11357-024-01499-0 · Cited by 3
“Finally, BayesAge 2.0 can predict biological age even when some features or measurements are missing from the dataset.”
InfluentialscCAT: Single-cell Combined graph Attentional clustering for scRNA-seq analysis
Gao, Y., Zhang, W., Zhang, Y., Shi, L., Shi, L., Wang, H., & Cheng, G. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107502 · Cited by 3
scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules
Liang, D.-M., & Du, P.-F. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf138 · Cited by 3
Chen, Z., Liu, L., Guo, X., Zhang, Y., Zhong, M., Xu, Y., Peng, T., Peng, T., Zhang, Y. E., Hou, Q., et al. (2025) · Molecular Neurobiology · doi:10.1007/s12035-025-04901-w · Cited by 3
“In the central nervous system (CNS), GPAM is predominantly expressed in astrocytes (ASTs), where its dysfunction disrupts astrocytic proliferation [1, 15, 16].”
IGF1R deficiency mitigates acute lung injury by promoting anti-inflammatory transcriptional profiles
Urtubia, A., Piñeiro‐Hermida, S., Alfaro‐Arnedo, E., Beni-Ledesma, J., Canalejo, M., de Toro, M., Pichel, J. G., & López, I. P. (2025) · Respiratory Research · doi:10.1186/s12931-025-03339-x · Cited by 3
“Then libraries were assessed for concentration and size, and run in a HiSeq 1500 PE100 lane to obtain high-throughput sequencing data.”
Systems analysis of benign bladder disorders: insights from omics analysis
Gheinani, A. H., Bigger-Allen, A. A., Wacker, A. L., et al. (2020) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00496.2019 · Cited by 3
A Potential miRNA‐mRNA Network for Dementia and Hernia Crosstalk
Chen, D., & Li, D. (2021) · BioMed Research International · doi:10.1155/2021/4324068 · Cited by 3
CBA: Cluster-Guided Batch Alignment for Single Cell RNA-seq
Yu, W., Mahfouz, A. E., & Reinders, M. J. T. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.644211 · Cited by 3
“Second, we used Mouse Cell Atlas (MCA) datasets 2 (Ha et al., 2018) which contains cells from 37 organs and the fluorescence activated cell sorting (FACS) datasets from Tabula Muris 3 (Tabula, 2018).”
MethodBinned multinomial logistic regression for integrative cell-type annotation
Motwani, K., Bacher, R. L., & Molstad, A. J. (2023) · The Annals of Applied Statistics · doi:10.1214/23-aoas1769 · Cited by 4
Kidney Development: Recent Insights from Technological Advances
Röck, R., Rizzo, L., & Lienkamp, S. S. (2022) · Physiology · doi:10.1152/physiol.00041.2021 · Cited by 4
Dermadi, D., Bscheider, M., Bjegovic, K., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/336313 · Cited by 3
Basal Contamination of Sequencing: Lessons from the GTEx datasetPreprint
Nieuwenhuis, T. O., Yang, S. Y., Verma, R., Pillalamarri, V., Arking, D. E., Rosenberg, A. Z., McCall, M. N., & Halushka, M. K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/602367 · Cited by 3
Bues, J., Biočanin, M., Pezoldt, J., Dainese, R., Chrisnandy, A., Rezakhani, S., Saelens, W., Gardeux, V., Gupta, R., Russeil, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.19.103812 · Cited by 3
Lee, R. D., Munro, S. A., Knutson, T. P., LaRue, R. S., Heltemes-Harris, L. M., & Farrar, M. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.30.178301 · Cited by 3
“The evaluation of different organs (Schaum et al., 2018) and niches (Baccin et al., 2020; Tikhonova et al., 2019) at single cell resolution has greatly expanded our understanding of the cellular diversity that is present within the bone marrow.”
BackgroundZanini, F., Che, X., Knutsen, C., Liu, M., Suresh, N. E., Domingo-Gonzalez, R., Dou, S. H., Zhang, D., Pryhuber, G., Jones, R. C., et al. (2022) · SSRN Electronic Journal · doi:10.1101/2021.04.27.441649 · Cited by 3
Fundamentals of Vaping-Associated Pulmonary Injury Leading to Severe Respiratory DistressPreprint
Esquer, C., Echeagaray, O., Firouzi, F., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.01.461568 · Cited by 3
Liau, E. S., Jin, S., Chen, Y., Liu, W.-S., Yong, L. W., Tsai, C.-T., Calon, M., Yu, J., Su, Y., Nedelec, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.29.462340 · Cited by 3
Vazquez, S. E., Mann, S. A., Bodansky, A., Kung, A. F., Quandt, Z., Ferré, E. M. N., Landegren, N., Eriksson, D., Bastard, P., Zhang, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.23.485509 · Cited by 3
“CGNL1 is an endothelial junction protein and is highly expressed in the cardiac endothelium (Chrifi et al., 2017; Schaum et al., 2018).”
