The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 2 of the 8 sources its claims cite were checked for support.
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
Open accessAbstract
Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.
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Claims the paper makes
44 Own result · 26 Own method · 20 Own interpretation · 5 No full text of the sources · 5 No basis given · 1 Supported · 1 Partly supported · 1 Sources not found · accuracy not yet measured
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In the paper
“Cluster co-membership alone, however, is insufficient to conclude that two cell populations from different organs represent the same or similar cell types; at any given resolution, unbiased clustering that groups related cells may also group unrelated cells21.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Partly supportedcites [21] Freytag, S., Tian, L., Lonnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res 7, 1297 (2018). sourceExperimental — accuracy not yet measured
The Results (Evaluation of clustering tools, Silver standard data; page unavailable) report that most clusters contained mixtures of different inferred cell types, supporting the caution that co-membership alone does not establish cell-type identity. The paper evaluates PBMC datasets, however, and does not test populations from different organs or establish that such populations were grouped together. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“transcriptional heterogeneity of brain endothelial cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clusters 1–3 contain mostly single positive T cells (Cd4+ or Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a cell population expressing Chodl in limb muscle”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, to determine which clusters are composed of related or unrelated cell types, we computed a heterogeneity score for each cluster (Methods), and found low scores for the biologically sensible clusters discussed above (Extended Data Fig. 9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To understand which TFs were most informative for specifying cell types, we performed variable selection from random forests (Methods) and determined that 136 TFs are needed to simultaneously define all cell types across all organs (Fig. 5f, Supplementary Table 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The compendium is comprised of single-cell transcriptomic data from 100,605 cells isolated from 20 organs from 3 female and 4 male, C57BL/6JN, 3-month-old mice (10–15 weeks), analogous to 20-year-old humans (Fig. 1).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting dendrogram closely resembles the dendrogram produced using all expressed genes, indicating that TFs can be used to reconstruct known cell ontology relationships between bulk populations (entanglement = 0.11; Extended Data Fig. 10a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonetheless, microfluidic droplets identified cell types missed by FACS in both organs, for example kidney mesangial cells, and splenic dendritic and natural killer cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the expression of MHC class II genes by adult mouse T cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 27
- It may state two separate claims as one. 45
Every source the paper cites for this claim was checked and supports it.
In the paper
“For each cluster, we provide annotations in the controlled vocabulary of a cell ontology19 to facilitate inter-experiment comparisons.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
What the sources say
- Supportedcites [19] Bakken, T. et al. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 18 (Suppl 17), 559 (2017). source
The Background section describes the Cell Ontology as a standard reference nomenclature needed to compare experimental results about cell types. It also says the ontology is used as a reference annotation vocabulary. The citing authors’ statement that they annotated each cluster describes their own work; the citation supports their choice of vocabulary and its purpose. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Single-cell transcriptomes were sequenced to an average depth of 814,488 reads per cell (FACS) and 7,709 unique molecular identifiers (UMIs) per cell (microfluidic droplet).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The bladder, liver, lung, mammary gland, trachea, tongue, and spleen show nearly twice as many genes detected per cell with FACS compared to microfluidic droplet, whereas heart and marrow show comparable numbers (Extended Data Fig. 5a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“Their data contains four times as many cells and included several sample types not present in our data, such as neonatal and fetal organs, cell lines, and young adult ovary, peripheral blood, placenta, prostate, small intestine, stomach, testis, and uterus.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then determined the TF sets distinguishing each individual cell type from all other cells. These sets vary substantially in size (from 2 to 813 TFs) and are not necessarily unique to each cell type (Fig. 5g–i; Supplementary Table 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“However, our FACS data contains four times as many genes per cell, and we analyzed several organs not present in the Han et al. data set, such as aorta, four brain regions, diaphragm, four fat types, four adult heart chambers, adult skin, tongue, and trachea.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while Cluster 2 contains mostly non-thymic T cells expressing the high affinity IL2 receptor (Il2ra, Il2rb), suggesting they are activated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 0 comprises thymic cells undergoing VDJ recombination characterized by RAG (Rag1, Rag2) and TdT (Dntt) expression, and includes uncommitted double positive T-cells (Cd4+, Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 2 contains B cells from fat, limb muscle, lung, spleen, marrow, and liver, but also cells annotated as leukocytes and lymphocytes from the thymus, heart, and limb muscle.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As expected, cells from different organs often mixed, with 25 of 54 clusters containing (at least five) cells from distinct organs (Fig. 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, when we repeated the analysis using cell surface markers, RNA splicing factors, or the two groups combined (equivalent to a random set of genes), the entanglement was 0.22, 0.25 and 0.34, respectively, suggesting that none of these molecular classes define cell type to the extent that transcription factors do.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, Cluster 1 also represents mature T cells, but primarily splenic.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This seems only to occur for populations smaller than ~30 cells, which comprise under 4% of the overall dataset, and represents a lower limit of sensitivity in the current release of data interpretation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Comparing methods shows organ-specific differences in the number of cells analyzed (Fig. 1b, c), reads per cell (Extended Data Fig. 3a, c) and genes per cell (Extended Data Fig. 3b, d).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 4 contains predominantly proliferating thymic T cells which may represent pre-T cells expanding following VDJ recombination.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, with both methods the most abundant cell types analyzed are epithelial cells and leukocytes, although FACS captured a larger diversity of cell types (Extended Data Fig. 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For those cell types captured by both methods, the proportion of each cell type is equivalent (Pearson correlation coefficient: spleen = 0.99, kidney = 0.99).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“For example, the median number of genes detected in bladder is ~4,900 (FACS), 2,900 (droplet), and 900 (microwell-seq), while in kidney it is ~1,400 (FACS), 1,900 (droplet), and 500 (microwell-seq).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, clusters 3 and 48 each contain endothelial cells from five or more organs, and clusters 1 and 24 contain mesenchymal and stromal cells from four or more organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We clearly observed these markers in several organs including limb muscle (Extended Data Fig. 11), but many showed little evidence of cellular activation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Dotted horizontal line demarcates the median saturation (~ 0.9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
