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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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 1,001–1,100 of 2,952
Huang, D., Jiao, X., Huang, S., Liu, J., Si, H., Qi, D., Pei, X., Lu, D., Wang, Y., & Li, Z. (2024) · The Ocular Surface · doi:10.1016/j.jtos.2024.06.005 · Cited by 23
Read, D. F., Booth, G. T., Daza, R. M., Jackson, D. L., Gladden, R. G., Srivatsan, S. R., Ewing, B., Franks, J. M., Spurrell, C. H., Gomes, A. R., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06582-y · Cited by 22
Life-long microbiome rejuvenation improves intestinal barrier function and inflammaging in mice
Sommer, F., Bernardes, J. P., Best, L., Sommer, N., Hamm, J., Messner, B., López-Agudelo, V. A., Fazio, A., Μαρίνος, Γ., Kadibalban, A. S., et al. (2025) · Microbiome · doi:10.1186/s40168-025-02089-8 · Cited by 26
Eosinophil innate immune memory after bacterial skin infection promotes allergic lung inflammation
Radhouani, M., Farhat, A., Hakobyan, A., Zahalka, S., Pimenov, L., Fokina, A., Hladik, A., Lakovits, K., Brösamlen, J., Dvorak, V., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.adp6231 · Cited by 25
Disentangling associations between complex traits and cell types with seismic
Lai, Q., Dannenfelser, R., Roussarie, J.-P., & Yao, V. (2025) · Nature Communications · doi:10.1038/s41467-025-63753-z · Cited by 26
Dawson, A., Wang, Y., Li, Y., LeMaire, S. A., & Shen, Y. H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.679995 · Cited by 22
“…stress may upregulate EC expression of the CD40 receptor, a member of the TNF family, and result in CD40-mediated EC activation, which stimulates ROS production, increased leukocyte adhesion, and inflammatory cell recruitment (Nakanishi et al., 2001; Schönbeck and Libby, 2001; Urbich et al., 2002).”
BackgroundYoshitake, R., Chang, G., Saeki, K., Ha, D., Wu, X., Wang, J., & Chen, S. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.850568 · Cited by 21
“(2021) did not include mammary gland fibroblasts, we integrated their steady-state mouse fibroblast atlas dataset with our own, as well as four other scRNA-seq datasets containing normal mouse mammary gland fibroblasts (Schaum et al., 2018; Li et al., 2020; Lo et al., 2020; Sebastian et al., 2020) (Figures 5A,B).”
BackgroundMethodLKB1 drives stasis and C/EBP-mediated reprogramming to an alveolar type II fate in lung cancer
Murray, C. W., Brady, J. J., Han, M., Cai, H., Tsai, M. K., Pierce, S. E., Cheng, R., Demeter, J., Feldser, D. M., Jackson, P. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28619-8 · Cited by 22
Ming, J., Lin, Z., Zhao, J., Wan, X., Ezran, C., Liu, S., Yang, C., & Wu, A. R. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac167 · Cited by 21
“The time taken to integrate 44 779 cells profiled using SS2 with 54 865 cells profiled using 10X for all tissues in Tabula Muris [12] using FIRM took about 20 min to an hour, varying based on the number of clusters in the 10X dataset.”
InfluentialGaedcke, S., Sinning, J., Dittrich‐Breiholz, O., Haller, H. G., Sörensen-Zender, I., Liao, C. M., Nordlohne, A., Sen, P., von Vietinghoff, S., DeLuca, D. S., et al. (2022) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00453.2021 · Cited by 21
Pires‐Afonso, Y., Muller, A., Grzyb, K., Oudin, A., Yabo, Y. A., Sousa, C., Scafidi, A., Poli, A., Cosma, A., Halder, R., et al. (2022) · Molecular Oncology · doi:10.1002/1878-0261.13287 · Cited by 25
Manivannan, S., Mansfield, C., Zhang, X., Kodigepalli, K. M., Majumdar, U., Garg, V., & Basu, M. (2022) · Communications Biology · doi:10.1038/s42003-022-03779-x · Cited by 25
Topchyan, P., Zander, R., Kasmani, M. Y., Nguyen, C. P., Brown, A. K., Lin, S., Burns, R., & Cui, W. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111736 · Cited by 22
Microbiota-host crosstalk in the newborn and adult rumen at single-cell resolution
Wu, J., Zhu, S., Tang, Y., Gu, F., Liu, J., & Sun, H. (2022) · BMC Biology · doi:10.1186/s12915-022-01490-1 · Cited by 25
“A previous study reported that the sufficiently rare cell type (smaller than about 30 cells) will be algorithmically grouped with a more populous cell type because entire cell clusters, rather than individual cells, were annotated in each tissue [89].”
