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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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.
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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.
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.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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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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.
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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.
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.
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 801–900 of 2,952
Vascular Homeostasis and Inflammation in Health and Disease—Lessons from Single Cell Technologies
Bondareva, O., & Sheikh, B. N. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21134688 · Cited by 31
“The first large-scale initiative to produce a reference single cell atlas of healthy wild type mice was pioneered by the Tabula Muris Consortium that sequenced more than 100,000 cells from 20 mouse organs [21].”
BackgroundPHAROH lncRNA regulates Myc translation in hepatocellular carcinoma via sequestering TIAR
Yu, A. T., Berasain, C., Bhatia, S., Rivera, K. D., Liu, B., Rigo, F., Pappin, D., & Spector, D. L. (2021) · eLife · doi:10.7554/elife.68263 · Cited by 32
Yuge, S., Nishiyama, K., Arima, Y., Hanada, Y., Oguri‐Nakamura, E., Hanada, S., Ishii, T., Wakayama, Y., Hasegawa, U., Tsujita, K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30197-8 · Cited by 33
Ünlü, G., Prizer, B., Erdal, R., Yeh, H.-W., Bayraktar, E. C., & Birsoy, K. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.024 · Cited by 34
Kim, T. K., Jeon, S., Park, S., Sonn, S., Seo, S., Suh, J., Jin, J., Kweon, H. Y., Kim, S., Moon, S. H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34433-z · Cited by 34
Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis
Raimundo, F., Vallot, C., & Vert, J. (2020) · Genome biology · doi:10.1186/s13059-020-02128-7 · Cited by 33
Zhang, Y., Xing, X., Long, B., Cao, Y., Hu, S., Li, X., Yu, Y., Tian, D., Sui, B., Luo, Z., et al. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.700 · Cited by 30
Age- and Microbiota-Dependent Cell Stemness Plasticity Revealed by Cattle Cell Landscape
Wu, J., Zhu, S., Tang, Y., Gu, F., Valencak, T. G., Liu, J., & Sun, H. (2023) · Research · doi:10.34133/research.0025 · Cited by 32
Spatially resolved multiomics on the neuronal effects induced by spaceflight in mice
Masarapu, Y., Cekanaviciute, E., Andrusivová, Ž., Westholm, J. O., Björklund, Å. K., Fallegger, R., Badia-i-Mompel, P., Boyko, V., Vasisht, S., Saravia-Butler, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48916-8 · Cited by 32
Septal LYVE1 + macrophages control adipocyte stem cell adipogenic potential
Yu, X., Hu, Y., Lim, H. Y., Li, Z., Jaitin, D. A., Yang, K. L., Kong, W. T., Xu, J., Bejarano, D. A., Bied, M., et al. (2025) · Science · doi:10.1126/science.adg1128 · Cited by 35
Deng, J., Ni, Z., Gu, W., Chen, Q., Nowak, W. N., Chen, T., Bhaloo, S. I., Zhang, Z., Hu, Y., Zhou, B., et al. (2020) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03480-4 · Cited by 32
Li, Y., Haug, S., Schlosser, P., Teumer, A., Tin, A., Pattaro, C., Köttgen, A., & Wuttke, M. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020010051 · Cited by 30
Chen, T. N., Gupta, A., Zalavadia, M., & Streets, A. (2020) · Lab on a Chip · doi:10.1039/d0lc00169d · Cited by 30
Machine learning methods to model multicellular complexity and tissue specificity
Sealfon, R. S. G., Wong, A. K., & Troyanskaya, O. G. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00339-3 · Cited by 30
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Fan, Z., Ardicoglu, R., Batavia, A. A., Rust, R., von Ziegler, L., Waag, R., Zhang, J., Desgeorges, T., Sturman, O., Dang, H., et al. (2023) · Angiogenesis · doi:10.1007/s10456-023-09870-z · Cited by 34
