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.
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- 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 2,101–2,200 of 2,952
EVALUATING THE COMPLEXITY OF GENE COEXPRESSION ESTIMATION FOR SINGLE-CELL DATA
Zhang, J., & Singh, R. (2023) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2023047230 · Cited by 2
Swamy, V. S., Batz, Z. A., & McGaughey, D. M. (2023) · Translational Vision Science & Technology · doi:10.1167/tvst.12.9.18 · Cited by 2
“44 As before, the scVI integration parameters of the pipeline were chosen rigorously by testing integration performance across a wide range of latent dimensions and number of highly variable genes in our scPOP framework 23 (Supplementary Fig.”
Omic horizon expression: a database of gene expression based on RNA sequencing data
Hu, Y., Xie, D., Li, X., Han, W., Chen, Y., Qi, H., & Wang, P. (2023) · BMC Genomics · doi:10.1186/s12864-023-09781-9 · Cited by 2
Introducing synthetic thermostable RNase inhibitors to single-cell RNA-seq
Noble, J. C., Lentini, A., Hagemann-Jensen, M., Sandberg, R., & Reinius, B. (2024) · Nature Communications · doi:10.1038/s41467-024-52717-4 · Cited by 2
“6 ), in line with liver and spleen lymphocyte/monocyte populations identified in previous studies 7,8 , demonstrating successful transcriptome capture of small cells from RNase-rich tissue.”
BackgroundKrolewski, R. C., Lin, B., Stampfer, S. D., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58779-w · Cited by 2
Opportunities for Single‐Cell Sequencing in Synthetic Biology
Wollny, D. (2020) · ChemSystemsChem · doi:10.1002/syst.202000016 · Cited by 2
Single-cell transcriptomics of murine mural cells reveals cellular heterogeneity
Guan, Y., Li, Y., Roosan, M. R., & Jing, Q. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1823-2 · Cited by 3
Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks
ZHENG, B., Lyu, M., Lin, S., & Brusić, V. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313427 · Cited by 2
“The Tabula Muris initiative [15] provided these data sets, but for our study we selected tissues and organs that had results from at least two independent samples.”
Methodvan de Grift, Y. B. C., Heijmans, N., & van Amerongen, R. (2020) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-020-09474-z · Cited by 2
STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mousePreprint
Gómez-Cabrero, D., Tarazona, S., Ferreirós-Vidal, I., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/587477 · Cited by 2
Commentary: U-CIRP-ing the neurological effects of deep hypothermic circulatory arrest
Sorabella, R. A., & Si, M. (2019) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2019.08.076 · Cited by 2
Molecular and Morphological Signatures of Chordate Development: Two Distinct Pathways, One TunicatePreprint
Kowarsky, M., Anselmi, C., Hotta, K., Burighel, P., Zaniolo, G., Caicci, F., Rosental, B., Neff, N. F., Ishizuka, K. J., Palmeri, K. J., et al. (2019) · SSRN Electronic Journal · doi:10.1101/801589 · Cited by 3
“…and cellular timelines of the stem and progenitor cells involved in embryonic development and adult stem cell maintenance to be produced for chordates (Briggs et al. 2018, Cao et al., 2019, Chan et al., 2018, Farrell et al., 2018, Nowotschin et al., 2019, Schaum et al., 2018, Wagner et al., 2018).”
