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.
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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.
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.
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- 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.
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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 101–200 of 2,952
Single-cell RNA sequencing in cardiovascular development, disease and medicine
Paik, D. T., Cho, S., Tian, L., Chang, H. Y., & Wu, J. C. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0359-y · Cited by 314
Ren, J., Isakova, A., Friedmann, D., Zeng, J., Grutzner, S. M., Pun, A., Zhao, G. Q., Kolluru, S. S., Wang, R., Lin, R., et al. (2019) · eLife · doi:10.7554/elife.49424 · Cited by 304
“Transcription factors Transcription factors (TFs) have been shown to be the best molecular feature for cell type definition (Schaum et al., 2018).”
BackgroundMethodInfluentialTutorial: guidelines for the computational analysis of single-cell RNA sequencing data
Andrews, T. S., Kiselev, V. Y., McCarthy, D. J., & Hemberg, M. (2020) · Nature Protocols · doi:10.1038/s41596-020-00409-w · Cited by 323
A systematic evaluation of single-cell RNA-sequencing imputation methods
Hou, W., Ji, Z., Ji, H., & Hicks, S. C. (2020) · Genome biology · doi:10.1186/s13059-020-02132-x · Cited by 307
Improved tools to study astrocytes
Yu, X., Nagai, J., & Khakh, B. S. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0264-8 · Cited by 298
Endothelial heterogeneity across distinct vascular beds during homeostasis and inflammation
Jambusaria, A., Hong, Z., Zhang, L., Srivastava, S., Jana, A., Tóth, P. T., Dai, Y., Malik, A. B., & Rehman, J. (2020) · eLife · doi:10.7554/elife.51413 · Cited by 299
Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes
Camuñas-Soler, J., Dai, X.-Q., Hang, Y., Bautista, A., Lyon, J. G., Suzuki, K., Kim, S. K., Quake, S. R., & MacDonald, P. E. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.04.005 · Cited by 286
“This technique has provided increasingly accurate cell-type classifications in diverse organs based on their transcriptional fingerprint (La Manno et al., 2016; Tabula Muris Consortium et al., 2018; Villani et al., 2017).”
MethodProfiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 347
The how and why of lncRNA function: An innate immune perspective
Robinson, E. K., Covarrubias, S., & Carpenter, S. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194419 · Cited by 282
Paik, D. T., Tian, L., Williams, I. M., Rhee, S., Zhang, H., Liu, C., Mishra, R., Wu, S. M., Red‐Horse, K., & Wu, J. C. (2020) · Circulation · doi:10.1161/circulationaha.119.041433 · Cited by 291
Gideon, H. P., Hughes, T. K., Tzouanas, C. N., Wadsworth, M. H., Tu, A. A., Gierahn, T. M., Peters, J. M., Hopkins, F. F., Wei, J.-R., Kummerlowe, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.04.004 · Cited by 282
“…the 10-week granulomas, we analyzed 109,584 cells, resolving 13 general cell types (Figures 2A, 2B, and S2A–S2G; Table S2; STAR Methods) (Tabula Muris Consortium et al., 2018; Han et al., 2018; Liberzon et al., 2011; Lopez et al., 2017; Varemo et al., 2013; Guo et al., 2018; Zilionis et al., 2019).”
MethodPredicting cell-to-cell communication networks using NATMI
Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A., & Forrest, A. R. R. (2020) · Nature Communications · doi:10.1038/s41467-020-18873-z · Cited by 285
Dissecting cellular crosstalk by sequencing physically interacting cells
Giladi, A., Cohen, M., Medaglia, C., Baran, Y., Li, B., Zada, M., Bost, P., Blecher‐Gonen, R., Salame, T., Mayer, J. U., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0442-2 · Cited by 276
Deciphering the cellular interplays underlying obesity-induced adipose tissue fibrosis
Marcelin, G., Silveira, A. L. M., Martins, L. B., Ferreira, A. V. M., & Clément, K. (2019) · Journal of Clinical Investigation · doi:10.1172/jci129192 · Cited by 291
“The progenitors also coexpressed PDGFRβ (35, 36).”