A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the MousePreprint
Pisco, A. O., McGeever, A., Schaum, N., Karkanias, J., Neff, N. F., Darmanis, S., Wyss‐Coray, T., & Quake, S. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/661728 · Cited by 57
Segers, V. F. M., Bringmans, T., & Keulenaer, G. W. D. (2023) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00256.2023 · Cited by 37
Kim, S. H., White, Z., Gainullina, A., Kang, S., Kim, J., Dominguez, J. R., Choi, Y., Cabrera, I., Plaster, M., Takahama, M., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.04.004 · Cited by 34
p73 regulates epidermal wound healing and induced keratinocyte programming
Beeler, J. S., Marshall, C. B., Ericsson, P. I. G., Shaver, T. M., Guasch, G. L. S., Lea, S. T., Johnson, K. N., Jin, H., Venters, B. J., Sanders, M. E., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218458 · Cited by 30
Impaired plasma membrane localization of ubiquitin ligase complex underlies 3-M syndrome development
Wang, P., Yan, F., Li, Z., Yu, Y., Parnell, S. E., & Xiong, Y. (2019) · Journal of Clinical Investigation · doi:10.1172/jci129107 · Cited by 31
Ding, H., Blair, A. P., Yang, Y., & Stuart, J. M. (2019) · Nature Communications · doi:10.1038/s41467-019-12924-w · Cited by 33
Integrative Cluster Analysis of Whole Hearts Reveals Proliferative Cardiomyocytes in Adult Mice
Galow, A.-M., Wolfien, M., Müller, P., Bartsch, M., Brunner, R. M., Hoeflich, A., Wolkenhauer, O., David, R., & Goldammer, T. (2020) · Cells · doi:10.3390/cells9051144 · Cited by 29
In vivo studies of glucagon secretion by human islets transplanted in mice
Tellez, K., Hang, Y., Gu, X., Chang, C., Stein, R. W., & Kim, S. K. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0213-x · Cited by 28
Single-cell and spatial transcriptomics approaches of cardiovascular development and disease
Roth, R., Kim, S., Kim, J., & Rhee, S. (2020) · BMB Reports · doi:10.5483/bmbrep.2020.53.8.130 · Cited by 32
Histamine triggers microglial responses indirectly via astrocytes and purinergic signaling
Xia, P., Logiacco, F., Huang, Y., Kettenmann, H., & Semtner, M. (2021) · Glia · doi:10.1002/glia.24039 · Cited by 30
“Histamine is a bioactive amine known to regulate many physiological processes in the periphery, such as allergic reactions, gastric acid secretion, and itch sensation (Thangam et al. (2018).”
BackgroundScalzo, R. L., Foright, R. M., Hull, S., Knaub, L. A., Johnson-Murguia, S., Kinanee, F., Kaplan, J., Houck, J. A., Johnson, G. C., Sharp, R. R., et al. (2021) · Endocrinology · doi:10.1210/endocr/bqab174 · Cited by 29
“To further understand what cells respond to changes in estrogen and ER signaling, we explored the Tabula Muris resource of single cell sequencing data from numerous murine tissues (without uterus, ovaries, and testes) (40).”
BackgroundHanners, N. W., Mar, K. B., Boys, I. N., Eitson, J. L., Cruz-Rivera, P. C. D. L., Richardson, R. B., Fan, W., Wight‐Carter, M., & Schoggins, J. W. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111266118 · Cited by 32
scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
Qian, K., Fu, S., Li, H., & Li, W. V. (2022) · Genome biology · doi:10.1186/s13059-022-02649-3 · Cited by 26
Shin, M., Chan, I. L., Cao, Y., Gruntman, A. M., Lee, J. T., Sousa, J., Rodríguez, T., Echeverria, D., Devi, G., Debacker, A. J., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac630 · Cited by 32
Ye, W., Lian, Q., Ye, C., & Wu, X. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.09.005 · Cited by 34
“So far, the most widely used datasets for evaluating pA tools for DNA sequences are the PASS dataset [68,69] of plant species, the ERPIN dataset [23] of human, and the DeepGSR dataset [27] of animal species; datasets for bulk RNA-seq are the MAQC dataset [123] and the HEK293 dataset [124]; and datasets for scRNA-seq a…”
MethodA Roadmap for the Human Oral and Craniofacial Cell Atlas
Caetano, A. J., Bionetwork, H. C. A. O. A. C., Sequeira, I., Byrd, K. M., Caetano, A., Sharpe, P. T., Volponi, A. A., Yianni, V., Bush, M., McKay, L. K., et al. (2022) · Journal of Dental Research · doi:10.1177/00220345221110768 · Cited by 30
