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 1,301–1,400 of 2,952
Cyp26b1 is a critical regulator of distal airway epithelial differentiation during lung development
Daniel, E., Barlow, H. R., Sutton, G. I., Gu, X., Htike, Y., Cowdin, M. A., & Cleaver, O. B. (2020) · Development · doi:10.1242/dev.181560 · Cited by 14
Paladin is a phosphoinositide phosphatase regulating endosomal VEGFR2 signalling and angiogenesis
Nitzsche, A., Pietilä, R. S., Love, D. T., Testini, C., Ninchoji, T., Smith, R., Ekvärn, E., Larsson, J., Roche, F. P., Egaña, I., et al. (2020) · EMBO Reports · doi:10.15252/embr.202050218 · Cited by 14
“:3) mouse (Wallgard et al , 2012; Egana et al , 2017; Schaum et al , 2018).”
Single-Cell RNA Sequencing Reveals Thoracolumbar Vertebra Heterogeneity and Rib-Genesis in Pigs
Li, J., Wang, L., Yu, D., Hao, J., Zhang, L., Adeola, A. C., Mao, B., Gao, Y., Wu, S., Zhu, C., et al. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2021.09.008 · Cited by 14
Kendal, A. R., Layton, T., Al‐Mossawi, H., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.09.869933 · Cited by 14
“Previous single cell transcriptomic studies of mouse muscle have demonstrated a number of different cell matrix associated cell populations including fibro-adipogenic progenitors (FAPs), muscle satellite cells (MuSCs), ITGA7+VCAM1-/Smooth Muscle-Mesenchymal Cells (SMMCs), and scleraxis (SCX) expressing cells (16) (32)…”
BackgroundMethodMolecular hallmarks of heterochronic parabiosis at single cell resolutionPreprint
Pálovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Webber, J. T., McGeever, A., Bonanno, L. N., Pisco, A. O., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.06.367078 · Cited by 14
“We discarded 435 cells with (1) fewer than 500 genes or (2) less than total 5,000 reads or (3) more than 30% ERCC 436 reads or (4) more than 10% mitochondrial reads or (5) more than 10% ribosomal reads.”
MethodThorner, K., Zorn, A. M., & Chaturvedi, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.07.459342 · Cited by 14
A Cellular Reference Resource for the Mouse Urinary BladderPreprint
Baker, D. S., Al‐Naggar, I. M., Sivajothi, S. K., Flynn, W. F., Amiri, A., Luo, D., Hardy, C. C., Kuchel, G. A., Smith, P. P., & Robson, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.20.461121 · Cited by 14
Avraham‐Davidi, I., Mages, S., Klughammer, J., Moriel, N., Imada, S., Hofree, M., Murray, E. M., Chen, J. H., Pelka, K., Mehta, A., et al. (2025) · eLife · doi:10.1101/2022.10.02.508492 · Cited by 24
Rezaei, A., Kocsis-Jutka, V., Gunes, Z. I., Zeng, Q., Kislinger, G., Bauernschmitt, F., Isilgan, H. B., Parisi, L. R., Kaya, T., Franzenburg, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58634-4 · Cited by 14
(2019) · doi:10.1101/609438 · Cited by 15
“70 The Tabula Muris Consortium. A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the Mouse. BioRxiv (Pre-print), doi:https://doi.org/10.1101/661728 (2019). 71 Ogrodnik, M. et al. Cellular senescence drives age-dependent hepatic steatosis. Nat Commun 8, 15691, doi:10.1038/ncomms15691 (2017). 72 Demaria…”
BackgroundInfluentialIntercellular signaling dynamics from a single cell atlas of the biomaterials response
Cherry, C., Maestas, D. R., Han, J., Andorko, J. I., Cahan, P., Fertig, E. J., Garmire, L. X., & Elisseeff, J. H. (2020) · doi:10.1101/2020.07.24.218537
Single-cell and spatial detection of senescent cells using DeepScence
Qu, Y., Ji, B., Dong, R., Gu, L., Chan, C., Xie, J., Glass, C., Wang, X.-F., Nixon, A. B., & Ji, Z. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101035 · Cited by 30
Bank1 modulates the differentiation and molecular profile of key B cell populations in autoimmunity
Hernández, G. G., Domínguez, T. M., Galicia, G., Morell, M. H., & Alarcón-Riquelme, M. E. (2024) · JCI Insight · doi:10.1172/jci.insight.179417 · Cited by 13
Bai, K., Moa, B., Shao, X., & Zhang, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.019 · Cited by 13
Mitochondrial control of fuel switching via carnitine biosynthesis
Auger, C., Nishida, H., Yuan, B., Silva, G. M., Fujimoto, M., Li, M., Katoh, D., Wang, D., Granath-Panelo, M., Shin, J., et al. (2026) · Science · doi:10.1126/science.ady5532 · Cited by 13
“Of note, SLC25A45 is highly expressed in the mouse kidney, where single-nuclei RNA-seq data ( 35 ) identified that epithelial cells of the proximal tubules and collecting ducts express Slc25a45 and carnitine synthesis enzymes, including Tmlhe and Aldh9a1 ( Supplementary Fig.7B ).”