Hughes, T. K., Wadsworth, M. H., Gierahn, T. M., Do, T. H., Weiss, D. I., Andrade, P. R., Ma, F., Silva, B. J. D. A., Shao, S., Tsoi, L. C., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.09.015 · Cited by 291
Cellular crosstalk in the development and regeneration of the respiratory system
Zepp, J. A., & Morrisey, E. E. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0141-3 · Cited by 265
Stem Cells and the Differentiation Hierarchy in Mammary Gland Development
Fu, N. Y., Nolan, E., Lindeman, G. J., & Visvader, J. E. (2019) · Physiological Reviews · doi:10.1152/physrev.00040.2018 · Cited by 280
Deep learning–based cell composition analysis from tissue expression profiles
Menden, K., Marouf, M., Oller, S., Dalmia, A., Magruder, D. S., Kloiber, K., Heutink, P., & Bonn, S. (2020) · Science Advances · doi:10.1126/sciadv.aba2619 · Cited by 260
Kwok, I., Becht, É., Xia, Y., Ng, M. S. F., Teh, Y. C., Tan, L., Evrard, M., Li, J. L. Y., Tran, H. T., Tan, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.06.005 · Cited by 270
Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set
Kebschull, J. M., Richman, E. B., Ringach, N., Friedmann, D., Albarran, E., Kolluru, S. S., Jones, R. C., Allen, W. E., Wang, Y., Cho, S. W., et al. (2020) · Science · doi:10.1126/science.abd5059 · Cited by 262
SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., & Jiang, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz166 · Cited by 250
APOE mediated neuroinflammation and neurodegeneration in Alzheimer’s disease
Parhizkar, S., & Holtzman, D. M. (2022) · Seminars in Immunology · doi:10.1016/j.smim.2022.101594 · Cited by 255
Unbiased spatial proteomics with single-cell resolution in tissues
Mund, A., Brunner, A.-D., & Mann, M. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.022 · Cited by 259
Dynamics of Cardiac Neutrophil Diversity in Murine Myocardial Infarction
Vafadarnejad, E., Rizzo, G., Krampert, L., Arampatzi, P., Arias-Loza, P.-A., Nazzal, Y., Rizakou, A., Knochenhauer, T., Bandi, S. R., Nugroho, V. A., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.317200 · Cited by 268
Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview
Slovin, S., Carissimo, A., Panariello, F., Grimaldi, A., Bouchè, V., Gambardella, G., & Cacchiarelli, D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1307-8_19 · Cited by 264
A role of PIEZO1 in iron metabolism in mice and humans
Ma, S., Dubin, A. E., Zhang, Y., Mousavi, S. A. R., Wang, Y., Coombs, A. M., Loud, M., Andolfo, I., & Patapoutian, A. (2021) · Cell · doi:10.1016/j.cell.2021.01.024 · Cited by 232
“Expression of Piezo1 in mice in liver cells and macrophages, related to Figure 2 A. Differential expression of Piezo1 (purple/blue color) in t-SNE projection ofmouse liver cells from single-cell RNaseq data (TabulaMuris Consortium et al., 2018).”
BackgroundAutomated methods for cell type annotation on scRNA-seq data
Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249
“In tests using the Tabula Muris Atlas as a training dataset [47], ACTINN was highly accurate in classifying strictly related cellular subtypes, and was robust against batch effects arising from the use of different sequencing techniques.”
