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.
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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 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.
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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
“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.
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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.
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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.
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.
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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.
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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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]
In 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
“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]
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 201–300 of 2,952
Stein-O’Brien, G., Clark, B. S., Sherman, T. D., Zibetti, C., Hu, Q., Sealfon, R. S. G., Liu, S., Qian, J. X., Colantuoni, C., Blackshaw, S., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.04.004 · Cited by 172
Machado, L., Geara, P., Camps, J., Dos Santos, M., Teixeira-Clerc, F., Van Herck, J., Varet, H., Legendre, R., Pawlotsky, J.-M., Sampaolesi, M., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.01.017 · Cited by 170
Liu, C. X., Li, R., Li, Y., Lin, X., Zhao, K., Liu, Q., Wang, S., Yang, X., Shi, X., Ma, Y., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.04.009 · Cited by 172
Universal Cell Embeddings: A Foundation Model for Cell BiologyPreprint
Rosen, Y., Roohani, Y., Agrawal, A., Samotorcan, L., Quake, S. R., & Leskovec, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568918 · Cited by 165
Barkaway, A., Rolas, L., Joulia, R., Bodkin, J. V., Lenn, T., Owen-Woods, C., Reglero-Real, N., Stein, M., Vázquez-Martínez, L., Girbl, T., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.04.025 · Cited by 178
Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID
Cortal, A., Martignetti, L., Six, E. M., & Rausell, A. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00896-6 · Cited by 165
“Finally, we assessed the reproducibility of Cell-ID gene signatures across datasets profiled with different single-cell omics technologies: scRNA-seq from the Tabula Muris mouse cell atlas 39 and single-cell ATAC-seq from the Mouse ATAC Atlas 40 (Supplementary Note 6).”
BackgroundMethodscJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning
Lin, Y., Wu, T.-Y., Wan, S., Yang, J. Y. H., Wong, W. H., & Wang, Y. X. R. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01161-6 · Cited by 166
Evaluation of Cell Type Annotation R Packages on Single-Cell RNA-Seq Data
Huang, Q., Liu, Y., Du, Y., & Garmire, L. X. (2021) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.07.004 · Cited by 149
Xi, H., Langerman, J., Sabri, S., Chien, P., Young, C. S., Younesi, S., Hicks, M. R., Gonzalez, K. M., Fujiwara, W., Marzi, J., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.04.017 · Cited by 160
Interleukin-36γ–producing macrophages drive IL-17–mediated fibrosis
Sommerfeld, S. D., Cherry, C., Schwab, R. M., Chung, L., Maestas, D. R., Laffont, P., Stein, J. E., Tam, A. J., Ganguly, S., Housseau, F., et al. (2019) · Science Immunology · doi:10.1126/sciimmunol.aax4783 · Cited by 160
The Human Cell Atlas from a cell census to a unified foundation model
Rood, J., Wynne, S., Robson, L., Hupalowska, A., Randell, J., Teichmann, S. A., & Regev, A. (2024) · Nature · doi:10.1038/s41586-024-08338-4 · Cited by 173
“Combining learnings from atlases, including those of model organisms 9,20 , with human genetic data, longitudinal donor and patient samples as well as high-throughput perturbation screens will further unlock mechanisms of human health and disease, to increase the predictive power of our models.”
Mitochondrial STAT3 exacerbates LPS-induced sepsis by driving CPT1a-mediated fatty acid oxidation
Li, R., Li, X., Zhao, J., Meng, F., Yao, C., Bao, E.-S., Sun, N., Chen, X., Cheng, W., Hua, H. M., et al. (2021) · Theranostics · doi:10.7150/thno.63751 · Cited by 159
Dao, L., You, Z., Lu, L., Xu, T., Sarkar, A. K., Zhu, H., Liu, M., Calandrelli, R., Yoshida, G., Lin, P., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.04.019 · Cited by 181
Mills, M. C., Tropf, F. C., Brazel, D. M., van Zuydam, N. R., Vaez, A., Agbessi, M. D. T., Ahsan, H., Alves, I., Andiappan, A. K., Arindrarto, W., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01135-3 · Cited by 166
False signals induced by single-cell imputation
Andrews, T. S., & Hemberg, M. (2018) · F1000Research · doi:10.12688/f1000research.16613.1 · Cited by 142
Sehgal, A., Irvine, K. M., & Hume, D. A. (2021) · Seminars in Immunology · doi:10.1016/j.smim.2021.101509 · Cited by 160
The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154
Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
Chen, B., Garmire, L., Calvisi, D. F., Chua, M.-S., Kelley, R. K., & Chen, X. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0240-9 · Cited by 154
Molecular hallmarks of heterochronic parabiosis at single-cell resolution
Pálovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Kern, F., Bonanno, L., Calcuttawala, K., Webber, J., et al. (2022) · Nature · doi:10.1038/s41586-022-04461-2 · Cited by 153
“All protocols used in this study are described in detail elsewhere 3,5 .”
