The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 2 of the 8 sources its claims cite were checked for support.
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
Open accessAbstract
Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.
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Claims the paper makes
44 Own result · 26 Own method · 20 Own interpretation · 5 No full text of the sources · 5 No basis given · 1 Supported · 1 Partly supported · 1 Sources not found · accuracy not yet measured
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In the paper
“Cluster co-membership alone, however, is insufficient to conclude that two cell populations from different organs represent the same or similar cell types; at any given resolution, unbiased clustering that groups related cells may also group unrelated cells21.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Partly supportedcites [21] Freytag, S., Tian, L., Lonnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res 7, 1297 (2018). sourceExperimental — accuracy not yet measured
The Results (Evaluation of clustering tools, Silver standard data; page unavailable) report that most clusters contained mixtures of different inferred cell types, supporting the caution that co-membership alone does not establish cell-type identity. The paper evaluates PBMC datasets, however, and does not test populations from different organs or establish that such populations were grouped together. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“transcriptional heterogeneity of brain endothelial cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clusters 1–3 contain mostly single positive T cells (Cd4+ or Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a cell population expressing Chodl in limb muscle”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, to determine which clusters are composed of related or unrelated cell types, we computed a heterogeneity score for each cluster (Methods), and found low scores for the biologically sensible clusters discussed above (Extended Data Fig. 9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To understand which TFs were most informative for specifying cell types, we performed variable selection from random forests (Methods) and determined that 136 TFs are needed to simultaneously define all cell types across all organs (Fig. 5f, Supplementary Table 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The compendium is comprised of single-cell transcriptomic data from 100,605 cells isolated from 20 organs from 3 female and 4 male, C57BL/6JN, 3-month-old mice (10–15 weeks), analogous to 20-year-old humans (Fig. 1).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting dendrogram closely resembles the dendrogram produced using all expressed genes, indicating that TFs can be used to reconstruct known cell ontology relationships between bulk populations (entanglement = 0.11; Extended Data Fig. 10a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonetheless, microfluidic droplets identified cell types missed by FACS in both organs, for example kidney mesangial cells, and splenic dendritic and natural killer cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the expression of MHC class II genes by adult mouse T cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 27
- It may state two separate claims as one. 45
Every source the paper cites for this claim was checked and supports it.
In the paper
“For each cluster, we provide annotations in the controlled vocabulary of a cell ontology19 to facilitate inter-experiment comparisons.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
What the sources say
- Supportedcites [19] Bakken, T. et al. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 18 (Suppl 17), 559 (2017). source
The Background section describes the Cell Ontology as a standard reference nomenclature needed to compare experimental results about cell types. It also says the ontology is used as a reference annotation vocabulary. The citing authors’ statement that they annotated each cluster describes their own work; the citation supports their choice of vocabulary and its purpose. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Single-cell transcriptomes were sequenced to an average depth of 814,488 reads per cell (FACS) and 7,709 unique molecular identifiers (UMIs) per cell (microfluidic droplet).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The bladder, liver, lung, mammary gland, trachea, tongue, and spleen show nearly twice as many genes detected per cell with FACS compared to microfluidic droplet, whereas heart and marrow show comparable numbers (Extended Data Fig. 5a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“Their data contains four times as many cells and included several sample types not present in our data, such as neonatal and fetal organs, cell lines, and young adult ovary, peripheral blood, placenta, prostate, small intestine, stomach, testis, and uterus.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then determined the TF sets distinguishing each individual cell type from all other cells. These sets vary substantially in size (from 2 to 813 TFs) and are not necessarily unique to each cell type (Fig. 5g–i; Supplementary Table 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“However, our FACS data contains four times as many genes per cell, and we analyzed several organs not present in the Han et al. data set, such as aorta, four brain regions, diaphragm, four fat types, four adult heart chambers, adult skin, tongue, and trachea.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while Cluster 2 contains mostly non-thymic T cells expressing the high affinity IL2 receptor (Il2ra, Il2rb), suggesting they are activated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 0 comprises thymic cells undergoing VDJ recombination characterized by RAG (Rag1, Rag2) and TdT (Dntt) expression, and includes uncommitted double positive T-cells (Cd4+, Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 2 contains B cells from fat, limb muscle, lung, spleen, marrow, and liver, but also cells annotated as leukocytes and lymphocytes from the thymus, heart, and limb muscle.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As expected, cells from different organs often mixed, with 25 of 54 clusters containing (at least five) cells from distinct organs (Fig. 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, when we repeated the analysis using cell surface markers, RNA splicing factors, or the two groups combined (equivalent to a random set of genes), the entanglement was 0.22, 0.25 and 0.34, respectively, suggesting that none of these molecular classes define cell type to the extent that transcription factors do.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, Cluster 1 also represents mature T cells, but primarily splenic.