The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; 2 of the 8 sources its claims cite were checked for support.
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
Open accessAbstract
Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.
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Claims the paper makes
44 Own result · 26 Own method · 20 Own interpretation · 5 No full text of the sources · 5 No basis given · 1 Supported · 1 Partly supported · 1 Sources not found · accuracy not yet measured
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In the paper
“Cluster co-membership alone, however, is insufficient to conclude that two cell populations from different organs represent the same or similar cell types; at any given resolution, unbiased clustering that groups related cells may also group unrelated cells21.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Partly supportedcites [21] Freytag, S., Tian, L., Lonnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res 7, 1297 (2018). sourceExperimental — accuracy not yet measured
The Results (Evaluation of clustering tools, Silver standard data; page unavailable) report that most clusters contained mixtures of different inferred cell types, supporting the caution that co-membership alone does not establish cell-type identity. The paper evaluates PBMC datasets, however, and does not test populations from different organs or establish that such populations were grouped together. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“transcriptional heterogeneity of brain endothelial cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clusters 1–3 contain mostly single positive T cells (Cd4+ or Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a cell population expressing Chodl in limb muscle”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, to determine which clusters are composed of related or unrelated cell types, we computed a heterogeneity score for each cluster (Methods), and found low scores for the biologically sensible clusters discussed above (Extended Data Fig. 9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To understand which TFs were most informative for specifying cell types, we performed variable selection from random forests (Methods) and determined that 136 TFs are needed to simultaneously define all cell types across all organs (Fig. 5f, Supplementary Table 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The compendium is comprised of single-cell transcriptomic data from 100,605 cells isolated from 20 organs from 3 female and 4 male, C57BL/6JN, 3-month-old mice (10–15 weeks), analogous to 20-year-old humans (Fig. 1).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting dendrogram closely resembles the dendrogram produced using all expressed genes, indicating that TFs can be used to reconstruct known cell ontology relationships between bulk populations (entanglement = 0.11; Extended Data Fig. 10a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nonetheless, microfluidic droplets identified cell types missed by FACS in both organs, for example kidney mesangial cells, and splenic dendritic and natural killer cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the expression of MHC class II genes by adult mouse T cells”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 27
- It may state two separate claims as one. 45
Every source the paper cites for this claim was checked and supports it.
In the paper
“For each cluster, we provide annotations in the controlled vocabulary of a cell ontology19 to facilitate inter-experiment comparisons.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
What the sources say
- Supportedcites [19] Bakken, T. et al. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 18 (Suppl 17), 559 (2017). source
The Background section describes the Cell Ontology as a standard reference nomenclature needed to compare experimental results about cell types. It also says the ontology is used as a reference annotation vocabulary. The citing authors’ statement that they annotated each cluster describes their own work; the citation supports their choice of vocabulary and its purpose. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Single-cell transcriptomes were sequenced to an average depth of 814,488 reads per cell (FACS) and 7,709 unique molecular identifiers (UMIs) per cell (microfluidic droplet).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The bladder, liver, lung, mammary gland, trachea, tongue, and spleen show nearly twice as many genes detected per cell with FACS compared to microfluidic droplet, whereas heart and marrow show comparable numbers (Extended Data Fig. 5a).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“Their data contains four times as many cells and included several sample types not present in our data, such as neonatal and fetal organs, cell lines, and young adult ovary, peripheral blood, placenta, prostate, small intestine, stomach, testis, and uterus.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then determined the TF sets distinguishing each individual cell type from all other cells. These sets vary substantially in size (from 2 to 813 TFs) and are not necessarily unique to each cell type (Fig. 5g–i; Supplementary Table 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“However, our FACS data contains four times as many genes per cell, and we analyzed several organs not present in the Han et al. data set, such as aorta, four brain regions, diaphragm, four fat types, four adult heart chambers, adult skin, tongue, and trachea.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while Cluster 2 contains mostly non-thymic T cells expressing the high affinity IL2 receptor (Il2ra, Il2rb), suggesting they are activated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 0 comprises thymic cells undergoing VDJ recombination characterized by RAG (Rag1, Rag2) and TdT (Dntt) expression, and includes uncommitted double positive T-cells (Cd4+, Cd8a+).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 2 contains B cells from fat, limb muscle, lung, spleen, marrow, and liver, but also cells annotated as leukocytes and lymphocytes from the thymus, heart, and limb muscle.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As expected, cells from different organs often mixed, with 25 of 54 clusters containing (at least five) cells from distinct organs (Fig. 3).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, when we repeated the analysis using cell surface markers, RNA splicing factors, or the two groups combined (equivalent to a random set of genes), the entanglement was 0.22, 0.25 and 0.34, respectively, suggesting that none of these molecular classes define cell type to the extent that transcription factors do.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, Cluster 1 also represents mature T cells, but primarily splenic.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This seems only to occur for populations smaller than ~30 cells, which comprise under 4% of the overall dataset, and represents a lower limit of sensitivity in the current release of data interpretation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Comparing methods shows organ-specific differences in the number of cells analyzed (Fig. 1b, c), reads per cell (Extended Data Fig. 3a, c) and genes per cell (Extended Data Fig. 3b, d).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 4 contains predominantly proliferating thymic T cells which may represent pre-T cells expanding following VDJ recombination.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, with both methods the most abundant cell types analyzed are epithelial cells and leukocytes, although FACS captured a larger diversity of cell types (Extended Data Fig. 4).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For those cell types captured by both methods, the proportion of each cell type is equivalent (Pearson correlation coefficient: spleen = 0.99, kidney = 0.99).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“For example, the median number of genes detected in bladder is ~4,900 (FACS), 2,900 (droplet), and 900 (microwell-seq), while in kidney it is ~1,400 (FACS), 1,900 (droplet), and 500 (microwell-seq).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, clusters 3 and 48 each contain endothelial cells from five or more organs, and clusters 1 and 24 contain mesenchymal and stromal cells from four or more organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We clearly observed these markers in several organs including limb muscle (Extended Data Fig. 11), but many showed little evidence of cellular activation.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Dotted horizontal line demarcates the median saturation (~ 0.9).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
