The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index; 7 of the 26 sources its claims cite were checked for support.
Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage
Open accessPublished version of a preprint
Abstract
Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury. Using scRNA-seq analysis, Bhattacharya and colleagues identify a subset of profibrotic lung macrophages that have a gene expression signature intermediate between those of monocytes and alveolar macrophages.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
30 Own result · 20 Own interpretation · 11 Not checked yet · 10 Own method · 3 No basis given · 1 Mixed support · accuracy not yet measured
Some cited sources support this claim and some don’t — read each source’s verdict below.
In the paper
“These observations indicate that the Pdgf pathway is crucial to disease pathogenesis.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
What the sources say
- Not checked yetcites [39] Richeldi, L., Collard, H. R. & Jones, M. G. Idiopathic pulmonary fibrosis. Lancet 389, 1941–1952 (2017). source
- Disputedcites [40] Noble, P. W., Barkauskas, C. E. & Jiang, D. Pulmonary fibrosis: patterns and perpetrators. J. Clin. Invest. 122, 2756–2762 (2012). sourceExperimental — accuracy not yet measured
The Figure 4 passage includes PDGF among pathways that mediate epithelial–mesenchymal interactions, but says their precise role in IPF is unknown and that evidence directly implicating them in pulmonary fibrosis pathogenesis is limited. It therefore does not support calling the PDGF pathway crucial to disease pathogenesis. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
- Partly supportedcites [41] Wollin, L. et al. Mode of action of nintedanib in the treatment of idiopathic pulmonary fibrosis. Eur. Respir. J. 45, 1434–1445 (2015). sourceExperimental — accuracy not yet measured
The “PDGF/PDGFR signalling in pulmonary fibrosis” section says PDGF appears to play an essential role in myofibroblast expansion and describes evidence linking it to fibrosis progression. That supports a role in disease pathogenesis, but the unqualified assertion that the pathway is crucial is stronger than the paper’s hedged conclusion. (LLM, conf=0.82)
- Supportedcites [42] Olson, L. E. & Soriano, P. Increased PDGFRα activation disrupts connective tissue development and drives systemic fibrosis. Dev. Cell 16, 303–313 (2009). source
The Discussion reports that increased PDGFRα signaling was sufficient to drive systemic connective tissue disease in mice. This supports the claim for the fibrotic disease studied, rather than disease pathogenesis generally. (LLM, conf=0.90)
- Supportedcites [43] Iwayama, T. et al. PDGFRα signaling drives adipose tissue fibrosis by targeting progenitor cell plasticity. Genes Dev. 29, 1106–1119 (2015). source
The paper’s Abstract describes PDGF as an important profibrotic signal and states that multiple studies have shown PDGF signaling is needed for fibrosis. This supports the claim as applied to fibrotic disease; the supplied passages do not identify another disease. (LLM, conf=0.88)
- Partly supportedcites [44] Horikawa, S. et al. PDGFRα plays a crucial role in connective tissue remodeling. Sci. Rep. 5, 17948 (2015). sourceExperimental — accuracy not yet measured
The Discussion reports that PDGFs are crucial in tissue remodeling during wound healing and tumor progression, and that PDGFRα mediates connective tissue remodeling in a mouse sponge-implantation model. It suggests PDGFRα could be a target for regulating remodeling in diverse diseases, but does not establish that the PDGF pathway is crucial to disease pathogenesis generally. Our copy provides no page numbers for these passages. (LLM, conf=0.83)
- Partly supportedcites [45] Hayes, B. J. et al. Activation of platelet-derived growth factor receptor alpha contributes to liver fibrosis. PLoS ONE 9, e92925 (2014). sourceExperimental — accuracy not yet measured
The Discussion says that PDGFRα plays a critical role in the development of fibrosis after chronic liver injury. That supports a role for PDGF signaling in liver-fibrosis pathogenesis, but the source does not establish that the pathway is crucial to disease pathogenesis generally; it also notes that other pathways contribute to fibrosis. (LLM, conf=0.83)
- Could not verifycites [46] Demaria, M. et al. An essential role for senescent cells in optimal wound healing through secretion of PDGF-AA. Dev. Cell 31, 722–733 (2014). sourceExperimental — accuracy not yet measured
The Results section, “Senescence-Associated PDGF-A Promotes Optimal Wound Closure,” shows that PDGF-AA is important for timely wound closure in mice. The Discussion likewise describes a beneficial role in cutaneous wound healing, not a role in disease pathogenesis. Searches for PDGF and disease pathogenesis found no passage in our copy addressing that claim. (LLM, conf=0.76)
hedgeddirect- 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.
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 these genes in SiglecF+CD11c+MHCIIhi cells at 4 weeks after injury was reduced to levels more similar to those detected in SiglecF+CD11c+ alveolar macrophages at steady-state”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
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.
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
“immunofluorescence of lung tissue indicated the expression of MAFB, a marker detected specifically in clusters C2 and C3, in CD68+ macrophages from patients with idiopathic lung fibrosis compared to healthy controls”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Cluster C2 and C3 markers are found in alternate lung fibrosis models
hedgedcorrelational- 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.
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 annotation scores for alveolar and interstitial macrophages were strongest at the poles of the macrophage cluster”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
plaincorrelational- 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.
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
“showed three clusters of fibroblasts, cluster A, B, and C”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > CX3CR1-expressing macrophages drive lung fibrosis
plain- 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.
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
“TdTomato-SiglecF+ alveolar macrophages (cluster C1) were detectable throughout the lung except in regions where Pdgfrb+ fibroblasts accumulated at sites of injury”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Cluster C2 macrophages localize to the fibrotic niche
plaincorrelational- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“SingleR differentiated cellular subtypes such as memory and naïve B and T cells”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Reference datasets enhance annotation of scRNA-seq
What the sources say
- Not checked yetcites [20] Guilliams, M. & Malissen, B. A death notice for in-vitro-generated GM-CSF dendritic cells? Immunity 42, 988–990 (2015). source
- Not checked yetcites [21] Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). source
- Not checked yetcites [22] Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018). source
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.
