The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index; none of the 26 sources its claims cite is checked for support yet.
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 · 12 Not checked yet · 10 Own method · 3 No basis given
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.
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 baseline, TdTomato+ cells represented 6% of the live lung cells by flow cytometry, likely representing the interstitial macrophages present in steady-state lung”
§ Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage > Results > Cluster C2 macrophages localize to the fibrotic niche
hedged- 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. 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 301–400 of 1,020
Staphylococcus aureus stimulates neutrophil itaconate production that suppresses the oxidative burst
Tomlinson, K. L., Riquelme, S. A., Baskota, S. U., Drikic, M., Monk, I. R., Stinear, T. P., Lewis, I. A., & Prince, A. S. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112064 · Cited by 103
Ma, J., Zhang, L., Zhan, H., Mo, Y., Ren, Z., Shao, A., & Lin, J. (2021) · Cell & Bioscience · doi:10.1186/s13578-021-00637-x · Cited by 106
B7-H3 as a Therapeutic Target in Advanced Prostate Cancer
Guo, C. W., Figueiredo, I., Gürel, B., Neeb, A., Seed, G., Crespo, M., Carreira, S., Rekowski, J., Buroni, L., Welti, J. C., et al. (2022) · European Urology · doi:10.1016/j.eururo.2022.09.004 · Cited by 113
Qi, J., Crinier, A., Escalière, B., Ye, Y., Wang, Z., Zhang, T., Batista, L., Liu, H., Hong, L., Wu, N., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100353 · Cited by 97
Francis, J. M., Leistritz-Edwards, D., Dunn, A., Tarr, C., Lehman, J. W., Dempsey, C., Hamel, A., Rayon-Estrada, V., Liu, G., Wang, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1126/sciimmunol.abk3070 · Cited by 90
Alvarez, M., Benhammou, J. N., Darci-Maher, N., French, S. W., Han, S. B., Sinsheimer, J. S., Agopian, V. G., Pisegna, J. R., & Pajukanta, P. (2022) · Genome Medicine · doi:10.1186/s13073-022-01055-5 · Cited by 94
Liang, J., Huang, G., Liu, X., Taghavifar, F., Liu, N., Wang, Y., Deng, N., Yao, C., Xie, T., Kulur, V., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci157338 · Cited by 99
Li, H., Liu, Z., Liu, L., Zhang, H., Han, C., Girard, L., Park, H., Zhang, A., Dong, C., Ye, J., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100554 · Cited by 102
Self-citationSingle-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis
Wang, Z.-Y., Keogh, A., Waldt, A., Cuttat, R., Neri, M., Zhu, S., Schuierer, S., Ruchti, A., Crochemore, C., Knehr, J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98806-y · Cited by 103
Peng, L., Wang, F., Wang, Z., Tan, J., Li, H., Tian, X., Liu, G., & Zhou, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac234 · Cited by 96
A comprehensive human embryo reference tool using single-cell RNA-sequencing data
Zhao, C., Reyes, Á. P., Schell, J. P., Weltner, J., Ortega, N. M., Zheng, Y., Björklund, Å. K., Baqué-Vidal, L., Sokka, J., Trokovic, R., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02493-2 · Cited by 185
Huuhtanen, J., Kasanen, H. H. E., Peltola, K. J., Lönnberg, T., Glumoff, V., Brück, O. E., Dufva, O., Peltonen, K., Vikkula, J., Jokinen, E., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci164809 · Cited by 103
Xiao, J., Yu, X., Meng, F., Zhang, Y., Zhou, W., Ren, Y., Li, J., Sun, Y., Sun, H., Chen, G., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06598-6 · Cited by 119
Nowicki-Osuch, K., Zhuang, L., Cheung, T. S., Black, E. L., Masqué‐Soler, N., Devonshire, G., Redmond, A. M., Freeman, A., di Pietro, M., Pilonis, N., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0824 · Cited by 87
