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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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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with fewer than 50,000 reads (FACS) or 1000 UMI (microfluidic droplet) were excluded.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No sample size choice was performed prior to the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells were lexicographically sorted by cell ID to ensure reproducibility.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cell types were assigned to each cluster using the abundance of known marker genes.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“When clusters appeared to be mixtures of cell types, they were refined either by increasing the resolution parameter for clustering or subsetting the data and rerunning steps 3–7.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Clustering
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All animal care and procedures were carried out in accordance with institutional guidelines approved by the VA Palo Alto Committee on Animal Research.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Sample plates were selected for downstream processing if the mean concentration of blanks (ERCC-containing, non-cell wells) was greater than 0 ng/μl, and, after linear regression of the values obtained from the Quant-iT dsDNA standard curve, the R2 value was greater than 0.98.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > cDNA synthesis and library preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Four 10–15 week old male and four virgin female C57BL/6JN (derived from C57BL/6J in 2016) mice were shipped from the National Institute on Aging colony at Charles River to the Veterinary Medical Unit (VMU) at the VA Palo Alto (VA).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tabula Muris provides a framework and description of many of the most populous and important cell populations within the mouse, and represents a foundation for future studies across a multitude of diverse physiological disciplines.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Randomization and blinding were not performed – the authors were aware of all data and metadata related variables during the entire course of the study.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection > Sample size, randomization, and blinding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“7) Full-length transcript data makes possible transcription factor, splice variant, and sequence variant analyses.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
It was unknown whether single-cell data from different animals, labs, and techniques could be synthesized into a general resource for biology.
“It therefore remains unknown whether these data can be synthesized as a more general resource for biology.”
The paper relies on this without support.
Comparing data collected from different animals by independent labs with varying experimental techniques is challenging.
“However, while these reports provide valuable characterization of individual organs, it is challenging to compare data collected from different animals by independent labs with varying experimental techniques.”
The paper relies on this without support.
Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.
“Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“A Tabula Muris compendium of single-cell RNA-Seq transcriptome data from 20 mouse organs and tissues (czbiohub.org) (14) revealed that ASGR1 transcripts are exclusively expressed in liver, specifically in hepatocytes (Fig. 1C).”
From Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR cleaved by furin (2021) · cites this paper as [14]
In the citing paper
“CD38 expression was specific to adipose-tissue-resident macrophages (CD68+CD64+F4/80+CD11b+CD11c−), not the non-resident population (CD68+CD64+F4/80+CD11b+CD11c+) (Fig. 4h)”
From Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages (2020) · cites this paper as [36]
In the citing paper
“Ageing is associated with increased activation of the NLRP3 inflammasome45,107, which is primarily expressed by myeloid immune cells107,108, and a subsequent increase in the expression of IL-1β, which is a major cytokine implicated in promoting ageing-related disease107.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
In the citing paper
“Multiple cells in the brain, including microglia, astrocytes, neurons and endothelial cells, express CD38 (REFS108,160), and treating microglia and astrocytes with inflammatory cytokines induces CD38 expression163,164.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
In the citing paper
“Transcription factors (TFs) have been shown to be the best molecular feature for cell type definition (Schaum et al., 2018).”
From Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei (2019) · cites this paper as Schaum et al., 2018
In the citing paper
“Furthermore, it has been shown that commonly used cell surface markers are limited in their ability to define cell identities (Tabula Muris Consortium et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tabula Muris Consortium et al, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
“liver, lung, mammary gland, pancreas, skin, spleen, thymus, tongue, and trachea from the same mouse were immediately processed into single cell suspensions.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“a) Barplot showing the heterogeneity score for each cluster containing multiple cell types. b-f) Heatmaps showing the average between-cell-type distances within select clusters”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“for clusters a) 1, b) 2, c) 3, d) 24, e) 48, f) 53.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Calculation of dissociation scores
“For each organ, principal component analysis was performed on a subset of 140 dissociation-related genes23.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > References
“23. Reichardt J & Bornholdt S Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys 74, 016110 (2006).”