hedgedn = 55,032- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fraction of all detected genes (defined as > 0 reads or UMIs) for each cell, across all organs, detected at increasing read or UMI thresholds for FACS (left; n= 44,949 individual cells), microfluidic droplet (middle; n= 55,656 individual cells), and microwell-Seq (right; n= 28,372 individual cells).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
plainn = 44,949- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The number of genes detected (threshold of > 0 reads or UMIs per cell) by FACS (red; n= 21,105 individual cells), microfluidic droplets (green; n = 55,032 individual cells), and microwell-Seq (blue; n= 25,891 individual cells) (Han et al.).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
What the sources say
- Not matchedcites Han et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainn = 21,105- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such gene expression profiles largely correlate (Pearson correlation coefficient: 0.74 – 0.90), suggesting that while biases between methods exist, both accurately recapitulate average cell type gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All organs used a standard annotation method; an example using liver is contained in the Organ Annotation Vignette.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This release allows for the exact replication of all results, facilitates in-depth analyses not completed here, and provides a comparative framework for future studies using the large variety of murine disease models.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, for nearly all reprogramming protocols the TFs used also specified the targeted cell type in our data (Supplementary Table 5), suggesting that our data can inform novel reprogramming schemes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Recently, Han et al.20 published a complementary scRNA-seq study across mouse organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many of these cell types have not previously been obtained in pure populations, and our data provide a wealth of new information on their characteristic gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is partially explained by cellular abundance and sampling depth (12,333 microfluidic droplet cells vs. 2,216 FACS cells, Supplementary Table 1), and possibly from cell capture and lysis biases between methods.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the dissociation- related satellite cell markers are not universal, and organs likely display unique dissociation-related expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these data are by no means a complete representation of all mouse organs and cell types, they provide a first draft attempt to create an organism-wide representation of cellular diversity.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enabled the direct comparison of cell type composition between organs, and the comparison of shared cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that most cells of similar cell types cluster together across organs and biological replicates shows that batch effects are not the main source of variance in the dataset.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“3) We annotated cell types using standard cell ontology terms, thereby enabling cross-organ and cross-experiment analyses”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“While this is known in human T cells, MHC class II was previously thought restricted to professional antigen presenting cells in mice22.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Not checked — no full text of the sourcecites [22] Holling, T. M., Schooten, E. & van Den Elsen, P. J. Function and regulation of MHC class II molecules in T-lymphocytes: of mice and men. Hum. Immunol. 65, 282–290 (2004). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“1) All data, analysis, and code is freely available”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is a predictable artifact of the annotation scheme: since entire clusters, rather than individual cells, were annotated in each organ, a sufficiently rare cell type that was algorithmically grouped with a more populous cell type will be mis-annotated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
boosteddirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All data, protocols, analysis scripts, and an interactive data browser are publicly shared (http://tabula-muris.ds.czbiohub.org/ ).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, the presence of such gene expression changes does not prevent cell type identification or the comparison of cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As the nomenclature is not identical, the analysis was performed between cell types that could be matched with a certain degree of confidence between the three methods (Supplementary Table 2).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Differential expression overlap analysis
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Littermates were not recorded or tracked, and mice were housed at the VA VMU for no longer than 2 weeks before euthanasia.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sample wells were then selected if their cDNA concentrations were at least one standard deviation greater than the mean concentration of the blanks.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sorters were calibrated using FACS buffer every day before collecting any cells, and also after every 8 sorted plates.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > FACS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A similar analysis was done globally for all FACS processed cells and for all microfluidic droplet processed cells to produce an unbiased clustering.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with fewer than 500 detected genes were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While it is not an exhaustive characterization of all mouse organs, it does provide a rich dataset of the most highly studied organs in biology.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This Tabula Muris, or “Mouse Atlas”, has many uses, including the discovery of new putative cell types, the discovery of novel gene expression in known cell types, and the ability to compare cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with fewer than 50,000 reads (FACS) or 1000 UMI (microfluidic droplet) were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No sample size choice was performed prior to the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells were lexicographically sorted by cell ID to ensure reproducibility.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cell types were assigned to each cluster using the abundance of known marker genes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“When clusters appeared to be mixtures of cell types, they were refined either by increasing the resolution parameter for clustering or subsetting the data and rerunning steps 3–7.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All animal care and procedures were carried out in accordance with institutional guidelines approved by the VA Palo Alto Committee on Animal Research.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sample plates were selected for downstream processing if the mean concentration of blanks (ERCC-containing, non-cell wells) was greater than 0 ng/μl, and, after linear regression of the values obtained from the Quant-iT dsDNA standard curve, the R2 value was greater than 0.98.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Four 10–15 week old male and four virgin female C57BL/6JN (derived from C57BL/6J in 2016) mice were shipped from the National Institute on Aging colony at Charles River to the Veterinary Medical Unit (VMU) at the VA Palo Alto (VA).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tabula Muris provides a framework and description of many of the most populous and important cell populations within the mouse, and represents a foundation for future studies across a multitude of diverse physiological disciplines.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Randomization and blinding were not performed – the authors were aware of all data and metadata related variables during the entire course of the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“7) Full-length transcript data makes possible transcription factor, splice variant, and sequence variant analyses.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
It was unknown whether single-cell data from different animals, labs, and techniques could be synthesized into a general resource for biology.
