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 901–1,000 of 1,020
Strunz, M., Simon, L. M., Ansari, M., Mattner, L. F., Angelidis, I., Mayr, C. H., Kathiriya, J. J., Yee, M., Ogar, P., Sengupta, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/705244 · Cited by 30
Induction of trained immunity by influenza vaccination - impact on COVID-19Preprint
Debisarun, P. A., Gössling, K. L., Bulut, O. P., et al. (2021) · medRxiv · doi:10.1101/2021.09.03.21263028 · Cited by 35
Noh, J., Lee, I. P., Han, N. R., Kim, M., Min, Y. K., Lee, S.-Y., Yun, S. H., Kim, S. I., Park, T., Chung, H., et al. (2022) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2022.07.005 · Cited by 36
Andrade, A. F., Annett, A., Karimi, E., Topouza, D. G., Rezanejad, M., Liu, Y., McNicholas, M., Santiago, E. G. G., Llivichuzhca-Loja, D., Gehlhaar, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52096-w · Cited by 40
Unterman, A., Sumida, T. S., Nouri, N., et al. (2020) · medRxiv · doi:10.1101/2020.07.16.20153437 · Cited by 35
scGraph: a graph neural network-based approach to automatically identify cell types
Yin, Q., Liu, Q., Fu, Z., Zeng, W., Zhang, B., Zhang, X., Jiang, R., & Lv, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac199 · Cited by 37
Peng, H., Jiang, L., Yuan, J., Wu, X., Chen, N., Liu, D., Liang, Y., Xie, Y., Jia, K., Li, Y., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.70054 · Cited by 38
Immunoglobulin G N-glycan markers of accelerated biological aging during chronic HIV infection
Giron, L. B., Liu, Q., Adeniji, O. S., Yin, X., Kannan, T., Ding, J., Lu, D. Y., Langan, S. J., Zhang, J., Azevedo, J. L. L. C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47279-4 · Cited by 33
Fu, W., Chen, S., Yang, R., Li, C., Gao, H., Li, J. H., & Zhang, X. (2022) · eLife · doi:10.7554/elife.79585 · Cited by 39
SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data
Prashant, N. M., Alomran, N., Chen, Y., Liu, H., Bousounis, P., Movassagh, M., Edwards, N. J., & Horvath, A. (2021) · BMC Genomics · doi:10.1186/s12864-021-07974-8 · Cited by 29
Zhang, J., Shen, Y., Chen, X.-Y., Jiang, M.-L., Yuan, F.-H., Xie, S.-L., Zhang, J., & Xu, F. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1115890 · Cited by 31
Zhu, Y., Zhao, Y., Wen, J., Liu, S., Huang, T., Hatial, I., Peng, X., Janabi, H. A., Huang, G., Mittlesteadt, J., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.ade4656 · Cited by 36
Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing
Xie, Y., Chen, H., Chellamuthu, V. R., Lajam, A. B. M., Albani, S., Low, A. H. L., Petretto, E. G., & Behmoaras, J. V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25073828 · Cited by 39
Dong, H., He, X., Zhang, L., Chen, W., Lin, Y., Liu, S., Wang, H., Nguyen, L. X. T., Li, M., Zhu, Y., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00736-x · Cited by 40
CAR T-cell-mediated delivery of bispecific innate immune cell engagers for neuroblastoma
Pascual‐Pasto, G., McIntyre, B. C., Hines, M. G., Giudice, A. M., Garcia-Gerique, L., Hoffmann, J., Mishra, P., Matlaga, S., Lombardi, S., Shraim, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51337-2 · Cited by 40
Li, X., Li, S., Wang, Y., Zhou, X., Wang, F., Muhammad, I., Luo, Y., Yandong, S., Liu, D., Wu, B., et al. (2024) · Theranostics · doi:10.7150/thno.96928 · Cited by 38
Alkhani, A., Levy, C. S., Tsui, M., Rosenberg, K., Polovina, K., Mattis, A. N., Mack, M., Dyken, S. J. V., Wang, B., Maher, J. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-64158-2 · Cited by 31