BackgroundMethodThe in vivo endothelial cell translatome is highly heterogeneous across vascular beds
Cleuren, A. C. A., van der Ent, M. A., Jiang, H., Hunker, K. L., Yee, A., Siemieniak, D. R., Molema, G., Aird, W. C., Ganesh, S. K., & Ginsburg, D. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912409116 · Cited by 152
Kinkhabwala, A., Herbel, C., Pankratz, J., Yushchenko, D. A., Rüberg, S., Praveen, P., Reiß, S., Rodriguez, F., Schaefer, D., Kollet, J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-05841-4 · Cited by 152
Ren, X., Zhong, G., Zhang, Q., Zhang, L., Sun, Y., & Zhang, Z. (2020) · Cell Research · doi:10.1038/s41422-020-0353-2 · Cited by 152
Reggio, A., Rosina, M., Palma, A., Perpetuini, A. C., Petrilli, L. L., Gargioli, C., Fuoco, C., Micarelli, E., Giuliani, G., Cerretani, M., et al. (2020) · Cell Death and Differentiation · doi:10.1038/s41418-020-0551-y · Cited by 160
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
Cao, Z., Lin, W., Shen, L., Yang, D.-C., & Gao, G. (2020) · Nature Communications · doi:10.1038/s41467-020-17281-7 · Cited by 152
Petitprez, F., Lévy, S., Sun, C., Meylan, M., Linhard, C., Becht, É., Elarouci, N., Tavel, D., Roumenina, L. T., Ayadi, M., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00783-w · Cited by 176
Filgueira, T. O., Castoldi, Â., Santos, L. E. R., de Amorim, G. J., Fernandes, M. S. D. S., Anastácio, W. D. L. D. N., Campos, E. Z., Santos, T. M., & Souto, F. O. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.587146 · Cited by 163
Moon, J. S., Goeminne, L. J. E., Kim, J. T., Tian, J. W., Kim, S., Nga, H. T., Kang, S. G., Kang, B. E., Byun, J., Lee, Y., et al. (2020) · Aging Cell · doi:10.1111/acel.13195 · Cited by 141
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145
The Golgi Outpost Protein TPPP Nucleates Microtubules and Is Critical for Myelination
Fu, M., McAlear, T. S., Nguyen, H., Osés-Prieto, J. A., Valenzuela, A., Shi, R., Perrino, J. J., Huang, T., Burlingame, A. L., Bechstedt, S., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.025 · Cited by 147
Mechanisms of tissue and cell-type specificity in heritable traits and diseases
Hekselman, I., & Yeger‐Lotem, E. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0200-9 · Cited by 156
Construction of a cross-species cell landscape at single-cell level
Wang, R., Zhang, P., Wang, J., Ma, L., Weigao, E., Suo, S., Jiang, M., Li, J., Chen, H., Sun, H., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac633 · Cited by 152
Deep soft K-means clustering with self-training for single-cell RNA sequence data
Chen, L., Wang, W., Zhai, Y., & Deng, M. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa039 · Cited by 145
MARS: discovering novel cell types across heterogeneous single-cell experiments
Brbić, M., Žitnik, M., Wang, S., Pisco, A. O., Altman, R. B., Darmanis, S., & Leskovec, J. (2020) · Nature Methods · doi:10.1038/s41592-020-00979-3 · Cited by 144
Farup, J., Just, J., de Paoli, F. V., Lin, L., Jensen, J., Billeskov, T. B., Sánchez-Román, I., Cömert, C., Møller, A. B., Madaro, L., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.10.001 · Cited by 162
miRNATissueAtlas2: an update to the human miRNA tissue atlas
Keller, A., Gröger, L., Tschernig, T., Solomon, J., Laham, O., Schaum, N., Wagner, V., Kern, F., Schmartz, G. P., Li, Y., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab808 · Cited by 143
“Mouse sample collection has been performed as described in the Tabula Muris Senis original manuscripts (32,47).”
