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
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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.
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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
“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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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
plainstates an association- 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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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
hedgedstates an association- 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
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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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- 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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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
plainstates an association- Asserted: passes — The authors put it forward as true.
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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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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
hedgedstates an association- 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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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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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
plainstates an association- 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
“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
hedgedstates an association- 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
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
hedgedstates an association- 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
plainstates an association- 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
“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
hedgedstates a possible cause- 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
“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
plainstates an association- 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
“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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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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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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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
plainstates an association- 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
plainstates an association- 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
“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
hedgedstates an association- 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
plainstates a cause- 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
hedgedstates an association- 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
hedgedstates a possible cause- 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
plainstates a cause- 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
plainstates a cause- 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.
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
boostedstates a causeSingle 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
hedgedstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
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Zhou, A., Jeansson, M., He, L., Wigge, L., Tonelius, P., Tati, R., Cederblad, L., Muhl, L., Uhrbom, M., Liu, J., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25084320 · Cited by 8
Graph attention autoencoder model with dual decoder for clustering single-cell RNA sequencing data
Wang, S., Zhang, Y., Zhang, Y., Zhang, Y., Pang, S., Su, J., & Liu, Y. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05442-w · Cited by 8
Basu, R., Kulkarni, P., Swegan, D., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25137438 · Cited by 8
“Cell type-specific expression varies from 11% (of ductal cells) to 92% (of beta cells) in the mouse pancreas ( Tabula muris [22], Figure S1).”
BackgroundRenigunta, V., Xhaferri, N., Shaikh, I. G., Schlegel, J. G., Bisen, R., Sanvido, I., Kalpachidou, T., Kummer, K. K., Oliver, D., Leitner, M. G., et al. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05312-1 · Cited by 9
Furtado, J., Geraldo, L. H., Leser, F. S., Bartkowiak, B., Poulet, M., Park, H., Robinson, M. E., Pibouin-Fragner, L., Eichmann, A., & Boyé, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2408674121 · Cited by 8
“ScRNA - seq showed that only a few retinal cell types expressed low levels of Ntn1 mRNA, suggesting that Netrin - 1 may come from the circulation, from the heart, lungs, trachea, or adipose tissue, all of which have higher Ntn1 mRNA levels as compared to the retina ( 56 ).”
Li, F., Hu, D., Dieter, C., Ansong, C. K., Sussel, L., & Orr, G. (2021) · Diabetes · doi:10.2337/db20-0802 · Cited by 8
Translational insights from single-cell technologies across the cardiovascular disease continuum
van Blokland, I. V., Groot, H. E., Franke, L., van der Wijst, M. G. P., & van der Harst, P. (2021) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2021.02.009 · Cited by 8
Rapti, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.787753 · Cited by 8
A downsampling method enables robust clustering and integration of single-cell transcriptome data
Ren, J., Zhang, Q., Zhou, Y., Hu, Y., Lyu, X., Fang, H., Yang, J., Yu, R., Shi, X., & Li, Q. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104093 · Cited by 11
Single-cell views of the Plasmodium life cycle
Real, E., & Mâncio-Silva, L. (2022) · Trends in Parasitology · doi:10.1016/j.pt.2022.05.009 · Cited by 8
Vandenbon, A., & Díez, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-38965-2 · Cited by 9
Gozes, I., Ivashko‐Pachima, Y., Kapitansky, O., Sayas, C. L., & Iram, T. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.05.014 · Cited by 8
Contreras, O. (2019) · Journal of Cell Communication and Signaling · doi:10.1007/s12079-019-00545-3 · Cited by 8
“…that these mesenchymal progenitors can be a double-edge knife depending on the type and extension of the injury (Contreras et al. 2016; Fiore et al. 2016; Gonzalez et al. 2017; Joe et al. 2010; Madaro et al. 2018; Lemos et al. 2015; Natarajan et al. 2010; Uezumi et al. 2010; Uezumi et al. 2011).”