Citations
Cites
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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
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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
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Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
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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
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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 601–700 of 2,952
scMAE: a masked autoencoder for single-cell RNA-seq clustering
Fang, Z., Zheng, R., & Li, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae020 · Cited by 53
“…studies (Pollen et al. 2014, Macosko et al. 2015, Baron et al. 2016, Shekhar et al. 2016, Tirosh et al. 2016, Bach et al. 2017, Cao et al. 2017, Guo et al. 2018, Hrvatin et al. 2018, Tabula Muris Consortium et al. 2018, Tosches et al. 2018, Wang et al. 2018, Young et al. 2018, Tran et al. 2020).”
BackgroundAn iron rheostat controls hematopoietic stem cell fate
Kao, Y.-R., Chen, J., Kumari, R., Ng, A. K. Y., Zintiridou, A., Tatiparthy, M., Ma, Y., Aivalioti, M. M., Moulik, D., Sundaravel, S., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.01.011 · Cited by 50
c-CSN: Single-Cell RNA Sequencing Data Analysis by Conditional Cell-Specific Network
Li, L., Dai, H., Fang, Z., & Chen, L. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.05.005 · Cited by 46
Gupta, A., Shamsi, F., Altemose, N., Dorlhiac, G. F., Cypess, A. M., White, A. P., Yosef, N., Patti, M., Tseng, Y.-H., & Streets, A. (2022) · Genome Research · doi:10.1101/gr.275509.121 · Cited by 54
“…only, with minimal detection efficiencies for mature adipocytes either in animal models (Burl et al. 2018; Hepler et al. 2018; Schwalie et al. 2018; The Tabula Muris Consortium 2018; Cho et al. 2019; Gu et al. 2019; Jaitin et al. 2019; Merrick et al. 2019; Henriques et al. 2020) or humans (Acosta…”
Regulation of Wnt receptor activity: Implications for therapeutic development in colon cancer
Zhong, Z. A., Michalski, M. N., Stevens, P. D., Sall, E. A., & Williams, B. O. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100782 · Cited by 49
Liu, Q., Zhang, K., Kang, Y., Li, Y., Deng, P., Li, Y., Tian, Y., Sun, Q., Tang, Y., Xu, K., et al. (2022) · Science Advances · doi:10.1126/sciadv.add6391 · Cited by 48
Fourth Report on Chicken Genes and Chromosomes 2022
Smith, J., Alfieri, J. M., Anthony, N., Arensburger, P., Athrey, G., Balacco, J., Balic, A., Bardou, P., Barela, P., Bigot, Y., et al. (2022) · Cytogenetic and Genome Research · doi:10.1159/000529376 · Cited by 58
STING is required for host defense against neuropathological West Nile virus infection
Wuertz, K. M., Treuting, P. M., Hemann, E. A., Esser‐Nobis, K., Snyder, A. G., Graham, J. B., Daniels, B. P., Wilkins, C. R., Snyder, J. M., Voss, K., et al. (2019) · PLoS Pathogens · doi:10.1371/journal.ppat.1007899 · Cited by 49
Amamoto, R., Garcia, M., West, E. R., Choi, J., Lapan, S. W., Lane, E. A., Perrimon, N., & Cepko, C. L. (2019) · eLife · doi:10.7554/elife.51452 · Cited by 46
Deletion of fibroblast activation protein provides atheroprotection
Stein, S., Weber, J., Nusser‐Stein, S., Pahla, J., Zhang, H. E., Mohammed, S. A., Oppi, S., Gaul, D. S., Paneni, F., Tailleux, A., et al. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvaa142 · Cited by 49
Mfsd2a: A Physiologically Important Lysolipid Transporter in the Brain and Eye
Wong, B. H., & Silver, D. L. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-981-15-6082-8_14 · Cited by 49
HiDeF: identifying persistent structures in multiscale ‘omics data
Zheng, F., Zhang, S., Churas, C., Pratt, D. R., Bahar, İ., & Ideker, T. (2021) · Genome biology · doi:10.1186/s13059-020-02228-4 · Cited by 46
Wanet, A., Bassal, M. A., Patel, S. B., Marchi, F., Mariani, S. A., Ahmed, N., Zhang, H., Borchiellini, M., Chen, S., Zhang, J., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.aba0178 · Cited by 47