In the paper
“Human CX3CR1 mRNA was also more highly expressed in lung microarray samples from 167 patients with lung fibrosis than in samples from 50 healthy controls”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Cluster C2 macrophages localize to the fibrotic niche
What the sources say
- Not checked yetcites [32] Naikawadi, R. P. et al. Telomere dysfunction in alveolar epithelial cells causes lung remodeling and fibrosis. JCI Insight 1, e86704 (2016). source
plaincorrelationaln = 167- 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.
- Rests on an assumption: Changes in expression of human orthologues in bulk lung microarray samples reflect changes in macrophage populations rather than other cell types.
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
“SingleR identified two subtypes of macrophages: alveolar and interstitial macrophages”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
plainn = 6- 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.
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
“Conditioned media from SiglecF+CD11c+MHCIIhi macrophages (corresponding to cluster C2) sorted from wild-type mice 14 days after bleomycin injury and cultured for 2 days enhanced gap closure by 3T3 fibroblasts compared to conditioned media from SiglecF+CD11c+MHCIIlo macrophages (corresponding to cluster C1) isolated from the same mice”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > CX3CR1-expressing macrophages drive lung fibrosis
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.
- Rests on an assumption: The 3T3 embryonic mouse fibroblast cell line is a valid proxy for lung fibroblasts in fibrosis for assessing migration and proliferation.
- It may state two separate claims as one. 19
- Probably not a substantial claim. 14
- The quote only partly states this claim. 1
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
“C1 had higher expression of alveolar macrophage genes than cluster C2, which in turn had higher alveolar macrophage genes than cluster C3”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
plaincorrelational- 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 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 C2 and C3 were enriched in cells isolated from mice with bleomycin-induced fibrosis and expressed Cx3cr1, Ccr2, Mafb and MHCII genes, consistent with monocytic origin”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
plaincorrelational- 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 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 A showed the highest expression of cell cycle genes and was predominantly composed of cells from bleomycin-treated mice”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > CX3CR1-expressing macrophages drive lung fibrosis
plaincorrelational- 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.
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
“We developed a computational tool for unbiased annotation as well as clustering of scRNA-seq, called SingleR (single cell recognition). SingleR assigns cellular identity for single cell transcriptomes by comparison to reference datasets of pure cell types sequenced by microarray or RNA-seq.”
- Also stated in: § Reference datasets enhance annotation of scRNA-seq · plain
“we developed a computational method called SingleR, which correlated single cell transcriptomes with reference transcriptomic datasets and improved its inferences iteratively”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Abstract
plaincorrelational- 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.
- Also stated in: § Reference datasets enhance annotation of scRNA-seq · plain
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
“This analysis identified three distinct subclusters (C1, C2 and C3), all three detected at varying levels in both steady-state and bleomycin-treated mice”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
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 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 C1 was equally present in steady-state lung and bleomycin injury and corresponded to the cells that were annotated as alveolar macrophages by the lung-specific datasets”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
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 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
“45% of cluster C2 cells (but only 7.3% of cluster C1 cells and 4.7% of cluster C3 cells) expressed at least 33% of cluster C1 genes and at least 33% of cluster C3 genes”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > SingleR identifies a transitional macrophage in fibrosis
plaincorrelational- 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.
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
“Pdgfra was not detected in the lung scRNA-seq dataset, which may reflect the limitation of the depth of sequencing with the single-cell method”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > CX3CR1-expressing macrophages drive lung fibrosis
hedgedconditional- 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.
In the paper
“This analysis showed a significant decrease in the expression of genes associated with alveolar macrophages (cluster C1) and a significant increase in the expression of MHC II genes (cluster C2 and C3) in the human fibrosis samples compared to the healthy controls”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Cluster C2 and C3 markers are found in alternate lung fibrosis models
What the sources say
- Not checked yetcites [32] Naikawadi, R. P. et al. Telomere dysfunction in alveolar epithelial cells causes lung remodeling and fibrosis. JCI Insight 1, e86704 (2016). source
plaincorrelationaln = 167- 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.
- Rests on an assumption: Changes in expression of human orthologues in bulk lung microarray samples reflect changes in macrophage populations rather than other cell types.
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
“At 21 days after bleomycin injury, bleomycin-injured, tamoxifen-induced Cx3cr1-CreERT2 / Rosa26-loxp-STOP-loxp-Diphteria Toxin A mice had significantly fewer macrophages quantified by immunofluorescent labeling of either Mertk+ or SiglecF+ cells in the lung”
- Also stated in: § Discussion · plain
“Deletion of these macrophages during the fibrotic period (days 8–21) eliminated Pdgfra+ or Pdgfrb+ fibroblasts and total lung collagen.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > CX3CR1-expressing macrophages drive lung fibrosis
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. The quote only partly states this claim.
- Also stated in: § Discussion · plain
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
“Altogether, the filtered data contained 8,366 cells and 13,861 genes (Supplementary Table 1).”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Drop-seq and data analysis
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.
In the paper
“a significant expansion of Pdgfra+ fibroblasts after bleomycin treatment has been reported in a study analyzing the fibroblast compartment in this model of fibrosis 35”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > CX3CR1-expressing macrophages drive lung fibrosis
What the sources say
- Not checked yetcites [35] Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013). source
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.