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This seems only to occur for populations smaller than ~30 cells, which comprise under 4% of the overall dataset, and represents a lower limit of sensitivity in the current release of data interpretation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Comparing methods shows organ-specific differences in the number of cells analyzed (Fig. 1b, c), reads per cell (Extended Data Fig. 3a, c) and genes per cell (Extended Data Fig. 3b, d).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 4 contains predominantly proliferating thymic T cells which may represent pre-T cells expanding following VDJ recombination.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, with both methods the most abundant cell types analyzed are epithelial cells and leukocytes, although FACS captured a larger diversity of cell types (Extended Data Fig. 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For those cell types captured by both methods, the proportion of each cell type is equivalent (Pearson correlation coefficient: spleen = 0.99, kidney = 0.99).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“For example, the median number of genes detected in bladder is ~4,900 (FACS), 2,900 (droplet), and 900 (microwell-seq), while in kidney it is ~1,400 (FACS), 1,900 (droplet), and 500 (microwell-seq).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, clusters 3 and 48 each contain endothelial cells from five or more organs, and clusters 1 and 24 contain mesenchymal and stromal cells from four or more organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We clearly observed these markers in several organs including limb muscle (Extended Data Fig. 11), but many showed little evidence of cellular activation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Dotted horizontal line demarcates the median saturation (~ 0.9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
hedgedn = 55,032- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fraction of all detected genes (defined as > 0 reads or UMIs) for each cell, across all organs, detected at increasing read or UMI thresholds for FACS (left; n= 44,949 individual cells), microfluidic droplet (middle; n= 55,656 individual cells), and microwell-Seq (right; n= 28,372 individual cells).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
plainn = 44,949- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The number of genes detected (threshold of > 0 reads or UMIs per cell) by FACS (red; n= 21,105 individual cells), microfluidic droplets (green; n = 55,032 individual cells), and microwell-Seq (blue; n= 25,891 individual cells) (Han et al.).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
What the sources say
- Not matchedcites Han et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainn = 21,105- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such gene expression profiles largely correlate (Pearson correlation coefficient: 0.74 – 0.90), suggesting that while biases between methods exist, both accurately recapitulate average cell type gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All organs used a standard annotation method; an example using liver is contained in the Organ Annotation Vignette.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This release allows for the exact replication of all results, facilitates in-depth analyses not completed here, and provides a comparative framework for future studies using the large variety of murine disease models.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, for nearly all reprogramming protocols the TFs used also specified the targeted cell type in our data (Supplementary Table 5), suggesting that our data can inform novel reprogramming schemes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Recently, Han et al.20 published a complementary scRNA-seq study across mouse organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many of these cell types have not previously been obtained in pure populations, and our data provide a wealth of new information on their characteristic gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is partially explained by cellular abundance and sampling depth (12,333 microfluidic droplet cells vs. 2,216 FACS cells, Supplementary Table 1), and possibly from cell capture and lysis biases between methods.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the dissociation- related satellite cell markers are not universal, and organs likely display unique dissociation-related expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these data are by no means a complete representation of all mouse organs and cell types, they provide a first draft attempt to create an organism-wide representation of cellular diversity.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enabled the direct comparison of cell type composition between organs, and the comparison of shared cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that most cells of similar cell types cluster together across organs and biological replicates shows that batch effects are not the main source of variance in the dataset.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“3) We annotated cell types using standard cell ontology terms, thereby enabling cross-organ and cross-experiment analyses”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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.
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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
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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 1,201–1,300 of 2,952
Zhao, S., Wang, L., Kleidonas, D., Qi, F., Liang, Y., Zheng, J., Umpierre, A. D., & Wu, L. (2025) · Science Advances · doi:10.1126/sciadv.ado7829 · Cited by 16
Cloud bursting galaxy: federated identity and access management
Jalili, V., Afgan, E., Taylor, J., & Goecks, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz472 · Cited by 21
Loss of RE-1 silencing transcription factor accelerates exocrine damage from pancreatic injury
Bray, J. K., Elgamal, O. A., Jiang, J., Wright, L. S., Sutaria, D. S., Badawi, M. A., Borcyk, M. G., Liu, X., Fredenburg, K. M., Campbell‐Thompson, M., et al. (2020) · Cell Death and Disease · doi:10.1038/s41419-020-2269-7 · Cited by 16
Towards the better understanding of myelopoiesis using single-cell technologies
Dress, R. J., Liu, Z., & Ginhoux, F. (2020) · Molecular Immunology · doi:10.1016/j.molimm.2020.04.020 · Cited by 16
Zhang, S., Liu, Y., Yang, J., Lin, Z., & Ng, M. K. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa064 · Cited by 15
Kapiainen, E., Kihlström, M. K., Pietilä, R. S., Kaakinen, M., Ronkainen, V., Tu, H., Heikkinen, A., Devarajan, R., Miinalainen, I., Laitakari, A., et al. (2020) · Cancer Research · doi:10.1158/0008-5472.can-19-1904 · Cited by 17
“On the basis of published RNAseq data, Tie2 (in ECs) and Angpt1 (in stromal cells) are constitutively expressed in the matured healthy lungs whereas Angpt2 (in ECs) levels are very low (45).”
MethodResultTransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments
Ribeiro, M. M., Okawa, S., & del Sol, A. (2020) · Stem Cells Translational Medicine · doi:10.1002/sctm.20-0227 · Cited by 15
“scRNA-seq data used in this study were obtained from the following sources.(29-31,35,41-48) For References 43, 48, the reprocessed data were retrieved from PangloaDB,(49) as the cell annotation was more accurate than the original one.”
MethodWilhelmi, I., Grunwald, S., Gimber, N., Popp, O., Dittmar, G., Arumughan, A., Wanker, E. E., Laeger, T., Schmoranzer, J., Daumke, O., et al. (2020) · Molecular Metabolism · doi:10.1016/j.molmet.2020.101151 · Cited by 15
“Original Article ELMO1 was excluded from further analysis since it is barely expressed in b -cells [38], and HOOK3 was excluded due to its described localization at the cis-Golgi [39].”
Pfisterer, U., Bräunig, J., Brattås, P. L., Heidenblad, M., Karlsson, G., & Fioretos, T. (2021) · Genes Chromosomes and Cancer · doi:10.1002/gcc.22944 · Cited by 17
CellMeSH: probabilistic cell-type identification using indexed literature
Mao, S., Zhang, Y., Seelig, G., & Kannan, S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab834 · Cited by 18
Wang, M., Ivanovska, I. L., Vashisth, M., et al. (2022) · APL Bioengineering · doi:10.1063/5.0080392 · Cited by 15
“The UMAP algorithm clusters and disperses lineages more reasonably than the original tSNE analysis (The Tabula Muris Consortium, 2018), but the same color code for tissues and same cluster labels are used here for comparison.”
MethodResultThe Application of Omics Technologies to Study Axon Regeneration and CNS Repair
Tedeschi, A., & Popovich, P. G. (2019) · F1000Research · doi:10.12688/f1000research.17084.1 · Cited by 16
Hallmarks of NLRP3 inflammasome activation are observed in organotypic hippocampal slice culture
Hoyle, C., Redondo‐Castro, E., Cook, J., Tzeng, T.-C. J., Allan, S. M., Brough, D., & Lemarchand, E. (2020) · Immunology · doi:10.1111/imm.13221 · Cited by 17
Wiring together large single-cell RNA-seq sample collectionsPreprint
Barkas, N., Petukhov, V., Nikolaeva, D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/460246 · Cited by 16
Ageing compromises mouse thymus function and remodels epithelial cell differentiationPreprint
Baran‐Gale, J., Morgan, M., Maio, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.02.973008 · Cited by 16
Molecular atlas of the human brain vasculature at the single-cell levelPreprint
Wälchli, T., Ghobrial, M., Schwab, M., Takada, S., Zhong, H., Suntharalingham, S., Vetiska, S., Rodrigues, D. R., Rehrauer, H., Wu, R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.18.464715 · Cited by 16
Deep Learning Applications in Single-Cell Omics Data AnalysisPreprint
Erfanian, N., Heydari, A. A., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Nasseri, S., Safarpour, H., & Sahebkar, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.26.470166 · Cited by 17
Identifying cell state–associated alternative splicing events and their coregulationPreprint
Najar, C. F. B. A., Burra, P., Yosef, N., & Lareau, L. F. (2022) · Genome Research · doi:10.1101/gr.276109.121 · Cited by 20
“To see if Psix could capture splicing associated with differences between groups of cells, we applied it to the brain neurons subset of the Tabula Muris Smart-seq2 data set (The Tabula Muris Consortium 2018).”
