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
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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.
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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
“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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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
plainstates an association- 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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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
hedgedstates an association- 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
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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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- 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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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
plainstates an association- Asserted: passes — The authors put it forward as true.
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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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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
hedgedstates an association- 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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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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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
plainstates an association- 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
“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
hedgedstates an association- 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
plainstates an association- Asserted: passes — The authors put it forward as true.
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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
hedgedstates an association- 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
plainstates an association- 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
“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
hedgedstates a possible cause- 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
“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
plainstates an association- 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
“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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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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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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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
plainstates an association- 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
plainstates an association- 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
“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
hedgedstates an association- 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
plainstates a cause- 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
hedgedstates an association- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Recently, Han et al.20 published a complementary scRNA-seq study across mouse organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
What the sources say
- Not checked — no full text of the sourcecites Han et al.20 Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107.e17 (2018). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many of these cell types have not previously been obtained in pure populations, and our data provide a wealth of new information on their characteristic gene expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Defining organ-specific cell types
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is partially explained by cellular abundance and sampling depth (12,333 microfluidic droplet cells vs. 2,216 FACS cells, Supplementary Table 1), and possibly from cell capture and lysis biases between methods.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Methodological comparison
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the dissociation- related satellite cell markers are not universal, and organs likely display unique dissociation-related expression profiles.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these data are by no means a complete representation of all mouse organs and cell types, they provide a first draft attempt to create an organism-wide representation of cellular diversity.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enabled the direct comparison of cell type composition between organs, and the comparison of shared cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that most cells of similar cell types cluster together across organs and biological replicates shows that batch effects are not the main source of variance in the dataset.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“3) We annotated cell types using standard cell ontology terms, thereby enabling cross-organ and cross-experiment analyses”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plainstates a cause- 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.
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.
- 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
“1) All data, analysis, and code is freely available”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is a predictable artifact of the annotation scheme: since entire clusters, rather than individual cells, were annotated in each organ, a sufficiently rare cell type that was algorithmically grouped with a more populous cell type will be mis-annotated.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Global clustering across organs
boostedstates a causeSingle 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.
- 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
“Importantly, the presence of such gene expression changes does not prevent cell type identification or the comparison of cell types across organs.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As the nomenclature is not identical, the analysis was performed between cell types that could be matched with a certain degree of confidence between the three methods (Supplementary Table 2).”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Differential expression overlap analysis
hedgedstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- 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
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
It was unknown whether single-cell data from different animals, labs, and techniques could be synthesized into a general resource for biology.
“It therefore remains unknown whether these data can be synthesized as a more general resource for biology.”
The paper relies on this without support.
Comparing data collected from different animals by independent labs with varying experimental techniques is challenging.
“However, while these reports provide valuable characterization of individual organs, it is challenging to compare data collected from different animals by independent labs with varying experimental techniques.”
The paper relies on this without support.
Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.
“Any individual single-cell sequencing experiment offers only a partial view of cell type diversity within an organism and gene expression within each cell type.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“A Tabula Muris compendium of single-cell RNA-Seq transcriptome data from 20 mouse organs and tissues (czbiohub.org) (14) revealed that ASGR1 transcripts are exclusively expressed in liver, specifically in hepatocytes (Fig. 1C).”
From Asialoglycoprotein receptor 1 is a novel PCSK9-independent ligand of liver LDLR cleaved by furin (2021) · cites this paper as [14]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Ageing is associated with increased activation of the NLRP3 inflammasome45,107, which is primarily expressed by myeloid immune cells107,108, and a subsequent increase in the expression of IL-1β, which is a major cytokine implicated in promoting ageing-related disease107.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
Experimental — accuracy not yet measuredIn the citing paper
“CD38 expression was specific to adipose-tissue-resident macrophages (CD68+CD64+F4/80+CD11b+CD11c−), not the non-resident population (CD68+CD64+F4/80+CD11b+CD11c+) (Fig. 4h)”
From Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages (2020) · cites this paper as [36]
In the citing paper
“Multiple cells in the brain, including microglia, astrocytes, neurons and endothelial cells, express CD38 (REFS108,160), and treating microglia and astrocytes with inflammatory cytokines induces CD38 expression163,164.”
From NAD+ metabolism and its roles in cellular processes during ageing (2021) · cites this paper as [108]
In the citing paper
“Transcription factors (TFs) have been shown to be the best molecular feature for cell type definition (Schaum et al., 2018).”
From Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei (2019) · cites this paper as Schaum et al., 2018
In the citing paper
“Furthermore, it has been shown that commonly used cell surface markers are limited in their ability to define cell identities (Tabula Muris Consortium et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Tabula Muris Consortium et al, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Abstract
“liver, lung, mammary gland, pancreas, skin, spleen, thymus, tongue, and trachea from the same mouse were immediately processed into single cell suspensions.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Mice and Organ Collection
“Isolating viable single cells from both pancreas and liver of the same mouse was not possible, therefore, 2 males and 2 females were used for each.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“a) Barplot showing the heterogeneity score for each cluster containing multiple cell types. b-f) Heatmaps showing the average between-cell-type distances within select clusters”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Extended Data
“for clusters a) 1, b) 2, c) 3, d) 24, e) 48, f) 53.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > Single Cell Methods > Calculation of dissociation scores
“For each organ, principal component analysis was performed on a subset of 140 dissociation-related genes23.”
§ Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris > References
“23. Reichardt J & Bornholdt S Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys 74, 016110 (2006).”
Citations
Cites
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Gene selection and classification of microarray data using random forest
Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 3,013
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3881 · Cited by 2,136
Dynamic Pattern Formation in a Vesicle-Generating Microfluidic Device
Thorsen, T., Roberts, R. W., Arnold, F. H., & Quake, S. R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.4163 · Cited by 2,052
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Research works printed but not linked: 1
- Kassambara, A. Practical guide to cluster analysis in R: unsupervised machine learning 1st edn (CreateSpace, North Charleston, 2017).
Cited by
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Items 2,801–2,900 of 2,952
Testing the social complexity hypothesis : a comparative study of vocal communication in macaques
Rebout, N. (2019) · theses.fr (ABES) · doi:10.70675/270b9842z6177z4b41zbe1dz29abd6323974
Schmitt, P. (2019) · theses.fr (ABES) · doi:10.70675/910c14fdzc720z4a3fza364z6abf25ce9c20
Identification of portal mesenchymal stem cells and derived myofibroblasts in liver fibrosis
Lei, L. (2020) · theses.fr (ABES) · doi:10.70675/3f63c15ezc769z48acz89d2z46ba95102826
Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePopPreprint
Yuan, H., Mandava, A., Samart, K., Ganz, J., & Krishnan, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.01.715877
Contrôle de l’homéostasie des macrophages de la pulpe rouge splénique par une niche fibroblastique
Bellomo, A. (2020) · theses.fr (ABES) · doi:10.70675/595aad77zb764z48bfz8e7dzc4d373e8001a
Rôle des macrophages dans le remodelage pathologique du tissu adipeux au cours de l'obésité
Askia, H. (2022) · theses.fr (ABES) · doi:10.70675/0d09beffzcdaez4950za6c0z29a0dd68a41b
Generation and characterization of the Il11-Cre knock-in mouse
Zhou, X. E., Xie, Z., Liu, W., Wang, Q., Lin, H., Li, R., Lang, Y., Chen, J., Yang, X. Y., Teng, Y., et al. (2026) · Transgenic Research · doi:10.1007/s11248-026-00491-y
Unsupervised representation learning for single-cell transcriptomic and epigenomic data
Raimundo, F. (2022) · theses.fr (ABES) · doi:10.70675/760cd5d0z17c6z4f4ezb476z68bd387daf22
Lu, C., Li, L., Chen, J., Chang, R., & Dong, H. (2026) · Journal of Diabetes · doi:10.1111/1753-0407.70225
Wang, J., Ke, D., Xia, J., Liu, A., Su, Y., & Zheng, C.-H. (2026) · BMC Biology · doi:10.1186/s12915-026-02598-4
Pan-organ poly(A) atlas reveals a post-transcriptional regulatory layer independent of RNA abundance
Lei, H., Long, Y., Wu, S., Wang, X. E., Peng, Y., Liu, Z., Lu, W., Shu, Y., Zhu, H., Zou, M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71703-6
“…selected to broadly represent the major mammalian physiological systems,includingthenervous,endocrine,cardiovascular,respiratory, digestive, urinary, musculoskeletal, immune, and reproductive systems, consistent with strategies employed in previous comprehensive mouse transcriptome atlases 36,37 .”