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 results are consistent with a common genetic signature and a generalizable pattern of macrophage response during fibrogenesis”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Cluster C2 and C3 markers are found in alternate lung fibrosis models
plaincorrelational- 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
“Our results also demonstrate the value of using reference-based annotation of single cell datasets in order to map the heterogeneity of canonical cell types, such as macrophages, and to enhance the differentiation of transcriptomically unique subclusters within the data.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
boosteddirect- 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
“SingleR uses the 80th percentile of correlation values, to prevent misclassification due to heterogeneity in the reference samples.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Cell Type Annotation of Single Cells (SingleR)
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 experiments were in accordance with protocols approved by the UCSF Institutional Animal Care and Use Committee.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Mice
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
“our annotation of cell states using SingleR relies on the particularities of the reference datasets”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
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
“Written informed consent was obtained from all subjects, and the study was approved by the UCSF institutional review board.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Human Lung Tissues
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
“Our analysis suggests an evolution of cluster C3 towards cluster C1, with C2 representing a transitional state.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
hedgeddirect- 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
“The ImmGen database is a large compendium of cell types from multiple organs, which greatly facilitated our clustering analysis by differential annotation.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
What the sources say
- Not checked yetcites [12] Heng, T. S. & Painter, M. W. The Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008). source
boosteddirect- 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.
In the paper
“Yang et al.32 performed gene expression profiling for 167 lung tissues from idiopathic interstitial pneumonia subjects and 50 healthy controls.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Analysis of published human lung microarray datasets
What the sources say
- Not checked yetcites Yang et al.32
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.
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
“Our studies indicate that these lungs are physiologically and pathologically normal.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Human Lung Tissues
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no 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
“suggesting that paracrine interactions between these macrophages and fibroblasts sustain fibroblast proliferation and tissue fibrosis”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
hedgeddirect- 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
“These findings underscore the potential therapeutic significance of our functional studies in mice.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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 be too vague to check against evidence.
- Rests on an assumption: The bleomycin-induced mouse lung fibrosis model recapitulates key features of human lung fibrosis, so findings in it can inform human disease.
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
“Strain-appropriate controls were used in all experiments”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Mice
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“Using only variable genes increases the ability to distinguish closely related cell types.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Cell Type Annotation of Single Cells (SingleR)
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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 the samples passed the quality control parameters established by Agilent.”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Online Methods > Microarray profiling in telomere dysfunction model of lung fibrosis
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 study identified a profibrotic macrophage subpopulation that localizes to sites of fibrotic scar with activating effects on the mesenchyme”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > 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.
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
“Mechanistic studies indicated that fibroblast migration and proliferation were dependent on secretion of Pdgf-aa by SiglecF+CD11c+MHCIIhi and CX3CR1+ cells, respectively”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Discussion
hedgeddirect- 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.
- Rests on an assumption: The 3T3 embryonic mouse fibroblast cell line is a valid proxy for lung fibroblasts in fibrosis for assessing migration and proliferation.
- Rests on an assumption: The Pdgf-aa blocking antibody specifically neutralizes Pdgf-aa without affecting other PDGF isoforms or unrelated pathways.
The paper relies on this without support.
The bleomycin-induced mouse lung fibrosis model recapitulates key features of human lung fibrosis, so findings in it can inform human disease.
“Our observation that ablation of macrophages expressing Cx3cr1 decreased fibroblasts and lung collagen content after bleomycin treatment has relevance for human disease.”
The paper relies on this without support.
The 3T3 embryonic mouse fibroblast cell line is a valid proxy for lung fibroblasts in fibrosis for assessing migration and proliferation.
“we performed in vitro assays with 3T3 fibroblasts, an embryonic mouse fibroblast cell line.”
The paper relies on this without support.
The Pdgf-aa blocking antibody specifically neutralizes Pdgf-aa without affecting other PDGF isoforms or unrelated pathways.
“Antibody blockade of Pdgf-aa inhibited the enhanced gap closure seen in SiglecF+CD11c+MHCIIhi compared to SiglecF+CD11c+MHCIIlo conditioned media”
The paper relies on this without support.
Changes in expression of human orthologues in bulk lung microarray samples reflect changes in macrophage populations rather than other cell types.
“we performed single-sample gene set enrichment analysis (ssGSEA)31 to quantify the expression of human orthologues of genes of interest in a published microarray dataset of 167 lung samples from patients with lung fibrosis and 50 healthy controls32”
The paper relies on this without support.
The human lung explants with regional heterogeneity are representative of idiopathic pulmonary fibrosis fibrotic foci.
“In three lung explants displaying such regional heterogeneity, we found that PDGF-AA+ macrophages were associated with the fibrotic rather than non-fibrotic regions”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The pathogenic role of Mo-AMs in lung fibrosis has been largely attributed to their activity in producing a number of pro-fibrotic mediators, such as TGF-β, PDGF-α, osteopontin (OPN), and MMPs1,3,11,13,15,18,28.”
From TREM2 promotes lung fibrosis via controlling alveolar macrophage survival and pro-fibrotic activity (2025) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“the M3-TAMs, actually the FABP4+ TAMs previously identified as the alveolar macrophages in the lung, were dominant in the lung metastatic osteosarcoma lesion [24].”
From Advances on immunotherapy for osteosarcoma (2024) · cites this paper as [24]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To identify myeloid clusters, we checked the expression of canonical myeloid marker genes (Supplementary Data 3). Furthermore, we used SingleR v2.0.074 for automated annotation based on the Novershtern immune reference dataset75 from the celldex v1.8.0 package74.”
From CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis (2024) · cites this paper as [74]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“To address the ambient RNA contamination SoupX version 1.5.2[54] was utilized to identify and remove ambient RNA background noise from the data.”