Wu, G., Lee, Y. Y., Gulla, E. M., Potter, A., Kitzmiller, J. A., Ruben, M. D., Salomonis, N., Whitsett, J. A., Francey, L. J., Hogenesch, J. B., et al. (2021) · eLife · doi:10.7554/elife.63003 · Cited by 50
Single-cell transcriptomic analysis of mIHC images via antigen mapping
Govek, K. W., Troisi, E. C., Miao, Z., Aubin, R. G., Woodhouse, S., & Cámara, P. G. (2021) · Science Advances · doi:10.1126/sciadv.abc5464 · Cited by 48
Understanding mast cell heterogeneity at single cell resolution
Cildir, G., Yip, K. H., Pant, H., Tergaonkar, V., Lopez, A. F., & Tumes, D. J. (2021) · Trends in Immunology · doi:10.1016/j.it.2021.04.004 · Cited by 46
D’Ercole, C., D’Angelo, P., Ruggieri, V., Proietti, D., Virtanen, L., Parisi, C., Riera, C. S., Renzini, A., Macone, A., Marzullo, M., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111861 · Cited by 48
Ubiquitin-specific proteases in inflammatory bowel disease-related signalling pathway regulation
Chen, R., Pang, X., Li, L., Zeng, Z., Chen, M., & Zhang, S. (2022) · Cell Death and Disease · doi:10.1038/s41419-022-04566-6 · Cited by 48
Goldfarb, C. N., Karri, K., Pyatkov, M., & Waxman, D. J. (2022) · Endocrinology · doi:10.1210/endocr/bqac059 · Cited by 46
Single-cell sequencing deconvolutes cellular responses to exercise in human skeletal muscle
Lovrić, A., Rassolie, A., Alam, S., Mandić, M., Saini, A. S., Altun, M., Fernandez‐Gonzalo, R., Gustafsson, T. P., & Rullman, E. (2022) · Communications Biology · doi:10.1038/s42003-022-04088-z · Cited by 51
Chen, J., Deng, J. C., Zemans, R. L., Bahmed, K., Kośmider, B., Zhang, M., Peters-Golden, M. L., & Goldstein, D. R. (2022) · Nature Communications · doi:10.1038/s41467-022-34593-y · Cited by 50
Myeloid Cell Derived IL1β Contributes to Pulmonary Hypertension in HFpEF
Agrawal, V., Kropski, J. A., Gokey, J. J., Kobeck, E., Murphy, M. B., Murray, K. T., Fortune, N. L., Moore, C. S., Meoli, D. F., Monahan, K., et al. (2023) · Circulation Research · doi:10.1161/circresaha.123.323119 · Cited by 52
Darbellay, F., & Necşulea, A. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz212 · Cited by 49
“Our analyses of cell-type specific gene markers, derived from single-cell transcriptomics analyses (Tabula Muris Consortium 2018; Green et al. 2018), confirms that this is indeed the case, as we see expected expression patterns in our transcriptome collection.”
BackgroundMethodInfluentialEmerging technologies to study glial cells
Hirbec, H., Déglon, N., Foo, L. C., Goshen, I., Grutzendler, J., Hangen, E., Kreisel, T., Linck, N., Muffat, J., Regio, S., et al. (2020) · Glia · doi:10.1002/glia.23780 · Cited by 51
“…tomography imaging is widely used to track microglial activation, including in patients where positive signal can be visible at early disease stages, for example, in patients with Alzheimer or Huntington disease (Edison, Donat, & Sastre, 2018; Hamelin et al., 2016; Tai et al., 2007; Figure 5a).”
Lombardo, J. A., Aliaghaei, M., Nguyen, Q., Kessenbrock, K., & Haun, J. B. (2021) · Nature Communications · doi:10.1038/s41467-021-23238-1 · Cited by 50
Cellular diversity and lineage trajectory: insights from mouse single cell transcriptomes
Tam, P., & Ho, J. W. K. (2020) · Development · doi:10.1242/dev.179788 · Cited by 44
Circadian asthma airway responses are gated by REV-ERBα
Durrington, H. J., Krakowiak, K., Meijer, P., Begley, N., Maidstone, R. J., Goosey, L. C., Gibbs, J. E., Blaikley, J., Gregory, L. G., Lloyd, C. M., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.02407-2019 · Cited by 49
Bone marrow Tregs mediate stromal cell function and support hematopoiesis via IL-10
Camacho, V., Matkins, V. R., Patel, S. B., Lever, J. M., Yang, Z., Li, Y., Landuyt, A. E., Dean, E. C., George, J. F., Yang, H. H., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.135681 · Cited by 54
“To resolve potential targets of Treg-secreted IL-10, we analyzed scRNA-seq data sets for the expression of IL-10 receptor α (IL-10Rα) across mature, progenitor, and stromal BM populations (78, 79).”