Backgroundβ-Cell-specific ablation of sirtuin 4 does not affect nutrient-stimulated insulin secretion in mice
Huynh, F. K., Peterson, B. S., Anderson, K. A., Lin, Z., Coakley, A. J., Llaguno, F. M. S., Nguyen, T.-T. N., Campbell, J. E., Stephens, S. B., Newgard, C. B., et al. (2020) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00170.2020 · Cited by 9
The Na + /K + ATPase Regulates Glycolysis and Modifies Immune Metabolism in TumorsPreprint
Sanderson, S. M., Xiao, Z., Wisdom, A. J., Bose, S., Liberti, M. V., Reid, M. A., Hocke, E., Gregory, S. G., Kirsch, D. G., & Locasale, J. W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018739 · Cited by 8
Shen, H., Shen, X., Hu, J., Liu, J., Zhang, C., Wu, D., Feng, M., Yang, M., Li, Y., Yang, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.31.478596 · Cited by 8
A tissue centric atlas of cell type transcriptome enrichment signaturesPreprint
Dusart, P., Öling, S., Struck, E. C., Norreen-Thorsen, M., Zwahlen, M., von Feilitzen, K., Oksvold, P., Bosić, M., Iglesias, M., Renné, T., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.10.520698 · Cited by 7
Novel Populations of Lung Capillary Endothelial Cells and Their Functional SignificancePreprint
James, J., Dekan, A., Niihori, M., McClain, N., Varghese, M. V., Bharti, D., Lawal, O., Padilla‐Rodriguez, M., Yi, D., Dai, Z., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2887159/v1 · Cited by 8
Arora, S., Yang, J., Akiyama, T., James, D. Q., Morrissey, A., Blanda, T. R., Badjatia, N., Lai, W., Ko, M. S. H., Pugh, B. F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.06.561228 · Cited by 8
“Gene set enrichment analysis of tissue-specific terms was performed using EnrichR [73], focusing on gene sets provided by Tabula Muris [74] and the Mouse Gene Atlas [75].”
Xu, Y., Zhang, W., Zheng, X., & Cai, X. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00587-7 · Cited by 7
Thuilliez, C., Moquin‐Beaudry, G., Khneisser, P., Costa, M. E. M. D., Karkar, S., Boudhouche, H., Drubay, D., Audinot, B., Geoerger, B., Scoazec, J., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae081 · Cited by 9
A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data
Madrigal, A., Lu, T., Soto, L. M., & Najafabadi, H. S. (2024) · Nature Communications · doi:10.1038/s41467-024-50963-0 · Cited by 8
CFTR represses a PDX1 axis to govern pancreatic ductal cell fate
Rotti, P. G., Yi, Y., Gasser, G. N., Yuan, F., Sun, X., Evans, I. A., Wu, P., Liu, G., Choi, S. H., Reeves, R. E., et al. (2024) · iScience · doi:10.1016/j.isci.2024.111393 · Cited by 7
Liao, P., Tong, S., Du, L., Mei, J., Wang, B., Lu, Y., Yao, M., Zhang, C., Liu, D., Zhong, Z., et al. (2025) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2024.09.013 · Cited by 8
“The utilization of scRNA-Seq in the bone marrow-blood system has revealed key hallmarks of aging in various tissues and cell types [15], as well as the crucial role of immune cell activation in the aging process [16].”