MethodThe cellular substrate of evolutionary novelty
Parker, J., & Pennell, M. W. (2025) · Current Biology · doi:10.1016/j.cub.2025.04.014 · Cited by 16
HiCat: a semi-supervised approach for cell type annotation
Bi, C., Bai, K., & Zhang, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf428 · Cited by 15
The Biodiversity Cell Atlas: mapping the tree of life at cellular resolution
Sebé-Pedrós, A., Tanay, A., Lawniczak, M., Arendt, D., Aerts, S., Archibald, J. M., Arnone, M. I., Blaxter, M., Cleves, P. A., Coelho, S. M., et al. (2025) · Nature · doi:10.1038/s41586-025-09312-4 · Cited by 22
Transforming microfluidics for single-cell analysis with robotics and artificial intelligence
Cheng, J., Anne, R., & Chen, Y. (2025) · Lab on a Chip · doi:10.1039/d5lc00216h · Cited by 21
Universal cell embedding provides a foundation model for cell biology
Rosen, Y., Roohani, Y., Agrawal, A., Samotorcan, L., Quake, S. R., & Leskovec, J. (2026) · Nature · doi:10.1038/s41586-026-10689-z · Cited by 258
Althagafi, A., Alsubaie, L., Kathiresan, N., Mineta, K., Aloraini, T., Mutairi, F. A., Alfadhel, M., Gojobori, T., Alfares, A. A., & Hoehndorf, R. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab859 · Cited by 16
“For the cell type, we downloaded single-cell RNAseq data from the Tabula Muris project (Tabula Muris Consortium et al. , 2018) in which genes are annotated with the CL.”
Cheng, Y., Kang, X.-Z., Cheng, T., Ye, Z., Tipoe, G. L., Yu, C., Wong, C., Liu, B., Chan, C., & Jin, D. (2022) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2022.01.017 · Cited by 15
BMP3b is a Novel Antifibrotic Molecule Regulated by Meflin in Lung Fibroblasts
Suzuki, A., Sakamoto, K., Nakahara, Y., Enomoto, A., Hino, J., Ando, A., Inoue, M., Shiraki, Y., Omote, N., Kusaka, M., et al. (2022) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2021-0484oc · Cited by 16
Molecular signatures and cellular diversity during mouse habenula development
van de Haar, L. L., Riga, D., Boer, J. E., Garritsen, O., Adolfs, Y., Sieburgh, T. E., van Dijk, R. E., Watanabe, K., van Kronenburg, N. C. H., Broekhoven, M. H., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111029 · Cited by 15
A Single-Cell Survey of Cellular Heterogeneity in Human Great Saphenous Veins
Sun, Y.-P., Hu, X., Zhang, K., Rao, M., Yin, P.-B., & Dong, R. (2022) · Cells · doi:10.3390/cells11172711 · Cited by 10
Single-Cell Genomics for Investigating Pathogenesis of Inflammatory Diseases
Jung, S., & Lee, J. S. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.0002 · Cited by 16
Karri, K., & Waxman, D. J. (2023) · RNA · doi:10.1261/rna.079580.123 · Cited by 15
“Raw sequencing data for hepatocytes (GSE109774) (Tabula Muris Consortium et al. 2018) and for the liver nonparenchymal cell clusters shown in Fig-ure2A (GSE137720, E-MTAB-8077, andGSE129516)(Dobieet al. 2019; Xiong et al. 2019; Kalucka et al. 2020) was obtained from healthy (untreated, normal chow…”
MethodMechanisms regulating vascular and lymphatic regeneration in the heart after myocardial infarction
Berkeley, B., Tang, M. N. H., & Brittan, M. (2023) · The Journal of Pathology · doi:10.1002/path.6093 · Cited by 16
Systemic proteome phenotypes reveal defective metabolic flexibility in Mecp2 mutants
Zlatic, S. A., Werner, E., Surapaneni, V., Lee, C. E., Gokhale, A., Singleton, K. S., Duong, D. M., Crocker, A. J., Gentile, K. L., Middleton, F. A., et al. (2023) · Human Molecular Genetics · doi:10.1093/hmg/ddad154 · Cited by 15
scTOP: physics-inspired order parameters for cellular identification and visualization
Yampolskaya, M., Herriges, M. J., Ikonomou, L., Kotton, D. N., & Mehta, P. (2023) · Development · doi:10.1242/dev.201873 · Cited by 15
Sun, S., Meng, Y., Li, M., Tang, X., Hu, W., Wu, W., Li, G., Pang, Q., Wang, W., & Liu, B. (2023) · Nature Aging · doi:10.1038/s43587-023-00512-z · Cited by 16
“With the benefits of single-cell transcriptome techniques, studies have been able to define various cell populations in BMNCs and BM niche ECs 35,36 .”
Brd4::Nutm1 fusion gene initiates NUT carcinoma in vivo
Zheng, D., Elnegiry, A. A., Luo, C., Bendahou, M. A., Xie, L., Bell, D., Takahashi, Y., Hanna, E. Y., Mias, G. I., Tsoi, M. F., et al. (2024) · Life Science Alliance · doi:10.26508/lsa.202402602 · Cited by 15
Swain, A. K., Pandit, V., Sharma, J., & Yadav, P. (2024) · Communications Biology · doi:10.1038/s42003-024-06349-5 · Cited by 18
“ThescRNA-seqdatasetsofmajortissueclasseswereobtainedfromdifferent databases,includingGEO 62 , human cell atlas 63 , single cell portal 64 of Broad InstituteandFigshare 65,66 .”