Glucagon-like peptide 1 receptor is a T cell-negative costimulatory molecule
Ben Nasr, M., Usuelli, V., Dellepiane, S., Seelam, A. J., Fiorentino, T. V., D’Addio, F., Fiorina, E., Xu, C., Xie, Y., Balasubramanian, H. B., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.05.001 · Cited by 102
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 114
Translational opportunities of single-cell biology in atherosclerosis
de Winther, M. P. J., Bäck, M., Evans, P. C., Gomez, D. A., Gonçalves, I., Jørgensen, H. F., Koenen, R. R., Lutgens, E., Norata, G. D., Osto, E., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac686 · Cited by 97
Expression of Immune Related Genes and Possible Regulatory Mechanisms in Alzheimer’s Disease
Lu, Y., Li, K., Hu, Y., & Wang, X. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.768966 · Cited by 93
Cardiac Mast Cells: Underappreciated Immune Cells in Cardiovascular Homeostasis and Disease
Varricchi, G., Marone, G., & Kovanen, P. T. (2020) · Trends in Immunology · doi:10.1016/j.it.2020.06.006 · Cited by 92
Diverse Injury Pathways Induce Alveolar Epithelial Cell CCL2/12, Which Promotes Lung Fibrosis
Yang, J., Agarwal, M., Ling, S., Teitz‐Tennenbaum, S., Zemans, R. L., Osterholzer, J. J., Sisson, T. H., & Kim, K. K. (2020) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2019-0297oc · Cited by 88
Midbrain organoids with an SNCA gene triplication model key features of synucleinopathy
Mohamed, N., Sirois, J., Ramamurthy, J., Mathur, M., Lépine, P., Deneault, É., Maussion, G., Nicouleau, M., Chen, C. X. ‐., Abdian, N., et al. (2021) · Brain Communications · doi:10.1093/braincomms/fcab223 · Cited by 91
Griffiths, J. I., Chen, J., Cosgrove, P. A., O’Dea, A. P., Sharma, P., Ma, C. X., Trivedi, M. S., Kalinsky, K. M., Wisinski, K. B., O'Regan, R. M., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00215-7 · Cited by 94
Patsalos, A., Halász, L., Medina-Serpas, M. A., Berger, W. K., Dániel, B., Tzerpos, P., Kiss, M., Nagy, G., Fischer, C., Simándi, Z., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20210420 · Cited by 94
Wang, L., Netto, K. G., Zhou, L., Liu, X., Wang, M., Zhang, G., Foster, P. S., Li, F., & Yang, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005590118 · Cited by 93
Shi, Y. X., Zhang, Q., Bi, H., Lu, M., Tan, Y., Zou, D., Ge, L., Chen, Z., Liu, C., Ci, W., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02651-9 · Cited by 94
Wang, L., Sfakianos, J. P., Beaumont, K. G., Akturk, G., Horowitz, A., Sebra, R. P., Farkas, A. M., Gnjatic, S., Hake, A., Izadmehr, S., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-4574 · Cited by 97
Monocytes and macrophages: emerging mechanisms and novel therapeutic targets in pulmonary fibrosis
Perrot, C. Y., Karampitsakos, Τ., & Herazo‐Maya, J. D. (2023) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00302.2023 · Cited by 96
Sengupta, S., Das, S., Crespo, Â. C., Cornel, A. M., Patel, A. G., Mahadevan, N. R., Campisi, M., Ali, A. K., Sharma, B., Rowe, J. H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00427-5 · Cited by 101
Song, J., Zhang, Y., Frieler, R. A., Andren, A. C., Wood, S. C., Tyrrell, D. J., Sajjakulnukit, P., Deng, J. C., Lyssiotis, C. A., Mortensen, R. M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci173034 · Cited by 100
Targeting the mevalonate pathway suppresses ARID1A-inactivated cancers by promoting pyroptosis
Zhou, W., Liu, H., Yuan, Z., Zundell, J. A., Towers, M., Lin, J., Lombardi, S., Nie, H., Murphy, B., Yang, T., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.03.002 · Cited by 99
Categorization of lung mesenchymal cells in development and fibrosis