Liu, J., Fan, X., Gu, C., Yang, Y., Wu, B., Chen, G., Hsieh, C., & Heng, P. (2025) · Advanced Science · doi:10.1002/advs.202412095 · Cited by 7
Somatic gene delivery faithfully recapitulates a molecular spectrum of high-risk sarcomas
Imle, R., Blösel, D., Kommoss, F. K. F., Placke, S., Stutheit-Zhao, E., Blume, C., Lupar, D., Schmitt, L., Winter, C., Wagner, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60519-5 · Cited by 9
Shao, J.-S., Lai, A. C., Huang, W., Wu, K.-C., Chi, P., Chang, Y., & Chang, Y. (2025) · Journal of Clinical Investigation · doi:10.1172/jci187372 · Cited by 9
Natural Killer Cell‐Mediated Antitumor Immunity: Molecular Mechanisms and Clinical Applications
Luo, N., Chen, C., Zhou, W., Hao, J., He, S., Liu, Y., Ku, Y., Huang, L., Zhang, C., Shu, Y., et al. (2025) · MedComm · doi:10.1002/mco2.70387 · Cited by 12
The stroke risk gene Foxf2 maintains brain endothelial cell function via Tie2 signaling
Völgyi, K., González-Gallego, J., Müller, S. A., Todorov, M. I., Şeker, F. B., Frerich, S., Cernilogar, F. M., Schröger, L., Malik, R., Cao, J., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02136-5 · Cited by 8
Cross-species cellular mapping and humanization of Fcγ receptors to advance antibody modeling
Damme, K. F. A. V., Sichien, D., der Borght, K. V., Moorleghem, J. V., Gassen, S. V., Jorssen, J., Winter, S. D., Leeuw, E. D., Wang, C., Chai, Q., et al. (2026) · Science Immunology · doi:10.1126/sciimmunol.ady7328 · Cited by 10
Bai, H., Lin, M., Meng, Y., Bai, H., & Cai, S. (2022) · Cellular Signalling · doi:10.1016/j.cellsig.2022.110361 · Cited by 8
Kopacz, A., Klóska, D., Fichna, J., et al. (2022) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2022.09.014 · Cited by 7
COMMD4 is a novel prognostic biomarker and relates to potential drug resistance mechanism in glioma
Liu, Z., Peng, L., Sun, Y., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.974107 · Cited by 7
“(Gordon and Galli, 1990; Falduto et al., 2022; Fereydouni et al., 2022) When exposed to FcϵRI, Human MCs produce substantial levels of granulocyte-macrophage colony-stimulating factor (GMCSF), according to Fereydouni et al. This is significant because both GM-CSF and TNF-α have been shown to…”
BackgroundJacob, J. B., Wei, K.-C., Bepler, G., Reyes, J. D., Cani, A. K., Polin, L., White, K., Kim, S., Viola, N. T., McGrath, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106320 · Cited by 9
Systems biology approaches to unravel lymphocyte subsets and function
Kim, Y., Greenleaf, W. J., & Bendall, S. C. (2023) · Current Opinion in Immunology · doi:10.1016/j.coi.2023.102323 · Cited by 7
“The recent publication of Tabula Sapiens [10] with nearly 500 000 cells from 24 tissues and organs, and similarly Tabula Muris a few years earlier [11], highlights the widespread application of scRNA-seq in different tissues as well as the abundance of public resources that can be utilized in a…”
Single-cell and spatial transcriptomics in endocrine research
Matsumoto, R., & Yamamoto, T. (2024) · Endocrine Journal · doi:10.1507/endocrj.ej23-0457 · Cited by 7
“This technique enables researchers to analyze the gene expression of each cell in its native environment with positional infor‐ mation, providing insights into the interactions between different cell types [51].”
Lin, Q., Yu, W.-J., Bai, J., Jiang, W., & Li, H. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167048 · Cited by 7
Single-cell classification, analysis, and its application using deep learning techniques
Premkumar, R., Srinivasan, A., Devi, K., Deepika, M., Gaayathry, E., Jadhav, P., Futane, A., & Narayanamurthy, V. (2024) · Biosystems · doi:10.1016/j.biosystems.2024.105142 · Cited by 8
Quantifying the clusterness and trajectoriness of single-cell RNA-seq data
Lim, H. S., & Qiu, P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011866 · Cited by 6
Xu, H., Ye, Y., Duan, R., Gao, Y., Hu, Y., & Gao, L. (2024) · Advanced Science · doi:10.1002/advs.202306770 · Cited by 6
Zhang, T., Ren, J., Li, L., Wu, Z., Zhang, Z., Dong, G., & Wang, G. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115976 · Cited by 8
Characterizing efficient feature selection for single-cell expression analysis
Cho, J., Baik, B., Hai, N. C. T., Park, D., & Nam, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae317 · Cited by 8
“ARI results for (C) sctransform and (D) log-normalization. endothelial cells by analyzing scRNA-seq data from the Tabula Muris consortium [ 20].”