MethodKIR + CD8 + T cells suppress pathogenic T cells and are active in autoimmune diseases and COVID-19
Li, J., Zaslavsky, M., Su, Y., Guo, J., Sikora, M. J., van Unen, V., Christophersen, A., Chiou, S.-H., Chen, L., Li, J., et al. (2022) · Science · doi:10.1126/science.abi9591 · Cited by 249
A subset of Kupffer cells regulates metabolism through the expression of CD36
Blériot, C., Barreby, E., Dunsmore, G., Ballaire, R., Chakarov, S., Ficht, X., De Simone, G., Andreata, F., Fumagalli, V., Guo, W., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.08.006 · Cited by 236
Proximal colon–derived O-glycosylated mucus encapsulates and modulates the microbiota
Bergstrom, K., Shan, X., Casero, D., Batushansky, A., Lagishetty, V., Jacobs, J. P., Hoover, C., Kondo, Y., Shao, B., Gao, L., et al. (2020) · Science · doi:10.1126/science.aay7367 · Cited by 248
A comprehensive library of human transcription factors for cell fate engineering
Ng, A. H. M., Khoshakhlagh, P., Arias, J. E. R., Pasquini, G., Wang, K., Swiersy, A., Shipman, S. L., Appleton, E., Kiaee, K., Kohman, R. E., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0742-6 · Cited by 243
Dell'Orso, S., Juan, A. H., Ko, K.-D., Naz, F., Perovanovic, J., Gutierrez-Cruz, G., Feng, X., & Sartorelli, V. (2019) · Development · doi:10.1242/dev.174177 · Cited by 243
Brain Endothelial Cells Are Exquisite Sensors of Age-Related Circulatory Cues
Chen, M. B., Yang, A. C., Yousef, H., Lee, D., Chen, W., Schaum, N., Lehallier, B., Quake, S. R., & Wyss-Coray, T. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.03.012 · Cited by 233
Context specificity of the EMT transcriptional response
Cook, D. P., & Vanderhyden, B. C. (2020) · Nature Communications · doi:10.1038/s41467-020-16066-2 · Cited by 240
Identification, discrimination and heterogeneity of fibroblasts
Lendahl, U., Muhl, L., & Betsholtz, C. (2022) · Nature Communications · doi:10.1038/s41467-022-30633-9 · Cited by 256
Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
He, S., Wang, L.-H., Liu, Y., Li, Y., Chen, H.-T., Xu, J.-H., Peng, W., Lin, G.-W., Wei, P.-P., Li, B., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02210-0 · Cited by 237
Advances in spatial transcriptomic data analysisPreprint
Dries, R., Chen, J. G., Rossi, N. D., Khan, M. M., Sistig, A., & Yuan, G. (2021) · Genome Research · doi:10.1101/gr.275224.121 · Cited by 246
“Together with the development of a rich set of computational methods for data analysis (for review, see Yuan 2019; Hie et al. 2020), the scRNA-seq field has fulfilled a key role in the discovery of novel cell types and laid the foundation for the creation of comprehensive cell atlases in different species (Han et al.…”
BackgroundInfluentialCellRank 2: unified fate mapping in multiview single-cell data
Weiler, P., Lange, M., Klein, M., Pe’er, D., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02303-9 · Cited by 285
“The considered datasets are bone marrow 77 (using 10x and SmartSeq2), C. elegans embryogenesis 78 (subsetted to ciliated neurons, hypodermis and seam, or muscle and mesoderm) and zebrafish embryogenesis 79 .”