BackgroundEnglund, J. I., Cojoc, H., Blaas, L., Ritchie, A., Pentinmikko, N., Döhla, J., Munne, P., Patarroyo, M. E., Klefström, J. T., Ivaska, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.27.889451 · Cited by 2
UNCURL-App: Interactive database-driven analysis of scRNA-Seq dataPreprint
Zhang, Y., Mao, S., Mukherjee, S., Kannan, S., & Seelig, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.043737 · Cited by 2
Contreras, O., Soliman, H., Théret, M., Rossi, F. M. V., & Brandan, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.05.895334 · Cited by 3
Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysisPreprint
Raimundo, F., Vallot, C., & Vert, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.27.064816 · Cited by 5
Gene expression variability in human and chimpanzee populations share common determinantsPreprint
Fair, B. J., Blake, L. E., Sarkar, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.11.146142 · Cited by 2
Scalzo, R. L., Foright, R. M., Hull, S., Knaub, L. A., Johnson-Murguia, S., Kinanee, F., Kaplan, J., Freije, B., Verzosa, G., Houck, J. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.21.259440 · Cited by 3
Coexpression reveals conserved mechanisms of transcriptional cell identityPreprint
Crow, M., Suresh, H., Lee, J., & Gillis, J. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.10.375758 · Cited by 2
Predicting the impact of sequence motifs on gene regulation using single-cell dataPreprint
Hepkema, J., Lee, N. K., Stewart, B. J., Ruangroengkulrith, S., Charoensawan, V., Clatworthy, M. R., & Hemberg, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.26.400218 · Cited by 2
Simultaneous Identification of Brain Cell Type and Lineage via Single Cell RNA SequencingPreprint
Anderson, D. J., Pauler, F. M., McKenna, A., Shendure, J., Hippenmeyer, S., & Horwitz, M. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.31.425016 · Cited by 2
Xiong, L., Tian, K., Li, Y., & Zhang, Q. C. (2021) · Research Square · doi:10.21203/rs.3.rs-398163/v1 · Cited by 2
Analysis of single-cell RNA sequencing data based on autoencodersPreprint
Tangherloni, A., Ricciuti, F., Besozzi, D., Lió, P., & Cvejic, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/727867 · Cited by 2
Recovering Single-cell Heterogeneity Through Information-based Dimensionality ReductionPreprint
DeMeo, B., & Berger, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.19.427303 · Cited by 2
Mehrabad, E. M., Bhaskara, A., & Spike, B. T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441691 · Cited by 2
“The approach will likely also have useful application to many of the emerging data sets and compendia that have been produced with the scRNA-Seq technology (e.g. Regev et al. , 2017, Consortium et al. , 2018).”
MethodUnbiased integration of single cell transcriptome replicatesPreprint
Loza, M., Teraguchi, S., Standley, D. M., & Díez, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.05.442380 · Cited by 2
One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq DataPreprint
Wang, C. X., Zhang, L., & Wang, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.12.443814 · Cited by 2
Spatial Transcriptional Mapping of the Human Nephrogenic ProgramPreprint
Lindström, N. O., Sealfon, R. S. G., Chen, X., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3749651 · Cited by 2
Manivannan, S., Mansfield, C., Zhang, X., Kodigepalli, K. M., Majumdar, U., Garg, V., & Basu, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.28.446177 · Cited by 2
Benchmarking UMI-based single cell RNA-sequencing preprocessing workflowsPreprint
You, Y., Tian, L., Su, S., Dong, X., Jabbari, J. S., Hickey, P. F., & Ritchie, M. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.17.448895 · Cited by 2
“The final dataset profiled mouse lung tissue from the Tabula Muris study, available under GEO under accession number GSE109774 (37).”
MethodInfluentialMa, S., Zhang, Y., Wang, B., Hu, Z., Zhang, J., & Wang, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.08.467820 · Cited by 2
“Dataset ncells ngenes ncell types (>10 cells) Reference Xin 1,449 33,889 4(4) [35] Baron Human 8,569 17,499 14(13) [36] TM 54,865 19,791 55(55) [37] AMB92 12,832 42,625 110(92) [38] Zheng 68K 68,579 20,387 11(11) [39]”
BackgroundMultiview Graph Learning for single-cell RNA sequencing dataPreprint
Karaaslanli, A., Saha, S., Aviyente, S., & Maiti, T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.05.467476 · Cited by 2
Unsupervised cell functional annotation for single-cell RNA-SeqPreprint
Li, D., Ding, J., & Bar‐Joseph, Z. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.20.469410 · Cited by 2
An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptomePreprint
Liu, S., Ezran, C., Wang, M. F. Z., Li, Z., Awayan, K., Long, J. Z., Vlaminck, I. D., Wang, S., Epelbaum, J., Kuo, C. S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.13.472243 · Cited by 2
Li, J., Zaslavsky, M., Su, Y., Sikora, M. J., van Unen, V., Christophersen, A. O., Chiou, S., Chen, L., Li, J., Ji, X., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.23.473930 · Cited by 2
Zhang, J., Zuo, E., Song, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.21.473566 · Cited by 2
Precise Transcript Reconstruction with End-Guided AssemblyPreprint