Mitochondrial RNA stimulates beige adipocyte development in young mice
Hoang, A. C., Yu, H., Lin, Y., Chen, J.-C., Chen, C., Diedrich, V., Herwig, A., Landgraf, K., Körner, A., & Rőszer, T. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00683-w · Cited by 33
Oderberg, I. M., & Goessling, W. (2023) · JCI Insight · doi:10.1172/jci.insight.163929 · Cited by 30
Nonmyocytes as electrophysiological contributors to cardiac excitation and conduction
Simón-Chica, A., Wülfers, E. M., & Kohl, P. K. (2023) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00184.2023 · Cited by 34
Strunz, M., Simon, L. M., Ansari, M., Mattner, L. F., Angelidis, I., Mayr, C. H., Kathiriya, J. J., Yee, M., Ogar, P., Sengupta, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/705244 · Cited by 30
STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse
Gómez-Cabrero, D., Tarazona, S., Ferreirós-Vidal, I., Ramírez, R. N., Schmidt, A., Reijmers, T. H., Paul, V. V. S., Marabita, F., Rodríguez‐Ubreva, J., García-Gómez, A., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0202-7 · Cited by 29
Gao, W. N. D., Gallardo-Dodd, C. J., & Kutter, C. (2021) · Genome Research · doi:10.1101/gr.275944.121 · Cited by 30
Ren, W., Hua, M., Cao, F., & Zeng, W. (2023) · Advanced Science · doi:10.1002/advs.202306128 · Cited by 32
Salo, A. M., Rappu, P., Koski, M. K., Karjalainen, E., Izzi, V., Drushinin, K., Miinalainen, I., Käpylä, J., Heino, J., & Myllyharju, J. (2023) · Matrix Biology · doi:10.1016/j.matbio.2023.12.001 · Cited by 35
Sullivan, J. M., Bagnell, A. M., Alevy, J., Avila, E. M., Mihaljević, L., Saavedra-Rivera, P. C., Kong, L., Huh, J. S., McCray, B. A., Aisenberg, W. H., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adk1358 · Cited by 29
Liver exerkine reverses aging- and Alzheimer’s-related memory loss via vasculature
Bieri, G., Pratt, K. J. B., Fuseya, Y., Aghayev, T., Sucharov, J., Horowitz, A. M., Philp, A. R., Fonseca-Valencia, K., Chu, R., Phan, M., et al. (2026) · Cell · doi:10.1016/j.cell.2026.01.024 · Cited by 35
Ma, J., Tran, G., Wan, A. M. D., Young, E. W. K., Kumacheva, E., Iscove, N. N., & Zandstra, P. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-86087-4 · Cited by 30
Zou, B., Zhang, T., Zhou, R., Jiang, X., Yang, H., Jin, X., & Bai, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.708981 · Cited by 31
“…has enabled the gene expression profiling of a large number of individual cells at a single-cell resolution, offering unprecedented insights into the transcriptomic characterization of cell heterogeneity and dynamics (Stegle et al., 2015; Consortium, 2018; Han et al., 2018; Svensson et al., 2018).”
BackgroundDuctal Macrophages Predominate in the Immune Landscape of the Lactating Mammary Gland
Hassel, C., Gausserès, B., Guzylack‐Piriou, L., & Foucras, G. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.754661 · Cited by 30
Ng, B., Viswanathan, S., Widjaja, A. A., Lim, W., Shekeran, S. G., Goh, J. W. T., Tan, J., Kuthubudeen, F. F., Lim, S. Y., Xie, C., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073549 · Cited by 30
ABC portal: a single-cell database and web server for blood cells
Gao, X., Hong, F., Hu, Z., Zhang, Z., Yang, L., Li, X., & Cheng, T. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac646 · Cited by 32
Lung Cell Atlases in Health and Disease
Adams, T. S., Marlier, A., & Kaminski, N. (2022) · Annual Review of Physiology · doi:10.1146/annurev-physiol-032922-082826 · Cited by 34
Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research
Feng, S., Li, J., Tian, J., Lu, S., & Zhao, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032271 · Cited by 29
Franchini, M., Pellecchia, S., Viscido, G., & Gambardella, G. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad024 · Cited by 30
“It provides a cost-effective way to study cellular tissue composition (1–5), dynamic processes during cell developmental stages (6), or the role of transcriptional heterogeneity in pathological conditions and in response to treatments (7).”