Harmonized cross-species cell atlases of trigeminal and dorsal root ganglia
Bhuiyan, S. A., Xu, M., Yang, L., Semizoglou, E., Bhatia, P., Pantaleo, K. I., Tochitsky, I., Jain, A., Erdoğan, B., Blair, S. J., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj9173 · Cited by 260
High throughput error corrected Nanopore single cell transcriptome sequencing
Lebrigand, K., Magnone, V., Barbry, P., & Waldmann, R. (2020) · Nature Communications · doi:10.1038/s41467-020-17800-6 · Cited by 236
Unraveling B cell trajectories at single cell resolution
Morgan, D., & Tergaonkar, V. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.01.003 · Cited by 239
eIF2B activator prevents neurological defects caused by a chronic integrated stress response
Wong, Y. L., LeBon, L., Basso, A. M., Kohlhaas, K. L., Nikkel, A. L., Robb, H. M., Donnelly-Roberts, D. L., Prakash, J., Swensen, A. M., Rubinstein, N. D., et al. (2019) · eLife · doi:10.7554/elife.42940 · Cited by 236
Lopez‐Anido, C., Vatén, A., Smoot, N. K., Sharma, N., Guo, V., Gong, Y., Gil, M. X. A., Weimer, A. K. K., & Bergmann, D. C. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.03.014 · Cited by 223
Regulation of adult stem cell quiescence and its functions in the maintenance of tissue integrity
de Morrée, A., & Rando, T. A. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00568-6 · Cited by 258
Wang, Z., Emmerich, A., Pillon, N. J., Moore, T. M., Hemerich, D., Cornelis, M. C., Mazzaferro, E., Broos, S., Ahluwalia, T. S., Bartz, T. M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01165-1 · Cited by 236
Depleting myeloid-biased haematopoietic stem cells rejuvenates aged immunity
Ross, J., Myers, L. M., Noh, J. J., Collins, M. M., Carmody, A., Messer, R. J., Dhuey, E., Hasenkrug, K. J., & Weissman, I. L. (2024) · Nature · doi:10.1038/s41586-024-07238-x · Cited by 245
Improved reconstruction of single-cell developmental potential with CytoTRACE 2
Kang, M., Armenteros, J. J. A., Gulati, G. S., Gleyzer, R., Avagyan, S., Brown, E. L., Zhang, W., Usmani, A., Earland, N. J., Wu, Z., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02857-2 · Cited by 292
The Multifaceted Biology of PCSK9
Seidah, N. G., & Prat, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnab035 · Cited by 232
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 229
Clarke, Z. A., Andrews, T. S., Atif, J., Pouyabahar, D., Innes, B. T., MacParland, S. A., & Bader, G. D. (2021) · Nature Protocols · doi:10.1038/s41596-021-00534-0 · Cited by 234
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253
“…(scRNA-seq) data, including projects like the Human Cell Atlas (https://www.humancellatlas.org/ accessed on 25 May 2023, [78]), Tabula Muris (https://tabula-muris.ds.czbiohub.org/ accessed on 25 May 2023, [79]), and the Mouse Cell Atlas (https://bis.zju.edu.cn/MCA/ accessed on 25 May 2023 [80]).”
MethodRare Pulmonary Neuroendocrine Cells Are Stem Cells Regulated by Rb, p53, and Notch
Ouadah, Y., Rojas, E., Riordan, D. P., Capostagno, S., Kuo, C. S., & Krasnow, M. A. (2019) · Cell · doi:10.1016/j.cell.2019.09.010 · Cited by 223
“Tabula Muris Consortium (2018).”
InfluentialInhibition of inflammatory microglia by dietary fiber and short-chain fatty acids
Caetano-Silva, M. E., Rund, L., Hutchinson, N. T., Woods, J. A., Steelman, A. J., & Johnson, R. W. (2023) · Scientific Reports · doi:10.1038/s41598-022-27086-x · Cited by 225
Expression Atlas update: gene and protein expression in multiple species
Moreno, P. A., Fexova, S. K., George, N., Manning, J. R., Miao, Z., Mohammed, S., Fuentes, A. M.-P., Füllgrabe, A., Bi, Y., Bush, N. G., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1030 · Cited by 223
Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis
Chen, Y., Wang, H., Yang, Q., Zhao, W., Chen, Y., Ni, Q., Li, W., Shi, J., Zhang, W., Li, L., et al. (2022) · Theranostics · doi:10.7150/thno.65694 · Cited by 233
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218
An entropy-based metric for assessing the purity of single cell populations
Liu, B., Li, C., Li, Z., Wang, D., Ren, X., & Zhang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-16904-3 · Cited by 217
“We reasoned that Tabula Muris can be considered as such a plausible reference dataset because it comprises cells from 20 organs, which represents a highly heterogeneous population and was sequenced with both 10X Genomics and Smart-seq2 protocols 2 .”