Rubio-Ponce, A., Hidalgo, A., & Ballesteros, I. (2020) · Current Opinion in Immunology · doi:10.1016/j.coi.2020.08.003 · Cited by 13
Stem/progenitor cells in normal physiology and disease of the pancreas
Fallas, M. E. A., Pedraza‐Arévalo, S., Cujba, A.-M., Manea, T., Lambert, C., Morrugares, R., & Sancho, R. (2021) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2021.111459 · Cited by 16
Ding, Q., Yang, W., Luo, M., Xu, C., Xu, Z., Pang, F., Cai, Y., Anashkina, A. A., Su, X., Chen, N., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac300 · Cited by 13
LncRNAEPRregulates intestinal mucus production and protects against inflammation and tumorigenesis
Briata, P., Mastracci, L., Zapparoli, E., Caputo, L., Ferracci, E., Silvestri, A., Garuti, A., Hamadou, M. H., Inga, A., Marcaccini, E., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad257 · Cited by 13
Zhao, S., Zheng, J., Wang, L., Umpierre, A. D., Parusel, S., Xie, M., Dheer, A., Ayasoufi, K., Johnson, A. J., Richardson, J. R., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02128-6 · Cited by 13
“The statistical details for each speci fi c experiment, including sample size and statistical methods, are described in the fi gure legends.”
BackgroundMethodUnsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics
Cheng, Y., Su, Y., Yu, Z., Liang, Y., Wong, K., & Li, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25631 · Cited by 14
Fouché, A., & Zinovyev, A. Y. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1191961 · Cited by 14
“Hand-to-hand with the surge of biological modalities, there has been an explosion in the number of available datasets helped by various scienti fi c initiatives to make biological data more easily available (Conesa and Beck, 2019); among these initiatives, one can mention atlases of entire organisms such as the Tabula…”
BackgroundThe evolving definition of plant cell type
Amini, S., Doyle, J. J., & Libault, M. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1271070 · Cited by 13
Ando, R., Shiraki, Y., Miyai, Y., Shimizu, H., Furuhashi, K., Minatoguchi, S., Kato, K., Kato, A., Iida, T., Mizutani, Y., et al. (2023) · The Journal of Pathology · doi:10.1002/path.6211 · Cited by 13
Age-related structural and functional changes of the intracardiac nervous system
Sassu, E., Tumlinson, G., Stefanovska, D., Fernández, M. C., Iaconianni, P., Madl, J., Brennan, T. A., Koch, M., Cameron, B. A., Preißl, S., et al. (2023) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2023.12.002 · Cited by 12
Hui, W., Song, T., Yu, L., & Chen, X. (2023) · Antioxidants · doi:10.3390/antiox13010042 · Cited by 13
Predicting cell types with supervised contrastive learning on cells and their types
Heryanto, Y. D., Zhang, Y., & Imoto, S. (2024) · Scientific Reports · doi:10.1038/s41598-023-50185-2 · Cited by 15
Tomita, S., Nakanishi, N., Ogata, T., Higuchi, Y., Sakamoto, A., Tsuji, Y., Suga, T., & Matoba, S. (2024) · Communications Biology · doi:10.1038/s42003-023-05693-2 · Cited by 13
“We investigated the expression of Caveolins, Cavins, and BMPR2 by cell type using previously reported single-cell RNA sequencing in mouse lung tissue 19 .”