hedgedn = 55,032- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fraction of all detected genes (defined as > 0 reads or UMIs) for each cell, across all organs, detected at increasing read or UMI thresholds for FACS (left; n= 44,949 individual cells), microfluidic droplet (middle; n= 55,656 individual cells), and microwell-Seq (right; n= 28,372 individual cells).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
plainn = 44,949- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The number of genes detected (threshold of > 0 reads or UMIs per cell) by FACS (red; n= 21,105 individual cells), microfluidic droplets (green; n = 55,032 individual cells), and microwell-Seq (blue; n= 25,891 individual cells) (Han et al.).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
What the sources say
- Not matchedcites Han et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainn = 21,105- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FACS detects more molecules per cell, but fewer cells. Microfluidic droplets detect fewer molecules per cell, but more cells.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Correlating bulk gene expression profiles
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such gene expression profiles largely correlate (Pearson correlation coefficient: 0.74 – 0.90), suggesting that while biases between methods exist, both accurately recapitulate average cell type gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All organs used a standard annotation method; an example using liver is contained in the Organ Annotation Vignette.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This release allows for the exact replication of all results, facilitates in-depth analyses not completed here, and provides a comparative framework for future studies using the large variety of murine disease models.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, for nearly all reprogramming protocols the TFs used also specified the targeted cell type in our data (Supplementary Table 5), suggesting that our data can inform novel reprogramming schemes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global transcription factor analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Recently, Han et al.20 published a complementary scRNA-seq study across mouse organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many of these cell types have not previously been obtained in pure populations, and our data provide a wealth of new information on their characteristic gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is partially explained by cellular abundance and sampling depth (12,333 microfluidic droplet cells vs. 2,216 FACS cells, Supplementary Table 1), and possibly from cell capture and lysis biases between methods.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the dissociation- related satellite cell markers are not universal, and organs likely display unique dissociation-related expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these data are by no means a complete representation of all mouse organs and cell types, they provide a first draft attempt to create an organism-wide representation of cellular diversity.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enabled the direct comparison of cell type composition between organs, and the comparison of shared cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that most cells of similar cell types cluster together across organs and biological replicates shows that batch effects are not the main source of variance in the dataset.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“3) We annotated cell types using standard cell ontology terms, thereby enabling cross-organ and cross-experiment analyses”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“While this is known in human T cells, MHC class II was previously thought restricted to professional antigen presenting cells in mice22.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
What the sources say
- Not checked — no full text of the sourcecites [22] Holling, T. M., Schooten, E. & van Den Elsen, P. J. Function and regulation of MHC class II molecules in T-lymphocytes: of mice and men. Hum. Immunol. 65, 282–290 (2004). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“1) All data, analysis, and code is freely available”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is a predictable artifact of the annotation scheme: since entire clusters, rather than individual cells, were annotated in each organ, a sufficiently rare cell type that was algorithmically grouped with a more populous cell type will be mis-annotated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
boosteddirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All data, protocols, analysis scripts, and an interactive data browser are publicly shared (http://tabula-muris.ds.czbiohub.org/ ).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
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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.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, the presence of such gene expression changes does not prevent cell type identification or the comparison of cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As the nomenclature is not identical, the analysis was performed between cell types that could be matched with a certain degree of confidence between the three methods (Supplementary Table 2).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Differential expression overlap analysis
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Littermates were not recorded or tracked, and mice were housed at the VA VMU for no longer than 2 weeks before euthanasia.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sample wells were then selected if their cDNA concentrations were at least one standard deviation greater than the mean concentration of the blanks.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sorters were calibrated using FACS buffer every day before collecting any cells, and also after every 8 sorted plates.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > FACS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A similar analysis was done globally for all FACS processed cells and for all microfluidic droplet processed cells to produce an unbiased clustering.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with fewer than 500 detected genes were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While it is not an exhaustive characterization of all mouse organs, it does provide a rich dataset of the most highly studied organs in biology.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This Tabula Muris, or “Mouse Atlas”, has many uses, including the discovery of new putative cell types, the discovery of novel gene expression in known cell types, and the ability to compare cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with fewer than 50,000 reads (FACS) or 1000 UMI (microfluidic droplet) were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No sample size choice was performed prior to the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells were lexicographically sorted by cell ID to ensure reproducibility.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cell types were assigned to each cluster using the abundance of known marker genes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“When clusters appeared to be mixtures of cell types, they were refined either by increasing the resolution parameter for clustering or subsetting the data and rerunning steps 3–7.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All animal care and procedures were carried out in accordance with institutional guidelines approved by the VA Palo Alto Committee on Animal Research.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sample plates were selected for downstream processing if the mean concentration of blanks (ERCC-containing, non-cell wells) was greater than 0 ng/μl, and, after linear regression of the values obtained from the Quant-iT dsDNA standard curve, the R2 value was greater than 0.98.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Four 10–15 week old male and four virgin female C57BL/6JN (derived from C57BL/6J in 2016) mice were shipped from the National Institute on Aging colony at Charles River to the Veterinary Medical Unit (VMU) at the VA Palo Alto (VA).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tabula Muris provides a framework and description of many of the most populous and important cell populations within the mouse, and represents a foundation for future studies across a multitude of diverse physiological disciplines.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Randomization and blinding were not performed – the authors were aware of all data and metadata related variables during the entire course of the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“7) Full-length transcript data makes possible transcription factor, splice variant, and sequence variant analyses.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
It was unknown whether single-cell data from different animals, labs, and techniques could be synthesized into a general resource for biology.