Purvis, G. S. D., McNeill, E., Wright, B., Channon, K. M., & Greaves, D. R. (2024) · Cells · doi:10.3390/cells13110916 · Cited by 19
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
Grobecker, P., Sakoparnig, T., & van Nimwegen, E. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012224 · Cited by 14
“Numerous cell atlas projects [1–5] using this approach are already published or in progress.”
Fu, Q., Dong, C., Liu, Y., Xia, X., Liu, G., Zhong, F., & Liu, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae392 · Cited by 18
“However, the cell labels in commonly utilized single-cell datasets are predominantly derived through computational means,including methods like StemID [7],densityMclust [8],nearest neighbor graph clustering [9], hierarchical clustering [10], and Spearman correlation analysis [11].”
MethodFan, X., Liu, J., Yang, Y., Gu, C., Han, Y., Wu, B., Jiang, Y., Chen, G., & Heng, P. (2024) · Communications Biology · doi:10.1038/s42003-024-07154-w · Cited by 17
The Transcriptome of Hepatic Fibrosis Revealed by Single‐Cell RNA Sequencing
Jophlin, L., Cao, S., & Shah, V. H. (2020) · Hepatology · doi:10.1002/hep.31155 · Cited by 15
Cardiac miRNA Expression and their mRNA Targets in a Rat Model of Prediabetes
Sághy, É., Vörös, I., Ágg, B., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21062128 · Cited by 15
Wolfien, M., Galow, A.-M., Müller, P., Bartsch, M., Brunner, R. M., Goldammer, T., Olaf Wolkenhauer, A., Hoeflich, A., & David, R. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvaa054 · Cited by 14
A Plasma Protein Network Regulates PM20D1 and N-Acyl Amino Acid Bioactivity
Kim, J. T., Jedrychowski, M. P., Wei, W., Fernández, D., Fischer, C. R., Banik, S. M., Spiegelman, B. M., & Long, J. Z. (2020) · Cell chemical biology · doi:10.1016/j.chembiol.2020.04.009 · Cited by 15
Transcriptome features of striated muscle aging and predictability of protein level changes
Yu, H., Li, L. Z., Kastury, N., Thomas, C. T., Lam, M. P. Y., & Lau, E. (2021) · Molecular Omics · doi:10.1039/d1mo00178g · Cited by 19
Brown, R. E., Jacobse, J., Anant, S. A., Blunt, K. M., Chen, B., Vega, P. N., Jones, C. T., Pilat, J. M., Revetta, F. L., Gorby, A. H., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.153045 · Cited by 15
“The Tabula Muris (41) was queried for Mtg16 expression using their web interface (https://tabula-muris.ds.czbiohub.org/).”
BackgroundMethodInfluentialA neural network-based method for exhaustive cell label assignment using single cell RNA-seq data
Li, Z., & Feng, H. (2022) · Scientific Reports · doi:10.1038/s41598-021-04473-4 · Cited by 14
The normal and fibrotic mouse lung classified by spatial proteomic analysis
Ciccimarra, R., Bolognesi, M. M., Zoboli, M., Cattoretti, G., Stellari, F. F., & Ravanetti, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-12738-9 · Cited by 16
RANK+TLR2+ myeloid subpopulation converts autoimmune to joint destruction in rheumatoid arthritis
Zhang, W., Noller, K., Crane, J. L., Wan, M., Wu, X., Cahan, P., & Cao, X. (2023) · eLife · doi:10.7554/elife.85553 · Cited by 15
“Clusters were annotated based on known marker gene expression and differential gene expression, and SingleCellNet ( Tan and Cahan, 2019a ) was used to classify individual cells based on a well-annotated reference dataset ( Tabula Muris Consortium et al., 2018 , Figure 1—figure supplement 1B ).”
A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining
Gambardella, G., & di Bernardo, D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00734 · Cited by 15
“We also applied our gf-icf pipeline to analyze an additional dataset consisting of 27,499 single-cell transcriptional profile from mouse retinal bipolar neurons (Shekhar et al., 2016) profiled using the Drop-seq platform.”
MethodInfluentialModeling metabolic variation with single-cell expression dataPreprint
Zhang, Y., Kim, M. S., Nguyen, E., & Taylor, D. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.28.923680 · Cited by 15
“Single-cell data processing We obtained the Tabula Muris mouse single-cell RNA-seq count matrix dataset from https://tabula-muris.ds.czbiohub.org (Schaum et al., 2018).”
