Identifying Fibroblast Phenotypes with Multiplex Imaging

Multiplexed imaging and single-cell RNA sequencing identify fibroblast phenotypes and reveal possible cellular mechanisms of inflammatory diseases

Normal colon imaged with Cell DIVE. 8 biomarkers are shown: CD20, CD4, CD31, SOX9, Vimentin, Ki67, Panck and SMA. Normal_colon_imaged_with_Cell_DIVE.jpg

Identifying fibroblasts phenotypes can help researchers understand the relationship between distinct diseases and inflammation mechanisms. Fibroblasts perform a wide variety of immune and repair roles throughout the body. Disruptions in these roles can lead to a variety of inflammatory diseases such as rheumatoid arthritis, ulcerative colitis, interstitial lung disease, and Sjögren’s syndrome. However, the heterogeneity of fibroblast subtypes has complicated the understanding of how these critical cells might be contributing to shared mechanisms of inflammatory disease. 

Method

Using single-cell RNA sequencing alongside multiplexed imaging, the authors identified two functional clusters of fibroblasts shared across these disease phenotypes that may drive illness. To begin, the authors generated single-cell RNA sequencing (ScRNA-seq) of CD45-EPCAM- stromal cells from patients suffering from one of these diseases. From this data, a variety of clustering approaches were used to define 14 separate phenotypic clusters of fibroblasts from diseased tissue in these patients. Then, the clusters most tightly associated with inflammation (as determined through immune cell infiltration) were identified through a combination of flow cytometry and scRNA-Seq. This left the authors with two phenotypic clusters of interest: SPARC+COL3A1+ and CXCL10+CCL19+ fibroblasts, denoted C4 and C11 respectively. These two clusters featured distinct gene clusters and transcription factor activity. C4 cells were more highly associated with a program of tissue remodelling, while C11 was associated with a variety of immune cell recruitment and interaction functions.

Next, the authors utilized multiplexed imaging through Cell DIVE to add spatial context to these clusters and assign them to niches within in inflamed synovium, lip, and gut tissue. Three main spatial niches were analyzed: vascular, lymphoid, and mural. Cell DIVE and spatial clustering analysis revealed that C4 fibroblasts were localized to mural zones and contractile cells, while C11 fibroblasts were found in T lymphocyte-enriched regions. This spatial data supports and confirms the sequencing-based analysis.

Conclusion

These data, and others, support a model of multiple, distinct diseases potentially sharing fundamental mechanisms of inflammation through fibroblast phenotypes. These results also illuminate the potential for various classes of fibroblasts to mediate separate functions during inflammation. Here, genomics data and multiplexed imaging with Cell DIVE are used in concert to understand phenotypic clusters of a complex cell type, understand their expression and signaling milieu, and plot their spatial characteristics within a tissue.

Read the full article:

A. P. Croft, J. Campos, K. Jansen, J. D. Turner, J. Marshall, M. Attar, L. Savary, C. Wehmeyer, A. J. Naylor, S. Kemble, J. Begum, K. Dürholz, H. Perlman, F. Barone, H. M. McGettrick, D. T. Fearon, K. Wei, S. Raychaudhuri, I. Korsunsky, M. B. Brenner, M. Coles, S. N. Sansom, A. Filer & C. D. Buckley:

Distinct fibroblast subsets drive inflammation and damage in arthritis

Nature volume 570, pages 246–251 (2019)

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