MethodAbe, K., Yamashita, A., Morioka, M., Horike, N., Takei, Y., Koyamatsu, S., Okita, K., Matsuda, S., & Tsumaki, N. (2023) · Nature Communications · doi:10.1038/s41467-023-36408-0 · Cited by 153
Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations
Mattei, D., Ivanov, A., van Oostrum, M., Pantelyushin, S., Richetto, J., Mueller, F., Beffinger, M. M., Schellhammer, L., vom Berg, J., Wollscheid, B., et al. (2020) · doi:10.1101/2020.05.14.095422 · Cited by 143
Single Cell RNA Sequencing in Atherosclerosis Research
Williams, J. W., Winkels, H., Durant, C. P., Zaitsev, K., Ghosheh, Y., & Ley, K. F. (2020) · Circulation Research · doi:10.1161/circresaha.119.315940 · Cited by 142
Cell type-selective secretome profiling in vivo
Wei, W., Riley, N. M., Yang, A. C., Kim, J. T., Terrell, S. M., Li, V. L., García-Contreras, M., Bertozzi, C. R., & Long, J. Z. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-00698-y · Cited by 141
Kendal, A. R., Layton, T., Al‐Mossawi, H., Appleton, L., Dakin, S. G., Brown, R., Loizou, C. L., Rogers, M., Sharp, R. J., & Carr, A. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-70786-5 · Cited by 141
Cardiac cell type–specific gene regulatory programs and disease risk association
Hocker, J. D., Poirion, O., Zhu, F., Buchanan, J., Zhang, K., Chiou, J., Wang, T., Zhang, Q., Hou, X., Li, Y. E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf1444 · Cited by 136
Davey, L. E., Malkus, P. N., Villa, M. M., Dolat, L., Holmes, Z. C., Letourneau, J., Ansaldo, E., David, L. A., Barton, G. M., & Valdivia, R. H. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01407-w · Cited by 143
Deep learning applications in single-cell genomics and transcriptomics data analysis
Erfanian, N., Heydari, A. A., Feriz, A. M., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Razavi, S. M., Nasseri, S., Safarpour, H., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115077 · Cited by 167
Spatial mapping of cellular senescence: emerging challenges and opportunities
Gurkar, A. U., Gerencser, A. A., Mora, A. L., Nelson, A. C., Zhang, A. R., Lagnado, A. B., Enninful, A., Benz, C., Furman, D., Beaulieu, D., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00446-6 · Cited by 141
Macrophage galactose lectin is critical for Kupffer cells to clear aged platelets
Deppermann, C., Kratofil, R. M., Peiseler, M., David, B. A., Zindel, J., Castanheira, F. V. S., van der Wal, F., Carestia, A., Jenne, C., Marth, J., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20190723 · Cited by 125
Yu, L., Cao, Y., Yang, J. Y. H., & Yang, P. (2022) · Genome biology · doi:10.1186/s13059-022-02622-0 · Cited by 136
“1a), we subsampled from the Tabula Muris dataset [34] to create three main settings including (i) varying the number of true cell types from 5 to 20 (increment by 1) while fixing the number of cells in each cell type as 200; (ii) varying the number of cells from 50 to 250 (increment by 50) while…”
BackgroundMethodInfluentialGasdermin-E mediates mitochondrial damage in axons and neurodegeneration
Neel, D. V., Basu, H., Gunner, G., Bergstresser, M., Giadone, R. M., Chung, H., Miao, R., Chou, V., Brody, E. M., Jiang, X., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.02.019 · Cited by 147
Amorim, A., De Feo, D., Friebel, E., Ingelfinger, F., Anderfuhren, C. D., Krishnarajah, S., Andreadou, M., Welsh, C. A., Liu, Z., Ginhoux, F., et al. (2022) · Nature Immunology · doi:10.1038/s41590-021-01117-7 · Cited by 140
R-spondin 3 promotes stem cell recovery and epithelial regeneration in the colon
Harnack, C., Berger, H., Antanaviciute, A., Vidal, R., Sauer, S., Simmons, A., Meyer, T. F., & Sigal, M. (2019) · Nature Communications · doi:10.1038/s41467-019-12349-5 · Cited by 137
CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data
Hu, Y., Peng, T., Gao, L., & Tan, K. (2021) · Science Advances · doi:10.1126/sciadv.abf1356 · Cited by 136
Giuliani, G., Rosina, M., & Reggio, A. (2021) · FEBS Journal · doi:10.1111/febs.16080 · Cited by 144
Distinct properties of adipose stem cell subpopulations determine fat depot-specific characteristics
Nahmgoong, H., Jeon, Y. G., Park, E. S., Choi, Y. H., Han, S. M., Park, J., Ji, Y., Sohn, J. H., Han, J. S., Kim, Y. Y., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2021.11.014 · Cited by 135