Fan, Q., Yan, R., Li, Y., Lu, L., Liu, J., Li, S., Fu, T., Xue, Y., Liu, J., & Li, Z. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25021208 · Cited by 13
“…M2 M Φ with high expression of Mrc1 (encoding CD206), Retnl α ( Fizz1) , and Chil3 ( Chi3l3 , Ym1 ) [32], expressing Cd209a , Ms4a4c , and Tnip3 [33,34], plasmacytoid dendritic cells To determine the distribution of these immune cell subpopulations, we calculated their number and percentage in the…”
BackgroundBraithwaite, A. T., Akbar, N., Pezzolla, D., Paget, D., Krausgruber, T., Bock, C., Carnicer, R., & Choudhury, R. P. (2024) · The FASEB Journal · doi:10.1096/fj.202302003r · Cited by 13
Keeping pace: the primary cilium as the conducting baton of the islet
Idevall‐Hagren, O., Nilsson, C. I., & Sanchez, G. (2024) · Diabetologia · doi:10.1007/s00125-024-06096-6 · Cited by 11
“Both alpha and delta cells express structural and functional cilia genes, including Arl13b , Kif3a and Ift88 [60], and ultrastructural examination has shown the presence of primary cilia on both cell types [5, 61, 62].”
BackgroundYe, F., Zhang, S., Fu, Y., Yang, L. X., Zhang, G., Wu, Y., Pan, J., Chen, H., Wang, X., Ma, L., et al. (2024) · Cell Discovery · doi:10.1038/s41421-023-00642-z · Cited by 13
Single-cell transcriptomics dissecting the development and evolution of nervous system in insects
Liu, W., & Li, Q. (2024) · Current Opinion in Insect Science · doi:10.1016/j.cois.2024.101201 · Cited by 14
Zhai, Y., Chen, L., & Deng, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae188 · Cited by 14
Xu, T., Zhang, Y., Li, D., Lai, C., Wang, S., & Zhang, S. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3401136 · Cited by 13
“Piezo channel family currently includes two members, Piezo1 (Fam38A) and Piezo2 (Fam38B), which have a high degree of sequence similarity [32], [33], [34].”
BackgroundCharacterizing Fibroblast Heterogeneity in Diabetic Wounds Through Single-Cell RNA-Sequencing
Wang, H. H., Korah, M. M., Jing, S. L., Berry, C. E., Griffin, M., Longaker, M. T., & Januszyk, M. (2024) · Biomedicines · doi:10.3390/biomedicines12112538 · Cited by 14
“Specific cell types can now be “isolated” in silico, most commonly in a two-step process where cells are first clustered using unsupervised algorithms before being assigned cell type identities according to cluster-level expression profiles [64].”
scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data
Min, W., Wang, Z., Zhu, F., Xu, T., Wu, G., & Wang, S. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822787 · Cited by 15
Wang, J., Chen, X.-Y., Zhang, Y., Shen, Y., Zhu, M.-L., Zhang, J., & Zhang, J. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1562848 · Cited by 15
A molecular cell atlas of mouse lemur, an emerging model primate
de Morrée, A., Vlaminck, I. D., Shapiro, L. J., Razafindrakoto, A., Ravelonjanahary, H. N., Wright, P. C., Yoder, A. D., Williams, C. V., Schopler, R. L., Radespiel, U., et al. (2025) · Nature · doi:10.1038/s41586-025-09113-9 · Cited by 15
The relevance of adhesion G protein-coupled receptors in metabolic functions
Kaczmarek, I., Suchý, T., Prömel, S., Schöneberg, T., Liebscher, I., & Thor, D. (2021) · Biological Chemistry · doi:10.1515/hsz-2021-0146 · Cited by 14
Olig2-astrocytes express neutral amino acid transporter SLC7A10 (Asc-1) in the adult brain
Tatsumi, K., Kinugawa, K., Isonishi, A., Kitabatake, M., Okuda, H., Takemura, S., Tanaka, T., Mori, E., & Wanaka, A. (2021) · Molecular Brain · doi:10.1186/s13041-021-00874-8 · Cited by 14
“Recently, a database of the transcriptomes of mouse single cells has been published [26].”