MethodEng, J. W. ‐., Kato, Y., Adachi, Y., Swaminathan, B., Naiche, L. A., Vadakath, R., Sakamoto, Y., Nakazawa, Y., Tachino, S., Ito, K., et al. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-24-0081 · Cited by 8
Presidential Address Kidney Week 2019
Rosenberg, M. E. (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.15011219 · Cited by 7
Single-cell RNA Sequencing: In-depth Decoding of Heart Biology and Cardiovascular Diseases
Chen, Z., Liang, W., Duru, F., & Chen, L. (2020) · Current Genomics · doi:10.2174/1389202921999200604123914 · Cited by 8
Theis, A., Singer, R. A., Garofalo, D. C., Paul, A., Narayana, A., & Sussel, L. (2021) · Development · doi:10.1242/dev.192401 · Cited by 7
Decoding Cell Death: From a Veritable Library of Babel toVade Mecum?
Hughes, L. D., Wang, Y., Meli, A. P., Rothlin, C. V., & Ghosh, S. (2021) · Annual Review of Immunology · doi:10.1146/annurev-immunol-102819-072601 · Cited by 8
Mircea, M., & Semrau, S. (2021) · Biochemical Society Transactions · doi:10.1042/bst20210135 · Cited by 9
Concordant and Heterogeneity of Single-Cell Transcriptome in Cardiac Development of Human and Mouse
Shang, M., Hu, Y., Cao, H., Lin, Q., Yi, N., Zhang, J., Gu, Y., Yang, Y., He, S., Lu, M., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.892766 · Cited by 7
Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation
Tercan, B., Aguilar, B. H., Huang, S., Dougherty, E. R., & Shmulevich, I. (2022) · iScience · doi:10.1016/j.isci.2022.104951 · Cited by 7
A two-stage digestion of whole murine knee joints for single-cell RNA sequencing
Leale, D. M., Li, L., Settles, M. L., Mitchell, K. A. R., Froenicke, L., Yik, J. H. N., & Haudenschild, D. R. (2022) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2022.100321 · Cited by 7
Mondal, A. K., Joshi, I., Singh, P. P., & Prathosh, A. P. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3227202 · Cited by 7
“Quake Smart seq2 Trachea [52]: This dataset contains cell samples obtained from trachea of a mouse.”
BackgroundMethodscENT for Revealing Gene Clusters From Single-Cell RNA-Seq Data
Rao, F., Chen, M., Yang, D., Morrell, B., Song, Q., & Zhu, W. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3242260 · Cited by 7
“Thecorrespondingdataanal-ysiscancontributetolearningthemechanismofwhyparticular cellsarevulnerableinAD[7],[8],[9],[10],[11],[12].”
Distinct Requirements for Adaptor Proteins NCK1 and NCK2 in Mammary Gland Development
Golding, A. P., Ferrier, B., New, L. A., et al. (2023) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-023-09541-1 · Cited by 7
“Analysis of single cell transcriptome data from 8-week old mouse mammary gland tissue in the Tabula Muris compendium [17] revealed widespread expression of Nck1 in luminal, basal, stromal and endothelial cells, while Nck2 transcripts were restricted to luminal and basal cells (Fig.”