BackgroundMethodOit3, a promising hallmark gene for targeting liver sinusoidal endothelial cells
Li, Z., Ruan, B., Yang, P., Liu, J., Song, P., Duan, J., & Wang, L. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01621-2 · Cited by 29
“33 The clusters of distinct organs and tissues were demonstrated in Fig.”
genesorteR: Feature Ranking in Clustered Single Cell DataPreprint
Ibrahim, M. M., & Kramann, R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/676379 · Cited by 27
Atlas of ACE2 gene expression in mammals reveals novel insights in transmisson of SARS-Cov-2Preprint
Sun, K., Gu, L., Ma, L., & Duan, Y. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.30.015644 · Cited by 38
Generation of mouse conditional knockout alleles in one step using thei-GONAD methodPreprint
Shang, R., Zhang, H., & Bi, P. (2020) · Genome Research · doi:10.1101/gr.265439.120 · Cited by 30
Noval, M. G., Spector, S. N., Bartnicki, E., Izzo, F., Narula, N., Yeung, S. T., Damani‐Yokota, P., Dewan, M. Z., Mezzano, V., Rodriguez-Rodriguez, B. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40047-w · Cited by 28
“Single-cell analysis of Tabula muris datasets Data from Tabula muris (Tabula Muris et al., 2018) was downloaded from https://tabula-muris.ds.czbiohub.org.”
Reduced FOXF1 links unrepaired DNA damage to pulmonary arterial hypertension
Isobe, S., Nair, R. V., Kang, H. Y., Wang, L., Moonen, J.-R. A. J., Shinohara, T., Cao, A., Taylor, S., Otsuki, S., Marciano, D. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43039-y · Cited by 32
Haberman, E. R., Sarker, G., Arús, B. A., Ziegler, K. A., Meunier, S., Martínez‐Sánchez, N., Freibergerová, E., Yilmaz-Özcan, S., Fernández-González, I., Zentai, C., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.11.006 · Cited by 28
Remodeling of the Cardiac Extracellular Matrix Proteome During Chronological and Pathological Aging
Santinha, D. R., Vilaça, A., Estronca, L. M. B. B., Schüler, S. C., Bartoli, C., Sandre‐Giovannoli, A. D., Figueiredo, A., Quaas, M., Pompe, W., Ori, A., et al. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100706 · Cited by 32
Advancing bioinformatics with language models: components, applications, and perspectives
Liu, J., Yang, M., Yu, Y., Xu, H., Wang, T., Li, K., & Zhou, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag367 · Cited by 66
Interpretable deep learning in single-cell omics
Wagle, M. M., Long, S., Chen, C., Liu, C., & Yang, P. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae374 · Cited by 32
“…the use of scRNA-seq has better facilitated biological discoveries than traditional bulk data such as in cell type annotation for model organisms (Iram et al. 2018, Jones et al. 2022) and cell lineage tracing during development and disease progression (Kester and Van Oudenaarden 2018, Wagner and…”
MethodYu, R., Zhang, C., Yuan, M., Ye, S., Hu, T., Chen, S., Liang, G., Liu, J., Ke, H., Huang, J., et al. (2025) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2025.101558 · Cited by 29
Biočanin, M., Bues, J., Dainese, R., Amstad, E., & Deplancke, B. (2019) · Lab on a Chip · doi:10.1039/c9lc00014c · Cited by 24
Intracellular Alpha-Synuclein and Immune Cell Function
Grozdanov, V., & Danzer, K. M. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.562692 · Cited by 27
“Several studies have suggested increased expression of alpha-synuclein in macrophages and astrocytes (Tanji et al., 2001, 2002) after stimulation with lipopolysaccharide and interleukins.”
BackgroundDill, T. L., Carroll, A., Pinheiro, A., Gao, J., & Naya, F. J. (2020) · Development · doi:10.1242/dev.194027 · Cited by 27
Gastrointestinal epithelial innate immunity—regionalization and organoids as new model
Kayisoglu, Ö., Schlegel, N., & Bartfeld, S. (2021) · Journal of Molecular Medicine · doi:10.1007/s00109-021-02043-9 · Cited by 34
Huang, X., Shen, W., Veizades, S. A., Liang, G., Sayed, N., & Nguyen, P. K. P. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.590377 · Cited by 28
“…artery disease (Nguyen et al., 2011; Madhavan et al., 2018), diabetes (DECODE Study Group, 2003), hypertension (Gillis and Sullivan, 2016), emphysema (Barnes, 2016), pulmonary artery hypertension (Lakshmanan et al., 2020), sarcopenia (Tay et al., 2015), and chronic kidney disease (Yu et al., 2012).”
