Improving fibroblast characterization using single-cell RNA sequencing: an optimized tissue disaggregation and data processing pipeline.
Updated August 30, 2022Single-cell RNA sequencing (scRNA-Seq) provides a valuable platform for characterising multicellular ecosystems. Fibroblasts are a heterogeneous cell type involved in many physiological and pathological processes, but remain poorly-characterised. Analysis of fibroblasts is challenging: these cells are difficult to isolate from tissues, and are therefore commonly under-represented in scRNA-seq datasets. Here, we describe an optimised approach for fibroblast isolation from human lung tissues. We demonstrate the potential for this procedure in characterising stromal cell phenotypes using scRNA-Seq, analyse the effect of tissue disaggregation on gene expression, and optimise data processing to improve clustering quality. Overall design: mRNA profiles of human lung samples (tumour, inflamed and normal lung) from 3 patients.
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Analysis Portals
NoneProject Label
HumanFibroblastCharacterisationSpecies
Sample Type
Anatomical Entity
Organ Part
Selected Cell Types
Model Organ
Disease Status (Specimen)
Disease Status (Donor)
Development Stage
Library Construction Method
Nucleic Acid Source
Paired End
falseAnalysis Protocol
raw_matrix_generationFile Format
Cell Count Estimate
3.2kDonor Count
3