“It therefore remains unknown whether these data can be synthesized as a more general resource for biology.”
The paper relies on this without support.
Comparing data collected from different animals by independent labs with varying experimental techniques is challenging.
“However, while these reports provide valuable characterization of individual organs, it is challenging to compare data collected from different animals by independent labs with varying experimental techniques.”
The paper relies on this without support.
Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.
“Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“A Tabula Muris compendium of single-cell RNA-Seq transcriptome data from 20 mouse organs and tissues (czbiohub.org) (14) revealed that ASGR1 transcripts are exclusively expressed in liver, specifically in hepatocytes (Fig. 1C).”
From Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR cleaved by furin (2021) · cites this paper as [14]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Ageing is associated with increased activation of the NLRP3 inflammasome45,107, which is primarily expressed by myeloid immune cells107,108, and a subsequent increase in the expression of IL-1β, which is a major cytokine implicated in promoting ageing-related disease107.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
Experimental — accuracy not yet measuredIn the citing paper
“CD38 expression was specific to adipose-tissue-resident macrophages (CD68+CD64+F4/80+CD11b+CD11c−), not the non-resident population (CD68+CD64+F4/80+CD11b+CD11c+) (Fig. 4h)”
From Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages (2020) · cites this paper as [36]
In the citing paper
“Multiple cells in the brain, including microglia, astrocytes, neurons and endothelial cells, express CD38 (REFS108,160), and treating microglia and astrocytes with inflammatory cytokines induces CD38 expression163,164.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
In the citing paper
“Transcription factors (TFs) have been shown to be the best molecular feature for cell type definition (Schaum et al., 2018).”
From Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei (2019) · cites this paper as Schaum et al., 2018
In the citing paper
“Furthermore, it has been shown that commonly used cell surface markers are limited in their ability to define cell identities (Tabula Muris Consortium et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tabula Muris Consortium et al, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
“liver, lung, mammary gland, pancreas, skin, spleen, thymus, tongue, and trachea from the same mouse were immediately processed into single cell suspensions.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“a) Barplot showing the heterogeneity score for each cluster containing multiple cell types. b-f) Heatmaps showing the average between-cell-type distances within select clusters”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“for clusters a) 1, b) 2, c) 3, d) 24, e) 48, f) 53.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Calculation of dissociation scores
“For each organ, principal component analysis was performed on a subset of 140 dissociation-related genes23.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > References
“23. Reichardt J & Bornholdt S Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys 74, 016110 (2006).”
Citations
Cites
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Gene selection and classification of microarray data using random forest
Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 3,013
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3881 · Cited by 2,136
Dynamic Pattern Formation in a Vesicle-Generating Microfluidic Device
Thorsen, T., Roberts, R. W., Arnold, F. H., & Quake, S. R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.4163 · Cited by 2,052
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Research works printed but not linked: 1
- Kassambara, A. Practical guide to cluster analysis in R: unsupervised machine learning 1st edn (CreateSpace, North Charleston, 2017).
Cited by
Sorted by citations
Items 901–1,000 of 2,952
A novel STING variant triggers endothelial toxicity and SAVI disease
Valeri, E., Breggion, S., Barzaghi, F., Alezz, M. A., Crivicich, G., Pagani, I., Forneris, F., Sartirana, C., Costantini, M., Costi, S., et al. (2024) · The Journal of Experimental Medicine · doi:10.1084/jem.20232167 · Cited by 28
“While the role of STING is mainly studied in cells of hematopoietic origin, it has been shown that STING is expressed by non-hematopoietic cells, such as lung epithelial cells and endothelial cells (Gao et al., 2022; Digre and Lindskog, 2021; Tabula Muris Consortium, 2018; Liu et al., 2014).”
Histone β‐hydroxybutyrylation is critical in reversal of sarcopenia
Wang, Q., Lan, X., Hao, K., Xu, S., Huang, C., Wang, J., Wang, X., Huang, T., Wu, X., Chen, M., et al. (2024) · Aging Cell · doi:10.1111/acel.14284 · Cited by 32
Redox signalling and regulation of the blood-brain barrier
van Leeuwen, E., Hampton, M. B., & Smyth, L. C. D. (2020) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2020.105794 · Cited by 26
Deppermann, C., Peiseler, M., Zindel, J., Zbytnuik, L. D., Lee, W., Pasini, E., Baciu, C., Matelski, J. J., Lee, Y., Kumar, D., et al. (2020) · Hepatology · doi:10.1002/hep.31499 · Cited by 25
mRNA Processing: An Emerging Frontier in the Regulation of Pancreatic β Cell Function
Moss, N. D., & Sussel, L. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00983 · Cited by 24
CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data
Wei, Z., & Zhang, S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab286 · Cited by 25
Golden, G. J., Toledo, A. G., Márki, A., Sorrentino, J. T., Morris, C., Riley, R. J., Spliid, C. B., Chen, Q., Cornax, I., Lewis, N. E., et al. (2021) · mBio · doi:10.1128/mbio.01181-21 · Cited by 25
“Partial desulfation of the chains occurs because most tissues also express Ndst2 (41).”