BackgroundFine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease
D’Antonio, M., Nguyen, J., Arthur, T. D., Arias, A. D., Arthur, T. D., Benaglio, P., Berggren, W. T., Borja, V., Belmonte, J. C. I., Cook, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36638-2 · Cited by 25
Gallanis, G. T., Sharif, G. M., Schmidt, M. O., Friedland, B., Battina, R., Rahhal, R., Davis, J. W., Khan, I., Wellstein, A., & Riegel, A. T. (2023) · Cancers · doi:10.3390/cancers15061908 · Cited by 25
Kannan, S., Miyamoto, M., Zhu, R., Lynott, M. R., Guo, J., Chen, E. Z., Colas, A. R., Lin, B. L., & Kwon, C. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112330 · Cited by 22
Cheng, Y., Fan, X., Zhang, J., & Li, Y. (2023) · Communications Biology · doi:10.1038/s42003-023-04928-6 · Cited by 22
“TM (Tabula Muris) 54865 55 10X Genomics Schaum et al.(48)”
Bauzá-Thorbrügge, M., Vujičić, M., Chanclón, B., Pálsdóttir, V., Pillon, N. J., Benrick, A., & Asterholm, I. W. (2023) · Metabolism · doi:10.1016/j.metabol.2023.155716 · Cited by 22
Rat Models of Hormone Receptor-Positive Breast Cancer
Nicotra, R., Lutz, C., Messal, H. A., & Jonkers, J. (2024) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-024-09566-0 · Cited by 30
FSTL-1 Attenuation Causes Spontaneous Smoke-Resistant Pulmonary Emphysema
Henkel, M., Partyka, J., Gregory, A. D., Forno, E., Cho, M. H., Eddens, T. J., Tout, A. R., Salamacha, N., Horne, W. T., Rao, K., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201905-0973oc · Cited by 23
Bayesian correlation is a robust gene similarity measure for single-cell RNA-seq data
Sánchez‐Taltavull, D., Perkins, T. J., Dommann, N., Mélin, N., Keogh, A., Candinas, D., Stroka, D., & Beldi, G. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa002 · Cited by 19
Quantitative single-cell interactomes in normal and virus-infected mouse lungs
Cain, M. P., Hernandez, B. J., & Chen, J. (2020) · Disease Models & Mechanisms · doi:10.1242/dmm.044404 · Cited by 22
Big Data Approaches in Heart Failure Research
Lanzer, J. D., Leuschner, F., Kramann, R., Levinson, R. T., & Sáez-Rodríguez, J. (2020) · Current Heart Failure Reports · doi:10.1007/s11897-020-00469-9 · Cited by 23
Zhou, X., Fu, A. K. Y., & Ip, N. Y. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.02.001 · Cited by 23
“Accordingly, reanalysis of publicly available data with mouse tissues revealed that APOE is predominately expressed in the liver, skin, brain, adipose tissue, heart, aorta, and lungs, particularly in Kupffer cells, hepatocytes, and leukocytes [14].”
BackgroundEssential guidelines for computational method benchmarking
Weber, L. M., Saelens, W., Cannoodt, R., Soneson, C., Hapfelmeier, A., Gardner, P. P., Boulesteix, A.-L., Saeys, Y., & Robinson, M. D. (2019) · Genome Biology · doi:10.1186/s13059-019-1738-8 · Cited by 21
Kondegowda, N. G., Filipowska, J., Do, J., Leon-Rivera, N., Li, R., Hampton, R. F., OGYAADU, S. J., Levister, C. M., Penninger, J. M., Reijonen, H., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf5238 · Cited by 22
“Analysis of the available single-cell RNA sequencing (RNA-seq) datasets of the mouse pancreas ( 32 ) shows that Rank and Opg are expressed mainly in β cells with some expression in other cell types (fig.”