BackgroundSingle-cell transcriptomics unveils gene regulatory network plasticity
Iacono, G., Massoni-Badosa, R., & Heyn, H. (2019) · Genome Biology · doi:10.1186/s13059-019-1713-4 · Cited by 218
“We apply this new metric to generate global, large scale regulatory networks for 11 mouse organs (Tabula Muris Consortium et al., 2018), for pancreas tissue from healthy individuals and patients with type 2 diabetes (Segerstolpe et al., 2016), and for a mouse model of Alzheimer’s disease (Keren-Shaul et al., 2017).”
MethodInfluentialA molecular cell atlas of the human lung from single cell RNA sequencingPreprint
Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/742320 · Cited by 216
Enhanced safety and efficacy of protease-regulated CAR-T cell receptors
Labanieh, L., Majzner, R. G., Klysz, D. D., Sotillo, E., Fisher, C., Vilches-Moure, J. G., Pacheco, K. Z., Malipatlolla, M. B., Xu, P., Hui, J. H., et al. (2022) · Cell · doi:10.1016/j.cell.2022.03.041 · Cited by 232
“Mouse liver, kidney, and spleen smart-seq datasets were accessed from the Tabula Muris cell atlas (Tabula Muris Consortium et al., 2018) and the mouse lung data was accessed from the Raredon et al. dataset (Raredon et al., 2019).”
MethodCoBATCH for High-Throughput Single-Cell Epigenomic Profiling
Wang, Q., Xiong, H., Ai, S., Yu, X., Liu, Y., Zhang, J., & He, A. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.015 · Cited by 213
Cell transcriptomic atlas of the non-human primate Macaca fascicularis
Han, L., Wei, X., Liu, C., Volpe, G., Zhuang, Z., Zou, X., Wang, Z., Pan, T., Yuan, Y., Zhang, X., et al. (2022) · Nature · doi:10.1038/s41586-022-04587-3 · Cited by 211
Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing
Li, C. M.-C., Shapiro, H., Tsiobikas, C., Selfors, L. M., Chen, H., Rosenbluth, J., Moore, K., Gupta, K. P., Gray, G. K., Oren, Y., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108566 · Cited by 184
“…knowledge of mammary epithelia in early and adult development (Bach et al., 2017; Giraddi et al., 2018; Nguyen et al., 2018; Pal et al., 2017; Wuidart et al., 2018), characterization of stromal cells has been limited (Han et al., 2018; Kanaya et al., 2019; Tabula Muris Consortium, 2018, 2020).”
McKellar, D. W., Walter, L. D., Song, L. T., Mantri, M., Wang, M. F. Z., Vlaminck, I. D., & Cosgrove, B. D. (2021) · Communications Biology · doi:10.1038/s42003-021-02810-x · Cited by 224
Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
Kimmel, J. C., Penland, L., Rubinstein, N. D., Hendrickson, D. G., Kelley, D. R., & Rosenthal, A. Z. (2019) · Genome Research · doi:10.1101/gr.253880.119 · Cited by 184
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189
He, J., Babarinde, I. A., Sun, L., Xu, S., Chen, R., Shi, J., Wei, Y., Li, Y., Ma, G., Zhuang, Q., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21808-x · Cited by 205
Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies
González-Silva, L., Quevedo, L., & Varela, I. (2020) · Trends in cancer · doi:10.1016/j.trecan.2019.11.010 · Cited by 205
Single-cell analyses of aging, inflammation and senescence
Uyar, B., Palmer, D. H., Kowald, A., Escobar, H. M., Barrantes, I., Möller, S., Akalin, A., & Fuellen, G. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101156 · Cited by 207
Single-Cell Analysis of Blood-Brain Barrier Response to Pericyte Loss
Mäe, M. A., He, L., Nordling, S., Vázquez-Liébanas, E., Nahar, K., Jung, B., Li, X., Tan, B. C., Foo, J. C., Cazenave‐Gassiot, A., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.317473 · Cited by 220
Gladyshev, V. N., Kritchevsky, S. B., Clarke, S. G., Cuervo, A. M., Fiehn, O., de Magalhães, J. P., Mau, T., Maes, M., Moritz, R. L., Niedernhofer, L. J., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00150-3 · Cited by 222
Single-Cell Transcriptomics: A High-Resolution Avenue for Plant Functional Genomics