BackgroundHuman placental biology at single‐cell resolution: a contemporaneous review
Barrozo, E. R., & Aagaard, K. (2021) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.16970 · Cited by 25
Robust and annotation-free analysis of alternative splicing across diverse cell types in mice
Benegas, G., Fischer, J., & Song, Y. S. (2022) · eLife · doi:10.7554/elife.73520 · Cited by 24
Tosevska, A., Ghosh, S., Ganguly, A., Cappelletti, M., Kallapur, S. G., Pellegrini, M. A., & Devaskar, S. U. (2022) · Scientific Reports · doi:10.1038/s41598-022-12340-z · Cited by 26
Distinct gene programs underpinning disease tolerance and resistance in influenza virus infection
Cohn, O., Yankovitz, G., Peshes‐Yaloz, N., Steuerman, Y., Frishberg, A., Brandes, R., Mandelboim, M., Hamilton, J. R., Hagai, T., Amit, I., et al. (2022) · Cell Systems · doi:10.1016/j.cels.2022.11.004 · Cited by 24
Kim, Y., Cho, B., Cook, D. P., Trcka, D., Wrana, J. L., & Ramalho‐Santos, M. (2023) · Cell Reports · doi:10.1016/j.celrep.2022.111978 · Cited by 31
Pilat, J. M., Brown, R. E., Chen, Z., Berle, N. J., Othon, A. P., Washington, M. K., Anant, S. A., Kurokawa, S., Ng, V. H., Thompson, J. J., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci165988 · Cited by 31
“Human CRC scRNA-Seq data (30) (GSE178341) are publicly available through NCBI’s Gene colon (24, 63).”
MethodLv, T., Xiong, X., Yan, W., Liu, M., Xu, H., & He, Q. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1325 · Cited by 26
LY6E is a pan-coronavirus restriction factor in the respiratory tract
Mar, K. B., Wells, A. I., Dyke, M. C. C. V., Lopez, A. H., Eitson, J. L., Fan, W., Hanners, N. W., Evers, B. M., Shelton, J. M., & Schoggins, J. W. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01431-w · Cited by 25
Combinatorial expression motifs in signaling pathways
Granados, A. A., Kanrar, N., & Elowitz, M. B. (2024) · Cell Genomics · doi:10.1016/j.xgen.2023.100463 · Cited by 25
Luo, L., Zhou, Y., Zhang, C., Huang, J., Du, J., Liao, J., Bergholt, N. L., Bünger, C. E., Xu, F., Lin, L., et al. (2020) · Stem Cell Research · doi:10.1016/j.scr.2020.101990 · Cited by 27
Spatial transcriptome sequencing of FFPE tissues at cellular level
Liu, Y., Enninful, A., Deng, Y., & Fan, R. (2020) · doi:10.1101/2020.10.13.338475 · Cited by 26
SEMA6A drives GnRH neuron-dependent puberty onset by tuning median eminence vascular permeability
Lettieri, A., Oleari, R., van den Munkhof, M. H., van Battum, E. Y., Verhagen, M. G., Tacconi, C., Spreafico, M., Paganoni, A. J. J., Azzarelli, R., Andrè, V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43820-z · Cited by 27
“Protein localization patterns were con fi rmed by analysis of the Tabula Muris single-cell RNA sequencing (scRNA-seq) database 46 .”
MethodSingle-cell transcriptomics in tissue engineering and regenerative medicine
Ruta, A., Krishnan, K., & Elisseeff, J. H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00132-7 · Cited by 25
“Healthy tissues extensively profiled in the Tabula Sapiens (human) 157 and Tabula Muris (mouse) 158 can serve as uninjured baseline references.”
Interleukin-6: An Under-Appreciated Inducer of Thermogenic Adipocyte Differentiation
Radványi, Á., & Rőszer, T. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052810 · Cited by 31
CRISPRi-based screens in iAssembloids to elucidate neuron-glia interactions
Li, E., Benitez, C., Boggess, S. C., Koontz, M., Rose, I. V. L., Martinez, D., Dräger, N. M., Teter, O. M., Samelson, A. J., Pierce, N., et al. (2025) · Neuron · doi:10.1016/j.neuron.2024.12.016 · Cited by 35
A clinical road map for single-cell omics
Skinnider, M. A., Courtine, G., Bloch, J., & Squair, J. W. (2025) · Cell · doi:10.1016/j.cell.2025.06.009 · Cited by 29
SpaIM: single-cell spatial transcriptomics imputation via style transfer
Li, B., Tang, Z., Budhkar, A., Liu, X., Zhang, T., Yang, B., Su, J., & Song, Q. (2025) · Nature Communications · doi:10.1038/s41467-025-63185-9 · Cited by 29
Dietrich, A., Merotto, L., Pelz, K., Eder, B., Zackl, C., Reinisch, K., Edenhofer, F., Marini, F., Sturm, G., List, M., et al. (2026) · Genome biology · doi:10.1186/s13059-026-03955-w · Cited by 30
“Second-generation deconvolution is a powerful technique to investigate the complexity of tissues, coupling the high resolution of single-cell data [4, 62–65] with the cost-effectiveness of bulk RNA-seq [18].”