Magallanes, M. E., Agustin, B., Feijoo, M., ́Elía, G. D., Naya, D. E., & Lessa, E. P. (2026) · BMC Genomics · doi:10.1186/s12864-026-12853-1
Feature-preserving manifold approximation and projection to analyze single-cell data
Yang, Y., Gong, J., Sun, H., Choo, A., Mar, J. C., Wei, Y., Zhang, Y., Zhang, W., Shu, M., Tuong, Z. K., et al. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00970-6
“We perform the accuracy evaluation on various datasets including pancreatic development data 18 , exhausted CD8 + T cell data 22 , COVID scRNA-seq data 43 , embryoid body scRNA-seq data 6 , PBMC68k 44 and Tabula Muris 45 , as well as synthetic scRNA-seq datasets of different schemes 41 , standard MNIST (Modified Natio…”
A New Adaptive Hybrid Representation Method for Single-Cell Data
Weng, B., Huang, R., Ren, L., Bai, R., Han, R., & Yang, H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462130
scConcept enables concept-level exploration of single-cell transcriptomic dataPreprint
Chen, H., & Li, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.719959
Zegeye, M. M., Veettil, J. T., Paramel, G. V., Kurt, S., Salihović, S., Ljungberg, L. U., Kumawat, A. K., & Sirsjö, A. (2026) · Molecular Medicine · doi:10.1186/s10020-026-01484-7 · Cited by 1
Abdelnaby, M., & Moussa, M. R. (2026) · Journal of Computational Biology · doi:10.1177/15578666261436821
Accurate profiling of single-cell alternative transcript start sites by correcting RNA degradation
Xu, Z., Zhou, Z., Tang, C., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72298-8
Cardiac GLP1R gene expression: A cross-species single-cell transcriptomic analysis
Dostal, C., Ernst, M., Reiner, J., Snelling, R., Hu, C., Pokreisz, P., Kiss, A., & Podesser, B. K. (2026) · Advances in Translational Research · doi:10.1556/1661.2026.00113
CHD4 and NKX2.2 Cooperate to Regulate β-Cell Function by Repressing Non–β-Cell Gene Programs
Sarbaugh, D., Oliveira, T. G., Guney, M. A., Casey, M. R., Hoelscher, V. M., Hill, C. J., Michel, C. R., Wells, K. L., Benninger, R. K. P., & Sussel, L. (2026) · Diabetes · doi:10.2337/db25-0945
Engulfment by brain macrophages in a short-lived vertebratePreprint
Nagvekar, R., Pogson, A. N., Kalakuntla, P. R., Barr, H. J., Jaimes, A. M. M., Perry, S., Costa, E. K., Chen, J., Boos, F., Negredo, P. N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.28.721434
Sex differences in endothelial cell biology: cellular phenotypes and molecular regulators
Pislaru, A., & Haas, T. L. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1830278 · Cited by 1
“Similarly, interrogation of the Tabula Muris dataset, which includes single cell transcriptomic data from multiple mouse tissues (Tabula Muris Consortium, 2018), led to the observation that tissues such as the aorta, brain, and lung contain distinct sex-speci fi c endothelial subpopulations, whereas…”
On the Role of Embeddings in Diffusion-Based Generation of scRNA-seq Data
Rackl, M., Fuchs, F., & Andreas Hotho, A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19096-3_25
Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq DataPreprint
Wei, X., Li, X., Liu, H., Du, G., Wei, F., & Shang, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.07.723423
Zhu, J., Han, Z., Gregorio, R. D., Kevin, C., Zhang, Z., Dong, X., Banerjee, K., Liu, K., Rea-Moreno, M., Kizilbash, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.07.723626
Multivariate genetic architecture of age-related eye disease
Gao, L., Wang, Q., & Zhu, C. (2026) · PLoS ONE · doi:10.1371/journal.pone.0349199
“To identify etiological cell types associated with ARED, we employed Cellular Expression Specificity Integration for Complex Traits (CELLECT) [25] with single-cell RNA sequencing data from the Tabula Muris dataset [26].”
Cell Segmentation as Strategic Decision Making
Zhong, Y., & Ren, X. (2026) · Research · doi:10.34133/research.1304
Li, H., & Li, G. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1807805
“We used the Tabula Muris resource, which contains transcriptomic profiles from Mus musculus spanning a wide range of organs and tissues (28), and statistical significance was defined using an FDR-corrected threshold of P < 0.05.”