Liu, X., Rowan, S. C., Liang, J., Yao, C., Huang, G., Deng, N., Xie, T., Wu, D., Wang, Y., Burman, A., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102551 · Cited by 91
Zheng, S., Zou, Y., Tang, Y., Yang, A., Liang, J.-Y., Wu, L., Tian, W., Xiao, W., Xie, X., Yang, L., et al. (2022) · OncoImmunology · doi:10.1080/2162402x.2021.2020984 · Cited by 92
Fitzgerald, K. C., Smith, M. D., Kim, S., Sotirchos, E. S., Kornberg, M. D., Douglas, M., Nourbakhsh, B., Graves, J., Rattan, R., Poisson, L., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100424 · Cited by 102
Routhu, N. K., Cheedarla, N., Gangadhara, S., Bollimpelli, V. S., Boddapati, A. K., Shiferaw, A., Rahman, S. A., Sahoo, A., Edara, V. V., Lai, L., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.02.001 · Cited by 85
Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data
Geuenich, M. J., Hou, J., Sunyun, L., Ayub, S., Jackson, H. W., & Campbell, K. R. (2021) · Cell Systems · doi:10.1016/j.cels.2021.08.012 · Cited by 78
Annotation of cell types (ACT): a convenient web server for cell type annotation
Quan, F., Liang, X., Cheng, M., Yang, H., Liu, K., He, S., Sun, S., Deng, M., He, Y., Liu, W., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01249-5 · Cited by 93
Ekiz, H. A., Conley, C. J., Stephens, W. Z., & O’Connell, R. M. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3538-2 · Cited by 81
Monocyte-derived alveolar macrophage apolipoprotein E participates in pulmonary fibrosis resolution
Cui, H., Jiang, D., Banerjee, S., Xie, N., Kulkarni, T., Liu, R.-M., Duncan, S. R., & Liu, G. (2020) · JCI Insight · doi:10.1172/jci.insight.134539 · Cited by 81
Jackson, Z., Hong, C., Schauner, R., Dropulić, B., Caimi, P. F., Lima, M. J. G. D., Giraudo, M. F., Gupta, K., Reese, J., Hwang, T. H., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1586 · Cited by 85
McEvoy, C. M., Murphy, J. M., Zhang, L., Clotet‐Freixas, S., Mathews, J. A., An, J., Karimzadeh, M., Pouyabahar, D., Su, S., Zaslaver, O., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35297-z · Cited by 93
Self-citationBA.2.12.1, BA.4 and BA.5 escape antibodies elicited by Omicron infectionPreprint
Cao, Y., Yisimayi, A., Jian, F., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.30.489997 · Cited by 90
An interactive murine single-cell atlas of the lung responses to radiation injury
Curras-Alonso, S., Soulier, J., Defard, T., Weber, C., Heinrich, S., Laporte, H., Leboucher, S., Lameiras, S., Dutreix, M., Favaudon, V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38134-z · Cited by 89
Selective vulnerability of layer 5a corticostriatal neurons in Huntington’s disease
Pressl, C., Mätlik, K., Kus, L., Darnell, P., Luo, J., Paul, M. R., Weiss, A. R., Liguore, W. A., Carroll, T. S., Davis, D. A., et al. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.009 · Cited by 101
Mages, S., Moriel, N., Avraham‐Davidi, I., Murray, E. M., Watter, J., Chen, F., Rozenblatt–Rosen, O., Klughammer, J., Regev, A., & Nitzan, M. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01657-3 · Cited by 105
Spatiotemporal insight into early pregnancy governed by immune-featured stromal cells
Yang, M., Ong, J., Meng, F., Zhang, F., Shen, H., Kitt, K., Liu, T., Tao, W., & Du, P. (2023) · Cell · doi:10.1016/j.cell.2023.08.020 · Cited by 91
Knudsen, E. S., Kumarasamy, V. M., Chung, S., Ruiz, A., Vail, P. J., Tzetzo, S. L., Wu, J., Nambiar, R., Sivinski, J., Chauhan, S. S., et al. (2020) · Gut · doi:10.1136/gutjnl-2020-321000 · Cited by 83
Hartenstein, V., & Martı́nez, P. (2019) · Cell and Tissue Research · doi:10.1007/s00441-019-03096-6 · Cited by 89