Transendothelial Insulin Transport is Impaired in Skeletal Muscle Capillaries of Obese Male Mice
Williams, I. M., McClatchey, P. M., Bracy, D. P., Bonner, J. S., Valenzuela, F. A., & Wasserman, D. H. (2020) · Obesity · doi:10.1002/oby.22683 · Cited by 23
SLIT3 deficiency attenuates pressure overload–induced cardiac fibrosis and remodeling
Gong, L., Wang, S., Shen, L., Liu, C., Shenouda, M., Li, B., Liu, X., Shaw, J. A., Wineman, A. S., Yang, Y., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.136852 · Cited by 24
Yeo, G. H. T., Lin, L., Qi, C. Y., Cha, M., Gifford, D. K., & Sherwood, R. I. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.04.020 · Cited by 26
Ramzy, A., Tudurí, E., Glavas, M. M., Baker, R. K., Mojibian, M., Fox, J. K., O’Dwyer, S. M., Dai, D. L., Hu, X., Denroche, H. C., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-67136-w · Cited by 25
Zonation of Pancreatic Acinar Cells in Diabetic Mice
Egozi, A., Halpern, K. B., Farack, L., Rotem, H., & Itzkovitz, S. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108043 · Cited by 26
“The tabula Muris dataset (Tabula Muris Consortium, 2018) was used to define genes that are specific to acinar cells.”
MethodInfluentialIntegrating multiple references for single-cell assignment
Duan, B., Chen, S., Chen, X., Zhu, C., Tang, C., Wang, S., Gao, Y., Fu, S., & Liu, Q. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab380 · Cited by 23
The role of exercise in rehabilitation of discharged COVID-19 patients
Bo, W., Xi, Y., & Tian, Z. (2021) · Sports Medicine and Health Science · doi:10.1016/j.smhs.2021.09.001 · Cited by 28
“25”
IL-12 regulates type 3 immunity through interfollicular keratinocytes in psoriasiform inflammation
Zwicky, P., Ingelfinger, F., de Melo, B. M. S., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abg9012 · Cited by 25
Li, P., Li, Q., Biswas, N., Xin, H., Diemer, T., Liu, L., Pérez, L., Paternostro, G., Piermarocchi, C., Domanskyi, S., et al. (2021) · EMBO Molecular Medicine · doi:10.15252/emmm.202114511 · Cited by 25
“Indeed, loss of CC has been reported to precede RPE degeneration (Curcio et al , 2000; Biesemeier et al , 2014).”
Fujii, T., Wada, S., Carballo, C. B., Bell, R. D., Morita, W., Nakagawa, Y., Liu, Y., Chen, D., Pannellini, T., Sokhi, U. K., et al. (2022) · JBMR Plus · doi:10.1002/jbm4.10635 · Cited by 24
“Data from the Tabula Muris project was used as a training dataset.((50))”
MethodKaragiannis, T. T., Monti, S., & Sebastiani, P. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.855076 · Cited by 26
“ScRNA-seq has been extensively applied in multiple research areas to study cell types and states, as well as cell types compositional changes, across diseases and conditions (Shalek et al., 2014; Baron et al., 2016; Muraro et al., 2016; Villani et al., 2017; Butler et al., 2018; Schaum et al., 2018; Mathys et al., 201…”
BackgroundKrishnan, J., de la Visitación, N., Hennen, E. M., Amarnath, V., Harrison, D. G., & Patrick, D. M. (2022) · Hypertension · doi:10.1161/hypertensionaha.122.19305 · Cited by 26
Pham, T. H. M., Xue, Y., Brewer, S. M., Bernstein, K. E., Quake, S. R., & Monack, D. M. (2023) · Science Advances · doi:10.1126/sciadv.add4333 · Cited by 25
“'B3_AACCCAAGTTGTCATG-1', 'B3_GATGGAGGTGCTCTCT-1', 'A4_CTACAGATCCACAGGC-1', 'A4_TAGGGTTCAAATGATG-1', 'B4_GTCACGGTCAGCCCAG-1') as well as any cell that expressed Pou6f1 > 1 log2 CPM, a transcription factor typically detected in the mouse brain(78, 79).”
Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data
Islam, M. T., & Xing, L. (2023) · Nature Communications · doi:10.1038/s41467-023-36383-6 · Cited by 26
Emergent statistical laws in single-cell transcriptomic data
Lazzardi, S., Valle, F., Mazzolini, A., Scialdone, A., Caselle, M., & Osella, M. (2023) · Physical review. E · doi:10.1103/physreve.107.044403 · Cited by 27
Uapinyoying, P., Hogarth, M. W., Battacharya, S., Mázala, D. A. G., Panchapakesan, K., Bönnemann, C. G., & Jaiswal, J. K. (2023) · iScience · doi:10.1016/j.isci.2023.107479 · Cited by 26
Soto‐Díaz, K., Juda, M. B., Blackmore, S., Walsh, C., & Steelman, A. J. (2019) · Journal of Neurochemistry · doi:10.1111/jnc.14930 · Cited by 25
txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility
Zhang, H., Mulqueen, R. M., Iannuzo, N., Farrera, D. O., Polverino, F., Galligan, J. J., Ledford, J. G., Adey, A., & Cusanovich, D. A. (2024) · Genome biology · doi:10.1186/s13059-023-03150-1 · Cited by 25
scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks
Liu, T., Jia, C., Bi, Y., Guo, X., Zou, Q., & Li, F. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae486 · Cited by 25
“Specifically, the NMI score of scDFN performed well on 15 datasets, with the highest score of 0.9693 recorded for the QS_Diaphragm dataset [44], and scDFER performed well on seven datasets, with the best score of 0.9637 for the QS_Limb_Muscle dataset (Fig.”
MethodInfluentialBenchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset
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. (2025) · Genome biology · doi:10.1186/s13059-025-03543-4 · Cited by 37
“The efficacy of our matching scRNA-seq dataset was assessed with cell type deconvolution, showing comparable results to an established public reference [47] and out-performing it in several instances.”
InfluentialThe ageing immune system as a driver of systemic ageing
Jang, I. H., Niedernhofer, L. J., Robbins, P. D., & Camell, C. D. (2026) · Nature reviews. Immunology · doi:10.1038/s41577-026-01269-3 · Cited by 35
“…in aged organs that stem from immune ageing, including tissue-specific dysfunctional interaction between immune cells, altered microenvironment architecture, increased fibrosis and loss of stromal cell identity, macrophage activation, and accumulation of lymphocytes 12,56,57,59,70,117–119 (Fig.”
Lack of Flvcr2 impairs brain angiogenesis without affecting the blood-brain barrier
Santander, N. G., Lizama, C. O., Meky, E. M., McKinsey, G. L., Jung, B., Sheppard, D., Betsholtz, C., & Arnold, T. D. (2020) · Journal of Clinical Investigation · doi:10.1172/jci136578 · Cited by 26
Norris, D. M., Yang, P., Shin, S., Kearney, A. L., Kim, H. J., Geddes, T. A., Senior, A. M., Fazakerley, D. J., Nguyen, L. K., James, D. E., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102118 · Cited by 19
Macrophage beta2-adrenergic receptor is dispensable for the adipose tissue inflammation and function
Petkevicius, K., Bidault, G., Virtue, S., Newland, S. A., Dale, M., Dugourd, A. J. F., Sáez-Rodríguez, J., Mallat, Z., & Vidal‐Puig, A. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101220 · Cited by 25
Hu, Y., Li, B., Zhang, W., Liu, N., Cai, P., Chen, F., & Qu, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab085 · Cited by 25
Hara, A., Kato, K., Ishihara, T., Kobayashi, H., Asai, N., Mii, S., Shiraki, Y., Miyai, Y., Ando, R., Mizutani, Y., et al. (2021) · Genes to Cells · doi:10.1111/gtc.12855 · Cited by 24
Englund, J. I., Ritchie, A., Blaas, L., Cojoc, H., Pentinmikko, N., Döhla, J., Iqbal, S., Patarroyo, M. E., & Katajisto, P. (2021) · Development · doi:10.1242/dev.199281 · Cited by 24
Hedgehog interacting protein–expressing lung fibroblasts suppress lymphocytic inflammation in mice
Yun, J. H., Lee, C., Liu, T., Liu, S., Kim, E. Y., Xu, S., Curtis, J. L., Pinello, L., Bowler, R. P., Silverman, E. K., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.144575 · Cited by 28
“impact of intrinsic hedgehog signaling in lung fibroblasts on the activation of lymphocytes (18, 19, 45).”
BackgroundLévesque, J.-P., SUMMERS, K. M., Bisht, K., Millard, S. M., Winkler, I. G., & Pettit, A. R. (2021) · Experimental Hematology · doi:10.1016/j.exphem.2021.08.011 · Cited by 26
Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations
Wang, M., Gu, M., Liu, L., Liu, Y., & Tian, L. (2021) · Vascular Health and Risk Management · doi:10.2147/vhrm.s288090 · Cited by 25
Yin, Q., Wang, Y., Guan, J., & Ji, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab508 · Cited by 23
Shomroni, O., Sitte, M., Schmidt, J., Parbin, S., Ludewig, F., Yigit, G., Zelarayan, L. C., Streckfuß‐Bömeke, K., Wollnik, B., & Salinas, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-07874-1 · Cited by 24
EMBEDR: Distinguishing Signal from Noise in Single-Cell Omics Data
Johnson, E. M., Kath, W. L., & Mani, M. (2021) · Patterns · doi:10.1016/j.patter.2022.100443 · Cited by 23
“We re-emphasize that the EMBEDR framework is (A and C) Four dimensionally reduced representations of RNA-seq measurements from 4,771 bone marrow cells collected by the Tabula Muris Consortium 8 generatedbyt-SNEat k Eff z 15(A)and150 (C) (perplexity = 10 and 120, respectively; custom variation of the openTSNE implement…”
MethodInfluentialPractical Considerations for Single‐Cell Genomics
Regan, C., & Preall, J. B. (2022) · Current Protocols · doi:10.1002/cpz1.498 · Cited by 27
Gehrke, N., Hofmann, L. J., Straub, B. K., Rühle, F., Waisman, A., Galle, P. R., & Schattenberg, J. M. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.1048 · Cited by 23
Smirnova, N. F., Riemondy, K. A., Bueno, M., Collins, S., Suresh, P., Wang, X., Patel, K., Cool, C. D., Königshoff, M., Sharma, N., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.156648 · Cited by 28
“Putative biological identities were assigned to each cluster, using patterns of established canonical markers as well as importing the murine gene expression atlas, which resulted in the identification of 11 cell types (12) (Supplemental Figure 1, B–D).”