Cheng, Y., Li, L., Wei, X., Xu, F., Huang, X., Qi, F., Zhang, Y., & Li, X. (2023) · Cancer Science · doi:10.1111/cas.15745 · Cited by 35
Preclinical optimization of a GPC2-targeting CAR T-cell therapy for neuroblastoma
Sun, M., Cao, Y., Okada, R., Reyes‐González, J. M., Stack, H. G., Qin, H., Li, N., Seibert, C., Kelly, M. C., Ruppin, E., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005881 · Cited by 34
Huang, Y., Li, Y.-C., You, Z.-H., Hu, L., Hu, P., Wang, L., Peng, Y., & Huang, Z.-A. (2025) · BMC Biology · doi:10.1186/s12915-025-02128-8 · Cited by 34
Genetic and Transcriptional Contributions to Relapse in Normal Karyotype Acute Myeloid Leukemia
Petti, A. A., Khan, S. M., Xu, Z., Helton, N., Fronick, C. C., Fulton, R., Ramakrishnan, S. M., Srivatsan, S. N., Heath, S. E., Westervelt, P., et al. (2021) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-21-0050 · Cited by 35
Liu, J., Xu, K., Xing, M., Zhuo, Y., Guo, J., Du, M., Wang, Q. M., An, Y., Li, J., Gao, P., et al. (2021) · Cell Discovery · doi:10.1038/s41421-021-00360-4 · Cited by 33
scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
Qian, K., Fu, S., Li, H., & Li, W. V. (2022) · Genome biology · doi:10.1186/s13059-022-02649-3 · Cited by 26
Li, H., Yu, M., Zhong, L., Feng, S., Xu, Y., Tang, L., Wu, C. J., Zhang, X., Gu, L., Diao, H., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1162032 · Cited by 38
Yan, Q., Liu, J., Liu, Y., Wen, Z., Jin, D., Wang, F., & Gao, L. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05210-6 · Cited by 40
Claussen, A. D., Quevedo, R. V., Kirk, J. R., Higgins, T., Mostaert, B. J., Rahman, M. T., Oleson, J., Hernandez, R., Hirose, K., & Hansen, M. R. (2022) · Hearing Research · doi:10.1016/j.heares.2022.108510 · Cited by 32
Fibroblast-Derived STC-1 Modulates Tumor-Associated Macrophages and Lung Adenocarcinoma Development
Kamata, T., So, T. Y., Ahmed, Q., Giblett, S. M., PATEL, B., Luo, J., Reddel, R. R., & Pritchard, C. A. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107802 · Cited by 35
Kang, X., Chen, Y., Yi, B., Yan, X., Jiang, C., Chen, B., Lu, L., Sun, Y., & Shi, R. (2021) · Journal of Cancer · doi:10.7150/jca.58076 · Cited by 34
Dong, H., Xie, C., Yao, Z., Zhao, R., Lin, Y., Luo, Y., Chen, S., Qin, Y., Chen, Y., & Zhang, H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.947841 · Cited by 30
Zaid, A. K. M. A., & Ariel, A. (2024) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2024.115204 · Cited by 36
Nath, S., Santamaría, J. C. M., Chu, Y., Choi, J. S., Conforti, P., Lin, J.-D., Sankowski, R., Amann, L., Galanis, C., Wu, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53217-1 · Cited by 34
Magidey, K., Cooper, T. J., Kveler, K., Normand, R., Zhang, T., Timaner, M., Raviv, Z., James, B. P., Gazit, R., Ronai, Z. A., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002856 · Cited by 34
Dandavate, V., Bolshette, N. B., Drunen, R. V., Manella, G., Bueno-Levy, H., Zerbib, M., Kawano, I., Golik, M., Adamovich, Y., & Asher, G. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.07.003 · Cited by 36
Xu, F., Chen, J., Li, Y., Ouyang, S., Yu, M., Wang, Y., Fang, X., He, K., & Yu, F. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.01.001 · Cited by 32
Wang, M., Deng, C., Yang, C. H., Yan, M., Lu, H., Zhang, Y., Liu, H., Tong, Z., Ma, J., Wang, J., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04600-x · Cited by 33