Rich‐Griffin, C., Stechemesser, A., Finch, J., Lucas, E. S., Ott, S., & Schäfer, P. (2019) · Trends in Plant Science · doi:10.1016/j.tplants.2019.10.008 · Cited by 199
Role of Venous Endothelial Cells in Developmental and Pathologic Angiogenesis
Lee, H., Xu, Y., He, L., Choi, W., Gonzalez, D. G., Jin, S., & Simons, M. (2021) · Circulation · doi:10.1161/circulationaha.121.054071 · Cited by 199
García‐Bonilla, L., Shahanoor, Z., Sciortino, R. K., Nazarzoda, O., Racchumi, G., Iadecola, C., & Anrather, J. (2024) · Nature Immunology · doi:10.1038/s41590-023-01711-x · Cited by 225
Triana, S., Vonficht, D., Jopp-Saile, L., Raffel, S., Lutz, R., Leonce, D., Antes, M., Hernández-Malmierca, P., Ordoñez-Rueda, D., Ramasz, B., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-01059-0 · Cited by 195
Cells of origin of lung cancers: lessons from mouse studiesPreprint
Ferone, G., Lee, M. C., Sage, J., & Berns, A. J. M. (2020) · Genes & Development · doi:10.1101/gad.338228.120 · Cited by 198
Spatially resolved single-cell translatomics at molecular resolution
(2023) · doi:10.1126/science.add3067 · Cited by 198
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195
“Hence, this ratio can provide insight into how transcript levels are changing over time and, thus, where cells might be ‘headed’ in the future state 50 .”
BackgroundMethodIntegration of whole transcriptome spatial profiling with protein markers
Ben‐Chetrit, N., Niu, X., Swett, A. D., Sotelo, J., Jiao, M. S., Stewart, C. M., Potenski, C. J., Mielinis, P., Roelli, P., Stoeckius, M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01536-3 · Cited by 214
Ageing compromises mouse thymus function and remodels epithelial cell differentiation
Baran‐Gale, J., Morgan, M. D., Maio, S., Dhalla, F., Calvo‐Asensio, I., Deadman, M. E., Handel, A. E., Maynard, A., Chen, S., Green, F., et al. (2020) · eLife · doi:10.7554/elife.56221 · Cited by 191
Integrative analyses of single-cell transcriptome and regulome using MAESTRO
Wang, C., Sun, D., Huang, X., Wan, C., Li, Z., Han, Y., Qin, Q., Fan, J., Qiu, X., Xie, Y., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02116-x · Cited by 185
“By default, the immune LM22 gene signature from CIBERSORT [42] is used to annotate the cell types (Additional file 2: Table S2); we have also included signatures from adult mouse brain and Tabula Muris in the MAESTRO package [43], and other user-defined signatures are also supported.”
MethodInfluentialGenetic mapping of etiologic brain cell types for obesity
Timshel, P. N., Thompson, J. J., & Pers, T. H. (2020) · eLife · doi:10.7554/elife.55851 · Cited by 202
Zhang, H., Tian, L., Shen, M., Tu, C., Wu, H., Gu, M., Paik, D. T., & Wu, J. C. (2019) · Circulation Research · doi:10.1161/circresaha.119.315491 · Cited by 187
SciBet as a portable and fast single cell type identifier
Li, C., Liu, B., Kang, B., Liu, Z., Liu, Y., Chen, C., Ren, X., & Zhang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-15523-2 · Cited by 176
“(1) and (2), ΔSi can be calculated as: ΔSi 1⁄4 Xn j1⁄41 Si0 Sij 1⁄4 Xn j1⁄41 ðlnXi0 þ hi lnXij hiÞ ð3Þ”
Schafflick, D., Wolbert, J., Heming, M., Thomas, C., Hartlehnert, M., Börsch, A., Ricci, A., Martín-Salamanca, S., Li, X., Lu, I., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00880-y · Cited by 188
Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D., Yang, P., & Yang, J. Y. H. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199389 · Cited by 168
The lung microenvironment shapes a dysfunctional response of alveolar macrophages in aging
McQuattie-Pimentel, A. C., Ren, Z., Joshi, N., Watanabe, S., Stoeger, T., Chi, M., Lu, Z., Sichizya, L., Aillon, R. P., Chen, C.-I., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci140299 · Cited by 177
“One cluster (cluster 2) was characterized by increased expression of Lyz1 (Supplemental Figure 1F) and has been noted in previous data sets (15, 16).”