BackgroundMethodProfiling Chromatin Accessibility at Single-Cell Resolution
Sinha, S., Satpathy, A. T., Zhou, W., Ji, H., Stratton, J. A., Jaffer, A., Bahlis, N. J., Morrissy, A. S., & Biernaskie, J. A. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.06.010 · Cited by 49
Sparrer, M. N., Hodges, N. F., Sherman, T. J., VandeWoude, S., Bosco‐Lauth, A. M., & Mayo, C. E. (2023) · Journal of Clinical Microbiology · doi:10.1128/jcm.01610-22 · Cited by 58
Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics
Palmer, J. A., Rosenthal, N. A., Teichmann, S. A., & Litviňuková, M. (2024) · Circulation Research · doi:10.1161/circresaha.124.323672 · Cited by 48
Lysosomes in the immunometabolic reprogramming of immune cells in atherosclerosis
Bonacina, F., Zhang, X., Manel, N., Yvan‐Charvet, L., Razani, B., & Norata, G. D. (2024) · Nature Reviews Cardiology · doi:10.1038/s41569-024-01072-4 · Cited by 53
“In the past 6 years, the availability of single-cell RNA sequencing data sets has allowed the analysis of genes associated with lysosome function, thus revealing novel layers of regulation of lymphocytes in atherosclerosis 137 .”
Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging.
Wu, S.-H. S., Lee, J., & Koo, B. (2019) · Europe PMC (PubMed Central) · doi:10.14348/molcells.2019.0006 · Cited by 46
“In available scRNA-seq datasets (The Tabula Muris Consortium, 2018), a notable portion of total reads (~20”
BackgroundThe emergence and promise of single-cell temporal-omics approaches
Lederer, A. R., & Manno, G. L. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.005 · Cited by 44
Mia, S., Kane, M. S., Latimer, M. N., Reitz, C. J., Sonkar, R., Benavides, G. A., Smith, S. R., Frank, S. J., Martino, T. A., Zhang, J., et al. (2020) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00709.2019 · Cited by 49
Deleersnijder, D., Callemeyn, J., Arijs, I., Naesens, M., Craenenbroeck, A. H. V., Lambrechts, D., & Sprangers, B. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021020157 · Cited by 49
scLink: Inferring Sparse Gene Co-Expression Networks from Single-Cell Expression Data
Li, W. V., & Li, Y. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.11.006 · Cited by 45
Single-cell RNA-seq reveals cellular heterogeneity of mouse carotid artery under disturbed flow
Li, F., Yan, K., Wu, L., Zheng, Z., Du, Y., Liu, Z., Zhao, L., Li, W., Sheng, Y., Ren, L., et al. (2021) · Cell Death Discovery · doi:10.1038/s41420-021-00567-0 · Cited by 49
“Several scRNA-seq studies characterized EC heterogeneity in single tissues [21–23] and multiple tissues [24, 25].”
BackgroundSingh, A. K., Mahalingam, R., Squillace, S., Jacobson, K. A., Tosh, D. K., Dharmaraj, S., Farr, S. A., Kavelaars, A., Salvemini, D., & Heijnen, C. J. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01315-w · Cited by 47
TAS-Seq is a robust and sensitive amplification method for bead-based scRNA-seq
Shichino, S., Ueha, S., Hashimoto, S., Ogawa, T., Aoki, H., Wu, B., Chen, C.-Y., Kitabatake, M., Ouji-Sageshima, N., Sawabata, N., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03536-0 · Cited by 53
“PCR products with hashtag libraries (for tumor-infiltrating leukocytes) were combined, and cDNA product was purified by 0.65× AmPure XP beads, and unbounded fraction was isolated.”