From Single-cell transcriptomics unveils skin cell specific antifungal immune responses and IL-1Ra- IL-1R immune evasion strategies of emerging fungal pathogen Candida auris (2024) · cites this paper as [54]
Experimental — accuracy not yet measuredIn the citing paper
“The pericellular fibrotic matrix is a remarkably dynamic environment, which exerts profound influences on cell behavior, and many of the key cell–cell and cell–matrix interactions which regulate fibrosis are mediated by members of the integrin family (noncovalent α/β heterodimers from 18 different α subunits and 8 β subunits resulting in 24 known members in humans)86,87.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [86]
In the citing paper
“integrins can mediate the translation of spatially-fixed extracellular signals into a wide variety of changes in cell behavior including cell adhesion, migration, proliferation, differentiation and apoptosis86,87.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [86]
In the citing paper
“Of key relevance to fibrosis, integrins can also potentiate signals from soluble pro-fibrogenic growth factors such as TGF-β1.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [86]
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Raghu, G., Collard, H. R., Egan, J. J., Martinez, F. J., Behr, J., Brown, K. K., Colby, T. V., Cordier, J.-F., Flaherty, K. R., Lasky, J. A., et al. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.2009-040gl · Cited by 7,186
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
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
xCell: digitally portraying the tissue cellular heterogeneity landscape
Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology · doi:10.1186/s13059-017-1349-1 · Cited by 4,397
Self-citationFate Mapping Reveals Origins and Dynamics of Monocytes and Tissue Macrophages under Homeostasis
Yona, S., Kim, K.-W., Wolf, Y., Mildner, A., Varol, D., Breker, M., Strauss-Ayali, D., Viukov, S., Guilliams, M., Misharin, A., et al. (2013) · Immunity · doi:10.1016/j.immuni.2012.12.001 · Cited by 3,245
Soft lithography for micro- and nanoscale patterning
Qin, D., Younan Xia, A., & Whitesides, G. M. (2010) · Nature Protocols · doi:10.1038/nprot.2009.234 · Cited by 2,399
Cited by
Sorted by citations
Items 401–500 of 1,020
Gu, Y., Lawrence, T., Mohamed, R., Liang, Y., & Yahaya, B. H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.923235 · Cited by 73
Analysis and Visualization of Spatial Transcriptomic Data
Liu, B., Li, Y., & Zhang, L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.785290 · Cited by 73
COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1+ neutrophils and CD14+ monocytes
MacDonald, L., Alivernini, S., Tolusso, B., Elmesmari, A., Somma, D., Perniola, S., Paglionico, A., Petricca, L., Bosello, S. L., Carfì, A., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.147413 · Cited by 72
IL7 genetic variation and toxicity to immune checkpoint blockade in patients with melanoma
Taylor, C. A., Watson, R. A., Tong, O., Ye, W., Nassiri, I., Gilchrist, J. J., Aires, A. V. D. L., Sharma, P. K., Koturan, S., Cooper, R. A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02095-5 · Cited by 74
Zheng, H., Rao, A., Đermadi, D., Toh, J., Jones, L. M., Donato, M., Liu, Y. E., Su, Y., Dai, C. L., Kornilov, S. A., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.03.002 · Cited by 69
Nociceptor-immune interactomes reveal insult-specific immune signatures of pain
Jain, A., Gyori, B. M., Hakim, S., Jain, A. K., Sun, L., Petrova, V., Bhuiyan, S. A., Zhen, S., Wang, Q., Kawaguchi, R., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01857-2 · Cited by 96
The regenerating skeletal muscle niche drives satellite cell return to quiescence
Cutler, A. A., Pawlikowski, B. T., Wheeler, J. R., Betta, N. D., Elston, T. L., O’Rourke, R., Jones, K. L., & Olwin, B. B. (2022) · iScience · doi:10.1016/j.isci.2022.104444 · Cited by 72
CXCL12+ tumor-associated endothelial cells promote immune resistance in hepatocellular carcinoma
Lu, Y., Liu, Y., Zuo, X., Li, G., Wang, J., Liu, J., Wang, X., Wang, S., Zhang, W., Zhang, K., et al. (2025) · Journal of Hepatology · doi:10.1016/j.jhep.2024.09.044 · Cited by 82
Self-citationStreamFlow: Cross-Breeding Cloud With HPC
Colonnelli, I., Cantalupo, B., Merelli, I., & Aldinucci, M. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.3019202 · Cited by 101
Fibroblasts and macrophages: Collaborators in tissue homeostasis
Franklin, R. A. (2021) · Immunological Reviews · doi:10.1111/imr.12989 · Cited by 71
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 90
Liu, X., Qin, X., Qin, H., Jia, C., Yuan, Y., Sun, T., Chen, B., Chen, C., & Zhang, H. (2021) · Angiogenesis · doi:10.1007/s10456-021-09795-5 · Cited by 70
An IL-9–pulmonary macrophage axis defines the allergic lung inflammatory environment
Fu, Y., Wang, J., Zhou, B., Pajulas, A., Gao, H., Ramdas, B., Koh, B., Ulrich, B. J., Yang, S., Kapur, R., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abi9768 · Cited by 78
NLRP3 Inflammasome Mediates Immune-Stromal Interactions in Vasculitis