BackgroundMethodThe phosphodiesterase 2A controls lymphatic junctional maturation via cGMP-dependent notch signaling
Carlantoni, C., Liekfeld, L. M. H., Hemkemeyer, S. A., Schreier, D., Saygı, C., Kurelić, R., Cardarelli, S., Kalucka, J. M., Schulte, C., Beerens, M., et al. (2023) · Developmental Cell · doi:10.1016/j.devcel.2023.12.002 · Cited by 8
Global IL4Rα blockade exacerbates heart failure after an ischemic event in mice and humans
Alvarez‐Argote, S., Almeida, V. A., Knas, M. C., Buday, S. L., Patterson, M., & O'Meara, C. C. (2024) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00010.2024 · Cited by 7
Evolution of glial cells: a non-bilaterian perspective
Sheloukhova, L., & Watanabe, H. (2024) · Neural Development · doi:10.1186/s13064-024-00184-4 · Cited by 9
The Alveolar Stem Cell Niche of the Mammalian Lung
Hogan, B. L. M. (2020) · Springer eBooks · doi:10.1007/978-981-15-1185-1_2 · Cited by 10
Kalafatakis, I., Kalafatakis, K., Tsimpolis, A., et al. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02108-4 · Cited by 7
Wangzhou, A., Paige, C. A., Ray, P. R., Dussor, G. O., & Price, T. J. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.03.017 · Cited by 8
Hu, L., & Wu, C. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-00977-0 · Cited by 7
Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space
Riva, S. G., Cazzaniga, P., & Tangherloni, A. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669807 · Cited by 7
Li, F., Du, Y., Hong, L., et al. (2022) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.17354 · Cited by 7
AAE-SC: A scRNA-Seq Clustering Framework Based on Adversarial Autoencoder
Wu, Y., Guo, Y., Xiao, Y., & Lao, S. (2020) · IEEE Access · doi:10.1109/access.2020.3027481 · Cited by 7
“In this experiment, three representative unbalanced single-cell datasets extracted from three mouse tissues in the Tabula Muris project [50] have been selected and some rare data clusters exist in these datasets.”
MethodEditorial: Thy1/CD90 Surface Glycoprotein: Sensor of Microenvironment?
Felley‐Bosco, E., & Leyton, L. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00162 · Cited by 7
A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experimentsPreprint
Verma, A., & Engelhardt, B. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.14.906313 · Cited by 6
Mincarelli, L., Uzun, V., Rushworth, S. A., Haerty, W., & Macaulay, I. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.06.027474 · Cited by 6
Functional Immune Deficiency Syndrome via Intestinal Infection in COVID-19Preprint
Prates, E. T., Garvin, M. R., Pavicic, M., Jones, P. C., Shah, M. B., Alvarez, C., Kainer, D., Demerdash, O., Amos, B. K., Geiger, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.06.028712 · Cited by 7
scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seqPreprint
Lin, Y., Wu, T.-Y., Wan, S., Yang, J. Y. H., Wong, W. H., & Wang, Y. X. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.31.424916 · Cited by 7
Single-cell transcriptomics for the 99.9% of species without reference genomesPreprint
Botvinnik, O. B., Vemuri, V. N. P., Pierce‐Ward, N. T., Logan, P., Nafees, S., Karanam, L. S., Travaglini, K. J., Ezran, C., Ren, L., Juang, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.09.450799 · Cited by 7
Sturm, G., Monzel, A. S., Karan, K. R., Michelson, J., Ware, S. A., Cárdenas, A., Lin, J., Bris, C., Santhanam, B., Murphy, M. P., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.12.468448 · Cited by 7
Comprehensive annotation of 3′UTRs from primary cells and their quantification from scRNA-seq dataPreprint
Fansler, M. M., Mitschka, S., & Mayr, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.22.469635 · Cited by 7
Dissecting the contributions of tumor heterogeneity on metastasis at single-cell resolutionPreprint
Winkler, J., Tan, W., Diadhiou, C. M. M., McGinnis, C. S., Abbasi, A. J., Hasnain, S., Durney, S., Atamaniuc, E., Superville, D. A., Awni, L., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.04.502697 · Cited by 7
BASOPHILS ACTIVATE PRURICEPTOR-LIKE VAGAL SENSORY NEURONSPreprint
Wang, J.-C., Nikpoor, A. R., Crosson, T., Kaufmann, E., Rafei, M., & Talbot, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.11.598517 · Cited by 6
Small-cohort GWAS discovery with AI over massive functional genomics knowledge graphPreprint
Huang, K., Zeng, T., Koc, S., Pettet, A., Zhou, J., Jain, M. S., Sun, D., Ruiz, C., Ren, H., Howe, L. J., et al. (2024) · medRxiv · doi:10.1101/2024.12.03.24318375 · Cited by 9
A Micropillar Array Based Microfluidic Device for Rare Cell Detection and Single-Cell Proteomics
Chen, K., & Wang, Z. (2023) · Methods and Protocols · doi:10.3390/mps6050080 · Cited by 6
Drougkas, K., Smerla, R., Skarlis, C., & Mavragani, C. P. (2023) · Journal of Molecular Pathology · doi:10.3390/jmp4040024 · Cited by 7
Huang, Y., Liu, Z., Li, M., Wang, D., Ye, J., Hu, Q., Zhang, Q., Lin, Y., Chen, R., Liang, X., et al. (2024) · Immunity & Ageing · doi:10.1186/s12979-024-00452-1 · Cited by 8
“…investigate their relationship with corresponding immune cells and the age-related changes in their cellular and molecular characteristics, we integrated splenic immune cell data from the Tabula Muris datasets [28, 31] (B cells, Mast cells, Macrophages) and splenic DCs from a prior study [32].”