Schon, M. A., Lutzmayer, S., Hofmann, F., & Nodine, M. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.12.476004 · Cited by 2
Projecting clumped transcriptomes onto single cell atlases to achieve single cell resolutionPreprint
Johansen, N., & Quon, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.26.489628 · Cited by 2
Rheinheimer, B. A., Pasquale, M. C., Core, N. G. A. C. B., Limesand, K. H., Hoffman, M. P., & Chibly, A. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.26.517939 · Cited by 2
Fan, Z., Ardicoglu, R., Batavia, A. A., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2361644/v1 · Cited by 2
LY6E protects mice from pathogenic effects of murine coronavirus and SARS-CoV-2Preprint
Mar, K. B., Dyke, M. C. C. V., Lopez, A. H., Eitson, J. L., Fan, W., Hanners, N. W., Evers, B. M., Shelton, J. M., & Schoggins, J. W. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.25.525551 · Cited by 2
de, M. M., Dey, S., Bocxlaer, K. V., Ashwin, H. M., Brown, N., Myburgh, E., Dey, N. S., Rani, G. F., Muscutt, E., Osman, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.03.526940 · Cited by 2
Comprehensive analysis methods for developmental GC exposed zebrafish v1Preprint
Choi, M., Cook, A., Eachus, H., Tochwin, A., Kuntz, S., & Ryu, S. (2023) · doi:10.17504/protocols.io.kxygx9ooog8j/v1 · Cited by 2
Casalino‐Matsuda, S. M., Chen, F., González-González, F. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.28.530480 · Cited by 2
Ji, L., Wang, A., Sonthalia, S., Seo, S., Naiman, D. Q., Laurent Younes, B., Colantuoni, C., & Geman, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.06.535943 · Cited by 2
“Using scRNA-seq data from blood and brain tissue, we show that this new method, CellCover, performs as good or better than DE and other methods in defining cell-type discriminating gene panels, while reducing gene redundancy and capturing cell-class-specific signals that are distinct from those…”
Collagen prolyl 4-hydroxylases have sequence specificity towards different X-Pro-Gly tripletsPreprint
Salo, A. M., Rappu, P., Koski, M. K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.28.546674 · Cited by 2
“We employed Tabula Sapiens (Tabula Sapiens Consortium∗, 2022) and Tabula Muris (Tabula Muris Consortium et al., 2018) datasets to analyze C-P4H and procollagen gene expression at the single cell level.”
MethodHiggs, M. J., Hill, M., John, R. M., & Isles, A. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.05.552036 · Cited by 2
Korin, B., Avraham, S., McKay, A. S., Durinck, S., Moncada, R. M., Menon, H., Darmanis, S., Liang, Y., Modrusan, Z. D., Webster, J. D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.10.552883 · Cited by 2
A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell dataPreprint
Madrigal, A., Lu, T., Soto, L. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.15.553327 · Cited by 2
Swain, A. K., Pandit, V., Sharma, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.07.556641 · Cited by 2
Li, C., Chen, S., Chen, Y., Bian, H., Hao, M., Wei, L., & Zhang, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.08.574757 · Cited by 2
Song, Y., Hu, Y., Dow, J. A. T., Perrimon, N., & Papatheodorou, I. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.09.574675 · Cited by 3
Janssens, J., Mangeol, P. J. J., Hecker, N., Partel, G., Spanier, K. I., Ismail, J. N., Hulselmans, G. J., Aerts, S., & Schnorrer, F. (2024) · eLife · doi:10.7554/elife.92618.1 · Cited by 1
CellDiffusion: Enhancing scRNA-seq data integration with accurate inference of cell-cell associationPreprint
Liu, Y., Huang, Z., Lu, S. L., Jiang, L., Lin, Z., Lee, J.-H., Kolachalama, V. B., Li, R., Xu, D. H., Wang, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.28.577667 · Cited by 2
Antibody-Fab and -Fc features promote Mycobacterium tuberculosis restrictionPreprint
Grace, P. S., Peters, J. M., Sixsmith, J. D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.07.617070 · Cited by 2
Signature Distance: Generalizing Energy StatisticsPreprint
Lazzaro, N., Marchesi, R., Leonardi, G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.23.619602 · Cited by 2
Ptpn14 Knockout Mice Reveal Critical Female-Specific Roles for the Hippo PathwayPreprint
McCrea, E. M., Makrides, N., Tabata, T., Vogel, H. S., Howitt, B. E., Reineking, W., Vilches-Moure, J. G., Wang, M., Zhang, X., & Attardi, L. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.13.632464 · Cited by 2
Defining the role of fibroblasts in skin expansionPreprint
Stewart, C. A., Alar, C., Bargsted, L., Kaklamanou, I., Gozza, G. A., Polito, M. P., Enzo, E., Sifrim, A., & Aragona, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.18.643853 · Cited by 3
Kaur, G., Lamb, T. W., Tjitropranoto, A., & Rahman, I. (2025) · eLife · doi:10.7554/elife.106380 · Cited by 2
scContrast: A contrastive learning based approach for encoding single-cell gene expression dataPreprint
Li, W., Murtaza, G., & Singh, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.07.647292 · Cited by 2
“We trained scContrast on 21 out of 25 tissue samples from the Tabula Muris dataset (Consortium, 2018), which includes data from multiple donor mice sequenced using both droplet-and plate-based sequencing platforms.”