MethodInfluentialtalklr uncovers ligand-receptor mediated intercellular crosstalkPreprint
Wang, Y. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.01.930602 · Cited by 13
Putative cell type discovery from single-cell gene expression data
Miao, Z., Moreno, P., Huang, N., Papatheodorou, I., Brazma, A., & Teichmann, S. A. (2020) · Nature Methods · doi:10.26226/morressier.5ebd45acffea6f735881af34 · Cited by 133
Lagger, C., Ursu, E., Equey, A., Avelar, R. A., Pisco, A. O., Tăcutu, R., & de Magalhães, J. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.13.456238 · Cited by 17
Efficient profiling of total RNA in single cells with STORM-seqPreprint
Johnson, B. K., Majewski, M. F., Jang, H. J., Siwicki, R. A., Wegener, M., Semwal, A., Morrison, J., Gallik, K., Hagemann-Jensen, M., Foy, K. K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484332 · Cited by 18
Ketone body metabolism declines with age in mice in a sex-dependent mannerPreprint
Eap, B., Nomura, M., Panda, O., Garcia, T. Y., King, C. D., Rose, J., Leone, T. C., Kelly, D. P., Schilling, B., & Newman, J. C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.05.511032 · Cited by 18
Angiocrine Signaling in Sinusoidal Health and Disease
Cooper, S. A., Kostallari, E., & Shah, V. H. (2023) · Seminars in Liver Disease · doi:10.1055/a-2128-5907 · Cited by 15
Teefy, B. B., Lemus, A. J. J., Adler, A., Xu, A., Bhala, R., Hsu, K., & Benayoun, B. A. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113237 · Cited by 20
Weber‐Stiehl, S., Taubenheim, J., Järke, L., Röcken, C., Schreiber, S., Aden, K., Kaleta, C., Rosenstiel, P. C., & Sommer, F. (2024) · BMC Medicine · doi:10.1186/s12916-024-03710-7 · Cited by 16
Chen, J.-J. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.108494 · Cited by 16
Innate immune training in the neonatal response to sepsis
Rincon, J. C., Wang, D., Polcz, V. E., Barrios, E. L., Dirain, M. L. S., Ungaro, R. F., Nacionales, D. C., Zeumer-Spataro, L., Xiao, F., Efron, P. A., et al. (2025) · Molecular Medicine · doi:10.1186/s10020-025-01179-5 · Cited by 15
Modeling the vertebrate regulatory sequence landscape by UUATAC-seq and deep learning
Han, X., Wu, H., Wang, X., Liu, D., Fu, Y., Yang, L. X., Wang, R., Zhang, P., Wang, J., Ma, L., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.020 · Cited by 16
Resistin-like molecule γ attacks cardiomyocyte membranes and promotes ventricular tachycardia
Kumowski, N., Pabel, S., Grune, J., Momin, N., Ninh, V. K., Stengel, L., Mentkowski, K. I., Iwamoto, Y., Zheng, Y., Lee, I.-H., et al. (2025) · Science · doi:10.1126/science.adp7361 · Cited by 15
Ji, S., Yang, Z., Gozali, L., Kenney, T., Kocabas, A. M., Park, C. J., & Hynes, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250669 · Cited by 12
“For each of the 16 genes examined expression was independently confirmed by scRNAseq (27) or by Tabula Muris (28).”
BackgroundTranscriptome Profiling of Mouse Corpus Callosum After Cerebral Hypoperfusion
Takase, H., Hamanaka, G., Ohtomo, R., Ishikawa, H., Chung, K. K., Mandeville, E. T., Lok, J. M., Fornage, M., Herrup, K., Tse, K., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.685261 · Cited by 15
Chai, K., Liang, J., Zhang, X., Cao, P., Chen, S.-F., Gu, H., Ye, W., Liu, R., Hu, W., Peng, C., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.707165 · Cited by 14
“MAPT encodes microtubule-associated protein tau, which promotes the stability and assembly of microtubules in axon of neurons (Dehmelt and Halpain, 2005; Irwin et al., 2013; Wang and Mandelkow, 2016; Saha and Sen, 2019; Vogels et al., 2019).”
BackgroundDiversity of human NK cell developmental pathways defined by single-cell analyses
Seo, S., & Mace, E. M. (2021) · Current Opinion in Immunology · doi:10.1016/j.coi.2021.11.001 · Cited by 14
One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data
Wang, C. X., Zhang, L., & Wang, B. (2022) · Genome biology · doi:10.1186/s13059-022-02659-1 · Cited by 14
“We also conduct cell type inference experiments on two mouse atlas datasets [16, 25] and two human dendritic datasets [7, 8], both achieving high accuracy in cell type assignment (see Additional file 3: Fig.”