BackgroundWang, X., Du, W., Li, Y., Yang, H., Zhang, Y., Akbar, R., Morgan, H., Peng, T., Chen, J., Sadayappan, S., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.173832 · Cited by 24
Data acquisition approaches for single cell proteomics
Ghosh, G., Shannon, A. E., & Searle, B. C. (2024) · PROTEOMICS · doi:10.1002/pmic.202400022 · Cited by 25
Chen, X., Shao, J., Brandenburger, I., Qian, W., Hahnefeld, L., Bonnavion, R., Cho, H., Wang, S., Hidalgo, J., Wettschureck, N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58706-5 · Cited by 24
“The IL-6 receptor is present on islet endocrine cells 32 , being particularly enriched on β -cells in mice 33 .”
Generation of Quiescent Cardiac Fibroblasts Derived from Human Induced Pluripotent Stem Cells
Zhang, H., Shen, M., & Wu, J. C. (2020) · Methods in molecular biology · doi:10.1007/7651_2020_300 · Cited by 23
Hortells, L., Meyer, E. C., Thomas, Z. M., et al. (2021) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2021.02.001 · Cited by 21
Luo, Q., Yu, Y., & Lan, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab547 · Cited by 21
“To test the performance of SIGNET on large dataset, we ran SIGNET on single cell data of 20 organs and tissues from mice (GSE109774, 53 760 cells from four male and three female mice [78]) and we obtained 84 regulons.”
MethodscPretrain: multi-task self-supervised learning for cell-type classification
Zhang, R., Luo, Y., Ma, J., Zhang, M., & Wang, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac007 · Cited by 21
Gpr125 is a unifying hallmark of multiple mammary progenitors coupled to tumor latency
Spina, E., Simundza, J., Incassati, A., Chandramouli, A., Kugler, M. C., Lin, Z., Khodadadi‐Jamayran, A., Watson, C. J., & Cowin, P. (2022) · Nature Communications · doi:10.1038/s41467-022-28937-x · Cited by 21
Onoda, N., Kawabata, A., Hasegawa, K., Sakakura, M., Urakawa, I., Seki, M., Zenkoh, J., Suzuki, A., & Suzuki, Y. (2022) · DNA Research · doi:10.1093/dnares/dsac007 · Cited by 20
Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM
Chen, X., Chen, S., & Thomson, M. W. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00263-8 · Cited by 19
Gupta, S. K., Kawaguchi, R., Heinrichs, E., Gallardo, S., Castellanos, S., Mandric, I., Novitch, B. G., & Butler, S. J. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111119 · Cited by 21
“An atlas of these combined datasets with dI annotation can be found here _3D_interactive.html?dl=0; file opens after being downloaded). unique to the in vivo dataset. genes. trachea datasets (TabulaMuris Consortium) shows the in vitro cells overlap only are colored using the in vivo dataset annotations.”
BackgroundInfluentialEndothelial cell diversity: the many facets of the crystal
Pérez, L., Li, P., & Ferrara, N. (2022) · FEBS Journal · doi:10.1111/febs.16660 · Cited by 22
Fisher, E. S., Amarante, M. A., Lowry, N., Lotz, S., Farjood, F., Temple, S., Hill, C. E., & Kiehl, T. R. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02627-3 · Cited by 31
“data Immune-relevant data subsets were collected from the TabulaMuris Consortium data [43].”
MethodDelcroix, V., Mauduit, O., Lee, H. S., Ivanova, A., Umazume, T., Knox, S. M., de Paiva, C. S., Dartt, D. A., & Makarenkova, H. P. (2023) · Cells · doi:10.3390/cells12101435 · Cited by 22
Sozio, F., Schioppa, T., Laffranchi, M., Salvi, V., Tamassia, N., Bianchetto-Aguilera, F. M., Tiberio, L., Bonecchi, R., Bosisio, D., Parmentier, M., et al. (2023) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-22-0951 · Cited by 22
Sultan, I., Ramste, M. O., Peletier, P., Hemanthakumar, K. A., Ramanujam, D. P., Tirronen, A., von Wright, Y., Antila, S., Saharinen, P., Eklund, L., et al. (2024) · Circulation Research · doi:10.1161/circresaha.123.324136 · Cited by 22
Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease
Hu, P., Rychik, J. R., Zhao, J., Bai, H., Bauer, A., Yu, W., Rand, E. B., Dodds, K. M., Goldberg, D., Tan, K., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adk6213 · Cited by 26
Direct conversion of cardiac fibroblasts into endothelial-like cells using Sox17 and Erg
Farber, G., Dong, Y., Wang, Q., Rathod, M., Wang, H., Dixit, M., Keepers, B., Xie, Y., Butz, K., Polacheck, W. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48354-6 · Cited by 22
“1b) 10,11 .”