Functional interrogation of lymphocyte subsets in alopecia areata using single-cell RNA sequencing
Lee, E. Y., Dai, Z., Jaiswal, A., Wang, E. H. C., Anandasabapathy, N., & Christiano, A. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305764120 · Cited by 36
Sim, N., Carter, J.-M., Deka, K., Tan, B. K. T., Sim, Y., Tan, S., & Li, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-50071-z · Cited by 37
Current annotation strategies for T cell phenotyping of single-cell RNA-seq data
Mullan, K. A., de Vrij, N., Valkiers, S., & Meysman, P. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1306169 · Cited by 31
Zhang, X., Jiang, W. M., Wu, X., Xie, C., Zhang, Y., Li, L., Gu, Y., Hu, Z., Zhai, X., Liang, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61619-y · Cited by 42
The versatile biochemistry of iron in macrophage effector functions
Behmoaras, J. V. (2020) · FEBS Journal · doi:10.1111/febs.15682 · Cited by 30
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
Fu, X., Cao, Y., Bian, B., Wang, C., Graham, J. D., Pathmanathan, N., Patrick, E., Kim, J., & Yang, J. Y. H. (2025) · Nature Methods · doi:10.1038/s41592-025-02795-z · Cited by 51
Simon, M., Stüve, P., Schmidleithner, L., Bittner, S., Beumer, N., Strieder, N., Schmidl, C., Pant, A., Gebhard, C., Eigenberger, A., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.06.015 · Cited by 37
Dong, K., Gu, D., Shi, J., Bao, Y., Fu, Z., Fang, Y., Qu, L., Zhu, W., Jiang, A., & Wang, L. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.874792 · Cited by 29
Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma
Dolgalev, I., Zhou, H., Murrell, N., Le, H., Sakellaropoulos, T., Coudray, N., Zhu, K., Vasudevaraja, V., Yeaton, A. H., Goparaju, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42327-x · Cited by 37
Early clonal extinction in glioblastoma progression revealed by genetic barcoding
Ceresa, D., Alessandrini, F., Lucchini, S., Marubbi, D., Piaggio, F., Vera, J. M. M., Ceccherini, I., Reverberi, D., Appolloni, I., & Malatesta, P. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.07.001 · Cited by 34
Increased Expression of Tim-3 Is Associated With Depletion of NKT Cells In SARS-CoV-2 Infection
Yang, J., Chang, T., Tang, L., Deng, H., Chen, D., Luo, J., Wu, H., Tang, T.-X., Zhang, C., Li, Z., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.796682 · Cited by 28
Recent progresses in plant single-cell transcriptomics
Zheng, D., Xu, J., Lu, Y., Chen, H., Chu, Q., & Fan, L. (2023) · Crop Design · doi:10.1016/j.cropd.2023.100041 · Cited by 33
Aldrich, A. L., Horn, C. M., Heim, C. E., Korshoj, L. E., & Kielian, T. (2021) · The Journal of Immunology · doi:10.4049/jimmunol.2001042 · Cited by 28
Antifibrotic Drug Nintedanib Inhibits CSF1R to Promote IL-4–associated Tissue Repair Macrophages
Watson, C. K., Schloesser, D., Fundel‐Clemens, K., Lerner, C., Gabler, S., Baskaran, P., Wohnhaas, C. T., Dichtl, S., Huber, H. J., Ask, K., et al. (2022) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2022-0021oc · Cited by 35
Tumor-associated neutrophils attenuate the immunosensitivity of hepatocellular carcinoma
Teo, J. M. N., Chen, Z., Chen, W., Tan, R. J. Y., Cao, Q., Chu, Y., Ma, D., Chen, L., Yu, H., Lam, K.-H., et al. (2024) · The Journal of Experimental Medicine · doi:10.1084/jem.20241442 · Cited by 32