BackgroundLucas, T. A., Zhu, L., & Buckwalter, M. S. (2021) · Glia · doi:10.1002/glia.23993 · Cited by 28
Altered islet prohormone processing: a cause or consequence of diabetes?
Ramzy, A., & Kieffer, T. J. (2021) · Physiological Reviews · doi:10.1152/physrev.00008.2021 · Cited by 27
ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks
Wang, Y., Liu, T., & Zhao, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac427 · Cited by 25
der Stede, T. V., Spaas, J., de Jager, S., Brandt, J. D., Hansen, C. C., Stautemas, J., Vercammen, B., Baere, S. D., Croubels, S., Assche, C. V., et al. (2023) · Acta Physiologica · doi:10.1111/apha.14020 · Cited by 27
Mast cell deficiency improves cognition and enhances disease-associated microglia in 5XFAD mice
Lin, C., Hérisson, F., Le, H., Jaafar, N., Chetal, K., Oram, M. K., Flynn, K. L., Gavrilles, E. P., Sadreyev, R. I., Schiffino, F. L., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113141 · Cited by 29
CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing
Sato, K., Tsuyuzaki, K., Shimizu, K., & Nikaido, I. (2019) · Genome biology · doi:10.1186/s13059-019-1639-x · Cited by 28
Molecular Cross-Validation for Single-Cell RNA-seqPreprint
Batson, J., Royer, L. A., & Webber, J. T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/786269 · Cited by 27
Bej, S., Galow, A.-M., David, R., Wolfien, M., & Olaf Wolkenhauer, A. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04469-x · Cited by 28
Hermans, F., Bueds, C., Hemeryck, L., Lambrichts, I., Bronckaers, A., & Vankelecom, H. E. J. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1021459 · Cited by 27
“Within the research community, large efforts are being made in combining sc omics datasets to establish comprehensive cell atlases of all tissues and organs, both in healthy and diseased conditions (Regev et al., 2017; Han et al., 2018; Schaum et al., 2018; Almanzar et al., 2020), epitomized in large consortia such as…”
BackgroundPan, C., Liu, J., Gao, Y., Yang, M., Hu, H., Liu, C., Qian, M., Yuan, H.-Y., Yang, S., Zheng, M.-H., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.10.018 · Cited by 27
An overview of computational methods in single-cell transcriptomic cell type annotation
Li, T., Wang, Z., Liu, Y., He, S., Zou, Q., & Zhang, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf207 · Cited by 28
A dynamic and multimodal framework to define microglial states
Sankowski, R., & Prinz, M. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01978-3 · Cited by 31
“The advent of single-cell RNA-sequencing data for hundreds of thousands of cells has further facilitated finely resolved cell atlases for whole organisms 42,43 .”
David, F. P. A., Litovchenko, M., Deplancke, B., & Gardeux, V. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa412 · Cited by 28
Cause or effect? The spatial organization of pathogens and the gut microbiota in disease
Nguyen, J., Pepin, D. M., & Tropini, C. (2021) · Microbes and Infection · doi:10.1016/j.micinf.2021.104815 · Cited by 27
Analysis of single-cell RNA sequencing data based on autoencoders
Tangherloni, A., Ricciuti, F., Besozzi, D., Lió, P., & Cvejic, A. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04150-3 · Cited by 30
“The former was generated by Han et al. [47] using Microwell-Seq (4239 cells) [47], while the latter by the Tabula Muris Consortium [48] using Smart-Seq2 (2715 cells).”