“It therefore remains unknown whether these data can be synthesized as a more general resource for biology.”
The paper relies on this without support.
Comparing data collected from different animals by independent labs with varying experimental techniques is challenging.
“However, while these reports provide valuable characterization of individual organs, it is challenging to compare data collected from different animals by independent labs with varying experimental techniques.”
The paper relies on this without support.
Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.
“Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“A Tabula Muris compendium of single-cell RNA-Seq transcriptome data from 20 mouse organs and tissues (czbiohub.org) (14) revealed that ASGR1 transcripts are exclusively expressed in liver, specifically in hepatocytes (Fig. 1C).”
From Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR cleaved by furin (2021) · cites this paper as [14]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Ageing is associated with increased activation of the NLRP3 inflammasome45,107, which is primarily expressed by myeloid immune cells107,108, and a subsequent increase in the expression of IL-1β, which is a major cytokine implicated in promoting ageing-related disease107.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
Experimental — accuracy not yet measuredIn the citing paper
“CD38 expression was specific to adipose-tissue-resident macrophages (CD68+CD64+F4/80+CD11b+CD11c−), not the non-resident population (CD68+CD64+F4/80+CD11b+CD11c+) (Fig. 4h)”
From Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages (2020) · cites this paper as [36]
In the citing paper
“Multiple cells in the brain, including microglia, astrocytes, neurons and endothelial cells, express CD38 (REFS108,160), and treating microglia and astrocytes with inflammatory cytokines induces CD38 expression163,164.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
In the citing paper
“Transcription factors (TFs) have been shown to be the best molecular feature for cell type definition (Schaum et al., 2018).”
From Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei (2019) · cites this paper as Schaum et al., 2018
In the citing paper
“Furthermore, it has been shown that commonly used cell surface markers are limited in their ability to define cell identities (Tabula Muris Consortium et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tabula Muris Consortium et al, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
“liver, lung, mammary gland, pancreas, skin, spleen, thymus, tongue, and trachea from the same mouse were immediately processed into single cell suspensions.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“a) Barplot showing the heterogeneity score for each cluster containing multiple cell types. b-f) Heatmaps showing the average between-cell-type distances within select clusters”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“for clusters a) 1, b) 2, c) 3, d) 24, e) 48, f) 53.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Calculation of dissociation scores
“For each organ, principal component analysis was performed on a subset of 140 dissociation-related genes23.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > References
“23. Reichardt J & Bornholdt S Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys 74, 016110 (2006).”
Citations
Cites
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Gene selection and classification of microarray data using random forest
Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 3,013
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3881 · Cited by 2,136
Dynamic Pattern Formation in a Vesicle-Generating Microfluidic Device
Thorsen, T., Roberts, R. W., Arnold, F. H., & Quake, S. R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.4163 · Cited by 2,052
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Research works printed but not linked: 1
- Kassambara, A. Practical guide to cluster analysis in R: unsupervised machine learning 1st edn (CreateSpace, North Charleston, 2017).
Cited by
Sorted by citations
Items 1,401–1,500 of 2,952
Hida, M., Yasuda, K., Toyokawa, M., Asada‐Utsugi, M., Toda, S., Yanagida, N., Takahashi, R., Kinoshita, A., & Maki, T. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149601 · Cited by 12
Henry, D., Baugé, E., Hennuyer, N., Ory, K., Derudas, B., Vallez, E., Deprince, A., Lefèbvre, P., Haas, J. T., Staels, B., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adj8192 · Cited by 14
scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications
Bai, K., Moa, B., Shao, X., & Zhang, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf446 · Cited by 11
“We also included the Tabula Muris dataset from The Tabula Muris Consortium (Schaum et al. ) [28].”
