Home
|
Inbox
|
Search
|
View Abstract
Single Cell Deconvolution of Fibroblast Heterogeneity in Mouse Pulmonary Fibrosis
Description
.abstract img { width:300px !important; height:auto; display:block; text-align:center; margin-top:10px } .abstract { overflow-x:scroll } .abstract table { width:100%; display:block; border:hidden; border-collapse: collapse; margin-top:10px } .abstract td, th { border-top: 1px solid #ddd; padding: 4px 8px; } .abstract tbody tr:nth-child(even) td { background-color: #efefef; } .abstract a { overflow-wrap: break-word; word-wrap: break-word; }
A4342 - Single Cell Deconvolution of Fibroblast Heterogeneity in Mouse Pulmonary Fibrosis
Author Block: T. Xie1, y. wang2, N. Deng3, J. Tang2, Y. Geng1, n. liu1, J. C. Liang1, P. W. Noble1, D. Jiang1; 1Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 3Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
RATIONALE
Fibroblast heterogeneity has long been recognized in the mouse and the human lungs, in homeostasis and disease states. However, there is no common consensus on fibroblast subtypes, lineages, biological properties, signaling, and plasticity, which severely hampered our understanding of the mechanisms of lung fibrosis.
METHODS
To comprehensively classify fibroblast populations in the lung with an unbiased approach, single cell RNA-sequencing was performed with mesenchymal preparations from either uninjured or bleomycin-treated αSMA-GFP:Tbx4-Cre:Rosa26-tdTomato mouse lungs. Significantly differentiated gene, signature gene, IncRNA, extracellular and plasma
membrane expressing gene, and top transcription factor analysis were performed. Single-cell R-analysis tools (SCRAT) based on self-organizing maps (SOM) machine learning were used.
RESULTS
Single cell transcriptome analyses classified and defined 6 mesenchymal cell types in normal lung and 7 in fibrotic lung. Signature genes, lncRNAs, extracellular and plasma membrane genes, and top transcription factors were identified for each mesenchymal subtype in normal and fibrotic lung. We identified a distinct cluster of lipofibroblasts associated with prominent adipose and immune related gene features. We discovered a fibrosis-induced mesenchymal subtype that have remarkable level of Pdgfrb. Furthermore, We reconstructed the differentiation potential of mesenchymal subtypes by machine learning and have made the assumption of subtype trans-differentiation potentials.
CONCLUSIONS
This collection of single cell transcriptome and the distinct classification of fibroblast subsets provided novelty and disease relevance, which is an invaluable resource for understanding the fibroblast landscape and their roles in fibrotic diseases.
Funding source:
These studies were supported by NIH grants P01 HL108793, R01 HL060539 (P.W.N.), and R01 HL122068 (D.J.).
Author Block: T. Xie1, y. wang2, N. Deng3, J. Tang2, Y. Geng1, n. liu1, J. C. Liang1, P. W. Noble1, D. Jiang1; 1Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 3Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
RATIONALE
Fibroblast heterogeneity has long been recognized in the mouse and the human lungs, in homeostasis and disease states. However, there is no common consensus on fibroblast subtypes, lineages, biological properties, signaling, and plasticity, which severely hampered our understanding of the mechanisms of lung fibrosis.
METHODS
To comprehensively classify fibroblast populations in the lung with an unbiased approach, single cell RNA-sequencing was performed with mesenchymal preparations from either uninjured or bleomycin-treated αSMA-GFP:Tbx4-Cre:Rosa26-tdTomato mouse lungs. Significantly differentiated gene, signature gene, IncRNA, extracellular and plasma
membrane expressing gene, and top transcription factor analysis were performed. Single-cell R-analysis tools (SCRAT) based on self-organizing maps (SOM) machine learning were used.
RESULTS
Single cell transcriptome analyses classified and defined 6 mesenchymal cell types in normal lung and 7 in fibrotic lung. Signature genes, lncRNAs, extracellular and plasma membrane genes, and top transcription factors were identified for each mesenchymal subtype in normal and fibrotic lung. We identified a distinct cluster of lipofibroblasts associated with prominent adipose and immune related gene features. We discovered a fibrosis-induced mesenchymal subtype that have remarkable level of Pdgfrb. Furthermore, We reconstructed the differentiation potential of mesenchymal subtypes by machine learning and have made the assumption of subtype trans-differentiation potentials.
CONCLUSIONS
This collection of single cell transcriptome and the distinct classification of fibroblast subsets provided novelty and disease relevance, which is an invaluable resource for understanding the fibroblast landscape and their roles in fibrotic diseases.
Funding source:
These studies were supported by NIH grants P01 HL108793, R01 HL060539 (P.W.N.), and R01 HL122068 (D.J.).
|
Home
|
Inbox
|
Search
|