Lo, E. K. W., Velazquez, J. J., Peng, D., Kwon, C., Ebrahimkhani, M. R., & Cahan, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.07.506886 · Cited by 3
Census: accurate, automated, deep, fast, and hierarchical scRNA-seq cell-type annotationPreprint
Ghaddar, B., & De, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512926 · Cited by 3
“We thus proceeded to assess their annotation performance on 44 other challenging normal and cancer datasets from human tissues and from the Tabula Muris 15 ; these data included 1,769,071 total cells from 105 harmonized cell labels.”
MethodGDF3 is an obesity-induced regulator of TGFβ superfamily signalingPreprint
Ramachandran, D., Kotikalapudi, N., Gipson, G. R., Troncone, L., Vestal, K., Maridas, D. E., Gulko, A., Tsai, L. T.-Y., Rosen, V., Yu, P. B., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.07.515236 · Cited by 3
Astrocytic Neuroligins Are Not Required for Synapse Formation or a Normal Astrocyte CytoarchitecturePreprint
Golf, S. R., Trotter, J. H., Nakahara, G., & Südhof, T. C. (2023) · doi:10.7554/elife.87589.1 · Cited by 6
Identification of cell types, states and programs by learning gene set representationsPreprint
Hediyeh-zadeh, S., Whitfield, H. J., Kharbanda, M., Curion, F., Bhuva, D. D., Theis, F. J., & Davis, M. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.08.556842 · Cited by 3
Investigating RNA splicing as a source of cellular diversity using a binomial mixture modelPreprint
Isaev, K., & Knowles, D. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.17.562774 · Cited by 3
Somatic gene delivery for flexible in vivo modeling of high-risk sarcomaPreprint
Imle, R., Blösel, D., Kommoss, F. K. F., Zhao, E. S., Autry, R. J., Blume, C., Lupar, D., Schmitt, L., Winter, C., Wagner, L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.30.577924 · Cited by 3
Rebboah, E., Rezaie, N., Williams, B. A., Weimer, A. K. K., Shi, M., Yang, X., Liang, H. Y., Dionne, L. A., Reese, F., Trout, D. E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598567 · Cited by 3
“The Tabula Muris Consortium, a widely used resource, recently captured over 350,000 cells in 6 age groups and 23 tissues and organs 8 , building on their previous Tabula Muris catalog of 100,000 cells from 20 organs and tissues using single-cell RNA-seq (scRNA-seq) 9 .”
Furtwängler, B., Üresin, N., Richter, S., Schuster, M. B., Barmpouri, D., Holze, H., Wenzel, A., Grønbæk, K., Theilgaard‐Mönch, K., Theis, F. J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.05.602277 · Cited by 3
Messina, O., Fiche, J.-B., Ganesh, G., Youhanna, C. E., Kemkem, Y., Mesnager, A., Ferrer, J. P., Villalba, A., Scharfmann, R., Akerman, İ., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.18.613689 · Cited by 4
Homan, E. A., Gilani, A., Rubio‐Navarro, A., Johnson, M. A., Schaepkens, O. M., Cortada, È., de Lima, R. P., Stoll, L., & Lo, J. C. (2024) · eLife · doi:10.7554/elife.100708 · Cited by 3
de Groot, D. H., Leonardo, S. X. M., Pachkov, M. V., & van Nimwegen, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.08.652944 · Cited by 3
Trumpp, M., Tan, W. H., Burdzinski, W., Basler, Y., Jatzlau, J., Knaus, P. I., & Winkler, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0291379 · Cited by 4
scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data
Guo, Z., Wu, Y., Wang, S., Zhang, Q., Shi, J., Wang, Y., & Chen, Z. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05579-4 · Cited by 3
Das, J., Bera, S., Ganguly, N., Guha, I., Ghosh, T., Bhuniya, A., Nandi, P. S., Chakravarti, M., Dhar, S., Sarkar, A., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1325161 · Cited by 2
Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network
Zhang, X., Qian, K., & Li, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae538 · Cited by 3
“The mouse atlas dataset is derived from the Microwell-seq [33] and Smart-seq2 [34] platforms.”