Liu, Q., Yang, L., Li, C., Han, L., Wilmot, J., Yang, G., Leinardi, R., Zhou, Q., Schröppel, A., Kutschke, D., et al. (2025) · Science Advances · doi:10.1126/sciadv.adx8586 · Cited by 18
Neuronatin is a modifier of estrogen receptor-positive breast cancer incidence and outcome
Plasterer, C., Tsaih, S., Peck, A. R., Chervoneva, I., O'Meara, C. C., Sun, Y., Lemke, A., Murphy, D., Smith, J. S., Ran, S., et al. (2019) · Breast Cancer Research and Treatment · doi:10.1007/s10549-019-05307-8 · Cited by 11
Big data analytics in single‐cell transcriptomics: Five grand opportunities
Bhattacharya, N., Nelson, C. C., Ahuja, G., & Sengupta, D. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1414 · Cited by 8
Innovation in culture systems to study muscle complexity
Moyle, L. A., Davoudi, S., & Gilbert, P. M. (2021) · Experimental Cell Research · doi:10.1016/j.yexcr.2021.112966 · Cited by 13
Transcriptional bursts and heterogeneity among cardiomyocytes in hypertrophic cardiomyopathy
Burkart, V., Kowalski, K., Aldag-Niebling, D., Beck, J., Frick, D. A., Holler, T., Radocaj, A., Piep, B., Zeug, A., Hilfiker‐Kleiner, D., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.987889 · Cited by 11
Patir, A., Raper, A., Fleming, R., Henderson, B. E. P., Murphy, L., Henderson, N. C., Clark, E. L., Freeman, T. C., & Barnett, M. W. (2023) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkad178 · Cited by 11
Decoding fibrosis in the human central nervous system
Holl, D., & Göritz, C. (2023) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00243.2023 · Cited by 12
Abl2 repairs microtubules and phase separates with tubulin to promote microtubule nucleation
Duan, D., Lyu, W., Chai, P., Ma, S., Wu, C., Wu, C., Xiong, Y., Šestan, N., Zhang, K., & Koleske, A. J. (2023) · Current Biology · doi:10.1016/j.cub.2023.09.018 · Cited by 10
Florio, F., Vencato, S., Papa, F. T., Libergoli, M., Kheir, E., Ghzaiel, I., Rando, T. A., Torrente, Y., & Biressi, S. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202317405 · Cited by 11
“The dataset referred to C1 and CD163 expression in mouse limbs is available in the following database: Tabula Muris Dataset: https://tabula-muris.ds. czbiohub.org/ (Tabula Muris Consortium et al , 2018).”
MethodFrankowska, M., Surówka, P., Gawlińska, K., Borczyk, M., Korostyński, M., Filip, M., & Smaga, I. (2024) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2023.1303718 · Cited by 13
Saxena, P., Sinha, A., & Singh, S. K. (2024) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2023.2300685 · Cited by 13
SRT-Server: powering the analysis of spatial transcriptomic data
Yang, S., & Zhou, X. (2024) · Genome Medicine · doi:10.1186/s13073-024-01288-6 · Cited by 10
“For the reference-free version of CARD, SRT-Server also provides gene marker sets from 20 normal tissue datasets in mice [37] and 8 normal tissues with 23 diseases status in humans (Additional file 1: Table S1 and S2).”
MethodA new atlas to study embryonic cell types in Xenopus
Petrova, K., Tretiakov, M., Kotov, A., Monsoro-Burq, A. H., & Peshkin, L. (2024) · Developmental Biology · doi:10.1016/j.ydbio.2024.04.003 · Cited by 11
Deep generative models in single-cell omics
Rivero-García, I., Torres, M., & Sánchez‐Cabo, F. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108561 · Cited by 13
Graelmann, F. J., Gondorf, F., Majlesain, Y., Niemann, B., Klepac, K., Gosejacob, D., Gottschalk, M., Mayer, M., Iriady, I., Hatzfeld, P., et al. (2024) · Molecular Metabolism · doi:10.1016/j.molmet.2024.101963 · Cited by 11
Choi, S., Lee, J. M., Kim, K. E. S., Park, J.-H., Kim, L., Park, J., Jeon, Y., Jhun, B. W., Kim, S.-Y., Hong, J. J., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105198 · Cited by 11
“Single-cell transcriptomic data analysis of adult mouse lungs was performed using the Tabula Muris database, 27 which can be accessed at https://tabula-muris.ds. czbiohub.org/.”
MethodA single-cell atlas of chromatin accessibility in mouse organogenesis
Sun, K., Liu, X., & Lan, X. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01435-6 · Cited by 11
Mulenge, F., Gern, O. L., Busker, L. M., Aringo, A., Ghita, L., Waltl, I., Pavlou, A., & Kalinke, U. (2024) · Journal of Neuroinflammation · doi:10.1186/s12974-024-03197-2 · Cited by 12
“Furthermore, with the emergence of large-scale consortia such as Human Cell Atlas project [49] and murine cellular atlases [50] that are aiming at establishing comprehensive transcriptional maps for multicellular tissues, the development of approaches that compensate or avoid such transcriptional…”
BackgroundSzabados, T., Makkos, A., Ágg, B., Benczik, B., Brenner, G. B., Szabó, M., Váradi, B., Vörös, I., Gömöri, K., Varga, Z. V., et al. (2024) · British Journal of Pharmacology · doi:10.1111/bph.17345 · Cited by 11
Seubert, A. C., Krafft, M., Bopp, S., Helal, M., Bhandare, P., Wolf, E., Alemany, A., Riedel, A., & Kretzschmar, K. (2024) · Stem Cell Reports · doi:10.1016/j.stemcr.2024.10.007 · Cited by 12
Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE
Hozumi, Y., & Wei, G. (2024) · PLoS ONE · doi:10.1371/journal.pone.0311791 · Cited by 11
Cell Storage Conditions Impact Single-Cell Proteomic Landscapes
Onat, B., Momenzadeh, A., Haghani, A., Jiang, Y., Song, Y., Parker, S. J., & Meyer, J. G. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00632 · Cited by 12
Towards multi-fusion graph neural network for single-cell RNA sequence clustering
Yang, C.-M., Huang, D., Xu, Y.-K., He, X., Zhang, G., & Wang, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129764 · Cited by 13
RAPID LEAF FALLING 1 facilitates chemical defoliation and mechanical harvesting in cotton
Zhang, B., Yue, D., Han, B., Bao, D., Zhang, X., Hao, X., Lin, X., Lindsey, K., Zhu, L., Jin, S., et al. (2025) · Molecular Plant · doi:10.1016/j.molp.2025.03.017 · Cited by 11
Functional mosaic organization of neuroligins in neuronal circuits
Qin, L., Guo, S., Han, Y., Wang, X., & Zhang, B. (2020) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03478-y · Cited by 10
Potts, H. G., Lemieux, M. E., Rice, E. S., Warren, W. C., Choudhury, R. P., & Mommersteeg, M. T. M. (2022) · Cells · doi:10.3390/cells11040608 · Cited by 11
“and has been applied to a range of model and non-model organisms, from the frequently used mouse (Mus musculus) [3] to the less commonly used earthworm (Eisenia andrei) [4].”