clustifyr: an R package for automated single-cell RNA sequencing cluster classification
Fu, R., Gillen, A. E., Sheridan, R. M., Tian, C., Daya, M., Hao, Y., Hesselberth, J. R., & Riemondy, K. A. (2020) · F1000Research · doi:10.12688/f1000research.22969.2 · Cited by 135
Youlten, S. E., Kemp, J. P., Logan, J. G., Ghirardello, E. J., Sergio, C. M., Dack, M. R. G., Guilfoyle, S. E., Leitch, V. D., Butterfield, N. C., Komla-Ebri, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22517-1 · Cited by 133
Mammalian gene expression variability is explained by underlying cell state
Foreman, R., & Wollman, R. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199146 · Cited by 126
Enzymatic Dissociation Induces Transcriptional and Proteotype Bias in Brain Cell Populations
Mattei, D., Ivanov, A., van Oostrum, M., Pantelyushin, S., Richetto, J., Mueller, F., Beffinger, M., Schellhammer, L., vom Berg, J., Wollscheid, B., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21217944 · Cited by 145
Demystifying “drop-outs” in single-cell UMI data
Kim, T. H., Zhou, X., & Chen, M. (2020) · Genome biology · doi:10.1186/s13059-020-02096-y · Cited by 128
scMatch: a single-cell gene expression profile annotation tool using reference datasets
Hou, R., Denisenko, E., & Forrest, A. R. R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz292 · Cited by 130
Contreras, O., Cruz-Soca, M., Théret, M., Soliman, H. S., Tung, L. W., Groppa, E., Rossi, F. M. V., & Brandan, E. (2019) · Journal of Cell Science · doi:10.1242/jcs.232157 · Cited by 143
Single-cell omics in ageing: a young and growing field
He, X., Memczak, S., Qu, J. T., Belmonte, J. C. I., & Liu, G. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0196-7 · Cited by 129
Rosen, Y., Brbić, M., Roohani, Y., Swanson, K., Li, Z., & Leskovec, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02191-z · Cited by 144
Direct contribution of skeletal muscle mesenchymal progenitors to bone repair
Julien, A., Kanagalingam, A., Martínez‐Sarrà, E., Megret, J., Luka, M., Ménager, M. M., Relaix, F., & Colnot, C. (2021) · Nature Communications · doi:10.1038/s41467-021-22842-5 · Cited by 128
Vadevoo, S. M. P., Gunassekaran, G. R., Lee, C., Lee, N., Lee, J., Chae, S., Park, J., Koo, J., & Lee, B.-H. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102434118 · Cited by 130
Donovan, M. K. R., D’Antonio‐Chronowska, A., D’Antonio, M., & Frazer, K. A. (2020) · Nature Communications · doi:10.1038/s41467-020-14561-0 · Cited by 119
A molecular map of murine lymph node blood vascular endothelium at single cell resolution
Brulois, K., Rajaraman, A., Szade, A., Nordling, S., Bogoslowski, A., Đermadi, D., Rahman, M., Kiefel, H., O’Hara, E., Koning, J. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17291-5 · Cited by 125
Ferrandi, P. J., Alway, S. E., & Mohamed, J. S. (2020) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00321.2020 · Cited by 124
Zhao, L., Li, Z., Vong, J. S. L., Chen, X., Lai, H. M., Yan, L. Y. C., Huang, J., Sy, S. K. H., Tian, X. Y., Huang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18249-3 · Cited by 125
scSorter: assigning cells to known cell types according to marker genes
Guo, H., & Li, J. (2021) · Genome biology · doi:10.1186/s13059-021-02281-7 · Cited by 132
Determining sequencing depth in a single-cell RNA-seq experiment
Zhang, M. J., Ntranos, V., & Tse, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14482-y · Cited by 112
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing
Tsuyuzaki, K., Sato, H., Sato, K., & Nikaido, I. (2020) · Genome biology · doi:10.1186/s13059-019-1900-3 · Cited by 130
Lee, R. D., Munro, S. A., Knutson, T. P., LaRue, R. S., Heltemes-Harris, L. M., & Farrar, M. A. (2021) · Nature Communications · doi:10.1038/s41467-021-27232-5 · Cited by 131
Martínez‐Sánchez, N., Sweeney, O., Sidarta-Oliveira, D., Caron, A., Stanley, S. A., & Domingos, A. I. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.017 · Cited by 127
“[PubMed: 25392498] Tabula Muris, C., Overall, C., Logistical, C., Organ, C., Processing, Library, P., Sequencing, Computational Data, A., Cell Type, A., Writing, G., Supplemental Text Writing, G. & Principal, I. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.”
Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing.
Choi, Y. H., & Kim, J. K. (2019) · Europe PMC (PubMed Central) · doi:10.14348/molcells.2019.2446 · Cited by 126
“Over the past decade, single-cell RNA sequencing (scRNAseq) technologies have been developed that provide an unbiased view of cell-to-cell variability in gene expression within a population of cells (Chen et al., 2018; Kolodziejczyk et al., 2015a; Tanay and Regev, 2017; Wagner et al., 2016).”
BackgroundMicheli, A. J. D., Swanson, J. B., Disser, N. P., Martinez, L. M., Walker, N. R., Oliver, D. J., Cosgrove, B. D., & Mendias, C. L. (2020) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00372.2020 · Cited by 121
Statistical mechanics meets single-cell biology
Teschendorff, A. E., & Feinberg, A. P. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00341-z · Cited by 127
Cherry, C., Maestas, D. R., Han, J., Andorko, J. I., Cahan, P., Fertig, E. J., Garmire, L. X., & Elisseeff, J. H. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00770-5 · Cited by 121
“We used integration techniques similar to those from the Human Cell Atlas 17 and the Tabula Muris Consortium 18 to pool multiple single cell data sets including sorted cells and to provide a single cell atlas of response to biomaterials.”
Bhattarai, P., Coşacak, M. İ., Mashkaryan, V., Demir, S., Popova, S. D., Govindarajan, N., Brandt, K., Zhang, Y., Chang, W., Ampatzis, K., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000585 · Cited by 121
scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics
Song, Q., Su, J., & Zhang, W. (2021) · Nature Communications · doi:10.1038/s41467-021-24172-y · Cited by 123
Cell of origin influences pancreatic cancer subtype
Flowers, B. M., Xu, H., Mulligan, A. S., Hanson, K. J., Seoane, J. A., Vogel, H., Curtis, C., Wood, L. D., & Attardi, L. D. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0633 · Cited by 118
Navigating the Depths and Avoiding the Shallows of Pancreatic Islet Cell Transcriptomes
Mawla, A. M., & Huising, M. O. (2019) · Diabetes · doi:10.2337/dbi18-0019 · Cited by 119
Sessa, A., Fagnocchi, L., Mastrototaro, G., Massimino, L., Zaghi, M., Indrigo, M. T., Cattaneo, S., Martini, D., Gabellini, C., Pucci, C., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.013 · Cited by 118
Diet, microbiota, and the mucus layer: The guardians of our health
Suriano, F., Nyström, E. E. L., Sergi, D., & Gustafsson, J. K. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.953196 · Cited by 126
He, L., Mäe, M. A., Muhl, L., Sun, Y., Pietilä, R., Nahar, K., Liébanas, E. V., Fagerlund, M. J., Oldner, A., Liu, J., et al. (2020) · doi:10.1101/2020.05.11.088500 · Cited by 116
Comparative analysis of sequencing technologies for single-cell transcriptomics
Natarajan, K. N., Miao, Z., Jiang, M., Huang, X., Zhou, H., Xie, J., Wang, C., Qin, S., Zhao, Z., Wu, L. L., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1676-5 · Cited by 115
Single-cell transcriptomics of the naked mole-rat reveals unexpected features of mammalian immunity
Hilton, H. G., Rubinstein, N. D., Janki, P., Ireland, A. T., Bernstein, N., Fong, N. L., Wright, K. M., Smith, M., Finkle, D., Martin-McNulty, B., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000528 · Cited by 119
“Consistent with previous reports [8,9], the splenic immune cell population of mice is dominated by lymphoid lineage cells (approximately 90% of the cells; Fig 1A and 1C).”