Sharma, P., Mishra, S. R., Kurban, H., & Dalkilic, M. M. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00709-4 · Cited by 2
Somatic mutations distinguish melanocyte subpopulations in human skin
Tandukar, B., Deivendran, D., Chen, L., Bahrani, N., Baskurt, D., Weier, B. A., Sharma, H., Cruz‐Pacheco, N., Hu, M., Marks, K., et al. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-01943-7 · Cited by 2
“The RNA was sequenced at higher depth than a typical cell atlas study 14,15 , providing greater resolution into potential sublineages of melanocytes.”
Resistance exercise and mechanical overload upregulate vimentin for skeletal muscle remodeling
Godwin, J. S., Michel, J. M., Libardi, C. A., Kavazis, A. N., Fry, C. S., Frugé, A. D., McCashland, M., Vechetti, I. J., McCarthy, J. J., Mobley, C. B., et al. (2025) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.01028.2024 · Cited by 2
Morin, A. M., Chu, C. P., & Pavlidis, P. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012962 · Cited by 2
“Similarly, we downloaded each of 20 tissue datasets from the Tabula Muris Consortium [30] (https://figshare.com/articles/dataset/Robject_files_for_tissues_ processed_by_Seurat/5821263; July 2023), which were also combined as one dataset.”
Rodella, A., Boscaro, C., Amendolagine, F. I., Migliozzi, L., Molon, B., Viola, A., Albiero, M., & Fadini, G. P. (2025) · iScience · doi:10.1016/j.isci.2025.112646 · Cited by 3
Yamada, K., Satoh, T., Yaoita, N., et al. (2025) · Cardiovascular Research · doi:10.1093/cvr/cvaf064 · Cited by 2
Integrated omics identifies molecular networks of brain-body interactions of brain injury
Deng, Y.-H., Wang, H., Zhang, J.-H., Wang, Y.-F., Wang, M.-Y., Pan, X., Zhang, Y.-D., Gan, Y.-H., He, Y. J., Xie, F. X., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113103 · Cited by 3
NF-κB RelA regulates temporal oligodendrocyte differentiation in the postnatal brains
Sompub, K., Bizen, N., Baldwin, A. S., & Takebayashi, H. (2025) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2025.1622874 · Cited by 2
“Specifically, enrichment analyses were performed using TRANSFAC (Wingender et al., 2000) 10.3389/fncel.2025.1622874 and JASPAR (Sandelin et al., 2004) databases for transcription factor binding motifs, and Cell Marker (Zhang et al., 2019) and Tabula Muris (Tabula Muris and Consortium, 2018) databases for cell type–spec…”
Influentialrobin2: accelerating single-cell data clustering evaluation
Policastro, V., Righelli, D., Cutillo, L., & Carissimo, A. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf184 · Cited by 3
“The proposed method was applied to two datasets from different species: a subset of the Tabula Muris dataset (Tabula 2018) derived from mouse tissues and human Peripheral Blood Mononuclear Cells (PBMC) from the singleCellMultimodal Bioconductor package (Eckenrode et al. 2023).”
ICAM-1 identifies preadipocytes and restricts white adipogenesis by adhering immune cells
Zheng, C., Ye, J., Yang, Q., Liu, K., Chen, C., Cao, J., Li, Q., Xue, Y., Ma, H., Rabson, A. B., et al. (2025) · Cell Death and Differentiation · doi:10.1038/s41418-025-01551-2 · Cited by 2
“Single-cell RNA sequencing (scRNA-seq) now enables in vivo mapping of adipose progenitor cellular hierarchies and high-resolution identi fi cation of ICAM-1 + preadipocytes [9 – 12].”