Porritt, R. A., Zemmour, D., Abe, M., Lee, Y., Narayanan, M., de Carvalho, T. T., Gomez, A. C., Martinon, D., Santiskulvong, C., Fishbein, M. C., et al. (2021) · Circulation Research · doi:10.1161/circresaha.121.319153 · Cited by 76
Monocyte-derived Kupffer cells dominate in the Kupffer cell pool during liver injury
Li, W., Yang, Y., Yang, L., Chang, N., & Li, L. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113164 · Cited by 74
Fibroblast‐specific IL11 signaling drives chronic inflammation in murine fibrotic lung disease
Ng, B., Dong, J., Viswanathan, S., Widjaja, A. A., Paleja, B. S., Adami, E., Ko, N. S. J., Wang, M., Lim, S., Tan, J., et al. (2020) · The FASEB Journal · doi:10.1096/fj.202001045rr · Cited by 69
Becker, L. M., Chen, S.-H., Rodor, J., de Rooij, L. P. M. H., Baker, A. H., & Carmeliet, P. (2022) · Cardiovascular Research · doi:10.1093/cvr/cvac018 · Cited by 81
Benchmarking UMI-based single-cell RNA-seq preprocessing workflows
You, Y., Tian, L., Su, S., Dong, X., Jabbari, J. S., Hickey, P. F., & Ritchie, M. E. (2021) · Genome biology · doi:10.1186/s13059-021-02552-3 · Cited by 77
Chow, R. D.-W., Michaels, T., Bellone, S., Hartwich, T. M. P., Bonazzoli, E., Iwasaki, A., Song, E., & Santin, A. D. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0686 · Cited by 78
Khazaei, S., Jay, N. D., Deshmukh, S., Hendrikse, L. D., Jawhar, W., Chen, C. C. L., Mikael, L. G., Faury, D., Marchione, D. M., Lanoix, J., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0461 · Cited by 76
Epithelial plasticity can generate multi-lineage phenotypes in human and murine bladder cancers
Sfakianos, J. P., Daza, J., Hu, Y., Anastos, H., Bryant, G., Bareja, R., Badani, K. K., Galsky, M. D., Elemento, O., Faltas, B. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16162-3 · Cited by 68
A single-cell atlas of the peripheral immune response to severe COVID-19Preprint
Wilk, A. J., Rustagi, A., Zhao, N. Q., et al. (2020) · medRxiv · doi:10.1101/2020.04.17.20069930 · Cited by 75
Human IL-23 is essential for IFN-γ–dependent immunity to mycobacteria
Philippot, Q., Ogishi, M., Bohlen, J., Puchan, J., Arias, A. A., Nguyen, T., Martín-Fernández, M., Conil, C., Rinchai, D., Momenilandi, M., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.abq5204 · Cited by 72
Derrien, J., Gastineau, S., Frigout, A., Giordano, N., Cherkaoui, M., Gaborit, V., Boinon, R., Douillard, E., Devic, M., Magrangeas, F., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00625-9 · Cited by 77
Cortellino, S., Raveane, A., Chiodoni, C., Delfanti, G., Pisati, F., Spagnolo, V., Visco, E., Fragale, G., Ferrante, F., Magni, S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111256 · Cited by 76
Risk-associated alterations in marrow T cells in pediatric leukemia
Bailur, J. K., McCachren, S. S., Pendleton, K. E., Vasquez, J. C., Lim, H. S., Duffy, A. M., Doxie, D. B., Kaushal, A., Foster, C., DeRyckere, D., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.140179 · Cited by 68
McCowan, J., Fercoq, F., Kirkwood, P. M., T’Jonck, W., Hegarty, L. M., Mawer, C. M., Cunningham, R., Mirchandani, A. S., Hoy, A. M., Humphries, D. C., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abj2132 · Cited by 72
Guo, S., Liu, X., Zhang, J., Huang, Z., Ye, P., Shi, J., Stalin, A., Wu, C., Lu, S., Zhang, F., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107066 · Cited by 78
Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods
Ryu, Y., Han, G. H., Jung, E., & Hwang, D. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.0009 · Cited by 76
Network modeling of single-cell omics data: challenges, opportunities, and progresses
Blencowe, M., Arneson, D., Ding, J., Chen, Y.-W., Saleem, Z., & Yang, X. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180176 · Cited by 73
Single substitution in H3.3G34 alters DNMT3A recruitment to cause progressive neurodegeneration
Khazaei, S., Chen, C. C. L., Andrade, A. F., Kabir, N., Azarafshar, P., Morcos, S. M., França, J. A., Lopes, M. D. C., Lund, P. J., Danieau, G., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.023 · Cited by 71
Ogishi, M., Arias, A. A., Yang, R., Han, J. E., Zhang, P., Rinchai, D., Halpern, J., Mulwa, J., Keating, N., Chrabieh, M., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20220094 · Cited by 78
Wang, X., Chen, Y., Li, Z., Huang, B., Xu, L., Lai, J., Lu, Y., Zha, X., Liu, B., Lan, Y., et al. (2021) · Advanced Science · doi:10.1002/advs.202101447 · Cited by 71
KDM6A epigenetically regulates subtype plasticity in small cell lung cancer
Duplaquet, L., Li, Y., Booker, M. A., Xie, Y., Olsen, S. N., Patel, R. A., Hong, D., Hatton, C., Denize, T., Walton, E., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01210-z · Cited by 77
Karampitsakos, Τ., Torrisi, S. E., Antoniou, K. M., Manali, E. D., Korbila, I. P., Παπαϊωάννου, Ο., Sampsonas, F., Katsaras, M., Vasarmidi, E., Papakosta, D., et al. (2021) · Respiratory Research · doi:10.1186/s12931-021-01725-9 · Cited by 71
Yaakov, T. B., Wasserman, T., Aknin, E., & Savir, Y. (2023) · eLife · doi:10.7554/elife.74915 · Cited by 69
Dynamic macrophage phenotypes in autoimmune and inflammatory rheumatic diseases