MethodCardiomyocyte Ploidy, Metabolic Reprogramming and Heart Repair
Elia, A., Mohsin, S., & Khan, M. (2023) · Cells · doi:10.3390/cells12121571 · Cited by 15
Ageing-associated phenotypes in mice
Xie, K., & Ehninger, D. (2023) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2023.111852 · Cited by 14
Cytokine receptor-like factor 1 (CRLF1) promotes cardiac fibrosis via ERK1/2 signaling pathway
Luo, S., Yang, Z., Chen, R., You, D., Teng, F., Yuan, Y., Liu, W., Li, J., & Zhang, H. (2023) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b2200506 · Cited by 14
Yan, X., Zheng, R., Chen, J., & Li, M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad505 · Cited by 15
“…high expression level of Dcn , which has a high expression enrichment in “fibroblast” of heart FACS data (Schaum et al. 2018), and show low expression level of Cav1 and Cdh5 , both of which have high gene expression enrichment in “endothelial” cells of heart FACS data (Schaum et al. 2018) (Fig.”
BackgroundInfluentialCavin-2 Promotes Fibroblast-to-Myofibroblast Trans-Differentiation and Aggravates Cardiac Fibrosis
Higuchi, Y., Ogata, T., Nakanishi, N., Nishi, M., Tsuji, Y., Tomita, S., Conway, S. J., & Matoba, S. (2023) · ESC Heart Failure · doi:10.1002/ehf2.14571 · Cited by 14
“19,20 Cavin-2 is expressed in various cells and is also expressed in human and mouse fi broblasts.”
BackgroundAldo-keto reductase 1B: Much learned, much more to do
Zhao, Y., Zhang, M., Li, H., Yang, Y., Lu, X., Yu, J., & Pan, L. (2023) · hLife · doi:10.1016/j.hlife.2023.12.002 · Cited by 16
Mononen, J., Taipale, M., Malinen, M., Velidendla, B., Niskanen, E. A., Levonen, A., Ruotsalainen, A., & Heikkinen, S. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1225 · Cited by 13
“05) was used to estimate cell type ratios using FACS-based mouse liver scRNA-seq dataset from Tabula Muris ( 38 ,39 ).”
MethodTHEMIS is a substrate and allosteric activator of SHP1, playing dual roles during T cell development
Zhang, J., Jiang, Z., Zhang, X., Yang, Z., Wang, J., Chen, J., Chen, L., Song, M., Zhang, Y., Huang, M., et al. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01131-3 · Cited by 14
A Single-Cell Transcriptome of Bovine Milk Somatic Cells
Zorc, M., Dolinar, M., & Dovč, P. (2024) · Genes · doi:10.3390/genes15030349 · Cited by 14
“Single-cell sequencing Single-cell sequencing of human and mouse mammary somatic cells revealed a much wider range of cell types, which are present in the milk [20,25]. of human and mouse mammary somatic cells revealed a much wider range of cell types, which present in the milk [20,25].”
BackgroundZhang, Y., Song, Z., Wu, R., Kong, X., Zhang, H., Li, S., Gong, X., Gong, S.-H., Cheng, J., Yuan, F., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113930 · Cited by 14
Single-cell transcriptome atlas of lamprey exploring Natterin- induced white adipose tissue browning
Pang, Y., Qin, Y., Du, Z., Liu, Q., Zhang, J., Han, K., Lu, J., Yuan, Z., Li, J., Pan, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56153-w · Cited by 14
Exciting times to study the identity and evolution of cell types
Sachkova, M. Y., & Burkhardt, P. (2019) · Development · doi:10.1242/dev.178996 · Cited by 15
Advances of single-cell genomics and epigenomics in human disease: where are we now?