Neural control of goblet cells in the gastrointestinal tract
Rowe, M. C., Pettersson, V. L., Carbone, S., Gustafsson, J. K., & Poole, D. P. (2025) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00191.2025 · Cited by 3
Tiberi, G., Scagliotti, A., Curcio, C., Candiello, E., Villegas, P. A. D., Brugiapaglia, S., Mucciolo, G., Roux, C., Castellano, G., Curto, R., et al. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70442 · Cited by 2
Wang, X., Mei, Y., Wang, X., Wu, H., Wang, R., Zhang, P., Zhang, G., Li, J., Jiang, M., Fang, X., et al. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70461 · Cited by 3
Gulej, R., Patai, R., Kiss, T., Chandragiri, S. S., Ekambaram, S., Nagaraja, R. Y., Nagy, D., Kordestan, K. V., Lakat, T., Tarantini, S., et al. (2025) · GeroScience · doi:10.1007/s11357-025-01953-7 · Cited by 4
A novel paradigm for single-cell annotation in stem cell research
Atkeson, T., Wilson, S. B., Ruiz-Morales, E. R., Little, M. H., Vento‐Tormo, R., Neavin, D. R., & Powell, J. E. (2025) · Stem Cell Reports · doi:10.1016/j.stemcr.2025.102707 · Cited by 3
TMPRSS11B promotes an acidified microenvironment and immune suppression in squamous lung cancer
Sunil, H. S., Clemenceau, J. R. V., Grichuk, A., Barnfather, I., Nakkireddy, S. R., Izzo, L., Feng, Q., Hartnett, W., Evers, B. M., Thomas, L., et al. (2025) · EMBO Reports · doi:10.1038/s44319-025-00631-1 · Cited by 3
Loss of mouse C19orf12 homolog disturbs tubular ER homeostasis and leads to neuroaxonal dystrophy
Wu, F., Siedlak, S. L., Bhatta, S., Khaled, S., Shao, C., Torres, S. L., Fujioka, H., & Wang, W. (2025) · Acta Neuropathologica Communications · doi:10.1186/s40478-025-02145-2 · Cited by 2
“We retrieved single-cell RNA sequencing data from the Tabula-Muris database [1], revealing that the mouse homologue, 1600014C10Rik, is expressed in neurons, astrocytes, oligodendrocytes, and other cell types in the mouse brain (Fig.”
Lin, S., Chen, Z., Cheng, J., Chen, X., Zhu, Y., Li, L., Huang, L., Chen, Y., Dai, S., & Xu, S. (2025) · Archives of Dermatological Research · doi:10.1007/s00403-025-04459-6 · Cited by 2
“We preprocessed and normalized single-cell RNA sequencing data from Tabula Muris [44] using CELLEX to calcu - late an expression-specific likelihood score for each gene.”
Zhou, Y., Huang, Y., Fan, Y., & Xue, F. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337741 · Cited by 3
“For the meta-analysis, the scRNAseq datasets were acquired from Tabula Muris (https://tabula-muris.ds.czbiohub.org/) [17] and ZEBRA (https://www.ccb.uni-saarland.de/zebra) [18].”
Dormancy in colorectal cancer: The functional core of resistance, metastasis, and relapse
Alvarado-Ortiz, E., Moreno-Londoño, Á. P., & Sarabia-Sánchez, M. Á. (2025) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105081 · Cited by 3
Wu, Q. C., Swaminathan, A., Winward, A., Lalonde, L., Hwang, Y., Littman, N., Socolovsky, M., & Klein, A. M. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003462 · Cited by 2
“Published scRNA-seq data sets containing bone marrow and splenic cells were curated and used as reference data sets [5,81,82] (GEO accessions: GSE89754, GSE132042, GSE132901, GSE261601).”
Benchmarking large language models for cell typing in single-cell RNA-Seq
Xiao, T., Hua, D., Wang, Y., Lu, X., & Zhang, C. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf677 · Cited by 6
scSCDT: Self-contrastive neural network with deep topology mining for scRNA-seq data clustering
Zhou, Z., Tang, B., Chen, F., Wang, W., Zhao, S., & Yu, N. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129751 · Cited by 5
Lee, A., Castellví, S. D. L., Sellers, P. R., Taneja, S., Ortiz, M. R., Kowdley, D., Leung, J. H., Pokharel, B., Ganesan, L. P., NEEDLEMAN, B. J., et al. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03943-5 · Cited by 2
“Eng also correlated with Pcxnc1 , a specific marker of LSEC, and Pde2a , a marker of differentiated LSEC [48, 49], suggesting that the majority of cluster II genes regulated by the AAC2-hIGFBP4 complex and AAC2 affect the function of the LSEC population (Fig.”
Tan, K., Bai, Y., Zhang, Y., Shu, Z., & Yu, Z. (2026) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2026.3655670 · Cited by 3
Single-nucleus multiple-organ chromatin accessibility landscape in the adult rat
Li, R., Yuan, Y., Duan, S., Deng, Q., Ma, W., Liu, C., Gao, P., Yuan, Y., & Liu, C. (2026) · GigaScience · doi:10.1093/gigascience/giag013 · Cited by 2
Iron deficiency induces maturation-dependent loss of pancreatic β-cells
Mulders, A. V., Willems, L., Coenen, S., Bourgeois, S., Yi, X., Tong, Y., Leuckx, G., Heremans, Y., Pierreux, J., Degroote, L., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69574-y · Cited by 2
“0.2 72 , based on known marker genes from a previous study 21 .”