MethodWu, J.-Y., Qin, J., Li, L., Zhang, K.-D., Chen, Y., Li, Y., Jin, T., & Xu, J. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/5633514 · Cited by 27
Williams, I. M., & Wasserman, D. H. (2021) · Endocrinology · doi:10.1210/endocr/bqab252 · Cited by 27
AGC1 Deficiency: Pathology and Molecular and Cellular Mechanisms of the Disease
Pardo, B., Herrada-Soler, E., Satrústegui, J., Contreras, L., & del Arco, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23010528 · Cited by 31
“Regarding the expression of aralar in oligodendrocytes, there is no evidence of the protein ARALAR in mouse oligodendrocytes in vivo [7], and transcriptomic analysis revealed that oligodendrocyte precursor cells (OPCs) and mature oligodendrocytes do express aralar mRNA but at lower levels than neurons [37,59].”
BackgroundTang, H., Yu, X., Liu, R., & Zeng, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab584 · Cited by 27
“(c) For the multiclass classification task, 20 organ-specific single-cell datasets from the Tabula Muris were used, and the accuracy and kappa measurements were applied to evaluate all methods.”
MethodInfluentialWidespread choroid plexus contamination in sampling and profiling of brain tissue
Olney, K. C., Todd, K. T., Pallegar, P. N., Jensen, T. D., Cadiz, M. P., Gibson, K. A., Barnett, J. H., de Ávila, C., Bouchal, S. M., Rabichow, B. E., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01416-3 · Cited by 26
Wang, J., Xu, J., Zang, G., Zhang, T., Wu, Q., Zhang, H., Chen, Y., Wang, Y., Qin, W., Zhao, S., et al. (2022) · Research · doi:10.34133/2022/9839368 · Cited by 27
A parameter-free deep embedded clustering method for single-cell RNA-seq data
Zeng, Y., Wei, Z., Zhong, F., Pan, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac172 · Cited by 28
Inference of age-associated transcription factor regulatory activity changes in single cells
Maity, A. K., Hu, X., Zhu, T., & Teschendorff, A. E. (2022) · Nature Aging · doi:10.1038/s43587-022-00233-9 · Cited by 27
PlaqView 2.0: A comprehensive web portal for cardiovascular single-cell genomics
Feng, W., Turner, A. W., Gancayco, C. A., Wong, D., Song, Y., Mosquera, J. V., Auguste, G. E., Hodonsky, C. J., Prabhakar, A., Ekiz, H. A., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.969421 · Cited by 26
“Similarly, we use Seurat’s label transfer function FindTransferAnchors() and TransferData() to predict cell identity using the Tabula Sapiens and Tabula Muris (41) references, depending on the original species.”
MethodSimBu : bias-aware simulation of bulk RNA-seq data with variable cell-type composition
Dietrich, A., Sturm, G., Merotto, L., Marini, F., Finotello, F., & List, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac499 · Cited by 27
Wang, Y., Yu, Z., Li, S., Bian, C., Liang, Y., Wong, K., & Li, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad075 · Cited by 26
Mincarelli, L., Uzun, V., Wright, D. J., Scoones, A., Rushworth, S. A., Haerty, W., & Macaulay, I. C. (2023) · Communications Biology · doi:10.1038/s42003-023-04936-6 · Cited by 25
Multi-View Clustering With Graph Learning for scRNA-Seq Data
Wu, W., Zhang, W., Hou, W., & Ma, X. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3298334 · Cited by 27
“Finally, we validate the performance of the MCGL algo-rithm on a large-scale scRNA-seq dataset utilizing the Tabula dataset [58], which characterizes the 12,000 cells of 15 types from different organs.”
MethodModulation of insulin secretion by RBFOX2-mediated alternative splicing
Moss, N. D., Wells, K. L., Theis, A., Kim, Y.-K., Spigelman, A. F., Liu, X., MacDonald, P. E., & Sussel, L. (2023) · Nature Communications · doi:10.1038/s41467-023-43605-4 · Cited by 27
Gao, S., Dai, Y. D., & Rehman, J. (2021) · Genome Research · doi:10.1101/gr.265595.120 · Cited by 24
Ma, Q., Tao, H., Li, Q., Zhai, Z., Zhang, X., Lin, Z., Kuang, N., & Pan, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac942 · Cited by 27
scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis
Tian, S.-W., Ni, J., Wang, Y., Zheng, C.-H., & Ji, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3319551 · Cited by 27
Madsen, S., Delgado, A. C., Cadilhac, C., Maillard, V., Battiston, F., Igelbüscher, C. M., Neck, S. D., Magrinelli, E., Jabaudon, D., Telley, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49449-w · Cited by 29
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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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