InfluentialSingle-Cell Analysis in Pharmacological Research for Blood Lipid Disorders
Dang, H.-L., Hoang, D.-A., & Chu, D.-T. (2026) · Springer eBooks · doi:10.1007/978-981-95-9789-5_16
Combinatorial transcription factor interactions drive modular gene regulatory networksPreprint
Duan, J., Li, B., Kulkarni, K., Orquera-Tornakian, G., Barth, D., Wang, L. H., Pandit, V., Liou, J., Munshi, N. V., & Hon, G. C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.20.726505
Endothelial soluble APP/APLP2 promote heart repair through KIT-mediated angiogenesis
Kawase, H., Liu, S., Kurz, S., Bengelsdorff, V., Bonnavion, R., Jin, Y., Roquid, K. A., Cho, H., Liang, G., Banićević, M., et al. (2026) · Science Advances · doi:10.1126/sciadv.aeh0301
Immune context governs stage-specific responses to depleting anti-PD-1 antibody in type 1 diabetes
Zhai, Y., Dong, S., Naatz, L. C., Zhang, T., Ye, X., Tantin, D. R., Andrews, T. S., & Chen, M. (2026) · Cell Biomaterials · doi:10.1016/j.celbio.2026.100490
“62 ReBaLa results were cross-validated with unsupervised clustering, following a principle that each cluster was assigned to a single cell type based on the dominant label within a given cluster (Figure S2).”
Hill, J. D., Dong, A. H. W., Liu, J., Barbezani, M. D., Andrews, T. S., & Klein, R. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.19.726300
An ancient retrotransposon provides species-specific tuning of IL-18 inflammatory signalingPreprint
Ordonez, A. D., Allen, H. L., Sanford, L., Ivancevic, A. M., Agyepong, A., Chaw, M., Bridges, J. P., Schountz, T., & Chuong, E. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.25.727421 · Cited by 1
Inhibition of IGF1R in Early MMTV-Wnt1 Mammary Tumors: A Transcriptomic Analysis
Bulatowicz, J. J., Lemenze, A. D., Dogan, E., Galifi, C. A., Maingrette, K., Shang, Q., & Wood, T. L. (2026) · Cancers · doi:10.3390/cancers18111749
“Utilizing this list and the large body of scientific literature investigating various populations of multipotent mammary gland epithelial cells during development, we annotated the reclustered tumor epithelial cells analogously to their developmental counterparts (Figure 4B, [45–47]).”
Sui, C., Li, J., Li, R., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9635765/v1
Guan, J., Kizil, B., Kalakoti, G., Kummerfeld, D.-M., Doroshenko, O., Pelcastre-Neri, V., Frigger, N. C., Cirri, E., Pömpner, N., Goyal, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.25.727504
Fu, Z., Liu, Y., Wang, J., & Wang, S. (2026) · Array · doi:10.1016/j.array.2026.100934
scMVAF: a multi-view adaptive fusion clustering approach for single-cell RNA-sequencing data
Wang, J., Long, Q., Tang, D., Deng, J., & Liang, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf169
Luo, J., Lu, Q., Zhang, X., & Wang, X. (2026) · Human Genetics · doi:10.1007/s00439-026-02841-6
“Moreover, consistent findings were observed in another mouse hematopoietic system (Schaum et al. 2018), where HSCs similarly exhibited higher ScSpTITH score compared to other cell populations (Fig.”
Chen, J., Zhong, X., Li, L., Feng, P., Duan, X., Mai, D., Liu, L., & Xu, J. (2026) · European Spine Journal · doi:10.1007/s00586-026-10039-7
Iba, T., Wakabayashi, T., Ito, R., Sugawara, A., Fujimura, S., Sawane, M., Yoshioka, K., Matsui, A., Morishige, J., Nagata, N., et al. (2026) · Stem Cell Reports · doi:10.1016/j.stemcr.2026.102931
(4829), Y. L., Zhao, Y., Zhang, Y., Qi, J., Gong, H., Zhang, Z., Li, W., Liu, L., Feng, B., Hang, Q., et al. (2026) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2026.106844 · Cited by 1
Enzyme tethering for in situ epigenomics
Ahmad, K., Brahma, S., Janssens, D. H., Kind, J., Khyzha, N., Meers, M. P., Ramachandran, S., Zheng, Y., & Henikoff, S. (2026) · Nature Reviews Methods Primers · doi:10.1038/s43586-026-00491-6 · Cited by 1
“Large-scale efforts, such as the Human Cell Atlas and other whole-organism single-cell maps, have relied primarily on RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC–seq) to define cellular identities and regulatory landscapes 210–213 .”