BackgroundJIND: joint integration and discrimination for automated single-cell annotation
Goyal, M., Serrano, G., Argemí, J., Shomorony, I., Hernáez, M., & Ochoa, I. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac140 · Cited by 11
KIT is dispensable for physiological organ vascularisation in the embryo
Tacconi, C., Plein, A., Colletto, C., Villa, E., Denti, L., Barone, C., Javanmardi, Y., Moeendarbary, E., Azzoni, E., Fantin, A., et al. (2022) · Angiogenesis · doi:10.1007/s10456-022-09837-6 · Cited by 11
“Taking advantage of the Tabula Muris single-cell RNA sequencing (scRNA-seq) database [21], we reduced dimensionality and applied Uniform Manifold Approximation and Projection (UMAP) to the datasets from adult brain, liver and lung to select the clusters containing Pecam1+ ECs and compare Kit transcript levels (Fig.”
Zandavi, S. M., Koch, F. C., Vijayan, A., Zanini, F., Valdés‐Mora, F., Gallego‐Ortega, D., & Vafaee, F. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac436 · Cited by 11
Rayees, S., Joshi, J. C., Joshi, B., Vellingiri, V., Banerjee, S., & Mehta, D. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.874197 · Cited by 12
“Compared to other lung cell types, AMs predominantly expressed Rac1 (Nicholas et al., 2018).”
BackgroundAttention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
Bhadani, R. K., Chen, Z., & An, L. (2023) · Genes · doi:10.3390/genes14020506 · Cited by 14
Martino, R. A., Wang, Q., Xu, H. Y., Hu, G. B., Bell, P. L., Arroyo, E. J., Sims, J. J., & Wilson, J. M. (2023) · Journal of Virology · doi:10.1128/jvi.00174-23 · Cited by 13
Liu, J., Zhang, X., Zhang, Y., Qian, M., Yang, M., Yang, S. T., & Wang, L. (2023) · The FASEB Journal · doi:10.1096/fj.202300433rr · Cited by 11
Louie, A. Y., Kim, J. S., Drnevich, J. M., Dibaeinia, P., Koito, H., Sinha, S., McKim, D. B., Soto‐Díaz, K., Nowak, R. A., Das, A., et al. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02862-2 · Cited by 13
“This finding is consistent with a lack of toll-like receptor 4 expression on OLs [56] and indicates that OLs did not secrete TNF in response to residual LPS in conditioned medium (Fig.”
BackgroundResultCha, J., Tong, X., Walker, E. M., Dahan, T., Cochrane, V. A., Ashe, S., Russell, R., Osipovich, A. B., Mawla, A. M., Guo, M. A., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.166386 · Cited by 12
Johansen, N., Hu, H., & Quon, G. (2023) · Nature Communications · doi:10.1038/s41467-023-40744-6 · Cited by 10
Cleuren, A. C. A., & Molema, G. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1252021 · Cited by 13
Casalino‐Matsuda, S. M., Chen, F., González-González, F. J., Matsuda, H., Nair, A., Abdala‐Valencia, H., Budinger, G. R. S., Dong, J., Beitel, G. J., & Sporn, P. H. S. (2024) · JCI Insight · doi:10.1172/jci.insight.170316 · Cited by 11
Hendrickson, P. G., Oristian, K. M., Browne, M. R., Luo, L., Ma, Y., Cardona, D. M., Nash, J. O., Ballester, P. L., Davidson, S., Shlien, A., et al. (2024) · Oncogene · doi:10.1038/s41388-024-02984-8 · Cited by 10
“However, as all animals died before they could reproduce, innovative solutions to prevent spontaneous recombination are needed to generate a CDS model that can be maintained through standard breeding and used to induce tumors in a controlled manner.”