InfluentialThe transcriptional portraits of the neural crest at the individual cell level
Erickson, A. G., Kameneva, P., & Adameyko, I. I. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.02.017 · Cited by 51
Wang, Y., Zhao, Y., Chen, S., Chen, X., Zhang, Y., Chen, H., Liao, Y., Zhang, J., Wu, D., Chu, H., et al. (2022) · 中国科学通报：英文版 · doi:10.1016/j.scib.2022.03.012 · Cited by 47
Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets
Zhao, J., Wang, G., Ming, J., Lin, Z., Wang, Y., Agarwal, S., Agrawal, A., Al‐Moujahed, A., Alam, A. A., Albertelli, M. A., et al. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00251-y · Cited by 43
“5 and 7).”
BackgroundMethodInfluentialScripture-Adams, D. D., Chesmore, K. N., Barthélémy, F., Wang, R. T., Nieves‐Rodriguez, S., Wang, D. W., Mokhonova, E. I., Douine, E. D., Wan, J., Little, I., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03938-0 · Cited by 46
Oh, J., An, M., Son, D., Choi, J., Cho, Y. B., Yoo, C. E., & Park, W. (2022) · Experimental & Molecular Medicine · doi:10.1038/s12276-022-00892-z · Cited by 52
Transcriptional reprogramming of skeletal muscle stem cells by the niche environment
Lazure, F., Farouni, R., Sahinyan, K., Blackburn, D. M., Hernández-Corchado, A., Perron, G., Lu, T., Osakwe, A., Ragoussis, J., Crist, C. G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36265-x · Cited by 57
Lei, I., Huang, W., Noly, P., Naik, S., Ghali, M., Liu, L., Pagani, F. D., Ela, A. S. A. A. A. E., Pober, J. S., Pitt, B., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.ade3782 · Cited by 44
A single cell transcriptional atlas of early synovial joint development
Bian, Q., Cheng, Y., Wilson, J. P., Su, E. Y., Kim, D. W., Wang, H., Yoo, S., Blackshaw, S., & Cahan, P. (2020) · Development · doi:10.1242/dev.185777 · Cited by 44
Kopf, A., Fortuin, V., Somnath, V. R., & Claassen, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009086 · Cited by 50
Rypdal, K. B., Erusappan, P. M., Melleby, A. O., Seifert, D. E., Palmero, S., Strand, M. E., Tønnessen, T., Dahl, C. P., Almaas, V. M., Hubmacher, D., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-99032-2 · Cited by 48
Integrated Single-Cell Atlases Reveal an Oral SARS-CoV-2 Infection and Transmission AxisPreprint
Huang, N., Pérez, P., Kato, T., Mikami, Y., Okuda, K., Gilmore, R. C., Conde, C. D., Gasmi, B., Stein, S. R., Beach, M. E., et al. (2020) · medRxiv · doi:10.1101/2020.10.26.20219089 · Cited by 44
Papantoniou, I., Hall, G. N., Loverdou, N., Lesage, R., Herpelinck, T., Mendes, L. F., & Geris, L. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.11.012 · Cited by 56
Neutrophil dynamics and inflammaging in acute ischemic stroke: A transcriptomic review
Bui, T. A., Jickling, G. C., & Winship, I. R. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1041333 · Cited by 46
Albors, A. R., Singer, G., Llorens-Bobadilla, E., Frisén, J., May, A. P., Ponting, C. P., & Storey, K. G. (2023) · Developmental Cell · doi:10.1016/j.devcel.2023.01.003 · Cited by 44
The shaky foundations of simulating single-cell RNA sequencing data
Crowell, H. L., Leonardo, S. X. M., Soneson, C., & Robinson, M. D. (2023) · Genome biology · doi:10.1186/s13059-023-02904-1 · Cited by 60
The Unmixing Problem: A Guide to Applying Single-Cell RNA Sequencing to Bone
Greenblatt, M. B., Ono, N., Ayturk, U. M., Debnath, S., & Lalani, S. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3802 · Cited by 43
Molecular and Morphological Signatures of Chordate Development: Two Distinct Pathways, One Tunicate
Kowarsky, M., Anselmi, C., Hotta, K., Burighel, P., Zaniolo, G., Caicci, F., Rosental, B., Neff, N. F., Ishizuka, K. J., Palmeri, K. J., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2020.108681 · Cited by 43
Programming to S1PR1 + Endothelial Cells Promotes Restoration of Vascular Integrity
Akhter, M. Z., Joshi, J. C., Ragunathrao, V. A. B., Maienschein‐Cline, M., Proia, R. L., Malik, A. B., & Mehta, D. (2021) · Circulation Research · doi:10.1161/circresaha.120.318412 · Cited by 46