BackgroundIntegration of single cell data by disentangled representation learning
Guo, T., Chen, Y., Shi, M., Li, X., & Zhang, M. Q. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab978 · Cited by 13
“Then, we tested methods on nine publicly available datasets of human PBMC generated by 10x(v2), 10x(v3), CEL-Seq2, Smart-seq2, Seq-Well, inDrops and Drop-seq.”
MethodIntrinsic entropy model for feature selection of scRNA-seq data
Li, L., Tang, H., Xia, R., Dai, H., Liu, R., & Chen, L. (2022) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjac008 · Cited by 13
Liu, Z., Jiang, M., Liakath‐Ali, K., Sclip, A., Ko, J., Zhang, R. S., & Südhof, T. C. (2022) · eLife · doi:10.7554/elife.70664 · Cited by 14
Automatic Cell Type Annotation Using Marker Genes for Single-Cell RNA Sequencing Data
Chen, Y., & Zhang, S. (2022) · Biomolecules · doi:10.3390/biom12101539 · Cited by 13
Hasper, J., Welle, K. A., Hryhorenko, J. R., Ghaemmaghami, S., & Buchwalter, A. L. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211393 · Cited by 10
scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
Wang, Z., Wang, H., Zhao, J., & Zheng, C.-H. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05339-4 · Cited by 13
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Zeng, P., Li, Y., Hu, P., Peng, D., Lv, J., & Peng, X. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02297 · Cited by 13
“To explore the potential in practical applications, we additionally evaluate FCMI on the single-cell mouse atlas dataset that is widely used in biological analysis, where the sensitive attributes correspond to different sequence techniques [10, 15].”
MethodSupervised clustering for single-cell analysis
Lee, J. T. H., & Hemberg, M. (2019) · Nature Methods · doi:10.1038/s41592-019-0534-4 · Cited by 13
Yuan, Y., Zhou, Y., Li, Y., Hill, C. N., Ewing, R. M., Jones, M. G., Davies, D. E., Jiang, Z., & Wang, Y. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21041371 · Cited by 13
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-seq Data
Chi, W., & Deng, M. (2020) · Genes · doi:10.3390/genes11050532 · Cited by 13
“A large volume of scRNA-seq datasets has been produced, including cell atlases for humans [5] and mice [6,7].”
BackgroundGenomic, transcriptomic, and protein landscape profile of CFTR and cystic fibrosis
Sanders, M., Lawlor, J. M. J., Li, X., Schuen, J. N., Millard, S. L., Zhang, X., Buck, L., Grysko, B., Uhl, K. L., Hinds, D., et al. (2020) · Human Genetics · doi:10.1007/s00439-020-02211-w · Cited by 11
Fisher, C. L., Fallot, L. B., Wan, T. C., et al. (2022) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.2c00076 · Cited by 13
Dudgeon, C., Harris, C. R., Chen, Y., Ghaddar, B., Sharma, A., Shah, M. M., Roberts, A. I., Casabianca, A., Collisson, E. A., Balachandran, V. P., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.13.037374 · Cited by 15
Grabauskas, G., Gao, J., Wu, X., Zhou, S.-Y., Turgeon, D. K., & Owyang, C. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.07.053 · Cited by 14
RetractedZebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish EmbryogenesisPreprint
Kim, Y. J., VijayKumar, S., Iovino, B. G., Granados, A. A., Ancheta, S., Frank, M., Zhao, X., Awayan, K., Seng, A., Borja, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.18.618987 · Cited by 16
“The past decade has witnessed a revolution in our ability to decode developmental programs at unprecedented resolution.”