An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome
Liu, S., Ezran, C., Wang, M. F. Z., Li, Z., Awayan, K., Agarwal, S., Agrawal, A., Al‐Moujahed, A., Alam, A. A., Albertelli, M. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46070-9 · Cited by 13
Application of Computational Biology to Decode Brain Transcriptomes
Li, J., & Wang, G. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.03.003 · Cited by 11
Dong, K., & Zhang, S. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa120 · Cited by 11
A comparison of alternative mRNA splicing in the CD4 and CD8 T cell lineages
Liu, X., Andrews, M. V., Skinner, J. P. J., Johanson, T. M., & Chong, M. M. W. (2021) · Molecular Immunology · doi:10.1016/j.molimm.2021.02.009 · Cited by 11
Neuronal Cell-type Engineering by Transcriptional Activation
Liu, S., Striebel, J., Pasquini, G., Ng, A. H. M., Khoshakhlagh, P., Church, G. M., & Busskamp, V. (2021) · Frontiers in Genome Editing · doi:10.3389/fgeed.2021.715697 · Cited by 11
SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data
Abugessaisa, I., Hasegawa, A., Noguchi, S., Cardon, M., Watanabe, K., Takahashi, M., Suzuki, H., Katayama, S., Kere, J., & Kasukawa, T. (2022) · iScience · doi:10.1016/j.isci.2022.103777 · Cited by 14
Dirichlet process mixture models for single-cell RNA-seq clustering
Adossa, N. A., Rytkönen, K. T., & Elo, L. L. (2022) · Biology Open · doi:10.1242/bio.059001 · Cited by 11
Bookend: precise transcript reconstruction with end-guided assembly
Schon, M. A., Lutzmayer, S., Hofmann, F., & Nodine, M. D. (2022) · Genome biology · doi:10.1186/s13059-022-02700-3 · Cited by 12
“Notably, large-scale Smart-seq2 experiments across multiple organisms have already been sequenced, including tens of thousands of cells from 20 mouse tissues and 24 human tissues by the Tabula Muris and Tabula Sapiens Consortia, respectively [62, 63].”
BackgroundBetsholtz, C. (2022) · Upsala Journal of Medical Sciences · doi:10.48101/ujms.v127.9051 · Cited by 12
Sneddon, A., Ravindran, A., Shanmuganandam, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48673-8 · Cited by 11
Micheli, A. J. D., Swanson, J. B., Disser, N. P., Martinez, L. M., Walker, N. R., Oliver, D. J., Cosgrove, B. D., & Mendias, C. L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/801266 · Cited by 12
Maynard, A., McCoach, C. E., Rotow, J. K., Harris, L., Haderk, F., Kerr, D. L., Yu, E. A., Schenk, E. L., Tan, W., Zee, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.08.868828 · Cited by 11
McKellar, D. W., Walter, L. D., Song, L. T., Mantri, M., Wang, M. F. Z., Vlaminck, I. D., & Cosgrove, B. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.01.407460 · Cited by 11
Brain and blood single-cell transcriptomics in acute and subacute phases after experimental strokePreprint
García‐Bonilla, L., Shahanoor, Z., Sciortino, R. K., Nazarzoda, O., Racchumi, G., Iadecola, C., & Anrather, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.31.535150 · Cited by 12
FICTURE: Scalable segmentation-free analysis of submicron resolution spatial transcriptomicsPreprint
Si, Y.-C., Lee, C., Hwang, Y., Yun, J. H., Cheng, W., Cho, C.-S., Quirós, M., Nusrat, A., Zhang, W., Jun, G., et al. (2023) · bioRxiv · doi:10.1101/2023.11.04.565621 · Cited by 25
“We simulated data based on ten selected cell types from a scRNA-seq reference dataset 27 , assigning 8 cell types to specific regions and scattering cells from the other 2 cell types throughout the space to mimic infiltrating cells (Fig.”
MethodCirculatory proteins shape microglia state and boost phagocytosisPreprint
Lu, N., Losada, P. M., Hahn, O., Saksena, A., Tapp, E., Chadarevian, J. P., Dong, W., Shi, S. M., Shuken, S. R., Guldner, I. H., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615861 · Cited by 12
Franklin, R., Zhang, B., Frazier, J., Chen, M., Do, B. T., Padayao, S., Wu, K., Heiden, M. G. V., Vakoc, C. R., Roe, J., et al. (2025) · Genes & Development · doi:10.1101/gad.352316.124 · Cited by 8
Hawkins, D. Y., Zuch, D. T., Huth, J., Rodriguez-Sastre, N., McCutcheon, K. R., Glick, A., Lion, A. T., Thomas, C. F., Descoteaux, A. E., Johnson, W. E., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad278 · Cited by 10
TLNRD1 is a CCM complex component and regulates endothelial barrier integrity
Ball, N. J., Ghimire, S., Follain, G., Pajari, A. O., Wurzinger, D., Vaitkevičiūtė, M., Cowell, A. R., Berki, B., Ivaska, J., Paatero, I., et al. (2024) · Journal of Cell Biology · doi:10.1083/jcb.202310030 · Cited by 11
Kupffer cells are essential for platelet-mediated thrombopoietin generation in the liver
Karakas, D., Li, J., Ma, W., Wu, X., Khoury, C. J., Masoud, M., Hoard, B., Liu, Y., Zhu, G., Chen, J., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2517319122 · Cited by 10
McCallinhart, P. E., Strawser, C., Garfinkle, E. A. R., Navarro, J. B., McAllister, C., Vetter, T. A., Lucchesi, P. A., Mardis, E. R., Mezache, L., Veeraraghavan, R., et al. (2025) · Basic Research in Cardiology · doi:10.1007/s00395-025-01144-7 · Cited by 11
“Differentially expressed genes defining each cluster identified using the ‘FindAllMarkers’ function in Seurat, expression of canonical cell type markers, and reference data [2, 35] were used to annotate cell types per cluster.”