EBF1 controls an embryonic artery-forming niche that reactivates in pulmonary arterial hypertensionPreprint
Tian, W., Wu, T.-H., Gu, S., Lee, S., Zhao, H., Andruska, A., Song, K. K., Chang, J., Huang, C., Vinh, R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651303 · Cited by 3
Hedaya, O. M., Jiang, F., Baliga, U. K., Ivanov, A., Chen, S., Schwartz, J. L., Kawakami, Y., Chen, Y., & Yao, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.18.654700 · Cited by 3
Golf, S. R., Trotter, J. H., Wang, J., Nakahara, G., Han, X., Wernig, M., & Südhof, T. C. (2025) · eLife · doi:10.7554/elife.87589.3 · Cited by 2
McCrea, E. M., Makrides, N., Tabata, T., Reineking, W., Vilches-Moure, J. G., Wang, M., Lake, J. S., Vogel, H. S., Howitt, B. E., Zhang, X., et al. (2025) · Genes & Development · doi:10.1101/gad.352620.125 · Cited by 2
“S2; The Tabula Muris Consortium 2018).”
Atlas of Lysosomal Aging Reveals a Molecular Clock of Storage Disorder-Associated MetabolitesPreprint
Puszynska, A. M., Nguyen, T. P., Cangelosi, A. L., Armani, A., Roberts, J. M., Singh, K. A., Cameron, J. C., Tseyang, T., Liu, G. Y., Lai, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.25.678303 · Cited by 2
Keller, A. R., Adeniyi, A., Akkaya-Colak, K. B., Festing, M. H., Schwieters, A., Kumar, R., Sledziona, J., Gupta, K., Stevens, P., Pietrzak, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.24.678435 · Cited by 3
“To further validate our findings, we analyzed data available from the Tabula Muris Consortium 28 , a multi-organ compendium of murine transcriptional profiles throughout 27 months of the mouse lifespan.”
Nanoscale spatial-omics via contrastive embedding of single-molecule localisation dataPreprint
Shirgill, S., Jensen, L. G., Nieves, D. J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.07.679170 · Cited by 2
“Single-cell transcriptomics has provided insights into developmental biology and the discovery of new cell types and states [3], while protein sequence databases enabled AlphaFold's breakthrough in structural biology [4].”
Monoaminergic neurons share transcriptional identity across Bilaterian animalsPreprint
Goulty, M., Lewis, C., Nicolini, F., Khalid, R., Syed, H., Oliveri, P., Thomas, M. G., Rosato, E., & Feuda, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.10.679534 · Cited by 3
Ali, M. A., Siam, M. H. B., Vardaman, D., Bolding, C., Brazell, J. N., Whatley, A. D., Risley, C. A., Tidwell, H., Hossain, S. N., Samal, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.10.693494 · Cited by 2
immgenT: A Comprehensive Reference of Convergent T-cell States in the MousePreprint
Magill, I., Casey, O., Mallah, D., Panigrahi, S. S., Zhou, L. L., del Río, O. B., Bangs, D. J., Bee, G. C. W., Borys, S. M., Choi, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.30.702892 · Cited by 3
From Cellular Diversity to the Roots of Variability
Kruger, R. P. (2018) · Cell · doi:10.1016/j.cell.2018.11.001 · Cited by 1
Insights from Studies of White Adipose Tissue Using Single-Cell Approaches
Mejhert, N., & Rydén, M. (2022) · Handbook of experimental pharmacology · doi:10.1007/164_2021_578 · Cited by 1
Iatsiuk, V., Malinka, F., Pícková, M., Turečková, J., Kléma, J., Špoutil, F., Novosadová, V., Procházka, J., & Sedláček, R. (2022) · BMC Gastroenterology · doi:10.1186/s12876-022-02265-2 · Cited by 1
“compensatory potential To determine cell-type specific distribution of Ub-ligases in the colon, we used published single-cell RNA sequencing data of the murine colon as a reference [22].”