Shen, R., Liu, L., Wu, Z., Zhang, Y., Yuan, Z., Guo, J., Yang, F., Zhang, C., Chen, B., Feng, W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35288-0 · Cited by 85
Self-citationPCMDB: a curated and comprehensive resource of plant cell markers
Jin, J., Lü, P., Xu, Y., Tao, J., Li, Z., Wang, S., Yu, S., Wang, C., Xie, X., Gao, J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab949 · Cited by 89
Ranjan, B., Sun, W., Park, J., Mishra, K., Schmidt, F., Xie, R., Alipour, F., Singhal, V., Joanito, I., Honardoost, M. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26085-2 · Cited by 82
Süß, P., Hoffmann, A., Rothe, T., Ouyang, Z., Baum, W., Staszewski, O., Schett, G. A., Prinz, M., Krönke, G., Glass, C. K., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.02.109 · Cited by 86
Immature natural killer cells promote progression of triple-negative breast cancer
Thacker, G., Henry, S., Nandi, A., Debnath, R., Singh, S., Nayak, A., Susnik, B., Boone, M., Zhang, Q. H., Kesmodel, S. B., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.abl4414 · Cited by 88
Automatic cell type identification methods for single-cell RNA sequencing
Xie, B., Jiang, Q., Mora, A., & Li, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.027 · Cited by 66
Hawley, J. E., Obradović, A., Dallos, M. C., Lim, E. A., Runcie, K. D., Ager, C. R., McKiernan, J., Anderson, C. B., DeCastro, G. J., Weintraub, J. L., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.10.006 · Cited by 93
Hanemaaijer, E. S., Margaritis, T., Sanders, K., Bos, F. L., Candelli, T., Al-Saati, H., van Noesel, M. M., Meyer‐Wentrup, F. A. G., van de Wetering, M., Holstege, F. C. P., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022350118 · Cited by 87
The role of lung macrophages in acute respiratory distress syndrome
Dang, W., Tao, Y., Xu, X., Zhao, H., Zou, L., & Li, Y. (2022) · Inflammation Research · doi:10.1007/s00011-022-01645-4 · Cited by 89
Mill, C. P., Fiskus, W. C., Dinardo, C. D., Birdwell, C. E., Davis, J. A., Kadia, T. M., Takahashi, K., Short, N. J., Daver, N. G., Ohanian, M. N., et al. (2021) · Blood · doi:10.1182/blood.2021013156 · Cited by 83
Viswanathan, G., Fradin, H., Nazo, N., Ali, S., Ganapathi, A. M., Cumming, I., Zhuang, Y., Choi, I., Warman, A., Jassal, C., et al. (2023) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202203-0441oc · Cited by 84
Váňová, T., Sedmík, J., Raška, J., Černá, K. A., Tauš, P., Pospisilova, V., Nezvedová, M., Fedorová, V., Kadakova, S., Klímová, H., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113310 · Cited by 81
Single-cell RNA sequencing reveals distinct immunology profiles in human keloid
Feng, C., Shan, M., Xia, Y., Zheng, Z., He, K., Wei, Y., Song, K., Meng, T., Liu, H., Hao, Y., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.940645 · Cited by 88
Lung dendritic-cell metabolism underlies susceptibility to viral infection in diabetes
Nobs, S. P., Kolodziejczyk, A. A., Adler, L. N., Horesh, N., Botscharnikow, C., Herzog, E., Mohapatra, G., Hejndorf, S., Hodgetts, R. J., Spivak, I., et al. (2023) · Nature · doi:10.1038/s41586-023-06803-0 · Cited by 91
Inhibitory input directs astrocyte morphogenesis through glial GABABR
Cheng, Y.-T., Luna-Figueroa, E., Woo, J., Chen, H., Lee, Z.-F., Harmancı, A. S., & Deneen, B. (2023) · Nature · doi:10.1038/s41586-023-06010-x · Cited by 85
Hamada, M., Tsunakawa, Y., Jeon, H., Yadav, M. K., & Takahashi, S. (2019) · EXPERIMENTAL ANIMALS · doi:10.1538/expanim.19-0076 · Cited by 83
KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection
Fagerberg, E., Attanasio, J., Dien, C., Singh, J., Kessler, E., Abdullah, L., Shen, J., Hunt, B. G., Connolly, K. A., Brouwer, E. D., et al. (2025) · Science · doi:10.1126/science.adn2337 · Cited by 90