ResultShao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158
The changing mouse embryo transcriptome at whole tissue and single-cell resolution
He, P., Williams, B. A., Trout, D. E., Marinov, G. K., Amrhein, H., Berghella, L., Goh, S.-T., Plajzer-Frick, I., Afzal, V., Pennacchio, L. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2536-x · Cited by 174
Single-cell atlas of the human brain vasculature across development, adulthood and disease
Wälchli, T., Ghobrial, M., Schwab, M., Takada, S., Zhong, H., Suntharalingham, S., Vetiska, S., Rodríguez, D. G., Wu, R., Rehrauer, H., et al. (2024) · Nature · doi:10.1038/s41586-024-07493-y · Cited by 217
Haage, V. C., Semtner, M., Vidal, R. O., Pérez-Hernández, D., Pong, W. W., Chen, Z., Hambardzumyan, D., Magrini, V. J., Ly, A., Walker, J., et al. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0665-y · Cited by 182
Rajbhandari, P., Arneson, D. V., Hart, S. K., Ahn, I. S., Diamante, G., SANTOS, L. V. D. S., Zaghari, N., Feng, A., Thomas, B. J., Vergnes, L., et al. (2019) · eLife · doi:10.7554/elife.49501 · Cited by 173
Cellular heterogeneity in adipose tissues
Corvera, S. (2021) · Annual Review of Physiology · doi:10.1146/annurev-physiol-031620-095446 · Cited by 171
Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress
Hagemann-Jensen, M., Ziegenhain, C., & Sandberg, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01311-4 · Cited by 196
Horns, F., Dekker, C. L., & Quake, S. R. (2020) · Cell Reports · doi:10.1016/j.celrep.2019.12.063 · Cited by 160
BMP6/TAZ-Hippo signaling modulates angiogenesis and endothelial cell response to VEGF
Pulkkinen, H. H., Kiema, M., Lappalainen, J. P., Toropainen, A., Beter, M., Tirronen, A., Holappa, L., Niskanen, H., Kaikkonen, M. U., Ylä‐Herttuala, S., et al. (2020) · Angiogenesis · doi:10.1007/s10456-020-09748-4 · Cited by 175
Transcriptional heterogeneity of fibroblasts is a hallmark of the aging heart
Vidal, R. O., Wagner, J. U. G., Braeuning, C., Fischer, C., Patrick, R., Tombor, L., Muhly-Reinholz, M., John, D., Kliem, M., Conrad, T., et al. (2019) · JCI Insight · doi:10.1172/jci.insight.131092 · Cited by 168
“We projected single heart cells from the Tabula Muris consortium (12) set onto our single nuclei to identify the similar cell types and show the reproducibility from both approaches.”
BackgroundMethodFrom FAANG to fork: application of highly annotated genomes to improve farmed animal production
Clark, E. L., Archibald, A. L., Daetwyler, H. D., Groenen, M. A. M., Harrison, P. W., Houston, R. D., Kühn, C., Lien, S., Macqueen, D. J., Reecy, J. M., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02197-8 · Cited by 167
Emerging Principles in Myelopoiesis at Homeostasis and during Infection and Inflammation
Schultze, J. L., Mass, E., & Schlitzer, A. (2019) · Immunity · doi:10.1016/j.immuni.2019.01.019 · Cited by 174
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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