MethodJones, S.-S., Ozturk, M., Kieswetter, N. S., Poswayo, S. K., Hazra, R., Tamgue, O., Parihar, S. P., Suzuki, H., Brombacher, F., & Guler, R. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.948047 · Cited by 1
“Databases, including the Tabula Muris (34), show that Lyl1 is mainly expressed in monocyte populations in mouse lung tissue (Supplementary Figure 1G).”
MethodInfluentialPositive Association of TEAD1 With Schizophrenia in a Northeast Chinese Han Population
Sun, Y., Wen, L., Luo, Y.-Y., Hu, W.-J., Ren, H., Lv, Y., Zhang, C., Gao, P., Xuan, L.-N., Wang, G.-Y., et al. (2023) · Psychiatry Investigation · doi:10.30773/pi.2023.0069 · Cited by 1
“According to the SZDB database 23,24 , a significant difference of the human TEAD1 gene expression was shown in stratum between the schizophrenia patients and healthy control subjects (corrected p=0.001).”
BackgroundInfluentialPPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data
Tadi, A. A., Alhadidi, D., & Rueda, L. G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108351 · Cited by 1
Protocol for quantifying stem-cell-derived cardiomyocyte maturity using transcriptomic entropy score
Chen, E. Z., Kannan, S., Murphy, S. A., Farid, M., & Kwon, C. (2024) · STAR Protocols · doi:10.1016/j.xpro.2024.103083 · Cited by 2
Generation of the Stmn2-Cre mouse line targeting arterial endothelial cells
Lang, Y., Wang, Q., Li, R., et al. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.05.007 · Cited by 1
scCross: efficient search for rare subpopulations across multiple single-cell samples
Gerniers, A., Nijssen, S., & Dupont, P. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae371 · Cited by 1
“A part of this cluster has been identified in the original publication related to this data (Tabula Muris Consortium 2018), but it was interpreted as a cluster of thymic T cells.”
Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation
Lin, Y., Pan, Z., Zeng, Y., Yang, Y., & Dai, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae370 · Cited by 1
Transcriptomic Signatures in Lung Allografts and Their Therapeutic Implications
Guinn, M. T., Fernandez, R., Lau, S., & Loor, G. (2024) · Biomedicines · doi:10.3390/biomedicines12081793 · Cited by 2
Tejima, K., Kozawa, S., & Sato, T. N. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011436 · Cited by 2
Uncovering the signaling networks of disseminated glioblastoma cells in vivo with INSIGHT
Ahn, R., D’Souza, A. D., Long, L., Cui, Y., Gantchev, J. K., Baquer, G., Burgenske, D. M., Bakken, K. K., Ott, L. L., Carlson, B. L., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76587-0 · Cited by 1
“Oligodendrocyte-speci fi c markers were retrieved from PanglaoDB 120 andTabulaMuris 121 ,andmarkersthathadevidenceinthe literature to be expressed highly in other cell types of the brain from these datasets were fi ltered out.”
Application of bioinformatic tools in cell type classification for single-cell RNA-seq data
Sujana, S. T. A., Shahjaman, M., & Singha, A. C. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108332 · Cited by 1
Markov-bridge generation of transition paths and its application to cell-fate choice
Treut, G. L., Ancheta, S., Huber, G., Orland, H., & Yllanes, D. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013010 · Cited by 4
Liu, Q., & Xu, Y. L. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.168936 · Cited by 1
KRT14 is a promising prognostic biomarker of breast cancer related to immune infiltration
Liao, S., Zhang, X., Chen, L., Zhang, J., Lu, W., Rao, M., Zhang, Y., Ye, Z., Ivanova, D., Li, F., et al. (2025) · Molecular Immunology · doi:10.1016/j.molimm.2025.02.016 · Cited by 2
Wang, X., Bao, M., Wang, H., Sun, R., Dai, W., Sun, K., Zhu, Y., Pu, Y., Chu, Y., Li, X., et al. (2025) · Molecular Ecology Resources · doi:10.1111/1755-0998.14101 · Cited by 1
CORN 2.0 - Condition Orientated Regulatory Networks 2.0
Leung, R. W. T., Zhang, X., Chen, Z., Liang, Y., Huang, S., Yang, Z., Zong, X., Jiang, X., Lin, R., Deng, W., et al. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.04.003 · Cited by 1
Zheng, Q., Wu, Y., Zhang, X., Zhang, Y., Zhu, Z., Luan, B., Zang, P., & Sun, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-96907-6 · Cited by 1
“The single-cell transcriptomic data from the model organism mus musculus shown that OASL1 was expressed by aortic endothelial cells 63,64 , suggesting OASL1 may participated in inflammatory vascular diseases such as atherosclerosis.”