Fibrillin-1 and asprosin, novel players in metabolic syndrome
SUMMERS, K. M., Bush, S. J., Davis, M. R., Hume, D. A., Keshvari, S., & West, J. A. (2022) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2022.106979 · Cited by 24
Gao, K. M., Marshak‐Rothstein, A., & Fitzgerald, K. A. (2023) · Current Opinion in Immunology · doi:10.1016/j.coi.2022.102280 · Cited by 22
scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings
Jiao, L., Wang, G., Dai, H., Li, X., Wang, S., & Song, T. (2023) · Biomolecules · doi:10.3390/biom13040611 · Cited by 26
Molecular Features and Stages of Pulmonary Fibrosis Driven by Type 2 Inflammation
Mattoo, H., Bangari, D. S., Cummings, S., Humulock, Z., Habiel, D. M., Xu, E., Pate, N., Resnick, R. M., Savova, V., Qian, G. Y., et al. (2023) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2022-0301oc · Cited by 25
Deep Learning in Single-cell Analysis
Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 38
“Tabula Muris Mouse 20 tissues 53, 760 cells SMART-Seq2 10x Genomics Tabula Muris [219]”
BackgroundZBTB46 coordinates angiogenesis and immunity to control tumor outcome
Kabir, A. U., Zeng, C., Subramanian, M., Wu, J., Kim, M., Krchma, K., Wang, X., Halabi, C. M., Pan, H., Wickline, S. A., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01936-4 · Cited by 26
Ushering in Integrated T Cell Repertoire Profiling in Cancer
Jiang, N. J., Schonnesen, A. A., & Ma, K.-Y. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.11.005 · Cited by 25
Axon Guidance-Related Factor FLRT3 Regulates VEGF-Signaling and Endothelial Cell Function
Jauhiainen, S., Laakkonen, J. P., Ketola, K., et al. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00224 · Cited by 24
de Moraes, R. C. M., Singulani, M. P., de Gonçalves, A. C., Portari, G., & Torrão, A. D. S. (2020) · Experimental Gerontology · doi:10.1016/j.exger.2020.111097 · Cited by 21
Patil, A., & Patil, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.01.389890 · Cited by 25
“Using single-cell RNA sequencing, it is now possible to get the detailed gene expression pattern in each cell for millions of cells in a single experiment under varying conditions 1 .”
BackgroundTabula Microcebus: A transcriptomic cell atlas of mouse lemur, an emerging primate model organismPreprint
Ezran, C., Liu, S., Chang, S., Ming, J., Botvinnik, O. B., Penland, L., Tarashansky, A. J., de Morrée, A., Travaglini, K. J., Zhao, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.12.469460 · Cited by 24
Quake, S. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.03.626516 · Cited by 35
Ohkubo, R., Mu, W., Wang, C.-L., Song, Z., Barthez, M., Wang, Y., Mitchener, N., Abdullayev, R., Lee, Y. R., Ma, Y., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111803 · Cited by 26
Deng, T., Chen, S., Zhang, Y., Xu, Y., Feng, D., Wu, H., & Sun, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad042 · Cited by 23
SPSB1‐mediated inhibition of TGF‐β receptor‐II impairs myogenesis in inflammation
Li, Y., Dörmann, N., Brinschwitz, B., Kny, M., Martin, E., Bartels, K., Li, N., Giri, P. V., Schwanz, S., Boschmann, M., et al. (2023) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.13252 · Cited by 25
Liu, N. M., Butcher, J. T., Nakano, A., & del Campo, A. (2023) · Aging · doi:10.18632/aging.204750 · Cited by 25
Mauduit, O., Delcroix, V., Wong, A., Ivanova, A., Miles, L. A., Lee, H. S., & Makarenkova, H. P. (2023) · The Ocular Surface · doi:10.1016/j.jtos.2023.12.003 · Cited by 24
A shorter splicing isoform antagonizes ZBP1 to modulate cell death and inflammatory responses
Nagata, M., Schäfer, Y. C., Wachsmuth, L., & Pasparakis, M. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00238-7 · Cited by 22
Reducing IgG accumulation via neonatal Fc receptor (FcRn) blockade relieves neuropathic pain
Fiore, N. T., Willcox, K. F., Dayani, D., Zuberi, Y. A., Heijnen, C. J., & Grace, P. M. (2025) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2025.01.015 · Cited by 25
“The engagement of FcRn in microglia and its potential contribution to IgG/Fc γ R remains to be explored since there is evidence that microglia express FcRn (Tabula Muris et al., 2018).”