Cutolo, M., Soldano, S., Smith, V. M., Gotelli, E., & Hysa, E. (2025) · Nature Reviews Rheumatology · doi:10.1038/s41584-025-01279-w · Cited by 103
Kurihara, C., Lecuona, E., Wu, Q., Yang, W., Nunez-Santana, F., Akbarpour, M., Liu, X., Ren, Z., Li, W., Querrey, M., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.147282 · Cited by 73
Jha, R. M., Rajasundaram, D., Sneiderman, C. T., Schlegel, B. T., O’Brien, C., Xiong, Z., Janesko‐Feldman, K. L., Trivedi, R., Vagni, V., Zusman, B. E., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.06.021 · Cited by 82
VEGF-C protects the integrity of the bone marrow perivascular niche in mice
Fang, S., Chen, S., Nurmi, H. J., Leppänen, V., Jeltsch, M., Scadden, D. T., Silberstein, L., Mikkola, H. K. A., & Alitalo, K. K. (2020) · Blood · doi:10.1182/blood.2020005699 · Cited by 64
Ni, S.-H., Xu, J., Sun, S., Li, Y., Zhou, Z., Li, H., Liu, X., Deng, J., Huang, Y., Chen, Z., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab193 · Cited by 68
Distinct CD8+ T cell dynamics associate with response to neoadjuvant cancer immunotherapies
Li, H., Zandberg, D. P., Kulkarni, A., Chiosea, S. I., Santos, P. M., Isett, B. R., Joy, M., Sica, G., Contrera, K. J., Tatsuoka, C. M., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.02.026 · Cited by 85
Critical downstream analysis steps for single-cell RNA sequencing data
Zhang, Z., Cui, F., Lin, C., Zhao, L., Wang, C., & Zou, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab105 · Cited by 72
Feng, W. H., Schriever, H. C., Jiang, S., Bais, A. S., Wu, H., Kostka, D., & Li, G. (2022) · Communications Biology · doi:10.1038/s42003-022-03346-4 · Cited by 70
Zindel, J., Mittner, J., Bayer, J., April-Monn, S., Kohler, A., Nusse, Y. M., Dosch, M., Büchi, I., Sánchez‐Taltavull, D., Dawson, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27612-x · Cited by 72
Trem2 Agonist Reprograms Foamy Macrophages to Promote Atherosclerotic Plaque Stability—Brief Report
Patterson, M. T., Xu, Y., Hillman, H., Osinski, V., Schrank, P. R., Kennedy, A. E., Barrow, F., Zhu, A., Tollison, S., Shekhar, S., et al. (2024) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.124.320797 · Cited by 76
Functional heterogeneity of alveolar macrophage population based on expression of CXCL2
Xu-Vanpala, S., Deerhake, M. E., Wheaton, J. D., Parker, M. E., Juvvadi, P. R., MacIver, N. J., Ciofani, M., & Shinohara, M. L. (2020) · Science Immunology · doi:10.1126/sciimmunol.aba7350 · Cited by 64
Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale
Yang, M., Yang, Y., Xie, C., Ni, M., Liu, J., Yang, H., Mu, F., & Wang, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00518-z · Cited by 66
Fischer, A. W., Jaeckstein, M. Y., Gottschling, K., Heine, M., Sass, F., Mangels, N., Schlein, C., Worthmann, A., Bruns†, O. T., Yuan, Y., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.12.001 · Cited by 70
Robertson, A. G., Meghani, K. A., Cooley, L. F., McLaughlin, K. A., Fall, L. A., Yu, Y., Castro, M. A. A., Groeneveld, C. S., de Reyniès, A., Nazarov, V. I., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37568-9 · Cited by 74
Wang, P., Luo, M., Zhou, W., Jin, X., Xu, Z., Yan, S., Li, Y., Xu, C., Cheng, R., Huang, Y., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.814239 · Cited by 68
Landscape of tumoral ecosystem for enhanced anti-PD-1 immunotherapy by gut Akkermansia muciniphila
Zhu, Z., Huang, J., Zhang, Y., Hou, W., Chen, F., Mo, Y., & Zhang, Z. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114306 · Cited by 74
Darci-Maher, N., Alvarez, M., Arasu, U. T., Selvarajan, I., Lee, S. H., Pan, D. Z., Miao, Z., Das, S. S., Kamińska, D., Örd, T., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104620 · Cited by 53
Zemmour, D., Charbonnier, L., Léon, J., Six, E. M., Keleş, S., Delville, M., Benamar, M., Barış, S., Zuber, J., Chen, K., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00910-8 · Cited by 65
Garcia‐Flores, V., Romero, R. J., Tarca, A. L., Peyvandipour, A., Xu, Y., Galaz, J., Miller, D., Chaiworapongsa, T., Chaemsaithong, P., Berry, S. M., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adh8335 · Cited by 76
Myeloid cell-derived creatine in the hypoxic niche promotes glioblastoma growth
Rashidi, A., Billingham, L. K., Zolp, A. J., Chia, T.-Y., Silvers, C., Katz, J. L., Park, C. H., Delay, S., Boland, L. K., Geng, Y., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.11.013 · Cited by 71
Deciphering spatial genomic heterogeneity at a single cell resolution in multiple myeloma
Merz, M., Merz, A. M. A., Wang, J., Wei, L., Hu, Q., Hutson, N., Rondeau, C., Celotto, K., Belal, A., Alberico, R. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28266-z · Cited by 72
The effect of background noise and its removal on the analysis of single-cell expression data
Janssen, P., Kliesmete, Z., Vieth, B., Adiconis, X., Simmons, S., Marshall, J. L., Aubin, C., Heyn, H., Levin, J. Z., Enard, W., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02978-x · Cited by 64
TREM2 promotes lung fibrosis via controlling alveolar macrophage survival and pro-fibrotic activity