Signaling and transcriptional networks governing late synovial joint development
Yea, J.-H., Bian, Q., Cheng, Y.-H., et al. (2026) · Developmental Biology · doi:10.1016/j.ydbio.2026.05.010
Zhang, X., & Yu, J.-A. (2026) · Regenesis repair rehabilitation. · doi:10.1016/j.rerere.2026.06.001
Chen, S., Chen, Y., Liu, Y., Yang, T., Liu, Y., Liao, X., Zeng, B., Liu, J., Huang, C., Luo, Y., et al. (2026) · Life Sciences · doi:10.1016/j.lfs.2026.124522
FIRM : an R package for large-scale flexible integration of single-cell data
Ding, S., Yu, Z., & Ming, J. (2026) · Statistical Theory and Related Fields · doi:10.1080/24754269.2026.2679093
Decoding exercise-induced delayed aging from a single-cell perspective
Zhao, Y., Zhou, X., & Ni, T. (2026) · Advanced Exercise and Health Science · doi:10.1016/j.aehs.2026.05.001
Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data
Yao, M., & Ren, L. (2026) · Journal of Computational Biology · doi:10.1177/15578666261453098
Primary cilia as platforms for islet neurotransmitter signaling
Townsend, S. E., & Hughes, J. W. (2026) · Endocrinology · doi:10.1210/endocr/bqag069
Evaluating cell type annotations in single-cell omics in the absence of ground truthPreprint
Garnica, J., Andreatta, M., & Carmona, S. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.10.731285
α-cell SLC38A5 supports amino acid-induced α-cell proliferation and glucagon secretion
Sellick, K., Schornack, A. M. R., Rodgers, T. J., Ogodo, F. O., Aramandla, M. P., Stanley, J. E., Siv, W., Shou, M., Shankar, N. C., Saraf, S. S., et al. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1830329
TLDc Family Genes Are Understudied but Promising Regulators of Protection against Oxidative Stress
Fadeev, V. S., Silaeva, Y. Y., & Dolmatova, D. M. (2026) · Russian Journal of Genetics · doi:10.1134/s102279542670016x
Thomas, M., Pacary, A., Arnaud, E., Bernard, D., Maugis-Rabusseau, C., Guissard, C., Arlat, A., Davezac, N., Lorsignol, A., Paupert, J., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-58327-y
The heterogeneity of dopamine-mediated vasodilation in human intrarenal arteries
Kang, X., Jin, H., Zheng, Y., et al. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.06.020
Hong, Y., Dang, B., Zhou, Z., Liang, C., Li, L., Gao, Q., ZHANG, J., & Cheng, S. (2026) · Translational research · doi:10.1016/j.trsl.2026.06.017 · Cited by 1
Wei, L., Sun, F., Pan, H., Yao, J., Wang, M., Kang, Y., & Yang, J. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1816627
Galli, C., Colleluori, G., Perugini, J., Scopini, E., Severi, I., Grandin, G., & Giordano, A. (2026) · Journal of Anatomy · doi:10.1111/joa.70195 · Cited by 2
Optimal transport for label transfer in single-cell multi-omics integration
Ren, J., Zhang, Z., Wang, J., Xie, L., Wang, J., Wang, M., & Liu, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag334 · Cited by 1
“By contrast, scRNA-seq benefits from high-throughput data generation, mature annotation resources, and extensive reference atlases [ 10, 11], making it well-suited for cell-type identification and functional annotation.”
Kohl, C., & Vingron, M. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014418
Fang, M., Zhou, B., Yu, P., et al. (2026) · Genes · doi:10.3390/genes17070751
Silberstein, E., Karaiskos, S., Tirrell, N., et al. (2026) · iScience · doi:10.1016/j.isci.2026.116611
Altulea, A., Mackedenski, S. J., Nehme, J., & Demaria, M. (2026) · The EMBO Journal · doi:10.1038/s44318-026-00845-6 · Cited by 1
“Results from Tabula Muris Senis (Consortium, 2018), Tabula Sapiens 2.0 (Consortium and Quake, 2025), and irradiated murine tissues (Curras-Alonso et al, 2023; Horie et al, 2023; Mills et al, 2025; Paldor et al, 2022; Shamseddine et al, 2023) (Fig.”