InfluentialWang, J., Xia, J., Su, Y., & Zheng, C.-H. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag094 · Cited by 2
Mascarenhas, R. D. S., Duarte, M. J., Tanaka, P. P., de Oliveira, E. H. D., Monteiro, C. J., Vieira-Machado, M. C., Donadi, E. A., & Passos, G. A. S. (2026) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiag038 · Cited by 2
Carbone, F. P., Hanau, S., & Bianchi, N. (2026) · Non-Coding RNA · doi:10.3390/ncrna12020011 · Cited by 2
A cell-nonautonomous heme acquisition pathway enables erythroid hemoglobinization under stress
Belot, A., Rock, A., Dutt, S., Haemmerle, G., Maros, A., Yuan, X., Otsuru, S., Bodine, D. M., & Hamza, I. (2026) · Science · doi:10.1126/science.aea0552 · Cited by 2
Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI
Weinberger, E., Qiu, W., Tian, W., Zeng, Q., Ergen, C., Kronfeld, O., Kim, M., Yosef, N., Ecker, J. R., & Lee, S. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01225-9 · Cited by 2
“Consortia such as the Human Cell Atlas 55 and HuBMAP 56 now routinely generate large-scale single-cell reference atlases to improve understanding of cellular heterogeneity across tissues, organs, developmental stages and other conditions 57–60 .”
Xu, C., Lv, X., Yang, S., Lv, Y., Zheng, Y., Wang, Y.-X., Hui, Y., Sun, G. Y., Zhao, X., Ma, L.-Y., et al. (2026) · Blood · doi:10.1182/blood.2025031474 · Cited by 3
Gene regulatory mechanisms underlying sex- and age-dependent pathophysiology of MASLD
Morral, N. (2026) · Clinical Science · doi:10.1042/cs20250052 · Cited by 2
Yao, J., Wang, Y., & Zhang, Y. (2026) · Immunity · doi:10.1016/j.immuni.2026.04.018 · Cited by 3
“Analysis of single-cell RNA-seq data 62,63 confirmed that basophils are the predominant source of Il4 mRNA in the bone marrow under steady-state conditions.”
Loss of Endothelial WWOX: A Risk Factor for ARDS in Smokers?
Tanimura, K., & Nyunoya, T. (2020) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2020-0444ed · Cited by 2
“However, according to the mouse single cell transcriptome data, other cell types including lung epithelial and myeloid cells express the WWOX gene more highly than endothelial cells (15).”
BackgroundTissue-specific smooth muscle cell subtypes identified by transcriptional profiling
Lin, C.-J., & Mecham, R. P. (2021) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2021.106055 · Cited by 2
Konopka, T., Vestito, L., & Smedley, D. P. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab026 · Cited by 2
de Magalhães, J. P., Lagger, C., & Tăcutu, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-815962-0.00006-8 · Cited by 2
The many roles of the Alzheimer-associated gene PM20D1
Garro, D., Mora-Cubillo, P., Fonseca-Bone, S., Picado-Martínez, M. J., Brenes, M. F., Raventós, H., & Chavarría‐Soley, G. (2022) · Journal of Translational Genetics and Genomics · doi:10.20517/jtgg.2022.10 · Cited by 3
Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders
Wang, X., Zhang, C., Wang, L., & Zheng, P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065502 · Cited by 3
Exploring the Molecular and Developmental Dynamics of Endothelial Cell Differentiation
Shin, Y. J., & Lee, J. H. (2023) · International Journal of Stem Cells · doi:10.15283/ijsc23086 · Cited by 3
Alani, M., Altarturi, H., Pars, S., Al-mhanawi, B., Wolvetang, E. J., & Shaker, M. R. (2024) · International Journal of Stem Cells · doi:10.15283/ijsc23170 · Cited by 2
Mis-expression of GATA6 re-programs cell fate during early hematopoiesis
Audiger, C., Laâbi, Y., Nie, J., Gibson, L., Wilson-Annan, J., Brook-Carter, P. T., Kueh, A. J., Harris, A. W., Naik, S. H., Nutt, S. L., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114159 · Cited by 2
Cardiac Development at a Single-Cell Resolution
Wei, N., Lee, C., Duan, L., Galdos, F. X., Samad, T., Raissadati, A., Goodyer, W. R., & Wu, S. M. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-44087-8_14 · Cited by 2