Anderson, D. J., Pauler, F. M., McKenna, A., Shendure, J., Hippenmeyer, S., & Horwitz, M. S. (2022) · Cell Systems · doi:10.1016/j.cels.2022.03.006 · Cited by 13
MNK1 and MNK2 Expression in the Human Dorsal Root and Trigeminal Ganglion
Shiers, S. I., Sahn, J. J., & Price, T. J. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.01.039 · Cited by 9
Molecular Heterogeneity of the Brain Endothelium
Alnaqbi, N., Mohammad, M. G., Hamoudi, R., Mabondzo, A. M., & Harati, R. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45040227 · Cited by 12
“Indeed, scRNA-seq data obtained from male and female mouse BECs revealed differences in the BEC transcriptomes based on the gender, indicating that sex influences the transcription and gene expression in BECs [11,27,33].”
BackgroundRegulation of adult stem cell function by ketone bodies
Andersen, O. E., Poulsen, J. V., Farup, J., & de Morrée, A. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1246998 · Cited by 13
He, Z., He, W., Hu, C., Liao, J., Deng, W., Sun, H., Huang, Q., Chen, W., Zhang, L., Liu, M., et al. (2023) · Aging Cell · doi:10.1111/acel.13982 · Cited by 10
Application and prospects of single-cell and spatial omics technologies in woody plants
Liang, S., Li, Y., Chen, Y., Huang, H., Zhou, R., & Ma, T. (2023) · Forestry Research · doi:10.48130/fr-2023-0027 · Cited by 12
Iijima, H., Zhang, F., Ambrosio, F., & Matsui, Y. (2023) · Aging Cell · doi:10.1111/acel.14043 · Cited by 11
dos Santos, O. A. L., Carneiro, R. L., Requião, R. D., Alves, M. R., Domitrovic, T., & Palhano, F. L. (2024) · Scientific Reports · doi:10.1038/s41598-023-50811-z · Cited by 10
Hyperoxia prevents the dynamic neonatal increases in lung mesenchymal cell diversity
Zanini, F., Che, X., Suresh, N. E., Knutsen, C., Klavina, P. A., Xie, Y., Domingo-Gonzalez, R., Liu, M., Kum, A., Jones, R. C., et al. (2024) · Scientific Reports · doi:10.1038/s41598-023-50717-w · Cited by 10
Schmitt, M. J., Dramaretska, Y., Serresi, M., Kertalli, S., Jiang, B., Yin, J., Aguzzi, A., Barozzi, I., & Gargiulo, G. (2024) · Nature Communications · doi:10.1038/s41467-024-45069-6 · Cited by 13
Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data
Wan, H. S., Yuan, M., Fu, Y., & Deng, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae047 · Cited by 11
ScLinear predicts protein abundance at single-cell resolution
Hanhart, D., Gossi, F., Rapsomaniki, M., Julio, M. K., & Chouvardas, P. (2024) · Communications Biology · doi:10.1038/s42003-024-05958-4 · Cited by 12
Brash, J. T., Diez-Pinel, G., Colletto, C., Castellan, R. F. P., Fantin, A., & Ruhrberg, C. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00436-w · Cited by 10
Ljungholm, P. L., Ermund, A., Garsveden, M. M. S., Pettersson, V. L., & Gustafsson, J. K. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-02975-4 · Cited by 11
“Mouse colonic goblet cells primarily express Ptger4 that signals via cAMP, and to a lower extent Ptger1 that signals via Ca 2+ , and Ptger3 that inhibits cAMP production [43].”
BackgroundEndothelin 3/EDNRB signaling induces thermogenic differentiation of white adipose tissue
Wang, C., Tsuji, T., Wu, L.-H., Yang, C., Huang, T. L., Sato, M. M., Shamsi, F., & Tseng, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-51579-0 · Cited by 11
Yu, W., Kastriti, M. E., Ishan, M., Choudhary, S. K., Kramer, N., Rashid, M. M., Do, H. G. T., Wang, Z., Xu, T., Schwabe, R. F., et al. (2024) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2024.1460669 · Cited by 10
EnhancerNet: a predictive model of cell identity dynamics through enhancer selection
Karin, O. (2024) · Development · doi:10.1242/dev.202997 · Cited by 10
Extracellular peroxiredoxin 5 exacerbates atherosclerosis via the TLR4/MyD88 pathway
Kweon, H. Y., Song, E. J., Jeong, S., Lee, S. N., Sonn, S., Seo, S., Jin, J., Kim, S., Kim, T. K., Moon, S. H., et al. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.119052 · Cited by 11
Jiao, W., Yang, S., Li, Y., Li, Y., Liu, S., Shi, J., Wang, G., & Yu, M. (2024) · Translational Cancer Research · doi:10.21037/tcr-24-539 · Cited by 11
HDAC1 fine-tunes Th17 polarization in vivo to restrain tissue damage in fungal infections
Penninger, P., Brezovec, H., Tsymala, I., Teufl, M., Phan-Canh, T., Bitencourt, T. A., Brinkmann, M., Glaser, W., Ellmeier, W., Bonelli, M. M., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114993 · Cited by 10
Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics
Zhang, X., Xie, J., Yang, Z., Yu, C. K. W., Hu, Y., & Qin, J. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.020 · Cited by 13
RGS2 is an innate immune checkpoint for suppressing Gαq-mediated IFNγ generation and lung injury
Joshi, J. C., Joshi, B., Zhang, C., Banerjee, S., Vellingiri, V., Ragunathrao, V. A. B., Anwar, M., Rokade, T. P., Zhang, L., Amin, M. R., et al. (2025) · iScience · doi:10.1016/j.isci.2025.111878 · Cited by 10
“34,35 Using lung lysates (Figures 4B and S5A) and BAL macrophages (Figures 4C and 4D), we confirmed that RGS2 was delivered and expressed in the macrophages.”