InfluentialLei, Y., Li, J., Xie, J., & Wang, T. (2026) · BMC Pulmonary Medicine · doi:10.1186/s12890-026-04476-9
“Single-cell transcriptomic references were obtained from the Tabula Muris compendium [33].”
Mansueto, A. J., Norris, A. C., Yazlovitskaya, E. M., Zhu, L., Lu, J., Koomen, D. C., May, J. C., Kim, M., Hermanson, J. N., Molday, R. S., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-57906-3
Dang, Y., Liu, W., Li, H., & Liu, B. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109239
AtlasLens: Metadata-centric exploration and analysis of single-cell atlasesPreprint
Ashrafiyan, S., Kosaretskii, I., & Schulz, M. H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.10.735823
“14 21 28 s C 1 C 2 C 3 C 4 r 5 r 6 C l Here, enrichment results from a DEA between regular ventricular cardiac myocyte and regular atrial cardiac myocyte for heart tissue in the Tabula Muris dataset are condensed into interpretable process categories such as ’Heart process’, ’Cardiac muscle cell potential’, and ’Mitoc…”
InfluentialThe perineurium integrates leptin with its sympathetic outflow to protect against obesity
Sarker, G., Haberman, E. R., Chandran, A., Monfeuga, T., Maroteau, C., Lundh, S. H., Sidarta-Oliveira, D., Raimondi, A., Ziegler, K. A., Gomes, T., et al. (2026) · Nature Metabolism · doi:10.1038/s42255-026-01555-3 · Cited by 2
“To this end, we analysed the co-expression of Lepr and Adrb2 within individual annotated cell types for each organ represented in the Tabula Muris dataset 18 .”
Telocytes Twenty Years on: A Critical Reappraisal of Identity, Function, and Pathological Relevance
Pavelescu, L. A., & Creţoiu, S. M. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27146204
Lu, J., Rao, S. R., Knowles, H. J., Zhan, H., Gámez, B., Qin, M., Platt, E., Frost, L. R., Allen, T.-J., Marshall, G. B., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02799-x · Cited by 2
Chen, J., Zhang, Z., Fu, Q., Hou, H., Xu, G., Li, Z., Lu, Z., Qiu, J., Wu, K., Gao, X., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1857945
“To identify etiological cell types associated with MGLSc, we applied CELL-type Expression-speci fi c integration for Complex Traits (CELLECT) (16) — a method designed for single-cell RNA sequencing (scRNA-seq) data of complex traits — using data from the Tabula Muris90 dataset (17).”
Direct interaction of Vδ7 TCRs with IL17RA drives the differentiation of T H 1-like γδT cells
Ye, K., Francis, N., Dunst, J., Borgenstam, A., Rocamonde-Lago, I., Köhler, S., You, Y., Dubnovitsky, A. P., Kramer, A., Meng, F., et al. (2026) · Science · doi:10.1126/science.adx9264 · Cited by 1
Zeng, W., Yang, X., & Xu, Y. (2026) · Genes · doi:10.3390/genes17070813
TNFAIP3/A20 dysfunction drives innate and sterile hyperinflammation
Damme, K. F. A. V., Hertens, P., Sichien, D., der Borght, K. V., Moorleghem, J. V., Prijck, S. D., Klarenbeek, A., Louagie, E., Lammens, I., Vanhee, S., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1856810
“To pro fi le Tnfaip3 expression in mice, we analyzed splenocytes of steady-state mice which we described previously (45), and non-immune cells from the Tabula Muris (44).”
scMoSAT: A Semi-supervised Transformer Framework for Cross-Modal Cell Type Annotation
Chen, Y., Xie, M., Wang, X., & Liu, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3397-7_21
Defining the role of αC helix interactions in the activation of the integrin αI domain
Pösö, L., Airenne, T. T., Käpylä, J., et al. (2026) · FEBS Journal · doi:10.1111/febs.70658
Organism-scale annotation with Pan-human AzimuthPreprint
Sarkar, S., Li, Z., Molla, G., Shenoy, A., Zhang, B. Y., Collins, D., Vasilevsky, N., Gaut, J. P., Puig-Barbe, A., Bueckle, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.16.738997 · Cited by 2
“This would bring mouse and other model organisms 75–77 under the umbrella of a unified hierarchical cell annotation framework, and may benefit from the recent development of ‘universal’ or ‘cross-species’ embedding frameworks that can jointly model data across multiple species 18,78–80 .”