MethodAntounians, L., Figueira, R. L., Kukreja, B., Litvack, M. L., Zani‐Ruttenstock, E., Khalaj, K., Montalva, L., Doktor, F., Obed, M., Blundell, M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn5405 · Cited by 23
“2, B and C) ( 18 – 22 ).”
Maynard, L. H., Smith, O., Tilmans, N. P., Tham, E., Hosseinzadeh, S., Tan, W., Leenay, R. T., May, A. P., & Paulk, N. K. (2019) · Human Gene Therapy Methods · doi:10.1089/hum.2019.110 · Cited by 22
“(7) Randomly primed DNA synthesis was performed in a thermocycler using a ramp of 0.”
MethodBreathing fresh air into respiratory research with single-cell RNA sequencing
Alexander, M. J., Budinger, G. R. S., & Reyfman, P. A. (2020) · European Respiratory Review · doi:10.1183/16000617.0060-2020 · Cited by 23
Northstar enables automatic classification of known and novel cell types from tumor samples
Zanini, F., Berghuis, B. A., Jones, R. C., di Robilant, B. N., Nong, R. Y., Norton, J. A., Clarke, M. F., & Quake, S. R. (2020) · Scientific Reports · doi:10.1038/s41598-020-71805-1 · Cited by 17
Molecular Mechanism of Congenital Heart Disease and Pulmonary Hypertension
Nakanishi, T., Baldwin, H. S., Fineman, J. R., & Yamagishi, H. (2020) · Springer eBooks · doi:10.1007/978-981-15-1185-1 · Cited by 21
de Biagi, C. A. O., Cury, S. S., Alves, C. P., Rabhi, N., Silva, W. A., Farmer, S. R., Carvalho, R. F., & Júnior, M. L. B. (2021) · Cells · doi:10.3390/cells10113073 · Cited by 25
Radionuclide partitioning coefficients in soils and plants and their correlation.
Sheppard, M. I. (1985) · PubMed · Cited by 21
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21
The changing mouse embryo transcriptome at whole tissue and single-cell resolutionPreprint
He, P., Williams, B. A., Trout, D. E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.14.150599 · Cited by 21
Unifying single-cell annotations based on the Cell OntologyPreprint
Wang, S., Pisco, A. O., McGeever, A., Brbić, M., Žitnik, M., Darmanis, S., Leskovec, J., Karkanias, J., & Altman, R. B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/810234 · Cited by 21
REST is a major negative regulator of endocrine differentiation during pancreas organogenesisPreprint
Rovira, M., Atla, G., Maestro, M. A., Grau, V., García-Hurtado, J., Maqueda, M., Mosquera, J. L., Yamada, Y., Kerr-Conte, J. A., Pattou, F. N., et al. (2021) · Genes & Development · doi:10.1101/gad.348501.121 · Cited by 21
Erasure of Biologically Meaningful Signal by Unsupervised scRNAseq Batch-correction MethodsPreprint
Tyler, S. R., Guccione, E., & Schadt, E. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.15.468733 · Cited by 20
Simulating multiple variability in spatially resolved transcriptomics with scCube
Qian, J., Bao, H., Shao, X., Yin, F., Liao, J., Chen, Z., Li, C., Guo, W., Hu, Y., Li, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49445-0 · Cited by 23
“In the current version, scCube includes about 300 trained models of various tissues derived from four human and mouse scRNA-seq atlas (Tabula Muris 40 , Tabula Sapiens 41 , MCA 42 , and HCL 43 ) as well as eight high-quality SRT datasets (Supplementary Data 1).”
