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.
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30 Own result · 20 Own interpretation · 12 Not checked yet · 10 Own method · 3 No basis given
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“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
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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
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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
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“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
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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
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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
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“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.
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- 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.
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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
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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.
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- 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.
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- 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.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clusters 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.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster 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.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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
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- 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.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“cluster 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.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.
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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.
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It may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Discussion · plain
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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
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
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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
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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.
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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.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.
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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.
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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.
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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.
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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.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All 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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“this 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.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
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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”
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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 101–200 of 1,020
Automated methods for cell type annotation on scRNA-seq data
Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249
Zhang, P., Rashidi, A., Zhao, J., Silvers, C., Wang, H., Castro, B. A., Ellingwood, A., Han, Y., Lopez‐Rosas, A., Ζαννίκου, Μ., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37328-9 · Cited by 243
CAR-macrophage therapy for HER2-overexpressing advanced solid tumors: a phase 1 trial
Reiss, K. A., Angelos, M. G., Dees, E. C., Yuan, Y., Ueno, N. T., Pohlmann, P. R., Johnson, M. L., Chao, J., Shestova, O. E., Serody, J. S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03495-z · Cited by 268
Divergent clonal differentiation trajectories of T cell exhaustion
Dániel, B., Yost, K. E., Hsiung, S., Sándor, K., Xia, Y., Qi, Y., Hiam-Galvez, K. J., Black, M., Raposo, C. J., Shi, Q., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01337-5 · Cited by 241
Wang, C., Yu, Q., Song, T., Wang, Z., Song, L., Yang, Y., Shao, J., Li, J., Ni, Y., Chao, N., et al. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01130-8 · Cited by 255
Characterization of the COPD alveolar niche using single-cell RNA sequencing
Sauler, M., McDonough, J. E., Adams, T. S., Kothapalli, N., Bärnthaler, T., Werder, R. B., Schupp, J. C., Nouws, J., Robertson, M. J., Coarfa, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28062-9 · Cited by 255
Li, F., Zhang, H., Huang, Y., Li, D., Zheng, Z., Xie, K., Cao, C., Wang, Q., Zhao, X., Huang, Z., et al. (2024) · Drug Resistance Updates · doi:10.1016/j.drup.2024.101059 · Cited by 259
Liang, B., Huang, Y., Zhong, Y., Li, Z., Ye, R., Wang, B., Zhang, B., Meng, H., Lin, X., Du, J., et al. (2022) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2022.128459 · Cited by 249
Does tissue imprinting restrict macrophage plasticity?
Guilliams, M., & Svedberg, F. R. (2021) · Nature Immunology · doi:10.1038/s41590-020-00849-2 · Cited by 232
Maternal-fetal immune responses in pregnant women infected with SARS-CoV-2
Garcia‐Flores, V., Romero, R. J., Xu, Y., Theis, K. R., Arenas‐Hernandez, M., Miller, D., Peyvandipour, A., Bhatti, G., Galaz, J., Gershater, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27745-z · Cited by 234
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253
Zhang, H., Christensen, C. L., Dries, R., Oser, M. G., Deng, J., Diskin, B., Li, F., Pan, Y., Zhang, X., Yin, Y., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.11.003 · Cited by 222
Complement activation induces excessive T cell cytotoxicity in severe COVID-19
Georg, P., Astaburuaga-García, R., Bonaguro, L., Brumhard, S., Michalick, L., Lippert, L. J., Kostevc, T., Gäbel, C., Schneider, M., Streitz, M., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.040 · Cited by 218
Cottam, M. A., Caslin, H. L., Winn, N. C., & Hasty, A. H. (2022) · Nature Communications · doi:10.1038/s41467-022-30646-4 · Cited by 216
In vivo Differentiation of Human Monocytes
Coillard, A., & Segura, É. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01907 · Cited by 218
Fu, J., Akat, K. M., Sun, Z., Zhang, W., Schlöndorff, D., Liu, Z., Tuschl, T., Lee, K., & He, C. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2018090896 · Cited by 218
Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
Zhang, D., Schroeder, A., Yan, H., Yang, H., Hu, J., Lee, M. Y. Y., Cho, K. S., Susztak, K., Xu, G. X., Feldman, M. D., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02019-9 · Cited by 219
Refining colorectal cancer classification and clinical stratification through a single-cell atlas
Khaliq, A. M., Erdoğan, C., Kurt, Z., Turgut, S. S., Grunvald, M. W., Rand, T., Khare, S., Borgia, J. A., Hayden, D. M., Pappas, S. G., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02677-z · Cited by 246
Obradovic, A., Graves, D. K., Korrer, M. J., Wang, Y., Roy, S., Naveed, A., Xu, Y., Luginbuhl, A., Curry, J., Gibson, M. K., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-3570 · Cited by 215
Stroke subtype-dependent synapse elimination by reactive gliosis in mice
Shi, X., Luo, L., Wang, J., Shen, H., Li, Y., Mamtilahun, M., Liu, C., Shi, R., Lee, J.-H., Tian, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27248-x · Cited by 230
Dissecting the treatment-naive ecosystem of human melanoma brain metastasis
Biermann, J., Melms, J. C., Amin, A. D., Wang, Y., Caprio, L., Karz, A., Tagore, S., Barrera, I., Ibarra-Arellano, M. A., Andreatta, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.007 · Cited by 207
Tan, Z., Chen, X., Zuo, J., Fu, S., Wang, H., & Wang, J. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04056-z · Cited by 214
Single cell analysis of M. tuberculosis phenotype and macrophage lineages in the infected lung
Pisu, D., Huang, L., Narang, V., Theriault, M. E., Lê-Bury, G., Lee, B. T. K., Lakudzala, A. E., Mzinza, D. T., Mhango, D. V., Mitini-Nkhoma, S. C., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20210615 · Cited by 199