Madrid, D. M. D. C., Gu, W., Karim, S. J. I., Lowke, M. T., Kelleher, A. M., Warren, W. C., & Driver, J. P. (2025) · The Journal of Immunology · doi:10.1093/jimmun/vkaf032 · Cited by 3
Unveiling cell-type-specific mode of evolution in comparative single-cell expression data
Qin, T., Zhang, H., & Zou, Z. (2025) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2025.04.022 · Cited by 2
scVDM: A Diffusion Model Integrated With Conditional VAE for Generative Single-Cell Tasks
Peng, D., Xie, L., Yang, H. T., & Zhang, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3585960 · Cited by 1
“This dataset includes two independent mouse cell atlas datasets: one generated by Han et al. [32] using Microwell-Seq, and the other by the Tabula Muris Consortium [33] using 10x Genomics and Smart-Seq2 protocols.”
Jie, M., Feng, T., Hu, F., Sun, X., Jia, S., Lu, Y., Dong, B., & Jiang, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-10421-3 · Cited by 1
Zhang, Y., Du, Y., & Fu, Y. (2025) · Gene Reports · doi:10.1016/j.genrep.2025.102297 · Cited by 1
Tang, B., Cheng, G., & Gao, X. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00738-y · Cited by 1
PuMA: PubMed gene/cell type-relation Atlas
Bickmann, L., Sandmann, S., Walter, C., & Varghese, J. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06236-8 · Cited by 1
Deep multi-kernel cell clustering for single-cell RNA sequencing data
Yao, M., & Ren, L. (2025) · Biochemical Engineering Journal · doi:10.1016/j.bej.2025.109877 · Cited by 1
Li, S., Yang, J., Wang, Y., Di, S., & Chen, L. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736942 · Cited by 1
“We demonstrate the superiority of the ESCFD by achieving state-of-the-art results on three public datasets, PBMC [40], Tabular Muris [5] and Brain Small, in comparison to prior works.”
scSAG$^{2}$E: Sparse Autoencoders With Gene Graph Embedding for scRNA-Seq Data Clustering
Xia, M., Long, Q., Shu, Z., Wang, H., & Yu, Z. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3600651 · Cited by 1
Metadata
- Authors
- The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators
- Venue
- Nature
- Published
- 2018-10
- Volume
- 562
- Issue
- 7727
- Pages
- 367-372
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6642641
- OpenAlex
- W2894687190
- Cited by
- 2,842
- References
- 32
- Citations by year
- 2026: 329 · 2025: 382 · 2024: 384 · 2023: 379 · 2022: 391 · 2021: 412 · 2020: 380 · 2019: 167 · 2018: 14
- FWCI
- 77.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Jagiellonian University Repository (Jagiellonian University) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Veterans Affairs (I01 BX002324, I01RX001222)
- National Institute on Aging (DP1AG053015, P01AG036695)
- National Heart, Lung, and Blood Institute (R00HL127267)
- National Cancer Institute (R01CA157877)
- National Institute on Deafness and Other Communication Disorders (R01DC016892)
- National Institute of Diabetes and Digestive and Kidney Diseases (K08DK101603, P30DK026743, P30DK116074)
- U.S. National Library of Medicine (DP1LM012179)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- Tabula Muris, mouse organs, single-cell transcriptomics, cell types, gene expression, cell atlas, T cells, endothelial cells, fluorescence-activated cell sorting, Mus musculus
- MeSH terms
- Animals, Cell Biology, Female, Flow Cytometry, Male, Mice, Inbred C57BL, Organ Specificity, T-Lymphocytes, Transcription Factors, Gene Expression Profiling, Endothelial Cells, Microfluidics, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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