Single-cell RNA sequencing reveals that lung mesenchymal progenitor cells in IPF exhibit pathological features early in their differentiation trajectory

Daniel J. Beisang, Karen Smith, Libang Yang, Alexey Benyumov, Adam Gilbertsen, Jeremy Herrera, Eric Lock, Emilian Racila, Colleen Forster, Brian J. Sandri, Craig A. Henke, Peter B. Bitterman

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

In Idiopathic Pulmonary Fibrosis (IPF), there is unrelenting scarring of the lung mediated by pathological mesenchymal progenitor cells (MPCs) that manifest autonomous fibrogenicity in xenograft models. To determine where along their differentiation trajectory IPF MPCs acquire fibrogenic properties, we analyzed the transcriptome of 335 MPCs isolated from the lungs of 3 control and 3 IPF patients at the single-cell level. Using transcriptional entropy as a metric for differentiated state, we found that the least differentiated IPF MPCs displayed the largest differences in their transcriptional profile compared to control MPCs. To validate entropy as a surrogate for differentiated state functionally, we identified increased CD44 as a characteristic of the most entropic IPF MPCs. Using FACS to stratify IPF MPCs based on CD44 expression, we determined that CD44hi IPF MPCs manifested an increased capacity for anchorage-independent colony formation compared to CD44lo IPF MPCs. To validate our analysis morphologically, we used two differentially expressed genes distinguishing IPF MPCs from control (CD44, cell surface; and MARCKS, intracellular). In IPF lung tissue, pathological MPCs resided in the highly cellular perimeter region of the fibroblastic focus. Our data support the concept that IPF fibroblasts acquire a cell-autonomous pathological phenotype early in their differentiation trajectory.

Original languageEnglish (US)
Article number11162
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
The authors would like to thank the University of Minnesota Genomics Center, the UMGC Pilot Grant Program, Minnesota Supercomputing Institute, Flow Cytometry core, and BioNet Histology core for their technical assistance. We thank Mark Peterson for his technical assistance, and Matthew Parker for preliminary analysis. We thank Dr. Teschendorff for his personal correspondence regarding network entropy analysis. This work was funded by R01HL125236 to P.B, R01HL125227A1 to C.H., and 5T32HL7741 to D.B.

Publisher Copyright:
© 2020, The Author(s).

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