Jung, S., Hwang, B., Shin, S. Y., Park, E., Park, S., Kim, C. W., Kim, E., Choo, E. H., Choi, I. J., Świrski, F. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32284-2 · Cited by 219
Amor, C., Fernández-Maestre, I., Chowdhury, S., Ho, Y.-J., Nadella, S., Graham, C., Carrasco, S. E., Nnuji-John, E., Feucht, J., Hinterleitner, C., et al. (2024) · Nature Aging · doi:10.1038/s43587-023-00560-5 · Cited by 228
Hoang, T. N., Pino, M., Boddapati, A. K., Viox, E. G., Starke, C. E., Upadhyay, A. A., Gumber, S., Nekorchuk, M., Busman-Sahay, K., Strongin, Z., et al. (2021) · Cell · doi:10.1016/j.cell.2020.11.007 · Cited by 212
Barrow, F., Khan, S., Fredrickson, G., Wang, H., Dietsche, K., Parthiban, P., Robert, S., Kaiser, T., Winer, S., Herman, A., et al. (2021) · Hepatology · doi:10.1002/hep.31755 · Cited by 210
PGE2 limits effector expansion of tumour-infiltrating stem-like CD8+ T cells
Lacher, S. B., Dörr, J., de Almeida, G. P., Hönninger, J., Bayerl, F., Hirschberger, A., Pedde, A., Meiser, P., Ramsauer, L., Rudolph, T. J., et al. (2024) · Nature · doi:10.1038/s41586-024-07254-x · Cited by 222
Mast Cells, Angiogenesis and Lymphangiogenesis in Human Gastric Cancer
Sammarco, G., Varricchi, G., Ferraro, V., Ammendola, M., Fazio, M. D., Altomare, D. F., Luposella, M., Maltese, L., Currò, G., Marone, G., et al. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20092106 · Cited by 202
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189
Diversity of Macrophages in Lung Homeostasis and Diseases
Hou, F., Xiao, K., Tang, L., & Xie, L. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.753940 · Cited by 226
A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis
Ramesh, A., Schubert, R. D., Greenfield, A. L., Dandekar, R., Loudermilk, R. P., Sabatino, J. J., Koelzer, M. T., Tran, E. B., Koshal, K., Kim, K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008523117 · Cited by 200
Conway, B. R., O'Sullivan, E. D., Cairns, C., O’Sullivan, J., Simpson, D. J., Salzano, A., Connor, K. L., Ding, P., Humphries, D. C., Stewart, K., et al. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020060806 · Cited by 201
Krämer, B., Knoll, R., Bonaguro, L., ToVinh, M., Raabe, J., Astaburuaga-García, R., Schulte-Schrepping, J., Kaiser, K. M., Rieke, G. J., Bischoff, J., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.09.002 · Cited by 250
García‐Bonilla, L., Shahanoor, Z., Sciortino, R. K., Nazarzoda, O., Racchumi, G., Iadecola, C., & Anrather, J. (2024) · Nature Immunology · doi:10.1038/s41590-023-01711-x · Cited by 225
Immunogenomic Landscape of Hematological Malignancies
Dufva, O., Pölönen, P., Brück, O. E., Keränen, M. A. I., Klievink, J., Mehtonen, J., Huuhtanen, J., Kumar, A., Malani, D., Siitonen, S. M., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.002 · Cited by 194
Monocyte Count as a Prognostic Biomarker in Patients with Idiopathic Pulmonary Fibrosis
Kreuter, M., Lee, J. S., Tzouvelekis, A., Oldham, J. M., Molyneaux, P. L., Weycker, D. A., Atwood, M., Kirchgaessler, K.-U., & Maher, T. M. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202003-0669oc · Cited by 191
Osr2 functions as a biomechanical checkpoint to aggravate CD8+ T cell exhaustion in tumor
Zhang, J., Li, J., Hou, Y., Lin, Y., Zhao, H., Shi, Y., Chen, K., Nian, C., Tang, J., Pan, L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.04.023 · Cited by 214
Extensive Remodeling of the Immune Microenvironment in B Cell Acute Lymphoblastic Leukemia
Witkowski, M. T., Dolgalev, I., Evensen, N. A., Ma, C., Chambers, T. M., Roberts, K. G., Sreeram, S., Dai, Y., Tikhonova, A. N., Lasry, A., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.015 · Cited by 200
Modeling Sporadic Alzheimer's Disease in Human Brain Organoids under Serum Exposure
Chen, X., Sun, G., Tian, E., Zhang, M., Davtyan, H., Beach, T. G., Reiman, E. M., Blurton‐Jones, M., Holtzman, D. M., & Shi, Y. (2021) · Advanced Science · doi:10.1002/advs.202101462 · Cited by 200
CNS fibroblasts form a fibrotic scar in response to immune cell infiltration.
Dorrier, C. E., Aran, D., Haenelt, E. A., Sheehy, R. N., Hoi, K. K., Pintarić, L., Chen, Y., Lizama, C. O., Cautivo, K. M., Weiner, G. A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00770-9 · Cited by 199