Adameyko, I. I., Bakken, T. E., Bhaduri, A., Chhatbar, C., Filbin, M. G., Gate, D., Hochgerner, H., Kim, C. N., Krull, J. E., Manno, G. L., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01827-9 · Cited by 26
A primer on global molecular responses to exercise in skeletal muscle: Omics in focus
Murach, K. A., & Bagley, J. R. (2025) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2025.101029 · Cited by 22
Tspan18 is a novel regulator of thrombo-inflammation
Gavin, R. L., Koo, C. Z., & Tomlinson, M. G. (2020) · Medical Microbiology and Immunology · doi:10.1007/s00430-020-00678-y · Cited by 20
LEFTY1 Is a Dual-SMAD Inhibitor that Promotes Mammary Progenitor Growth and Tumorigenesis
Zabala, M., Lobo, N. A., Antony, J., Heitink, L. S., Gulati, G. S., Lam, J. S., Parashurama, N., Sanchez, K., Adorno, M., Sikandar, S. S., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.06.017 · Cited by 20
The Long Pentraxin PTX3 Controls Klebsiella Pneumoniae Severe Infection
Asgari, F., Supino, D., Parente, R., Polentarutti, N., Stravalaci, M., Porte, R., Pasqualini, F., Barbagallo, M., Perucchini, C., Recordati, C., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.666198 · Cited by 23
singlecellVR: Interactive Visualization of Single-Cell Data in Virtual Reality
Stein, D. F., Chen, H., Vinyard, M. E., Qin, Q., Combs, R. D., Zhang, Q., & Pinello, L. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.764170 · Cited by 22
“To demonstrate this utility, we processed (using Scanpy and scvr) and visualized on singlecellVR, the scRNA-seq data from the Chan-Zuckerberg Biohub Tabula Muris project, a dataset consisting of 44,949 cells and 20 tissues from seven mice [17].”
MethodPark, Y., Hauschild, A.-C., & Heider, D. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab104 · Cited by 22
Cardiovascular consequences of vaping
Echeagaray, O., Savko, C., Gallo, A., & Sussman, M. A. (2022) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000952 · Cited by 20
Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq
Ghaddar, B., & De, S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac333 · Cited by 19
“(D) UMAP of 9,552 murine immune cells from Tabula Muris (Schaum et al., 2018) colored by cell type.”
BackgroundMethodInfluentialMicallef, P., Vujičić, M., Wu, Y., Peris, E., Wang, Y., Chanclón, B., Ståhlberg, A., Cardell, S., & Asterholm, I. W. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.914956 · Cited by 20
“In support of this, publicly available single cell transcriptomics data show that the highest C1qtnf3 expression within adipose tissue SVF is found in the mesenchymal stem cell cluster while other cell types such as lymphocytes and myeloid and endothelial cells express very modest levels (45).”
MethodSystemic LSD1 Inhibition Prevents Aberrant Remodeling of Metabolism in Obesity
Ramms, B., Pollow, D. P., Zhu, H., Nora, C., Harrington, A. R., Omar, I., Gordts, P. L. S. M., Wortham, M., & Sander, M. (2022) · Diabetes · doi:10.2337/db21-1131 · Cited by 21
Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline
Mikolajewicz, N., Gacesa, R., Aguilera-Uribe, M., Brown, K. R., Moffat, J., & Han, H. R. (2022) · Communications Biology · doi:10.1038/s42003-022-04093-2 · Cited by 21
Higgs, M. J., Hill, M., John, R. M., & Isles, A. R. (2022) · BMC Genomics · doi:10.1186/s12864-022-08986-8 · Cited by 20
Wnt7a deficit is associated with dysfunctional angiogenesis in pulmonary arterial hypertension
Chakraborty, A., Nathan, A., Orcholski, M. E., Agarwal, S., Shamskhou, E. A., Auer, N., Mitra, A., Guardado, E. S., Swaminathan, G., Condon, D. F., et al. (2023) · European Respiratory Journal · doi:10.1183/13993003.01625-2022 · Cited by 20
“The lack of a phenotype in Wnt7a ECKO may result from AT1 cells and other intraparenchymal cells serving as sources of Wnt7a in the lung [35], thereby compensating for the lack of Wnt7a production by ECs.”