Type I IFN drives neutrophil swarming, impeding lung T cell–macrophage interactions and TB control
Branchett, W. J., Stavropoulos, E., Shields, J. M., Al‐Dibouni, A., Cardoso, M. S., Fernandes, A. I., Moreira-Teixeira, L., Slawinski, H., Mikolajczak, A., Rodgers, A., et al. (2025) · The Journal of Experimental Medicine · doi:10.1084/jem.20250466 · Cited by 28
“5.1 (Fu et al., 2020) using the Mouse Cell Atlas (Han et al., 2018), Tabula Muris 10X and SmartSeq2 (Schaum et al., 2018), and ImmGen (Gautier et al., 2012; Heng et al., 2008) as reference datasets.”
InfluentialAngel, A., Naom, L., Nabet-Levy, S., & Aran, D. (2025) · Genome biology · doi:10.1186/s13059-025-03784-3 · Cited by 35
A metabolic atlas of mouse aging
Pilley, S. E., Awad, D., Latumalea, D., New, C., Esparza, E., Wang, S. Y., Shi, X., Zhang, L., Unfried, M., Lee, J. H., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.10.016 · Cited by 26
Cardiac endurance training alters plasma profiles of circular RNA MBOAT2
Meinecke, A., Mitzka, S., Just, A., Cushman, S., Stojanović, S. D., Xiao, K., Mooren, F. C., Fiedler, J., & Thum, T. (2020) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00067.2020 · Cited by 23
An, X., Ogawa-Wong, A. N., Carmody, C., Ambrosio, R., Cicatiello, A. G., Luongo, C., Salvatore, D., Handy, D. E., Larsen, P. R., Wajner, S. M., et al. (2020) · Thyroid · doi:10.1089/thy.2020.0291 · Cited by 23
Control of the Anterior Pituitary Cell Lineage Regulator POU1F1 by the Stem Cell Determinant Musashi
Allensworth‐James, M. L., Banik, J., Odle, A. K., Hardy, L. L., Lagasse, A., Moreira, A. R. S., Bird, J. T., Thomas, C. L., Avaritt, N. L., Kharas, M. G., et al. (2020) · Endocrinology · doi:10.1210/endocr/bqaa245 · Cited by 24
Droplet microfluidics on analysis of pathogenic microbes for wastewater-based epidemiology
Ou, Y., Cao, S., Zhang, J., Dong, W., Yang, Z., & Yu, Z. (2021) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2021.116333 · Cited by 23
Degani, N., Lubelsky, Y., Perry, R. B., Ainbinder, E., & Ulitsky, I. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009681 · Cited by 22
Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research
Mädler, S. C., Julien-Laferrière, A., Wyss, L., Phan, M., Sonrel, A., Kang, A. S. W., Ulrich, E., Schmucki, R., Zhang, J. D., Ebeling, M., et al. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab102 · Cited by 28
Shi, B., Wu, Y., Chen, H., Ding, J., & Qi, J. (2021) · Cell Proliferation · doi:10.1111/cpr.13170 · Cited by 23
Rypdal, K. B., Melleby, A. O., Robinson, E. L., Li, J., Palmero, S., Seifert, D. E., Martin, D., Clark, C., López, B., Andreassen, K. E., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-04361-1 · Cited by 25
Tian, H., Xing, J., Tang, X., Sheng, X., Chi, H., & Zhan, W. (2023) · Aquaculture · doi:10.1016/j.aquaculture.2023.739238 · Cited by 23
Multilingual translation for zero-shot biomedical classification using BioTranslator
Xu, H., Woicik, A., Poon, H., Altman, R. B., & Wang, S. (2023) · Nature Communications · doi:10.1038/s41467-023-36476-2 · Cited by 29
Liu, J., Zeng, W., Kan, S., Li, M., & Zheng, R. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad475 · Cited by 24
Sema3a-Nrp1 Signaling Mediates Fast-Twitch Myofiber Specificity of Tw2+ Cells
Li, S., Karri, D., Sanchez‐Ortiz, E., Jaichander, P., Bassel‐Duby, R., Liu, N., & Olson, E. N. (2019) · Developmental Cell · doi:10.1016/j.devcel.2019.08.002 · Cited by 23
Đermadi, D., Bscheider, M., Bjegovic, K., Lazarus, N. H., Szade, A., Hadeiba, H., & Butcher, E. C. (2020) · iScience · doi:10.1016/j.isci.2020.100842 · Cited by 23
A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data
Srinivasan, S., Leshchyk, A., Johnson, N. T., & Korkin, D. (2020) · RNA · doi:10.1261/rna.074427.119 · Cited by 24
“Our next step is to improve the execution time of DUSC for very large data sets containing 100,000+ cells, which are highly heterogeneous and may include a certain cell type hierarchy (Tabula Muris Consortium 2018).”
MethodConde, C. D., Xu, C., Jarvis, L., Gomes, T., Howlett, S., Rainbow, D., Suchanek, O., King, H., Mamanova, L., Polanski, K., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.28.441762 · Cited by 27
Attention-based deep clustering method for scRNA-seq cell type identification
Li, S., Guo, H., Zhang, S., Li, Y., & Li, M. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011641 · Cited by 29
“In this section, we tested AttentionAE-sc on 16 scRNA-seq datasets [28, 29, 30, 31, 32, 33] from different organs or platforms (containing 870–9552 individual cells or 4–11 cell groups).”
MethodYoon, S.-H., Cho, B., Lee, D., Kim, H., Shim, J., & Nam, J. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1011077 · Cited by 22
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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