Self-citationSong, G., Shi, Y., Zhang, M., Goswami, S., Afridi, S., Meng, L., Ma, J., Chen, Y., Lin, Y., Zhang, J., et al. (2020) · Cell Discovery · doi:10.1038/s41421-020-00214-5 · Cited by 175
Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease
Green, G. S., Fujita, M., Yang, H.-S., Taga, M., Cain, A., McCabe, C., Comandante-Lou, N., White, C. C., Schmidtner, A. K., Zeng, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07871-6 · Cited by 204
Lipid-loaded tumor-associated macrophages sustain tumor growth and invasiveness in prostate cancer
Masetti, M., Carriero, R., Portale, F., Marelli, G., Morina, N., Pandini, M., Iovino, M., Partini, B., Erreni, M., Ponzetta, A., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20210564 · Cited by 202
Unterman, A., Sumida, T. S., Nouri, N., Yan, X., Zhao, A., Gasque, V., Schupp, J. C., Asashima, H., Liu, Y., Cosme, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27716-4 · Cited by 181
Sun, J., Zhao, J., Jiang, F., Wang, L., Xiao, Q., Han, F., Chen, J., Yuan, S., Wei, J., Larsson, S. C., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01229-9 · Cited by 197
Yi, M., Niu, M., Wu, Y., Ge, H., Jiao, D., Zhu, S., Zhang, J., Yan, Y., Zhou, P., Chu, Q., et al. (2022) · Journal of Hematology & Oncology · doi:10.1186/s13045-022-01363-8 · Cited by 184
High-efficient engineering of osteo-callus organoids for rapid bone regeneration within one month
Xie, C., Liang, R., Ye, J., Peng, Z., Sun, H., Zhu, Q., Shen, X., Hong, Y., Wu, H., Sun, W., et al. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121741 · Cited by 198
Induction of trained immunity by influenza vaccination - impact on COVID-19
Debisarun, P. A., Gössling, K. L., Bulut, O. P., Kılıç, G., Zoodsma, M., Liu, Z., Oldenburg, M., Rüchel, N., Zhang, B., Xu, C., et al. (2021) · PLoS Pathogens · doi:10.1371/journal.ppat.1009928 · Cited by 179
Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID
Cortal, A., Martignetti, L., Six, E. M., & Rausell, A. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00896-6 · Cited by 165
Guided construction of single cell reference for human and mouse lung
Guo, M., Morley, M. P., Jiang, C., Wu, Y., Li, G., Du, Y., Zhao, S., Wagner, A. F., Cakar, A. C., Kouril, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40173-5 · Cited by 173
Pine, A. R., Cirigliano, S. M., Nicholson, J., Hu, Y., Linkous, A. G., Miyaguchi, K., Edwards, L., Singhania, R., Schwartz, T. H., Ramakrishna, R. R., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0057 · Cited by 175
Origin and ontogeny of lung macrophages: from mice to humans
Evren, E., Ringqvist, E. E., & Willinger, T. (2019) · Immunology · doi:10.1111/imm.13154 · Cited by 185
SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
Cao, Y., Wang, X., & Peng, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00490 · Cited by 166
Pei, W., Shang, F., Wang, X., Fanti, A.-K., Greco, A., Busch, K., Klapproth, K., Zhang, Q., Quedenau, C., Sauer, S., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.07.018 · Cited by 183
Shao, X., Lu, X., Liao, J., Chen, H., & Fan, X. (2020) · Protein & Cell · doi:10.1007/s13238-020-00727-5 · Cited by 164
Blockade of the co-inhibitory molecule PD-1 unleashes ILC2-dependent antitumor immunity in melanoma
Jacquelot, N., Seillet, C., Wang, M., Pizzolla, A., Liao, Y., Hediyeh-zadeh, S., Grisaru‐Tal, S., Louis, C., Huang, Q., Schreuder, J., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00943-z · Cited by 186
IFNγ Is Critical for CAR T Cell–Mediated Myeloid Activation and Induction of Endogenous Immunity
Alizadeh, D., Wong, R. A., Gholamin, S., Maker, M. S., Aftabizadeh, M., Yang, X., Pecoraro, J. R., Jeppson, J. D., Wang, D., Aguilar, B., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1661 · Cited by 189
Adaptive immune responses to SARS-CoV-2 infection in severe versus mild individuals
Zhang, F., Gan, R., Zhen, Z., Hu, X., Li, X., Zhou, F., Liu, Y., Chen, C., Xie, S., Zhang, B., et al. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00263-y · Cited by 156
Next-generation computational tools for interrogating cancer immunity
Finotello, F., Rieder, D., Hackl, H., & Trajanoski, Z. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0166-7 · Cited by 161
Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158
The inflammatory speech of fibroblasts
Schuster, R., Rockel, J. S., Kapoor, M., & Hinz, B. (2021) · Immunological Reviews · doi:10.1111/imr.12971 · Cited by 181
Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D., Yang, P., & Yang, J. Y. H. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199389 · Cited by 168
Bowles, K. R., Silva, M. C., Whitney, K., Bertucci, T., Berlind, J. E., Lai, J. D., Garza, J. C., Boles, N. C., Mahali, S., Strang, K. H., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.003 · Cited by 175
Zheng, H., Liu, H., Ge, Y., & Wang, X. (2021) · Cancer Cell International · doi:10.1186/s12935-021-02252-9 · Cited by 153
Inborn errors of OAS–RNase L in SARS-CoV-2–related multisystem inflammatory syndrome in children
Lee, D., Pen, J. L., Yatim, A., Dong, B., Aquino, Y., Ogishi, M., Pescarmona, R., Talouarn, E., Rinchai, D., Zhang, P., et al. (2022) · Science · doi:10.1126/science.abo3627 · Cited by 159
Hu, G., Su, Y., Kang, B. H., Fan, Z., Dong, T., Brown, D. R., Cheah, J. H., Wittrup, K. D., & Chen, J. (2021) · Nature Communications · doi:10.1038/s41467-021-21066-x · Cited by 162
Single‐cell analyses reveal suppressive tumor microenvironment of human colorectal cancer
Mei, Y., Xiao, W., Hu, H., Lu, G., Chen, L., Sun, Z., Lü, M., Ma, W., Jiang, T., Gao, Y., et al. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.422 · Cited by 151
Failure of human rhombic lip differentiation underlies medulloblastoma formation
Hendrikse, L. D., Haldipur, P., Saulnier, O., Millman, J., Sjoboen, A. H., Erickson, A. W., Ong, W., Gordon, V. D., Coudière-Morrison, L., Mercier, A. L., et al. (2022) · Nature · doi:10.1038/s41586-022-05215-w · Cited by 167
Liu, Y., Ge, J., Chen, Y., Liu, T., Chen, L., Liu, C., Ma, D., Chen, Y., Cai, Y., Xu, Y., et al. (2023) · Advanced Science · doi:10.1002/advs.202205395 · Cited by 167
Wang, Z., Xie, L., Ding, G., Song, S., Chen, L., Li, G., Xia, M., Han, D., Zheng, Y., Liu, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25771-5 · Cited by 163
Cao, Y., Yisimayi, A., Bai, Y., Huang, W., Li, X., Zhang, Z., Yuan, T., An, R., Wang, J., Xiao, T., et al. (2021) · Cell Research · doi:10.1038/s41422-021-00514-9 · Cited by 140
Interferon-stimulated neutrophils as a predictor of immunotherapy response
Benguigui, M., Cooper, T. J., Kalkar, P., Schif‐Zuck, S., Halaban, R., Bacchiocchi, A., Kamer, I., Deo, A., Manobla, B., Rottem, M., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.005 · Cited by 179
Smalley, I., Chen, Z., Phadke, M. A., Li, J., Yu, X., Wyatt, C. J., Evernden, B. R., Messina, J. L., Sarnaik, A. A., Sondak, V. K., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-1694 · Cited by 159
Interleukin-36γ–producing macrophages drive IL-17–mediated fibrosis
Sommerfeld, S. D., Cherry, C., Schwab, R. M., Chung, L., Maestas, D. R., Laffont, P., Stein, J. E., Tam, A. J., Ganguly, S., Housseau, F., et al. (2019) · Science Immunology · doi:10.1126/sciimmunol.aax4783 · Cited by 160
Regulation of epithelial transitional states in murine and human pulmonary fibrosis
Wang, F., Ting, C., Riemondy, K. A., Douglas, M., Foster, K., Patel, N., Kaku, N., Linsalata, A. E., Nemzek, J. A., Varisco, B. M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci165612 · Cited by 176
Single-Cell Analysis Reveals the Range of Transcriptional States of Circulating Human Neutrophils
Wigerblad, G., Cao, Q., Brooks, S. R., Naz, F., Gadkari, M., Jiang, K., Gupta, S., O’Neil, L. J., Dell’Orso, S., Kaplan, M. J., et al. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2200154 · Cited by 166
Jeong, H. Y., Ham, I.-H., Lee, S. H., Ryu, D., Son, S.-Y., Han, S.-U., Kim, T.-M., & Hur, H. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-0792 · Cited by 156
Pro-inflammatory and proliferative microglia drive progression of glioblastoma
Liu, H., Sun, Y., Zhang, Q., Jin, W., Gordon, R. E., Zhang, Y., Wang, J., Sun, C., Wang, Z., Qi, X., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109718 · Cited by 144
Janosevic, D., Myslinski, J., McCarthy, T. W., Zollman, A., Syed, F., Xuei, X., Gao, H., Liu, Y., Collins, K. S., Cheng, Y., et al. (2021) · eLife · doi:10.7554/elife.62270 · Cited by 149