Liu, L., Shi, Y., He, S., Yang, J., Song, S., Wang, D., Wang, Z., Zhou, H., Deng, X., Zou, S., et al. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07071-y · Cited by 13
“S9) [ 26, 27].”
Giorgino, F., Alzaïd, F., Stoian, A. P., Chan, J. C. N., Ji, L., Lumu, W., Manrique-Hurtado, H., Mauricio, D., Saboo, B., Senior, P. A., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06504-5 · Cited by 18
Ma, Y., Liu, C., Xi, G., et al. (2022) · Disease Markers · doi:10.1155/2022/3843954 · Cited by 12
RetractedYang, X., Dai, Z., Gao, C., Yin, Y., Shi, C., Liu, R., Zhuge, Q., Huang, Y. M., Zhou, B., Han, Z., et al. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x221105995 · Cited by 12
Functional Annotation of Custom Transcriptomes
Hamid, F., Alasoo, K., Vilo, J., & Makeyev, E. V. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2521-7_9 · Cited by 12
Single-Cell RNA Sequencing Reveals New Basic and Translational Insights in the Cystic Fibrosis Lung
Januska, M. N., & Walsh, M. J. (2022) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2022-0038tr · Cited by 11
Zandavi, S. M., Liu, D., Chung, Y. Y., Anaissi, A., & Vafaee, F. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10357-4 · Cited by 15
“7 marrow 10X [38] Chromium GSE132042 mouse marrow 4112 23433 9 UMI”
BackgroundInfluentialPlatform-agnostic CellNet enables cross-study analysis of cell fate engineering protocols
Lo, E. K. W., Velazquez, J. J., Peng, D., Kwon, C., Ebrahimkhani, M. R., & Cahan, P. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2023.06.008 · Cited by 14
Ke, M., Fang, Y., Cai, H., Lu, J., Yang, L., Wang, Y., Wu, R., Zhang, X., Lv, Y., & Dong, J. (2023) · International Journal of Biological Sciences · doi:10.7150/ijbs.84768 · Cited by 12
Alcober-Boquet, L., Kraus, N., Huber, L. S., Vutukuri, R., Fuhrmann, D. C., Stroß, C., Schaefer, L., Scholich, K., Zeuzem, S., Piiper, A., et al. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00413.2023 · Cited by 14
scFed: federated learning for cell type classification with scRNA-seq
Wang, S., Shen, B., Guo, L., Shang, M., Liu, J., Sun, Q., & Shen, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad507 · Cited by 13
“The Tabula Muris (TM) dataset represents relatively large scRNA-seq dataset ( > 50 000 cells)[27].”
BackgroundRöthe, J., Kraft, R., Ricken, A. M., Kaczmarek, I., Matz‐Soja, M., Winter, K., Dietzsch, A. N., Buchold, J., Ludwig, M., Liebscher, I., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-05783-9 · Cited by 12
Making mouse transcriptomics deconvolution accessible with immunedeconv
Merotto, L., Sturm, G., Dietrich, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae032 · Cited by 12
“We considered a subset of the “mammary gland” dataset from the Tabula Muris study (Tabula Muris Consortium et al. 2018) and simulated 20 pseudo-bulk samples using the R package SimBu (Dietrich et al. 2022) (details in Supplementary Methods).”