Mammary duct luminal epithelium controls adipocyte thermogenic programme
Patel, S., Sparman, N. Z. R., Arneson, D. V., Alvarsson, A., SANTOS, L. V. D. S., Duesman, S. J., Centonze, A., Hathaway, E., Ahn, I. S., Diamante, G., et al. (2023) · Nature · doi:10.1038/s41586-023-06361-5 · Cited by 21
He, Y., Heng, Y., Qin, Z., Wei, X., Wu, Z., & Qu, J. Y. (2023) · Science Advances · doi:10.1126/sciadv.adi1891 · Cited by 21
Notch signaling in thyrocytes is essential for adult thyroid function and mammalian homeostasis
Mosteiro, L., Nguyen, T. T. T., Hankeová, S., Álvarez‐Sierra, D., Reichelt, M., Vandriel, S. M., Lai, Z., Choudhury, F. K., Sangaraju, D., Kamath, B. M., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00937-1 · Cited by 21
ANKS1B encoded AIDA-1 regulates social behaviors by controlling oligodendrocyte function
Cho, C. H., Deyneko, I. V., Cordova-Martinez, D., Vazquez, J., Maguire, A. S., Diaz, J. R., Carbonell, A. U., Tindi, J. O., Cui, M., Fleysher, R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43438-1 · Cited by 20
“Recent large-scale single-cell transcriptomic initiatives suggest that AIDA-1 is expressed in oligodendrocytes and OPCs 30 – 33 , as well as in newborn OPCs linked to lesions in multiple sclerosis (MS) 34 .”
InfluentialscTPC: a novel semisupervised deep clustering model for scRNA-seq data
Qiu, Y., Yang, L., Jiang, H., & Zou, Q. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae293 · Cited by 22
Exercise activates interferon response of the liver via Gpld1 to enhance antiviral innate immunity
Ren, T., He, J., Zhang, T., Niu, A.-X., Yuan, Y., Zuo, Y., Miao, Y., Hongguang, Z., Zang, L., Qiao, C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk5011 · Cited by 23
Abedini‐Nassab, R., & Mahdaviyan, N. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2992551 · Cited by 20
Defining the combinatorial space of PKC::CARD‐CC signal transduction nodes
Staal, J., Driege, Y., Haegman, M., Kreike, M., Iliaki, S., Vanneste, D., Lork, M., Afonina, I. S., Braun, H., & Beyaert, R. (2020) · FEBS Journal · doi:10.1111/febs.15522 · Cited by 20
PRL-2 phosphatase is required for vascular morphogenesis and angiogenic signaling
Poulet, M., Sirois, J., Boyé, K., Uetani, N., Hardy, S., Daubon, T., Dubrac, A., Tremblay, M. L., & Bikfalvi, A. (2020) · Communications Biology · doi:10.1038/s42003-020-01343-z · Cited by 23
Fear Deficits in HypomyelinatedTpppKnock-Out Mice
Nguyen, H., Meservey, L. M., Ishiko, N., Zhou, M., Huang, T., & Fu, M. (2020) · eNeuro · doi:10.1523/eneuro.0170-20.2020 · Cited by 20
“Tppp mRNA is highly expressed by oligodendrocytes, but extremely low in other brain cells (Zhang et al., 2014; Schaum et al., 2018) and in other non-brain tissues (Schaum et al., 2018).”
BackgroundSingle cell profiling of total RNA using Smart-seq-totalPreprint
Isakova, A., Neff, N. F., & Quake, S. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.02.131060 · Cited by 19
Gan, C., Yaqoob, U., Lu, J., Xie, M., Anwar, A., Jalan-Sakrikar, N., Jerez, S., Sehrawat, T. S., Navarro-Corcuera, A., Kostallari, E., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.174775 · Cited by 21
“…https://fantom.gsc.riken.jp/zenbu/ gLyphs/#config=FANTOM5_promoterome_hg19;loc=hg19:chr10:104151762.104164391+), as well as in the mouse liver (Supplemental Figure 3, B and C) (31), and accounted for COL4 upregulation in a murine nonalcoholic steatohepatitis model (Supplemental Figure 3D) (32).”
BackgroundInfluentialShu, Z., Xia, M., Tan, K., Zhang, Y., & Yu, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae562 · Cited by 21
“These datasets include the following: Klein [48], Young [49], Quake_Smart-seq2_Trachea (Trachea) [50], Quake_Smart-seq2_Diaphragm (Diaphragm) [50], and Quake_10x_Limb_Muscle (Muscle) [50], Quake_10x_Bladder (Bladder) [50], Pollen [51], Wang_Lung [52].”