Gurusamy, M., Tischner, D., Shao, J., Klatt, S., Zukunft, S., Bonnavion, R., Günther, S., Siebenbrodt, K., Kestner, R., Kuhlmann, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26882-9 · Cited by 45
Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
Heydari, A. A., & Sindi, S. S. (2023) · Biophysics Reviews · doi:10.1063/5.0091135 · Cited by 44
Single-cell atlas of the aging mouse colon
Širvinskas, D., Omrani, O., Lu, J. N., Rasa, S. M. M., Křepelová, A., Adam, L., Käppel, S., Sommer, F., & Neri, F. (2022) · iScience · doi:10.1016/j.isci.2022.104202 · Cited by 46
“, 2013) could modulate CD8 T cell survival or exhaustion in chronic infection, whereas IL-7 (Schluns et al., 2000) could mediate the homeostasis of naı̈ve and memory CD8 T cells.”
The ulcerative colitis-associated gene FUT8 regulates the quantity and quality of secreted mucins
Cantero-Recasens, G., Burballa, C., Ohkawa, Y., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205277119 · Cited by 43
A single-cell atlas of murine reproductive tissues during preterm labor
Garcia‐Flores, V., Romero, R. J., Peyvandipour, A., Galaz, J., Pusod, E., Panaitescu, B., Miller, D., Xu, Y., Li, T., Liu, Z., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2022.111846 · Cited by 44
MHA, an interactive website for scRNA‐seq data of male genitourinary development and disease
Zhao, L., Zhao, Y., Yao, C., Li, Z., Dai, Y., & Tang, Y. (2023) · Andrology · doi:10.1111/andr.13402 · Cited by 45
van Kuijk, K., McCracken, I. R., Tillie, R. J. H. A., Asselberghs, S., Kheder, D. A., Muitjens, S., Jin, H., Taylor, R. S., Schreur, R. W., Kuppe, C., et al. (2023) · Cardiovascular Research · doi:10.1093/cvr/cvad016 · Cited by 44
An APOBEC3 Mutational Signature in the Genomes of Human-Infecting Orthopoxviruses
Forni, D., Cagliani, R., Pozzoli, U., et al. (2023) · mSphere · doi:10.1128/msphere.00062-23 · Cited by 43
“expression restricted to hematopoietic cells and lymphoid tissues (18).”
BackgroundClustering ensemble in scRNA-seq data analysis: Methods, applications and challenges
Nie, X., Qin, D., Zhou, X., Duo, H., Hao, Y., Li, B., & Liang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106939 · Cited by 44
Sympathetic Innervation Regulates Osteocyte‐Mediated Cortical Bone Resorption during Lactation
Guo, Q., Chen, N., Cheng, Q., Qi, C., Noller, K., Wan, M., Liu, X., Zhang, W., Cahan, P., & Cao, X. (2023) · Advanced Science · doi:10.1002/advs.202207602 · Cited by 49
The computational capabilities of many-to-many protein interaction networks
Klumpe, H. E., García‐Ojalvo, J., Elowitz, M. B., & Antebi, Y. E. (2023) · Cell Systems · doi:10.1016/j.cels.2023.05.001 · Cited by 43
scDiffusion: conditional generation of high-quality single-cell data using diffusion model
Luo, E., Hao, M., Wei, L., & Zhang, X. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae518 · Cited by 66
Muscle aging and sarcopenia: The pathology, etiology, and most promising therapeutic targets
Grima-Terrén, M., Campanario, S., Ramírez‐Pardo, I., Cisneros, A., Hong, X., Perdiguero, E., Serrano, A. L., Isern, J., & Muñoz‐Cánoves, P. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101319 · Cited by 52
Guillaume, M., Riant, É., Fabre, A., Raymond‐Letron, I., Buscato, M., Davezac, M., Tramunt, B., Montagner, A., Smati, S., Zahreddine, R., et al. (2019) · Hepatology Communications · doi:10.1002/hep4.1363 · Cited by 45
He, D., Mao, A., Zheng, C., Kan, H., Zhang, K., Zhang, Z., Feng, L., & Ma, X. (2020) · National Science Review · doi:10.1093/nsr/nwaa038 · Cited by 44
Wu, W., Liu, Z., & Ma, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa433 · Cited by 43
“Large-scale datasets include Tabula [39] for mouse and Birey [41] for forebrain spheroids, PBMC [8] of peripheral blood mononuclear cells and COVID-19 [42] of bronchoalveolar lavage fluid (BALF) for human.”