Láníčková, T., Hensler, M., Kašíková, L., Vosahlikova, S., Angelidou, A., Pasulka, J., Griebler, H., Drozenová, J., Mojžíšová, K., Vankerckhoven, A., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-1594 · Cited by 34
Okato, A., Utsumi, T., Ranieri, M., Zheng, X., Zhou, M., Pereira, L. D., Chen, T., Kita, Y., Wu, D., Hyun, H., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci169241 · Cited by 34
Gut Microbiota‐Butyrate‐PPARγ Axis Modulates Adipose Regulatory T Cell Population
Chen, B., Guan, L., Wu, C., Gong, Y., Wu, L., Zhang, M., Cao, Z., Chen, Y., Yang, C., Wang, B., et al. (2025) · Advanced Science · doi:10.1002/advs.202411086 · Cited by 40
Fibroblast atlas: Shared and specific cell types across tissues
Liu, K., Cui, Y., Han, H., Guo, E., Shi, X., Xiong, K., Zhang, N., Zhai, S., Sang, S., Liu, M., et al. (2025) · Science Advances · doi:10.1126/sciadv.ado0173 · Cited by 41
Mao, X., Zhou, D., Lin, K., Zhang, B., Gao, J. T., Ling, F., Zhu, L., Yu, S., Chen, P., Zhang, C., et al. (2022) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-022-03278-2 · Cited by 33
Beliën, J., Swinnen, S., D’hondt, R., Verdú de Juan, L., Dedoncker, N., Matthys, P., Bauer, J., Vens, C., Moylett, S., & Dubois, B. (2024) · Nature Communications · doi:10.1038/s41467-024-49312-y · Cited by 32
Advances in single-cell sequencing: insights from organ transplantation
Wang, Y., Wang, J., Schnieke, A., & Fischer, K. (2021) · Military Medical Research · doi:10.1186/s40779-021-00336-1 · Cited by 34
Raut, R. D., Chakraborty, A. K., Neogi, T., Albro, M. B., Snyder, B. D., Schaer, T. P., Zhang, C., Grinstaff, M. W., & Bais, M. V. (2025) · Communications Biology · doi:10.1038/s42003-025-08586-8 · Cited by 54
The effector program of human CD8 T cells supports tissue remodeling
Delacher, M., Schmidleithner, L., Simon, M., Stüve, P., Sanderink, L., Hotz‐Wagenblatt, A., Wuttke, M., Schambeck, K., Ruhland, B., Hofmann, V., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20230488 · Cited by 30
Galeano Niño, J. L., Wu, H., LaCourse, K. D., Srinivasan, H., Fitzgibbon, M., Minot, S. S., Sather, C., Johnston, C. D., & Bullman, S. (2023) · Nature Protocols · doi:10.1038/s41596-023-00888-7 · Cited by 36
Ionizing irradiation-induced Fgr in senescent cells mediates fibrosis
Mukherjee, A., Epperly, M. W., Shields, D. S., Hou, W., Fisher, R., Hamade, D. F., Wang, H., Huq, M. S., Bao, R., Tabib, T., et al. (2021) · Cell Death Discovery · doi:10.1038/s41420-021-00741-4 · Cited by 33
Chen, J., Liu, Z., Wu, Z., Li, W., & Tan, X. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1132424 · Cited by 32
Neurodevelopmental hijacking of oligodendrocyte lineage programs drives glioblastoma infiltration
Wu, Y., Wu, B. Z., Ellenbogen, Y., Kant, J. A., Yu, P., Li, X., Caloren, L. C., Sotov, V., Tran, C. N., Restrepo, M., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.04.022 · Cited by 35
scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data
Nguyen, V., & Griss, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04574-5 · Cited by 28
Zhang, K., Yue, J., Yin, L., Chen, J., Chen, Y., Hu, L., Shen, J., Yu, N., Gong, Y., & Liu, Z. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.052 · Cited by 31
Borcherding, N. C., Severson, K. J., Henderson, N., Ortolan, L. D. S., Rosenthal, A. C., Bellizzi, A. M., Liu, V., Link, B. K., Mangold, A. R., & Jabbari, A. (2022) · Blood Advances · doi:10.1182/bloodadvances.2021005991 · Cited by 30
Du, J., Wei, R., Jiang, S., Jiang, H., Li, L., Qiang, W., He, H., Shi, L., Ma, Q., Yu, K., et al. (2022) · American Journal of Hematology · doi:10.1002/ajh.26583 · Cited by 32