Identification of GLS as a cuproptosis-related diagnosis gene in acute myocardial infarction
Liu, Z., Wang, L., Xing, Q., Liu, X., Hu, Y., Li, W., Yan, Q., Liu, R., & Huang, N. J. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1016081 · Cited by 82
Lee, J., Boyce, S., Powers, J., Baer, C. E., Sassetti, C. M., & Behar, S. M. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008621 · Cited by 70
Hackert, N. S., Radtke, F. A., Exner, T., Lorenz, H., Müller‐Tidow, C., Nigrovic, P. A., Wabnitz, G. H., & Grieshaber‐Bouyer, R. (2023) · Nature Communications · doi:10.1038/s41467-023-43573-9 · Cited by 95
Pan-cancer classification of single cells in the tumour microenvironment
Nofech-Mozes, I., Soave, D. M., Awadalla, P., & Abelson, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37353-8 · Cited by 90
Mitophagy Promotes Resistance to BH3 Mimetics in Acute Myeloid Leukemia
Glytsou, C., Chen, X., Zacharioudakis, E., Al-Santli, W., Zhou, H., Nadorp, B., Lee, S., Lasry, A., Sun, Z., Papaioannou, D., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0601 · Cited by 91
Neuronal activity-dependent mechanisms of small cell lung cancer pathogenesis
Savchuk, S., Gentry, K. M., Wang, W., Carleton, E., de Biagi, C. A. O., Luthria, K. D., Yalçın, B., Ni, L., Farnsworth, H. C., Davis, R. A., et al. (2025) · Nature · doi:10.1038/s41586-025-09492-z · Cited by 138
Miao, Y., Pek, N. M. Q., Tan, C. P. B., Jiang, C., Yu, Z., Iwasawa, K., Shi, M. Y., Kechele, D. O., Sundaram, N., Pastrana-Gomez, V., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.041 · Cited by 104
Kim, M., Min, Y. K., Jang, J., Park, H., Lee, S., & Lee, C. H. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002503 · Cited by 79
Ryu, D., Kim, S. J., Hong, Y., Jo, A., Kim, N., Kim, H., Lee, H., Kım, K., & Park, W. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0694 · Cited by 80
Yamashita, N., Long, M. D., Fushimi, A., Yamamoto, M., Hata, T., Hagiwara, M., Bhattacharya, A., Hu, Q., Wong, K., Liu, S., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002115 · Cited by 84
Zhang, H., Zhang, G., Xu, P., Yu, F., Li, L., Huang, R., Zhang, Z., Kadier, K., Wang, Y., Gu, Q., et al. (2025) · Med Research · doi:10.1002/mdr2.70004 · Cited by 84
Multiple Fibroblast Subtypes Contribute to Matrix Deposition in Pulmonary Fibrosis
Liu, X., Dai, K., Zhang, X., Huang, G., Lynn, H., Rabata, A., Liang, J., Noble, P. W., & Jiang, D. (2023) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2022-0292oc · Cited by 81
Chen, H., Li, Z., Qiu, L., Dong, X., Chen, G., Shi, Y., Cai, L., Liu, W., Ye, H., Zhou, Y., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-004389 · Cited by 85
Seyfferth, C., Eekhout, T., Shi, D., Neumann, M., Berg, L. S., Ke, Y., Shahan, R., Cox, K. L., Gómez-Cano, F., Nelissen, H., et al. (2024) · The Plant Cell · doi:10.1093/plcell/koae003 · Cited by 88
Fitzgerald, G., Turiel, G., Gorski, T., Soro-Arnáiz, I., Zhang, J., Casartelli, N. C., Masschelein, E., Maffiuletti, N. A., Sutter, R., Leunig, M., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04504-y · Cited by 85
Yu, B., Zhang, Q., Lin, L., Zhou, X., Ma, W., Wen, S., Li, C., Wang, W., Wu, Q., Wang, X., et al. (2023) · Cell Discovery · doi:10.1038/s41421-022-00506-y · Cited by 80
Bai, Y., Hu, M., Chen, Z., Wei, J., & Du, H. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.767070 · Cited by 74
Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics
Subramanian, A., Alperovich, M., Yang, Y., & Li, B. (2022) · Genome biology · doi:10.1186/s13059-022-02820-w · Cited by 93
Chen, C.-C., Tran, W., Song, K., Sugimoto, T., Obusan, M. B., Wang, L., Sheu, K. M., Cheng, D., Ta, L. H., Varuzhanyan, G., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.10.009 · Cited by 77