ResultThe Comparison of Two Single-cell Sequencing Platforms: BD Rhapsody and 10x Genomics Chromium
Gao, C., Zhang, M., & Chen, L. (2020) · Current Genomics · doi:10.2174/1389202921999200625220812 · Cited by 122
Lee, Y., Bogdanoff, D., Wang, Y., Hartoularos, G. C., Woo, J. M., Mowery, C. T., Nisonoff, H. M., Lee, D. S., Sun, Y., Lee, J., et al. (2021) · Science Advances · doi:10.1126/sciadv.abg4755 · Cited by 110
Schwann cell precursors represent a neural crest‐like state with biased multipotency
Kastriti, M. E., Faure, L., Ahsen, D. V., Bouderlique, T., Boström, J., Solovieva, T., Jackson, C. R., Bronner-Fraser, M. E., Meijer, D., Hadjab, S., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021108780 · Cited by 125
Towards multimodal foundation models in molecular cell biology
Cui, H., Tejada-Lapuerta, A., Brbić, M., Sáez-Rodríguez, J., Cristea, S., Goodarzi, H., Lotfollahi, M., Theis, F. J., & Wang, B. (2025) · Nature · doi:10.1038/s41586-025-08710-y · Cited by 151
“Cross-species datasets may also provide helpful evolutionary context 84,85 .”
Generation of a Single-Cell RNAseq Atlas of Murine Salivary Gland Development
Hauser, B. R., Aure, M. H., Kelly, M. C., Hoffman, M. P., & Chibly, A. M. (2020) · iScience · doi:10.1016/j.isci.2020.101838 · Cited by 112
EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data
Teschendorff, A. E., Zhu, T., Breeze, C. E., & Beck, S. (2020) · Genome biology · doi:10.1186/s13059-020-02126-9 · Cited by 118
“Although the MCA1 breast tissue atlases failed to capture adipocytes, we nevertheless proceeded to construct scRNA-Seq reference matrices from each of the SmartSeq2 and 10X platforms (the “Methods” section).”
BackgroundMethodResultInfluentialstPlus: a reference-based method for the accurate enhancement of spatial transcriptomics
Chen, S., Boheng, Z., Chen, X., Xuegong, Z., & Rui, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab298 · Cited by 114
Semisupervised adversarial neural networks for single-cell classificationPreprint
Kimmel, J. C., & Kelley, D. R. (2021) · Genome Research · doi:10.1101/gr.268581.120 · Cited by 112
Renal-Tubule Epithelial Cell Nomenclature for Single-Cell RNA-Sequencing Studies
Chen, L., Clark, J. Z., Nelson, J. W., Kaissling, B., Ellison, D. H., & Knepper, M. A. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2019040415 · Cited by 109
Microbial regulation of hexokinase 2 links mitochondrial metabolism and cell death in colitis
Hinrichsen, F., Hamm, J., Westermann, M., Schröder, L. C., Shima, K., Mishra, N., Walker, A., Sommer, N., Klischies, K., Prasse, D., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.11.004 · Cited by 120
Individual cell types in C. elegans age differently and activate distinct cell-protective responses
Roux, A. E., Yuan, H., Podshivalova, K., Hendrickson, D. G., Kerr, R. A., Kenyon, C. J., & Kelley, D. R. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112902 · Cited by 116
Understanding the heterogeneity of resident liver macrophages
Blériot, C., & Ginhoux, F. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02694 · Cited by 107
Zhao, L., Jin, Y., Donahue, K., Tsui, M., Fish, M., Logan, C. Y., Wang, B., & Nusse, R. (2019) · Hepatology · doi:10.1002/hep.30563 · Cited by 111
Yang, J., Vamvini, M. T., Nigro, P., Ho, L., Galani, K., Alvarez, M., Tanigawa, Y., Renfro, A., Carbone, N. P., Laakso, M., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.004 · Cited by 110
“…annotated cell clusters using SciBet (Li et al., 2020a), SingleR (Aran et al., 2019), and cell type-specific markers from Tabula Muris (Tabula Muris Consortium et al., 2018) and tissue-focused studies in the field (Giordani et al., 2019; Merrick et al., 2019; Oprescu et al., 2020; Ramirez et al.,…”
MethodBenchmarking strategies for cross-species integration of single-cell RNA sequencing data
Song, Y., Miao, Z., Brāzma, A., & Papatheodorou, I. (2023) · Nature Communications · doi:10.1038/s41467-023-41855-w · Cited by 112
“Our quantitative scoring system of species mixing and biology conservation is supported by two assumptions: (1) cell type annotations are accurate; (2) known cell type homology from literature is confident.”
BackgroundMethod
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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