Ren, Y., Shen, Y., Jin, L., Huang, Y. N., Deng, Y., Xiao, Y., & Wang, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.20.739409
Single-cell resolution of skeletal muscle aging: Current paradigms and perspectives
Wang, Y., Yang, Y., Guan, W., Yu, W., Li, N., & Wang, D. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103272 · Cited by 1
The Single-Cell Atlas Revolution: Integrating Lineage, Space, and Evolution to Decode Animal Biology
Rehman, S. U., Ali, R., Husien, H. M., & Wang, M. (2026) · DNA and Cell Biology · doi:10.1177/10445498261460199
scCoExpress: A Sparsity-Aware R Package for Gene Co-Expression Analysis in Single-Cell Datasets
Lande, K., Evensen, K. G., & Williams, A. E. (2026) · F1000Research · doi:10.12688/f1000research.186153.1
Li, C. Y., Gou, X., Zhao, Y., Zhang, L., Liu, S., Deng, S., Li, Q., & Qi, S. (2026) · CNS Neuroscience & Therapeutics · doi:10.1002/cns.71058
Cross-modal mapping of cancer stem-like cell plasticity using deep learning
Chowdhury, D., Priyadarshi, S., Biswas, S., Neekhra, B., Gupta, D., & Haldar, S. (2026) · NAR Cancer · doi:10.1093/narcan/zcag015
An Imputation Method for Single cell RNA-seq Data based on Ladder Variational Autoencoder
Ha, T. Q., Vo, T. L., Hoang, K. T. X., Nguyen, Q. A., & Vinh, L. V. (2026) · doi:10.1145/3815970.3815981
Liang, Y., Liu, K., Wang, X., Lu, C., Cheng, Y., Yang, Y., Wen, Y., Jiang, X., Zhang, W., Zhao, L., et al. (2026) · Redox Biology · doi:10.1016/j.redox.2026.104331 · Cited by 1
“We first analyzed the relative expression of classical ferroptosis biomarkers (Gpx4, Acsl4, Tfrc) across lung cell types using the single-cell database Tabula Muris [30].”
Li, Y., Zhou, W., Xiao, Y., Ma, J., Wei, L., & Jiang, L. (2026) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl50499
Andre, D., Fabrizius, A., Hagmaier, M., Waisman, A., Reifenberg, K., Burmester, T., Reuss, S., & Hankeln, T. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.07.062
Schöneich, J., Dupont, A., Schlößer, S., Cheng, M., Schmitz, M. A., Richter, I., Gubbi, N. M. K. P., Zhang, K., Maié, T., Laouina, A., et al. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003212
Engulfment by brain macrophages in a short-lived vertebratePreprint
Nagvekar, R., Pogson, A. N., Kalakuntla, P. R., Barr, H. J., Jaimes, A. M. M., Perry, S., Costa, E. K., Chen, J., Boos, F., Negredo, P. N., et al. (2026) · eLife · doi:10.7554/elife.112503.1
Engulfment by brain macrophages in a short-lived vertebratePreprint
Nagvekar, R., Pogson, A. N., Kalakuntla, P. R., Barr, H. J., Jaimes, A. M. M., Perry, S., Costa, E. K., Chen, J., Boos, F., Negredo, P. N., et al. (2026) · eLife · doi:10.7554/elife.112503
Acri, D. J., Mustaklem, R., Horan-Portelance, L., Dabin, L. C., Park, J. H., Hartigan, K. A., Kersey, H. N., Mesecar, M. E., Gibbs, J. R., Cookson, M. R., et al. (2026) · bioRxiv · doi:10.64898/2026.07.30.741795
Combinatorial transcription factor interactions drive modular gene regulatory networksPreprint
Duan, J., Li, B., Kulkarni, K., Orquera-Tornakian, G., Barth, D., Wang, L. H., Pandit, V., Liou, J., Munshi, N. V., & Hon, G. C. (2026) · eLife · doi:10.7554/elife.111977.1
Long, Y., Zhang, W., Cao, L., Huang, X., & Wang, Y. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014602
“…human pancreas, BMMC dataset [30] from bone marrow and Immune dataset from multi-tissue immune system [31], as well as four mouse datasets, BaronMouse dataset [32] from mouse pancreas, ChenMouse dataset [33] and AMB dataset [34] from mouse visual cortex, and TM dataset [35] from mouse musculus.”
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
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