ResultPriam, P., Krasteva, V., Rousseau, P., Polsinelli, A., Côté, L., Desanlis, I., Farah, A., Lavallée, V., Kmita, M., & Lessard, J. A. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.08.007 · Cited by 6
sigRGCN: A Robust Residual Graph Convolutional Network for scRNA-Seq Data Clustering
Shu, Z., Xia, M., Tan, K., Yu, Z., & Wu, X. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2024.3515053 · Cited by 7
Bjerre, F. A., Nielsen, J. V., Burton, M., Dhumale, P., Jørgensen, M. G., Hansen, S. T., Lund, L. C., Thomassen, M., Sørensen, J. A., Andersen, D. C., et al. (2025) · Stem Cell Research & Therapy · doi:10.1186/s13287-025-04234-4 · Cited by 8
“GO-terms were also extracted from Enrichr [20] as was cell identity predictions from the CellMarker2024-, Tabula Sapiens [18]- and Tabula Muris databases [19] (Extended Data Table 4).”
Strategies for studying sex differences in brain aging
Ansere, V. A., Kim, S., Marino, F., Morillo, K. S., Dubal, D. B., Murphy, C. T., Suh, Y., & Benayoun, B. A. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.02.001 · Cited by 7
Wells, C., Sorgenfrei, J., Johnson, S. L., Albertson, D., Rutter, J. P., & Baker, S. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319350 · Cited by 6
“Instead, a related mitochondrial kinase (Ubiquitous creatine kinase mitochondrial, UbCKmit, encoded by Ckmt1 in mice), is exclusively expressed at high levels by neurons [26].”
Redhead, C., Taye, N., Chin-Young, B., Oguntuyo, K., Cummins, J. H., Hart, K. J., Han, W. M., & Hubmacher, D. (2025) · iScience · doi:10.1016/j.isci.2025.112712 · Cited by 6
CD2AP at the junction of nephropathy and Alzheimer’s disease
Vandal, M., Janmaleki, M., Rea, I. M., Gunn, C., Hirai, S., Biernaskie, J. A., Chun, J., Gordon, G. R., Shaw, A. S., Sanati‐Nezhad, A., et al. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00852-x · Cited by 9
CD38 ‐Targeting Peptide Vaccine Ameliorates Aging‐Associated Phenotypes in Mice
Yu, S., Zhi-qiang, L., Tang, Y., Chen, Y., Ma, Y., Du, K., Zong, Z., Feng, K., Wei, Y., Chen, L., et al. (2025) · Aging Cell · doi:10.1111/acel.70147 · Cited by 8
“With aging, CD38 is highly expressed in the spleen, mesenteric fat, liver, etc. (Schaum et al. 2018; Wang et al. 2021; Schaum et al. 2020).”