Li, Y., Wei, J., Sun, Y., Zhou, W., Ma, X., Guo, J., Zhang, H., & Jin, T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115819 · Cited by 33
Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology
Ortega-Batista, A., Jaén-Alvarado, Y., Moreno-Labrador, D., Gómez, N., García, G., & Guerrero, E. N. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26052074 · Cited by 37
Inhibition of CHIT1 as a novel therapeutic approach in idiopathic pulmonary fibrosis
Sklepkiewicz, P. L., Dymek, B., Mlącki, M., Koralewski, R., Mazur, M., Nejman‐Gryz, P., Korur, S., Zagożdżon, A., Rymaszewska, A., von der Thüsen, J. H., et al. (2022) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2022.174792 · Cited by 27
Macrophages and cadherins in fibrosis and systemic sclerosis
To, S., & Agarwal, S. K. (2019) · Current Opinion in Rheumatology · doi:10.1097/bor.0000000000000657 · Cited by 30
Kar, A., Alvarez, M., Garske, K. M., Huang, H., Lee, S. H., Deal, M., Das, S. S., Koka, A., Jamal, Z. N., Mohlke, K. L., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01291-x · Cited by 28
Gao, M., Wang, M., Zhou, S., Hou, J., He, W., Shu, Y., & Wang, X. (2024) · Cancer Cell International · doi:10.1186/s12935-024-03592-y · Cited by 38
Defining cellular population dynamics at single-cell resolution during prostate cancer progression
Germanos, A. A., Arora, S., Zheng, Y., Goddard, E. T., Coleman, I. M., Ku, A. T., Wilkinson, S. C., Song, H., Brady, N. J., Amezquita, R. A., et al. (2022) · eLife · doi:10.7554/elife.79076 · Cited by 30
Zong, Y., Yang, Y., Zhao, J., Li, L., Luo, D., Hu, J., Gao, Y., Li, W., Li, N., & Jiang, L. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1292146 · Cited by 32
Dietary Fiber as a Counterbalance to Age-Related Microglial Cell Dysfunction
Vailati-Riboni, M., Rund, L. A., Caetano‐Silva, M. E., Hutchinson, N. T., Wang, S. S., Soto‐Díaz, K., Woods, J. A., Steelman, A. J., & Johnson, R. W. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.835824 · Cited by 35
Gao, H., Di, J., Clausen, B. H., Wang, N., Zhu, X., Zhao, T., Chang, Y., Pang, M., Yang, Y., He, R., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112629 · Cited by 36
Jiang, Y., Yu, W., Hu, T., Peng, H., Hu, F., Yuan, Y., Liu, X., Lai, S., Zhou, J., & Dong, X. S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1335333 · Cited by 32
Microenvironmental regulation of T-cells in pulmonary hypertension
Plecitá‐Hlavatá, L., Brázdová, A., Křivonosková, M., Hu, C., Phang, T. L., Tauber, J. A., Li, M., Zhang, H., Höetzenecker, K., Crnković, S., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1223122 · Cited by 30
STOmicsDB: a database of Spatial Transcriptomic dataPreprint
Xu, Z., Wang, W., Yang, T., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.11.481421 · Cited by 31
Chen, C., Chung, W., Chang, J. W., Yang, C., Hui‐Kang, D., Teng, Y.-C., Lu, C., Chen, W., Yang, H.-Y., Tsai, C.-C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54180-7 · Cited by 33
Du, J., Kageyama, S., Yamashita, R., Tanaka, K., Okumura, M., Motegi, A., Hojo, H., Nakamura, M., Hirata, H., Sunakawa, H., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05080-x · Cited by 33
Lee, H., Joo, J., Sohn, D. H., Kang, J., Yu, Y., Park, H. R., & Kim, Y. H. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03702-2 · Cited by 26
Advances in Targeted Therapy for Progressive Fibrosing Interstitial Lung Disease