Cui, H., Banerjee, S., Xie, N., Hussain, M., Jaiswal, A., Liu, H., Kulkarni, T., Antony, V. B., Liu, R.-M., Colonna, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57024-0 · Cited by 74
Stromal Heterogeneity in the Human Proliferative Endometrium—A Single-Cell RNA Sequencing Study
Queckbörner, S., von Grothusen, C., Boggavarapu, N. R., Francis, R. M., Davies, L. C., & Gemzell‐Danielsson, K. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11060448 · Cited by 68
Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma
Chen, Z., Yang, X., Bi, G., Liang, J., Hu, Z., Zhao, M., Li, M., Lu, T., Zheng, Y., Sui, Q., et al. (2020) · International Journal of Biological Sciences · doi:10.7150/ijbs.42080 · Cited by 58
Shytaj, I. L., Procopio, F. A., Tarek, M., Carlón-Andrés, I., Tang, H., Goldman, A. R., Munshi, M., Pal, V. K., Forcato, M., Sreeram, S., et al. (2021) · EMBO Molecular Medicine · doi:10.15252/emmm.202013901 · Cited by 64
Singh, S., Lee, N., Pedroza, D. A., Bado, I., Hamor, C., Zhang, L., Aguirre, S., Hu, J., Shen, Y., Xu, Y., et al. (2022) · Cancer Research · doi:10.1158/0008-5472.can-21-3714 · Cited by 64
Fraser, E., Denney, L., Antanaviciute, A., Blirando, K., Vuppusetty, C., Zheng, Y., Repapi, E., Iotchkova, V., Taylor, S., Ashley, N., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.623430 · Cited by 71
Araujo, H. A., Pechuan-Jorge, X., Zhou, T., Do, M. T., Hu, X., Rojas Alvarez, F. R., Salvatierra, M. E., Ibarguen, H. P., Lee, R., Raghulan, R., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0421 · Cited by 70
A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumorsPreprint
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/761429 · Cited by 66
Pervizaj-Oruqaj, L., Ferrero, M. R., Matt, U., & Herold, S. (2024) · European Respiratory Review · doi:10.1183/16000617.0263-2023 · Cited by 78
HER3 Is an Actionable Target in Advanced Prostate Cancer
Gil, V., Miranda, S., Riisnaes, R., Gurel, B., D’Ambrosio, M., Vasciaveo, A., Crespo, M., Ferreira, A., Brina, D., Troiani, M., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-3360 · Cited by 68
Zhang, Y., Tang, T., Wang, B., Wen, Y., Feng, Y., Yin, Q., Jiang, W., Zhang, Y., Li, Z., Wu, M., et al. (2024) · Advanced Science · doi:10.1002/advs.202309752 · Cited by 72
Li, Y., Jiang, M., Aye, L., Luo, L., Zhang, Y., Xu, F., Wei, Y., Peng, D., He, X., Gu, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45340-w · Cited by 71
Advances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 70
Macrophage-Derived Cathepsin S Remodels the Extracellular Matrix to Promote Liver Fibrogenesis
Zuo, T., Xie, Q., Liu, J., Yang, J., Shi, J., Kong, D., Wang, Y., Zhang, Z., Gao, H., Zeng, D.-B., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.05.039 · Cited by 70
Dufva, O., Gandolfi, S., Huuhtanen, J., Dashevsky, O., Duàn, H., Saeed, K., Klievink, J., Nygren, P. J., Bouhlal, J., Lahtela, J., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.11.008 · Cited by 77
Phosphoproteomics identifies microglial Siglec‐F inflammatory response during neurodegeneration
Morshed, N., Ralvenius, W. T., Nott, A., Watson, L. A., Rodriguez, F. H., Akay, L. A., Joughin, B. A., Pao, P., Penney, J., LaRocque, L. M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209819 · Cited by 48
Insitutype: likelihood-based cell typing for single cell spatial transcriptomicsPreprint
Danaher, P. J., Zhao, E. J., Yang, Z. G., Ross, D., Gregory, M., Reitz, Z., Kim, T. K., Baxter, S. K., Jackson, S. W., He, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512902 · Cited by 65
Washer, S. J., Perez‐Alcantara, M., Chen, Y., Steer, J., James, W., Trynka, G., Bassett, A. R., & Cowley, S. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-23477-2 · Cited by 60
Patel, R. S., Tomlinson, J. E., Divers, T. J., de Walle, G. R. V., & Rosenberg, B. R. (2021) · BMC Biology · doi:10.1186/s12915-020-00947-5 · Cited by 54
Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases
Tagore, S., Caprio, L., Amin, A. D., Bestak, K., Luthria, K. D., D’Souza, E. K., Barrera, I., Melms, J. C., Wu, S., Abuzaid, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03530-z · Cited by 80
Liu, C., Wang, K., Liu, W., Zhang, J., Fan, Y., & Sun, Y. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2024.04.019 · Cited by 75
de Rooij, L. P. M. H., Becker, L. M., Teuwen, L.-A. M. N., Boeckx, B., Jansen, S., Feys, S., Verleden, S. E., Liesenborghs, L., Stalder, A. K., Libbrecht, S., et al. (2022) · Cardiovascular Research · doi:10.1093/cvr/cvac139 · Cited by 61
Li, H., Wang, H., Sokulsky, L. A., Liu, S., Yang, R. R., Liu, X., Zhou, L., Li, J., Huang, C., Li, F., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.09.032 · Cited by 63
Deciphering Brain Complexity using Single-Cell Sequencing
Mu, Q., Chen, Y., & Wang, J. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.07.007 · Cited by 61
Carli, J. F. M., Trahan, G. D., Jones, K. L., Hirsch, N. M., Rolloff, K. P., DUNN, E. Z., Friedman, J. E., Barbour, L. A., Hernandez, T. L., MacLean, P. S., et al. (2020) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-020-09466-z · Cited by 58
Liang, Y., Tan, Y., Guan, B., Guo, B., Xia, M., Li, J., Shi, Y. X., Yu, Z., Zhang, Q., Liu, D., et al. (2022) · Theranostics · doi:10.7150/thno.77281 · Cited by 60