Kim, H., Park, I., Park, J.-E., Kim, J. K., Seo, M., & Kim, J. K. (2025) · Nature Communications · doi:10.1038/s41467-025-60702-8 · Cited by 9
FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data
Bakhtiari, M., Bonn, S., Theis, F. J., Zolotareva, O. I., & Baumbach, J. (2025) · Genome biology · doi:10.1186/s13059-025-03684-6 · Cited by 7
Zhang, P., Song, C., Shi, J., Wei, Z., Wang, J., Huang, W., Zhang, R., Wang, J., Yang, X., Wang, G., et al. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00875-4 · Cited by 7
“2j) and the BMECs-spe-cific expression pattern of ENG was validated by single-cell RNA-seq database ( Muris. et al. [33]), qPCR, and Western Blotting (Supplementary Fig.”
Wang, C., Huang, M., Han, J., Sun, M., Li, H., Zhang, Y., Liu, H., & Han, L. G. (2025) · BMEMat · doi:10.1002/bmm2.70035 · Cited by 6
The curious case of anti-PEG antibodies
Denman, D. S., & Dalhaimer, P. (2025) · Nanoscale · doi:10.1039/d5nr02301g · Cited by 8
Xia, Y., Zheng, M., Li, X., Song, K., & Teng, Z. (2025) · iScience · doi:10.1016/j.isci.2025.113526 · Cited by 6
Impact of Endothelial Diversity and Dysfunction on Cardiovascular Disease
Williams, A. E., Dunaway, L. S., Juśkiewicz, Z. J., Schug, W. J., Cortese-Krott, M. M., Koval, M. H., & Isakson, B. E. (2025) · Comprehensive physiology · doi:10.1002/cph4.70064 · Cited by 9
Beyond transcription: RNA-binding proteins steering angiogenesis
Hamm, K., Clifford, E. W., Kozak, K., Cullison, J., Iqbal, H., Yan, R., Xing, Y., & Liu, Z. (2025) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00465.2025 · Cited by 5
Zarodniuk, M., Wenninger, A., Najera, J., Lee, J., Markillie, J., MacKenzie, C., Bergqvist-Patzke, J., Batista, B., Panchbhavi, M. A., Rumbach, R., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2513172122 · Cited by 5
“To further define the glial cell types contributing to this response, we performed cell type deconvolution using the Tabula Muris single - cell RNA - seq atlas (31 ).”
Zhang, P., & Liu, B. (2021) · Disease Markers · doi:10.1155/2021/8116633 · Cited by 6
“Single-cell RNA-seq data of arterial and cardiac tissues were downloaded for analysing target gene expression at the single-cell level [38, 39].”
MethodPetersen, J., Englmaier, L., Artemov, A. V., Poverennaya, I. V., Mahmoud, R., Bouderlique, T., Tesarova, M., Deviatiiarov, R. M., Szilvásy‐Szabó, A., Akkuratov, E. E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38663-7 · Cited by 6
An information-theoretic approach to single cell sequencing analysis
Casey, M. J., Fliege, J., Sánchez-García, R. J., & MacArthur, B. D. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05424-8 · Cited by 6
Glucose metabolism partially regulates β‐cell function through epigenomic changes
Kim, Y. K., Won, K., & Sussel, L. (2024) · Journal of Diabetes Investigation · doi:10.1111/jdi.14173 · Cited by 7
“LSD1 is a component of transcriptional complexes within the nucleus that regulates nutrient responses in various cell types, including hepatocytes, adipocytes and pancreatic b -cells 27 .”
BackgroundSelvarajan, I., Kiema, M., Huang, R., Li, J., Zhu, J., Pölönen, P., Örd, T., Õunap, K., Godiwala, M., Golebiewski, A. K., et al. (2024) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.123.318964 · Cited by 6
“Our analysis suggests that compared with haplotype 1, only haplotype 2 showed allele-specific enhancer activity, while hap3 showed no effect (Figure 2E), pinpointing rs880890 as A , Single-cell RNA-seq data from Tabula Muris 52 representing data across 20 organs and tissues from mice show that CALCRL (calcitonin recep…”
Influential
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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