Leblanc, V., Trinh, D. L. N., Aslanpour, S., Hughes, M. L., Livingstone, D., Jin, D., Ahn, B. Y., Blough, M. D., Cairncross, J. G., Chan, J. A.-W., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2022.02.016 · Cited by 159
Song, G., Shi, Y., Lu, M., Ma, J., Huang, S., Zhang, J., Wu, Y. C., Li, J., Lin, Y., Yang, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29164-0 · Cited by 167
Single‐cell transcriptome atlas of human mesenchymal stem cells exploring cellular heterogeneity
Wang, Z., Chai, C., Wang, R., Feng, Y., Huang, L., Zhang, Y., Xiao, X., Yang, S., Zhang, Y., & Zhang, X. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.650 · Cited by 164
Evaluation of Cell Type Annotation R Packages on Single-Cell RNA-Seq Data
Huang, Q., Liu, Y., Du, Y., & Garmire, L. X. (2021) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.07.004 · Cited by 149
A common framework of monocyte-derived macrophage activation
Sanin, D. E., Ge, Y., Marinković, E., Kabat, A. M., Castoldi, Â., Caputa, G., Grzes, K. M., Curtis, J. D., Thompson, E. A., Willenborg, S., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abl7482 · Cited by 161
scMatch: a single-cell gene expression profile annotation tool using reference datasets
Hou, R., Denisenko, E., & Forrest, A. R. R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz292 · Cited by 130
Long-term histone lactylation connects metabolic and epigenetic rewiring in innate immune memory
Ziogas, A., Novakovic, B., Ventriglia, L., Galang, N., Tran, K. A., Li, W., Matzaraki, V., van Unen, N., Schlüter, T., Ferreira, A. V., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.048 · Cited by 191
Macrophage: Key player in the pathogenesis of autoimmune diseases
Yang, S. C., Zhao, M., & Jia, S. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1080310 · Cited by 165
Intratumoral CD103+ CD8+ T cells predict response to PD-L1 blockade
Banchereau, R., Chitre, A. S., Scherl, A., Wu, T. D., Patil, N. S., de Almeida, P. E., Kadel, E. E., Madireddi, S., Au‐Yeung, A., Takahashi, C., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002231 · Cited by 153
Chi, H., Zhao, S., Yang, J., Gao, X., Peng, G., Zhang, J., Xie, X., Song, G., Xu, K., Xia, Z., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1137025 · Cited by 153
Crinier, A., Dumas, P., Escalière, B., Pipéroglou, C., Gil, L., Villacreces, A., Vely, F., Ivanović, Z., Milpied, P. J., Narni-Mancinelli, É., et al. (2020) · Cellular and Molecular Immunology · doi:10.1038/s41423-020-00574-8 · Cited by 147
Notaras, M., Lodhi, A., Dündar, F., Collier, P., Sayles, N. M., Tilgner, H., Greening, D., & Colak, D. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01316-6 · Cited by 150
Song, P., Li, W., Guo, L., Ying, J., Gao, S., & He, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.850745 · Cited by 134
Interfacing Seurat with the R tidy universe
Mangiola, S., Doyle, M., & Papenfuss, A. T. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab404 · Cited by 156
Putative cell type discovery from single-cell gene expression data
Miao, Z., Moreno, P., Huang, N., Papatheodorou, I., Brazma, A., & Teichmann, S. A. (2020) · Nature Methods · doi:10.26226/morressier.5ebd45acffea6f735881af34 · Cited by 133
Landscape of mast cell populations across organs in mice and humans
Tauber, M., Basso, L., Martin, J.-C., Bostan, L. E., Pinto, M. M., Thierry, G. R., Houmadi, R., Serhan, N., Loste, A., Blériot, C., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20230570 · Cited by 151
Alveolar and lung interstitial macrophages: Definitions, functions, and roles in lung fibrosis
Shi, T., Denney, L., An, H., Ho, L., & Zheng, Y. (2020) · Journal of Leukocyte Biology · doi:10.1002/jlb.3ru0720-418r · Cited by 146
High-throughput single-сell sequencing in cancer research
Jia, Q., Chu, H., Jin, Z., Long, H., & Zhu, B. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00990-4 · Cited by 157
Rodor, J., Chen, S.-H., Scanlon, J. P., Monteiro, J. P., Caudrillier, A., Sweta, S., Stewart, K. R., Shmakova, A., Dobie, R., Henderson, B. E. P., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab296 · Cited by 138
Bhattacharya, M., & Ramachandran, P. (2023) · Nature Immunology · doi:10.1038/s41590-023-01551-9 · Cited by 148
Self-citation
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).
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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