Adapting systems biology to address the complexity of human disease in the single-cell era
Fischer, D. S., Villanueva, M. A., Winter, P. S., & Shalek, A. K. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00821-6 · Cited by 32
CRATER tumor niches facilitate CD8+ T cell engagement and correspond with immunotherapy success
Ludin, A., Stirtz, G. L., Tal, A., Nirmal, A. J., Pfaff, K. L., Manos, M. P., Besson, N. R., Eskndir, N., Porter, B. A., Jones, S. M., et al. (2025) · Cell · doi:10.1016/j.cell.2025.09.021 · Cited by 22
Data Denoising and Post-Denoising Corrections in Single Cell RNA Sequencing
Agarwal, D., Wang, J., & Zhang, N. R. (2020) · Statistical Science · doi:10.1214/19-sts7560 · Cited by 19
“Transfer learning from public datasets is particularly enticing in light of recent initiatives to build detailed cellular atlases for each anatomic organ in mouse (Han et al., 2018; Tabula Muris Consortium, 2018) and human (Rozenblatt-Rosen et al., 2017).”
BackgroundUnderstanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution
Marín‐Sedeño, E., de Morentin, X. M., Pérez-Pomares, J. M., Gómez-Cabrero, D., & Ruiz‐Villalba, A. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.645276 · Cited by 18
KIT Is Required for Fetal Liver Hematopoiesis
Fantin, A., Plein, A., Tacconi, C., Villa, E., Ceccacci, E., Denti, L., & Ruhrberg, C. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.648630 · Cited by 18
“5 in the mouse before colonizing the bone marrow just before birth (Taoudi et al., 2008; Rybtsov et al., 2014, 2011; Azzoni et al., 2018).”
BackgroundElamaa, H., Kaakinen, M., Nätynki, M., Szabó, Z., Ronkainen, V., Äijälä, V. K., Mäki, J. M., Kerkelä, R., Myllyharju, J., & Eklund, L. (2022) · Angiogenesis · doi:10.1007/s10456-021-09828-z · Cited by 20
Ascensión, A. M., Araúzo-Bravo, M. J., & Izeta, A. (2022) · Life · doi:10.3390/life12010067 · Cited by 19
Kohler, K. T., Goldhammer, N., Demharter, S., Pfisterer, U., Khodosevich, K. V., Rønnov‐Jessen, L., PETERSEN, O. W., Villadsen, R., & Kim, J. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00444-8 · Cited by 20
Clayton, D. R., Ruiz, W. G., Dalghi, M. G., Montalbetti, N., Carattino, M. D., & Apodaca, G. L. (2022) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00135.2022 · Cited by 17
Integration of single-cell multi-omics data by regression analysis on unpaired observations
Yuan, Q., & Duren, Z. (2022) · Genome biology · doi:10.1186/s13059-022-02726-7 · Cited by 19
Decoding brain memory formation by single-cell RNA sequencing
Sardoo, A. M., Zhang, S., Ferraro, T. N., Keck, T. M., & Chen, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac412 · Cited by 20
Shu, Z., Long, Q., Zhang, L., Yu, Z., & Wu, X. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01305 · Cited by 19
Dai, L., Guo, X., Xing, Z., Tao, Y., Liang, W., Shi, Z., Hu, W., Zhou, S., & Wang, X. (2023) · BMC Cancer · doi:10.1186/s12885-023-10575-1 · Cited by 20
Frazier, A. P., Mitchell, D. N., Given, K. S., Hunn, G., Burch, A. M., Childs, C. R., Moreno‐Garcia, M., Corigilano, M. R., Quillinan, N., Macklin, W. B., et al. (2023) · Glia · doi:10.1002/glia.24349 · Cited by 20
Tumor-derived interleukin-1α and leukemia inhibitory factor promote extramedullary hematopoiesis
Barisas, D. A. G., Kim, M., Subramanian, M., Kabir, A. U., Krchma, K., Wu, J., Zinselmeyer, B. H., Stewart, C. L., & Choi, K. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3001746 · Cited by 19
“This PDGFRα+/β+ surface phenotype matches mesenchymal stem cells as identified by single-cell RNA-sequencing (scRNA-seq) of limb muscles [36,37].”
Background
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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