Vascular development, remodeling and maturation
Furtado, J., & Eichmann, A. (2024) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2024.02.001 · Cited by 13
Xie, J., Ruan, S., Tu, M., Yuan, Z., Hu, J., Li, H., & Li, S. (2024) · Oncogene · doi:10.1038/s41388-024-03074-5 · Cited by 12
“…of α = 2/3 and applying the Laplacian smoothing fi lter iteratively for t times, the fi ltered representation of the reconstruction matrix ^ X can be denoted as: The Self-supervised discriminative embedding To enhance the effectiveness of node embedding learning and improve clustering…”
Characterization of FGF21 Sites of Production and Signaling in Mice
Sullivan, A. I., Jensen-Cody, S. O., Claflin, K. E., Vorhies, K., Flippo, K. H., & Potthoff, M. J. (2024) · Endocrinology · doi:10.1210/endocr/bqae120 · Cited by 11
nsDCC: dual-level contrastive clustering with nonuniform sampling for scRNA-seq data analysis
Wang, L., Li, W., Zhou, F., Yu, K., Feng, C., & Zhao, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae477 · Cited by 12
Rajala, R., Cleuren, A. C. A., & Griffin, C. T. (2024) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.124.321353 · Cited by 12
MetaQ: fast, scalable and accurate metacell inference via single-cell quantization
Li, Y., Li, H., Lin, Y., Zhang, D., Peng, D., Liu, X., Xie, J., Hu, P., Chen, L., Luo, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56424-6 · Cited by 15
“The originalcLISIandiLISIrangein[1, K ]and[1, B ],respectively.”
scCompass: An Integrated Multi‐Species scRNA‐seq Database for AI‐Ready
Wang, P., Liu, W., Wang, J., Liu, Y., Li, P., Xu, P., Cui, W., Zhang, R., Long, Q., Hu, Z., et al. (2025) · Advanced Science · doi:10.1002/advs.202500870 · Cited by 17
Yuan, F., Pan, X., Zeng, T., Zhang, Y., Chen, L., Gan, Z., Huang, T., & Cai, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00350 · Cited by 12
Fragmentation of Small-Cell Lung Cancer Regulatory States in Heterotypic Microenvironments
Schaff, D. L., Singh, S., Kim, K., Sutcliffe, M. D., Park, K.-S., & Janes, K. A. (2021) · Cancer Research · doi:10.1158/0008-5472.can-20-1036 · Cited by 12
“PNECs normally receive Il33 stimulation as an alarmin from ATII cells during lung injury or infection (69), but Il33 is also highly expressed in hepatocytes and liver sinusoids (40,70).”
BackgroundMethodInfluentialShaw, I., O'Sullivan, E. D., Pisco, A. O., Borthwick, G., Gallagher, K., Péault, B., Hughes, J., & Ferenbach, D. A. (2021) · Stem Cells Translational Medicine · doi:10.1002/sctm.20-0392 · Cited by 12
Single-Cell Analysis for Whole-Organism Datasets
Pisco, A. O., Tojo, B. M., & McGeever, A. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-031008 · Cited by 11
Qian, L., Li, Y., Cao, Y., Meng, G., Peng, J., Li, H., Wang, Y., Xu, T., Zhang, L., Sun, B., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.689068 · Cited by 15
“Two single-cell datasets [GSE109774 (27) and GSE103867 (28)], which contain nearly 100,000 cells from 20 mouse organs and two HCC cell lines (HuH-1 and HuH-7), separately, were used to validate the expression patterns of glycolysis and KBM at the single-cell level in normal tissue and tumor cell lines.”