Regional and aging-specific cellular architecture of non-human primate brains
Wang, Y.-M., Wang, W., Pan, Y., Zeng, L., Wu, J., Wang, Z., Zhuang, X.-L., Li, M., Cooper, D. N., Wang, S., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01469-x · Cited by 10
Characterizing primary and secondary senescence in vivo
Sogabe, Y., Shibata, H., Kabata, M., Tanaka, A., Mitsunaga, K., Sunadome, K., Nakajima‐Koyama, M., Hirano, M., Nishida, E., Woltjen, K., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00917-y · Cited by 14
“NC, 1,000; up, top 100; down, top 100. b , Gene expression patterns of caMEK1 mCherry and caMKK6 mCherry in the liver were compared with aged mouse liver data (GSE132040) 50 .”
InfluentialCober, N. D., McCourt, E. R., Godoy, R. S., Deng, Y., Schlosser, K., Qamsari, E. S., Azami, J., Salehisiavashani, E., Cook, D. P., Lemay, S.-E. S., et al. (2025) · Cardiovascular Research · doi:10.1093/cvr/cvaf146 · Cited by 12
Holdener, C., & Vlaminck, I. D. (2026) · Communications Biology · doi:10.1038/s42003-026-09898-z · Cited by 12
Multiomics uncovers developing immunological lineages in human
Stephenson, E., Webb, S., & Haniffa, M. A. (2021) · European Journal of Immunology · doi:10.1002/eji.202048769 · Cited by 11
Miscell: An efficient self-supervised learning approach for dissecting single-cell transcriptome
Shen, H., Li, Y., Feng, M., Shen, X., Wu, D., Zhang, C., Yang, Y., Yang, M., Hu, J., Liu, J., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103200 · Cited by 10
Mouse genomic and cellular annotations
Long, H., Reeves, R., & Simon, M. M. (2022) · Mammalian Genome · doi:10.1007/s00335-021-09936-7 · Cited by 11
Unbiased integration of single cell transcriptome replicates
Loza, M., Teraguchi, S., Standley, D. M., & Díez, D. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqac022 · Cited by 13
Single-Cell RNA Sequencing Reveals Novel Genes Regulated by Hypoxia in the Lung Vasculature
Thomas, S., Manivannan, S., Garg, V., & Lilly, B. (2022) · Journal of Vascular Research · doi:10.1159/000522340 · Cited by 12
Modeling Human Heart Development and Congenital Defects Using Organoids: How Close Are We?
Jiang, S., Feng, W. H., Chang, C. A., & Li, G. (2022) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd9050125 · Cited by 10
“In contrast, the AVC CMs express Rspo3, Tbx3, and Bmp2, and the OFT CMs express Rspo3 and Cxcl12 [32,44] (Figure 1A).”
BackgroundBanerjee, P., Olmsted‐Davis, E. A., Deswal, A. M., Nguyen, M. T. H., Koutroumpakis, E., Palaskas, N., Lin, S. H., Kotla, S., Reyes‐Gibby, C. C., Yeung, S. J., et al. (2022) · The Journal of Cardiovascular Aging · doi:10.20517/jca.2022.13 · Cited by 10
Wan, H. S., Chen, L., & Deng, M. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.12.008 · Cited by 10
“The annotation results of scmap (A), ItClust (B), scArches (C), and s Consortium dataset [3] profiled mouse cells using Smart-seq2 (SS2) and 10x Genomics to conduct two sets of cross-tissue experiments.”
BackgroundMethodYan, X., Zheng, R., Wu, F., & Li, M. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad099 · Cited by 10
“…which has enabled an unprecedented resolution of cellular heterogeneity in complex tissues and has profoundly changed our understandings of cell-to-cell heterogeneity in various biological areas (Cao and Gao, 2022; Heath et al. , 2016; Lawson et al. , 2018; Tabula Muris Consortium et al. , 2018).”
Rheinheimer, B. A., Pasquale, M. C., Core, N. G. A. C. B., Hoffman, M. P., Hoffman, M. P., & Chibly, A. M. (2023) · iScience · doi:10.1016/j.isci.2023.106660 · Cited by 13
Fan, Y., Wang, Y., Wang, F., Huang, L., Yang, Y., Wong, K., & Li, X. (2023) · Advanced Science · doi:10.1002/advs.202205442 · Cited by 9
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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