BackgroundMethodZummo, F. P., Krishnanda, S. I., Georgiou, M., O'Harte, F. P. M., Parthsarathy, V., Cullen, K. S., Honkanen‐Scott, M., Shaw, J., Lovat, P. E., & Arden, C. (2021) · Autophagy · doi:10.1080/15548627.2021.1956123 · Cited by 43
Differentiation reveals latent features of aging and an energy barrier in murine myogenesis
Kimmel, J. C., Yi, N., Roy, M., Hendrickson, D. G., & Kelley, D. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109046 · Cited by 42
Liver RBFOX2 regulates cholesterol homeostasis via Scarb1 alternative splicing in mice
Paterson, H. A. B., Yu, S., Artigas, N., Prado, M. A., Haberman, N., Wang, Y.-F., Jobbins, A. M., Pahita, E., Mokochinski, J. B., Hall, Z., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00681-y · Cited by 45
“1h), and showed that hepatocytes account for most of the Rbfox2 liver expression, although Rbfox2 was also detected in endothelial cells, on the basis of the analysis of a mouse single-cell RNA-seq dataset 18 (Fig.”
Godoy, R. S., Cook, D. P., Cober, N. D., McCourt, E. R., Deng, Y., Wang, L., Schlosser, K., Rowe, K., & Stewart, D. J. (2023) · eLife · doi:10.7554/elife.80900 · Cited by 40
“Cell clusters were characterized using an automated annotation tool ( Tan and Cahan, 2019 ) and by cross-referencing differential gene expression of individual clusters to previously characterized lung cells of the Tabula Muris cell atlas ( Tabula Muris Consortium et al., 2018 ).”
Emerging perspectives on cytoglobin, beyond NO dioxygenase and peroxidase
Mathai, C., Jourd’heuil, F. L., Lopez‐Soler, R. I., & Jourd’heuil, D. (2020) · Redox Biology · doi:10.1016/j.redox.2020.101468 · Cited by 42
Gene expression variability in human and chimpanzee populations share common determinants
Fair, B. J., Blake, L. E., Sarkar, A., Pavlovic, B. J., Cuevas, C., & Gilad, Y. (2020) · eLife · doi:10.7554/elife.59929 · Cited by 38
GIMAP5 maintains liver endothelial cell homeostasis and prevents portal hypertension
Drzewiecki, K., Choi, J., Brancale, J., Leney-Greene, M. A., Sarı, S., Dalgıç, B., Aksu, A. Ü., Şahin, G. E., Ozen, A., Barış, S., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20201745 · Cited by 42
“Analysis of publicly available single-cell RNA sequencing (scRNA-seq) from mouse and human healthy livers confirmed Gimap5 gene expression in hepatic endothelial cells (Aizarani et al., 2019; Tabula Muris Consortium, 2018).”
BackgroundLarouche, J. A., Mohiuddin, M., Choi, J. J., Ulintz, P., Fraczek, P. M., Sabin, K., Pitchiaya, S., Kurpiers, S. J., Castor‐Macias, J. A., Liu, W., et al. (2021) · eLife · doi:10.7554/elife.66749 · Cited by 42
How many markers are needed to robustly determine a cell's type?
Fischer, S., & Gillis, J. (2021) · iScience · doi:10.1016/j.isci.2021.103292 · Cited by 44
“Recent atlas efforts based on single-cell technologies have led to comprehensive cell type taxonomies that include amultitude of novel cell types (Tasic et al., 2018; Zeisel et al., 2018; Schaum et al., 2018; Packer et al., 2019; Cao et al., 2020).”