Campbell, K. A., Colacino, J. A., Puttabyatappa, M., Dou, J., Elkin, E. R., Hammoud, S. S., Domino, S. E., Dolinoy, D. C., Goodrich, J. M., Loch-Caruso, R. K., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04623-6 · Cited by 84
Molecular programs of fibrotic change in aging human lung
Lee, S., Islam, M. N., Boostanpour, K., Aran, D., Jin, G., Christenson, S., Matthay, M. A., Eckalbar, W. L., DePianto, D. J., Arron, J. R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26603-2 · Cited by 78
Self-citationMumme, H., Thomas, B. E., Bhasin, S. S., Krishnan, U., Dwivedi, B., Perumalla, P., Sarkar, D., Ulukaya, G. B., Sabnis, H. S., Park, S. I., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41994-0 · Cited by 91
McCluskey, D., Benzian-Olsson, N., Mahil, S. K., Hassi, K., Wohnhaas, C. T., Burden, A. D., Griffiths, C. E. M., Ingram, J. R., Levell, N. J., Parslew, R., et al. (2022) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2022.04.027 · Cited by 77
Fu, M., Shu, S., Peng, Z., Liu, X., Chen, X., Zeng, Z., Yang, Y., Cui, H., Zhao, R., Wang, X., et al. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.123.319828 · Cited by 90
Quek, C. Y. J., Pratapa, A., Bai, X., Al‐Eryani, G., da Silva, I. P., Mayer, A. T., Bartoniček, N., Harvey, K., Maher, N. G., Conway, J. W., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114392 · Cited by 91
Schmassmann, P., Roux, J., Buck, A., Tatari, N., Hogan, S. A., Wang, J., Mantuano, N. R., Wieboldt, R., Lee, S., Snijder, B., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adf5302 · Cited by 82
A pan-cancer single-cell RNA-seq atlas of intratumoral B cells
Fitzsimons, E., Qian, D., Enica, A., Thakkar, K., Augustine, M., Gamble, S., Reading, J. L., & Litchfield, K. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.09.011 · Cited by 88
Watson, R. A., Tong, O., Cooper, R. A., Taylor, C. A., Sharma, P. K., Aires, A. V. D. L., Mahé, E. A., Ruffieux, H., Nassiri, I., Middleton, M. R., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abj8825 · Cited by 76
Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing
Wu, W., Zhang, J., Cao, X., Cai, Z., & Zhao, F. (2022) · Nature Communications · doi:10.1038/s41467-022-30963-8 · Cited by 77
Feng, D., Zhu, W., Wang, J., Li, D., Shi, X., Xiong, Q., You, J., Han, P., Qiu, S., Wei, Q., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00526-7 · Cited by 94
Naqvi, I. A., Giroux, N. S., Olson, L. B., Morrison, S. A., Llanga, T., Akinade, T., Zhu, Y., Zhong, Y., Bose, S., Arvai, S., et al. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121393 · Cited by 83
Spatial transcriptomics reveals human cortical layer and area specification
Qian, X., Coleman, K., Jiang, S., Kriz, A. J., Marciano, J. H., Luo, C., Cai, C., Manam, M. D., Caglayan, E., Lai, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09010-1 · Cited by 109
The myogenesis program drives clonal selection and drug resistance in rhabdomyosarcoma
Patel, A. G., Chen, X. '., Huang, X., Clay, M. R., Komarova, N. L., Krasin, M. J., Pappo, A. S., Tillman, H. S., Orr, B. A., McEvoy, J. D., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.04.003 · Cited by 79
Zhang, S., Liu, Z., Wu, D., Chen, L., & Xie, L. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.596318 · Cited by 74
Parry, E. M., Lemvigh, C. K., Deng, S., Dangle, N. J., Ruthen, N. G., Knisbacher, B. A., Broséus, J., Hergalant, S., Guièze, R., Li, S., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.08.013 · Cited by 69
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).
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