MethodFundamentals of vaping-associated pulmonary injury leading to severe respiratory distress
Esquer, C., Echeagaray, O., Firouzi, F., Savko, C., Shain, G., Bose, P., Rieder, A., Rokaw, S., Witon-Paulo, A., Gude, N. A., et al. (2021) · Life Science Alliance · doi:10.26508/lsa.202101246 · Cited by 14
Axon Guidance Molecules in the Islets of Langerhans
Waters, B. J., & Blum, B. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.869780 · Cited by 12
GranatumX: A Community-Engaging, Modularized, and Flexible Webtool for Single-Cell Data Analysis
Garmire, D. G., Zhu, X., Mantravadi, A., Huang, Q., Yunits, B., Liu, Y., Wolfgruber, T., Poirion, O., Zhao, T., Arisdakessian, C. G., et al. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2021.07.005 · Cited by 12
Liao, C., Barrow, F., Venkatesan, N., Nakao, Y., Mauer, A. S., Fredrickson, G., Song, M. J., Sehrawat, T. S., Dasgupta, D., Graham, R. P., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1130184 · Cited by 12
Harnessing computational spatial omics to explore the spatial biology intricacies
Yuan, Z., & Yao, J. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.006 · Cited by 12
Carnosine synthase deficiency aggravates neuroinflammation in multiple sclerosis
Spaas, J., der Stede, T. V., de Jager, S., Berends, A. V. D. W., Tiane, A., Baelde, H. J., Baba, S. P., Eckhardt, M., Wolfs, E., Vanmierlo, T., et al. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102532 · Cited by 12
SLIT3-mediated fibroblast signaling: a promising target for antifibrotic therapies
Gong, L., & Si, M. (2023) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00216.2023 · Cited by 11
Multidimensional Single-Cell Analyses in Organ Development and Maintenance
Zhang, Y., & Liu, F. (2019) · Trends in Cell Biology · doi:10.1016/j.tcb.2019.02.006 · Cited by 12
Powell, T. R., Duarte, R. R. R., Hotopf, M. H., Hatch, S. L., Rougvie, M. D. M., Breen, G. D., Lewis, C. M., & Nixon, D. F. (2020) · Scientific Reports · doi:10.1038/s41598-020-59256-0 · Cited by 13
“SELCoH aimed to investigate mental and physical health in the general population in London, UK.”
BackgroundMethodBehavioral strategies to prevent and mitigate COVID-19 infection
Hutchinson, N. T., Steelman, A. J., & Woods, J. A. (2020) · Sports Medicine and Health Science · doi:10.1016/j.smhs.2020.09.001 · Cited by 14
Experimental and computational technologies to dissect the kidney at the single-cell level
Kuppe, C., Perales-Patón, J., Sáez-Rodríguez, J., & Kramann, R. (2020) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaa233 · Cited by 12
Vascular biotransformation of organic nitrates is independent of cytochrome P450 monooxygenases
Lopez, M., Malacarne, P. F., Gajos‐Draus, A., Ding, X., Daiber, A., Lundberg, J. O., Offermanns, S., Brandes, R. P., & Rezende, F. (2021) · British Journal of Pharmacology · doi:10.1111/bph.15362 · Cited by 12
“To investigate whether POR/CYP can biotransform NO prodrugs, we selected CYPs which are expressed in murine and human aorta using published sequencing data as reference (Fleming, 2001; Kalluri et al., 2019; Tabula Muris Consortium, 2018).”
MethodBuchmann, G., Schürmann, C., Spaeth, M., Abplanalp, W. T., Tombor, L. S., John, D., Warwick, T., Rezende, F., Weigert, A., Shah, A. M., et al. (2021) · Redox Biology · doi:10.1016/j.redox.2021.102050 · Cited by 12
Adipose tissue and adipose secretome in systemic sclerosis
Brezovec, N., Burja, B., & Lakota, K. (2021) · Current Opinion in Rheumatology · doi:10.1097/bor.0000000000000838 · Cited by 12
scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methodsPreprint
Tian, L., Dong, X., Freytag, S., Cao, K. L., Su, S., JalalAbadi, A., Amann‐Zalcenstein, D., Weber, T. S., Seidi, A., Jabbari, J. S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/433102 · Cited by 11
Walter, L. D., Orton, J. L., Fong, E. H. H., Maymí, V. I., Rudd, B. D., Elisseeff, J. H., & Cosgrove, B. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.25.542370 · Cited by 12
Spotlight on 10x Visium: a multi-sample protocol comparison of spatial technologiesPreprint
Du, M. R. M., Wang, C., Law, C. W., Amann‐Zalcenstein, D., Anttila, C. J. A., Ling, L., Hickey, P. F., Sargeant, C. J., Chen, Y., Ioannidis, L. J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.13.584910 · Cited by 12
The Four Core Genotypes mouse model: evaluating the impact of a recently discovered translocation
Wiese, C. B., Soliman, B., & Reue, K. (2024) · Biology of Sex Differences · doi:10.1186/s13293-024-00665-5 · Cited by 14
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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