BackgroundMeasuring cardiomyocyte cell-cycle activity and proliferation in the age of heart regeneration
Auchampach, J. A., Han, L., Huang, G. N., Kühn, B., Lough, J., O'Meara, C. C., Payumo, A. Y., Rosenthal, N. A., Sucov, H. M., Yutzey, K. E., et al. (2022) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00666.2021 · Cited by 45
Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor
Fischer, S., Crow, M., Harris, B. D., & Gillis, J. A. (2021) · Nature Protocols · doi:10.1038/s41596-021-00575-5 · Cited by 36
“names = FALSE) global_aurocs = MetaNeighborUS( trained_model = biccn_subclasses, dat = tasic, study_id = tasic$study_id, cell_type = tasic$primary_type, fast_version = TRUE ) gabaergic_tasic = splitTestClusters(global_aurocs, k = 4)[[2]] # GOOD gabaergic_biccn = splitTrainClusters(global_aurocs[gabaergic_tasic,], k =…”
MethodInfluentialWilliams, K., Yokomori, K., & Mortazavi, A. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.835099 · Cited by 46
“High-Resolution Transcriptome Methods Single-cell RNA-seq (scRNA-seq) revolutionized the field of transcriptomics, and multiple studies have applied this to assay mononucleated satellite cells (Schaum et al., 2018; Giordani et al., 2019; Rubenstein et al., 2020).”
BackgroundHistorical and current perspectives on blood endothelial cell heterogeneity in the brain
Matsuoka, R. L., Buck, L. D., Vajrala, K. P., Quick, R. E., & Card, O. A. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04403-1 · Cited by 40
FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics
Si, Y., Lee, C., Hwang, Y., Yun, J. H., Cheng, W., Cho, C., Quirós, M., Nusrat, A., Zhang, W., Jun, G., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02415-2 · Cited by 59
“We simulated data based on ten cell types from a single-cell RNA-seq reference dataset 30 , assigning eight cell types to specific regions and scattering cells from the other two cell types throughout the space to mimic infiltrating cells (Fig.”
Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics
Furtwängler, B., Üresin, N., Richter, S., Schuster, M. B., Barmpouri, D., Holze, H., Wenzel, A., Grønbæk, K., Theilgaard‐Mönch, K., Theis, F. J., et al. (2025) · Science · doi:10.1126/science.adr8785 · Cited by 56
Glucokinase intrinsically regulates glucose sensing and glucagon secretion in pancreatic alpha cells
Moede, T., Leibiger, B., Sánchez, P. V., Daré, E., Köhler, M. G., Muhandiramlage, T. P., Leibiger, I. B., & Berggren, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-76863-z · Cited by 41
Miao, W., Jiang, L., Xu, F., Lyu, J., Jiang, X., He, M., Liu, Y., Yang, T., Leak, R. K., Stetler, R. A., et al. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102125 · Cited by 44
MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data
Liu, S., Thennavan, A., Garay, J. P., Marron, J. S., & Perou, C. M. (2021) · Genome biology · doi:10.1186/s13059-021-02445-5 · Cited by 42
“The TM 10X-mammary and TM Smartseq-mammary datasets were downloaded from the Tabula Muris Consortium website [57].”
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A robust and scalable graph neural network for accurate single-cell classification
Zeng, Y., Wei, Z., Pan, Z., Lu, Y., & Yang, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab570 · Cited by 42
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Cardiomyocyte ploidy is dynamic during postnatal development and varies across genetic backgrounds
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Vogt, C. C., Zipple, M. N., Sprockett, D. D., Miller, C. H., Hardy, S. X., Arthur, M. K., Greenstein, A. M., Colvin, M. S., Michel, L. M., Moeller, A. H., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-01809-0 · Cited by 44
High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer
Koblan, L. W., Yost, K. E., Zheng, P., Colgan, W. N., Jones, M. G., Yang, D., Kumar, A., Sandhu, J., Schnell, A., Sun, D., et al. (2025) · Science · doi:10.1126/science.adx3800 · Cited by 53
The Roles of TGF-β Signaling in Cerebrovascular Diseases
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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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