Yuan, L., Sun, S., Li, Z., Zhang, S., Wang, X., & Geng, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5692-6_2 · Cited by 2
Verstappe, B., & Scott, C. L. (2024) · Clinical & Experimental Immunology · doi:10.1093/cei/uxae077 · Cited by 3
LHPP deficiency aggravates liver fibrosis through TGF‐β/Smad3 signaling
Zhang, Y., Li, B., Zhang, W., Chen, P., Liu, L., Nie, Y., Huang, C., & Zhu, X. (2024) · The FASEB Journal · doi:10.1096/fj.202400117rr · Cited by 2
Chen, L., Chen, P.-L., Jiang, S., Lee, H.-L., Liu, Y., Chueh, A., Lin, J.-H., Chen, C. G., Hung, C., & Hsu, K. (2024) · Function · doi:10.1093/function/zqae052 · Cited by 2
Sheikhi, A., Baghaie, L., Rahbarizadeh, F., et al. (2025) · Vaccines · doi:10.3390/vaccines13020199 · Cited by 2
Wang, L., Liu, S., Zhang, S. X., et al. (2025) · eLife · doi:10.7554/elife.99790.4 · Cited by 2
Brash, J. T., Diez-Pinel, G., Rinaldi, L., Castellan, R. F. P., Fantin, A., & Ruhrberg, C. (2025) · Angiogenesis · doi:10.1007/s10456-025-09971-x · Cited by 2
scTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data
Zou, Z., Liu, Y., Bai, Y., Luo, J., & Zhang, Z. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012904 · Cited by 2
“(2) Mouse brain, we selected mouse brain datasets from MCA [25], the Tabula Muris (TMS) dataset [37] and Romanov et al. [38].”
Xiong, H., Shen, P., Luo, Q., Zhang, L., Li, B., Ding, Z., & Wang, L. (2025) · Clinical Genetics · doi:10.1111/cge.14766 · Cited by 2
“We utilized the Tabula Muris90 dataset, which includes transcriptomic data from 100 000 cells and 20 organs and tissues from mice ( Mus musculus ) [26].”
Challenges and Opportunities in Spatiotemporal Models of Mammalian Gastrulation
Rubinstein, H., Mayshar, Y., & Stelzer, Y. (2025) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-101323-125216 · Cited by 3
Choi, Y., Kim, J., Moon, C., Baik, J., Lee, H. J., & Son, Y. (2025) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2025.08.014 · Cited by 2
Karatepe, K., de Faria, B. M., Zhang, J., Chen, X., Pinto, H. B., Fyodorov, D. V., Sefik, E., Willcockson, M. A., Flavell, R. A., Skoultchi, A. I., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2509412122 · Cited by 2
Sousa, A. G. G., Smolander, J., Junttila, S., & Elo, L. L. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1128 · Cited by 2
Rőszer, T. (2020) · Progress in inflammation research · doi:10.1007/978-3-030-50480-9_10 · Cited by 2
TCF7 is not essential for glucose homeostasis in mice
Kaur, K. D., Wong, C. K., Baggio, L. L., Beaudry, J. L., Fuchs, S., Panaro, B. L., Matthews, D., Cao, X., & Drucker, D. J. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101213 · Cited by 2
“Adult mouse islet data were obtained from GSE84133 [26] and GSE109774 [27].”
MethodBayesian information sharing enhances detection of regulatory associations in rare cell types
Wu, A. P., Peng, J., Berger, B., & Cho, H. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab269 · Cited by 2
Huang, M., Yang, Y., Wen, X., Xu, W., Lü, N., Sun, X. S., Tu, J., & Lu, Z. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab581 · Cited by 3
“Next, to explore the application value of this parallel model on a much larger dataset, we selected a larger dataset with a scale of 53 760 cells (14).”
MethodXing, C., Guo, S., Li, W., Deng, W., Ning, M., Lok, J. M., Arai, K., & Lo, E. H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822546-2.00028-9 · Cited by 2
UNIFAN: A Tool for Unsupervised Single-Cell Clustering and Annotation
Li, D., Ding, J., & Bar‐Joseph, Z. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2022.0251 · Cited by 2
“Users can download the example Tabula Muris data https://figshare.com/ndownloader/ files/24351086 (The Tabula Muris Consortium, 2018) and run the provided getExample.py script to preprocess the data using SCANPY (Wolf et al, 2018).”
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.
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