Rade, M., Grieb, N., Weiss, R., Sia, J., Fischer, L., Born, P., Boldt, A., Fricke, S., Franz, P., Scolnick, J. A., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00763-8 · Cited by 71
Yang, L., Gao, L., Yang, Y., Shi, D., Zhang, Z., Wang, X., Huang, Y., Wu, J., Meng, J., Li, H., et al. (2025) · Journal of Neuroinflammation · doi:10.1186/s12974-025-03396-5 · Cited by 72
Transcriptional census of epithelial-mesenchymal plasticity in cancer
Cook, D. P., & Vanderhyden, B. C. (2022) · Science Advances · doi:10.1126/sciadv.abi7640 · Cited by 59
Heterogeneity analysis of astrocytes following spinal cord injury at single‐cell resolution
Hou, J., Bi, H., Ge, Q., Teng, H., Wan, G., Yu, B., Jiang, Q. Y., & Gu, X. (2022) · The FASEB Journal · doi:10.1096/fj.202200463r · Cited by 68
Khan, S. M., Desai, R. R., Coxon, A. T., Livingstone, A. J., Dunn, G. P., Petti, A. A., & Johanns, T. M. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005293 · Cited by 60
CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data
Xu, J., Zhang, A., Liu, F., Chen, L., & Zhang, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad195 · Cited by 66
Tang, Y., Kwiatkowski, D. J., & Henske, E. P. (2022) · Nature Communications · doi:10.1038/s41467-022-32673-7 · Cited by 66
Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells
Bunis, D. G., Bronevetsky, Y., Krow‐Lucal, E., Bhakta, N. R., Kim, C. C., Nerella, S., Jones, N. G., Mendoza, V. F., Bryson, Y. J., Gern, J. E., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2020.108573 · Cited by 58
The role of macrophages in fibrosis of chronic kidney disease
Li, G., Yang, H., Zhang, D., Zhang, Y., Liu, B., Wang, Y.-X., Zhou, H., Xu, Z.-X., & Wang, Y. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.117079 · Cited by 73
Szabó, P. M., Vajdi, A., Kumar, N., Tolstorukov, M. Y., Chen, B. J., Edwards, R. H., Ligon, K. L., Chasalow, S. D., Chow, K.-H., Shetty, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-28480-9 · Cited by 71
Biram, A., Liu, J., Hezroni, H., Davidzohn, N., Schmiedel, D., Khatib-Massalha, E., Haddad, M., Grenov, A., Lebon, S., Salame, T., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.01.013 · Cited by 60
Single-cell sequencing reveals antitumor characteristics of intratumoral immune cells in old mice
Zhang, C., Lei, L., Yang, X., Ma, K., Zheng, H., Su, Y., Jiao, A., Wang, X., Liu, H., Zou, Y., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002809 · Cited by 50
Single-Cell RNA Sequencing in Parkinson’s Disease
Ma, S., & Lim, S. B. (2021) · Biomedicines · doi:10.3390/biomedicines9040368 · Cited by 50
A comparison of automatic cell identification methods for single-cell RNA-sequencing dataPreprint
Abdelaal, T., Michielsen, L., Cats, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/644435 · Cited by 64
Wilson, T. L., Kim, H., Chou, C., Langfitt, D. M., Mettelman, R. C., Minervina, A. A., Allen, E. K., Métais, J., Pogorelyy, M. V., Riberdy, J. M., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1508 · Cited by 58
Pearce, H., Croft, W. D., Nicol, S. M., Margielewska‐Davies, S., Powell, R., Cornall, R. J., Davis, S. J., Marcon, F., Pugh, M., Fennell, É., et al. (2023) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-22-0121 · Cited by 60
Metadata
- Authors
- Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., Butte, A. J., & Bhattacharya, M.
- Affiliations
- University of California, San Francisco (US)
- Gladstone Institutes (US)
- QB3 (US)
- Corresponding author
- Bhattacharya, Mallar
- ORCID
- Venue
- Nature Immunology
- Published
- 2019-01-14
- Volume
- 20
- Issue
- 2
- Pages
- 163-172
- ISSN
- 1529-2908 (ISSN-L)
- 1529-2916
- PMC
- PMC6340744
- OpenAlex
- W2907514116
- Semantic Scholar
- a4d99547655d02c1cfa5d8c627a10248507b96f2
- Cited by
- 4,984
- References
- 56
- Citations by year
- 2026: 831 · 2025: 1024 · 2024: 966 · 2023: 856 · 2022: 662 · 2021: 394 · 2020: 181 · 2019: 63
- FWCI
- 249.29
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Immunology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Gladstone Institutes
- National Institutes of Health (HHSN272201200028C)
- University of California, San Francisco
- National Heart, Lung, and Blood Institute (L30 HL110167, R01HL131560, R01HL139897)
- National Institute of Allergy and Infectious Diseases (HHSN272201200028C)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Surgery › Transplantation: Methods and Outcomes
- Keywords
- pulmonary fibrosis, macrophage heterogeneity, bleomycin-induced lung fibrosis, idiopathic pulmonary fibrosis, monocyte-derived macrophages, alveolar macrophages, CX3CR1, fibrotic niche, macrophage activation
- MeSH terms
- CX3C Chemokine Receptor 1, Animals, Bleomycin, Cells, Cultured, Disease Models, Animal, Female, Humans, Lung, Macrophage Activation, Male, Antigens, Differentiation, Myelomonocytic, Up-Regulation, Macrophages, Alveolar, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Idiopathic Pulmonary Fibrosis, Single-Cell Analysis, Sialic Acid Binding Immunoglobulin-like Lectins
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review