Kamies, R., & Martinez‐Jimenez, C. P. (2020) · Mammalian Genome · doi:10.1007/s00335-020-09834-4 · Cited by 15
Comparison of Scanpy-based algorithms to remove the batch effect from single-cell RNA-seq data
Li, J., Yu, C., Ma, L., Wang, J., & Guo, G. (2020) · Cell Regeneration · doi:10.1186/s13619-020-00041-9 · Cited by 14
Uncovering cellular networks in branching morphogenesis using single-cell transcriptomics
Goodwin, K., & Nelson, C. M. (2020) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2020.09.004 · Cited by 13
Salem, N. A., Mahnke, A. H., Tseng, A. M., Garcia, C. R., Jahromi, H. K., Geoffroy, C. G., & Miranda, R. C. (2020) · Neurotoxicology and Teratology · doi:10.1016/j.ntt.2020.106943 · Cited by 13
CoolMPS for robust sequencing of single-nuclear RNAs captured by droplet-based method
Hahn, O., Fehlmann, T., Zhang, H., Munson, C. N., Vest, R. T., Borcherding, A., Liu, S., Villarosa, C., Drmanac, S., Drmanac, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1127 · Cited by 16
Joint Characterization of Multiscale Information in High Dimensional Data
Sousa, D. J., & Small, C. (2021) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2021.1113 · Cited by 14
“As the t-SNE hyperparameter of learning rate increases between the minimum recommended value of 50 [20,21], the scikit-learn default of 200, and the data-adaptive recommended value of 833, central clusters corresponding to equal-valued grayscale matrix diagonals contract slightly to leave more empty space near the cen…”
BackgroundMethodRole of macrophages and phagocytes in orchestrating normal and pathologic hematopoietic niches
Lévesque, J.-P., SUMMERS, K. M., Millard, S. M., Bisht, K., Winkler, I. G., & Pettit, A. R. (2021) · Experimental Hematology · doi:10.1016/j.exphem.2021.07.001 · Cited by 17
Macrophage deletion of Noc4l triggers endosomal TLR4/TRIF signal and leads to insulin resistance
Qin, Y., Jia, L., Liu, H., Ma, W., Ren, X., Li, H., Liu, Y., Li, H., Ma, S., Liu, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26408-3 · Cited by 18
Nested Stochastic Block Models applied to the analysis of single cell data
Morelli, L., Giansanti, V., & Cittaro, D. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04489-7 · Cited by 14
Ritso, M., Tung, L. W., & Rossi, F. M. V. (2021) · Experimental Cell Research · doi:10.1016/j.yexcr.2021.112947 · Cited by 14
Epithelial LIF signaling limits apoptosis and lung injury during bacterial pneumonia
Na, E., Allen, E., Baird, L. A., Odom, C. V., Korkmaz, F. T., Shenoy, A. T., Matschulat, A. M., Jones, M. R., Kotton, D. N., Mizgerd, J. P., et al. (2022) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00325.2021 · Cited by 15
Xiao, W., Pinilla-Baquero, A., Faulkner, J., Song, X., Prabhakar, P. K., Qiu, H. Y., Moremen, K. W., Ludwig, A., Dempsey, P. J., Azadi, P., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-08227-8 · Cited by 15
Contemplating Dichotomous Nature of Gamma Delta T Cells for Immunotherapy
Bhat, J., Placek, K., & Faissner, S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.894580 · Cited by 14
“Though the transcriptome and epigenome of gd T cells are already available, the focus is now shifted to single-cell studies (Table 1) as it allows to create a compendium of cell types as exemplified in mice (104) and humans (105, 106).”
BackgroundA pulmonologist's guide to perform and analyse cross-species single lung cell transcriptomics
Pennitz, P., Kirsten, H., Friedrich, V. D., Wyler, E., Goekeri, C., Obermayer, B., Heinz, G. A., Mashreghi, M., Büttner, M., Trimpert, J., et al. (2022) · European Respiratory Review · doi:10.1183/16000617.0056-2022 · Cited by 14
GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets
Yan, X., Zheng, R., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac311 · Cited by 14
Defining cardiac fibrosis complexity and regulation towards therapeutic development
Claridge, B., Drack, A., Pinto, A. R., & Greening, D. W. (2023) · Clinical and Translational Discovery · doi:10.1002/ctd2.163 · Cited by 13
Osipovich, A. B., Dudek, K. D., Trinh, L. T., Kim, L. H., Shrestha, S., Cartailler, J., & Magnuson, M. A. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010729 · Cited by 14
“Our qPCR data and as well as Tabula Muris scRNA-seq data indicate that mouse Zfp92 is highly expressed in pancreatic endocrine islet cells, with lower levels expressed in brain, testis, and intestinal tissues.”
BackgroundThe Human Cell Atlas: making ‘cell space’ for disease
Ponting, C. P. (2019) · Disease Models & Mechanisms · doi:10.1242/dmm.037622 · Cited by 14
Decoding the body language of immunity: Tackling the immune system at the organism level
Chevrier, N. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.10.010 · Cited by 13
“Multi-tissue profiling approaches will further help to decipher interorgan circuits when combined with, for example, (1) ongoing efforts to map the cellular composition of organs at large [62-64], or by focusing on immune cells [65,66], (2) single-cell measurement tools that are becoming increasingly multiparametric […”
BackgroundSingle-cell analyses uncover granularity of muscle stem cells
Saber, J., Lin, A. Y., & Rudnicki, M. A. (2020) · F1000Research · doi:10.12688/f1000research.20856.1 · Cited by 13
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
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