Gibson, C. D., Kugler, M. C., Deshwal, H., Munger, J. S., & Condos, R. (2020) · Lung · doi:10.1007/s00408-020-00370-1 · Cited by 31
Tang, Z., Gao, J., Wu, J., Zeng, G., Liao, Y., Song, Z., Liang, X., Hu, J., Hu, Y., Liu, M., et al. (2021) · Stem Cell Research & Therapy · doi:10.1186/s13287-021-02469-5 · Cited by 33
UGRP1-modulated MARCO+ alveolar macrophages contribute to age-related lung fibrosis
Chen, Y., Hao, X., Li, M., Tian, Z., & Cheng, M. (2023) · Immunity & Ageing · doi:10.1186/s12979-023-00338-8 · Cited by 25
Bioinformatics roadmap for therapy selection in cancer genomics
Jiménez‐Santos, M. J., García‐Martín, S., Torre, C. F., Domenico, T. D., Goméz-López, G., & Al‐Shahrour, F. (2022) · Molecular Oncology · doi:10.1002/1878-0261.13286 · Cited by 35
Tissue niche influences immune and metabolic profiles to Staphylococcus aureus biofilm infection
Roy, Z. A. V., Arumugam, P., Bertrand, B. P., Shinde, D. D., Thomas, V. C., & Kielian, T. (2024) · Nature Communications · doi:10.1038/s41467-024-53353-8 · Cited by 36
Oxidative Stress Response Biomarkers of Ovarian Cancer Based on Single-Cell and Bulk RNA Sequencing
Zheng, M., Hu, Y., Liu, O., Li, S., Wang, Y., Li, X., Liu, J., Yang, Q. F., Li, X., & Lin, B. (2023) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2023/1261039 · Cited by 31
Gu, M., He, T., Yuan, Y., Duan, S., Li, X., & Shen, C. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.753386 · Cited by 32
McKinnon, B. D., Lukowski, S. W., Mortlock, S., Crawford, J., Atluri, S. C., Subramaniam, S., Johnston, R. L., Nirgianakis, K., Tanaka, K., Amoako, A. A., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03541-3 · Cited by 28
Alveolar Organoids in Lung Disease Modeling
Purev, E., Bahmed, K., & Kośmider, B. (2024) · Biomolecules · doi:10.3390/biom14010115 · Cited by 34
Horn, C. M., Arumugam, P., Roy, Z. A. V., Heim, C. E., Fallet, R. W., Bertrand, B. P., Shinde, D. D., Thomas, V. C., Romanova, S. G., Bronich, T. K., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci174051 · Cited by 33
Marteau, V., Nemati, N., Handler, K., Raju, D., Kirchmair, A., Rieder, D., Soto, E. K., Fotakis, G., de Lange, G., Carollo, S., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2025.12.003 · Cited by 40
Decoding organ fibrosis: mechanistic insights and emerging therapeutic strategies
Chen, X., Zhang, J., Guo, L., Wu, C., Zhou, J., Xu, M., Mo, L., Li, Y., & He, J. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02532-0 · Cited by 40
Ji, L., & Guo, W. (2022) · Pharmacological Research · doi:10.1016/j.phrs.2022.106588 · Cited by 30
Kim, S. H., White, Z., Gainullina, A., Kang, S., Kim, J., Dominguez, J. R., Choi, Y., Cabrera, I., Plaster, M., Takahama, M., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.04.004 · Cited by 34
Meng, F.-L., Huang, X., Qin, W., Liu, K.-B., Wang, Y., Li, M., Ren, Y., Li, Y.-Z., & Sun, Y. (2023) · Biomarker Research · doi:10.1186/s40364-023-00523-3 · Cited by 27
Cao, X., Huang, Y., You, Z., Shang, X., Hu, L., Hu, P., & Huang, Z.-A. (2024) · Genome biology · doi:10.1186/s13059-024-03357-w · Cited by 30
Tumor-Associated Microglia Secrete Extracellular ATP to Support Glioblastoma Progression
Wu, C. Y.-J., Chen, Y., Lin, Y.-J., Wei, K., Chang, K., Feng, L.-Y., Chen, K., Li, G., Ren, A. L., Nitta, R. T., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-24-0018 · Cited by 31
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
