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Updated: Jul 23, 2025

Isolation of Normal and Cancer-associated Fibroblasts from Fresh Tissues by Fluorescence Activated Cell Sorting FACS

Isolation of Normal and Cancer-associated Fibroblasts from Fresh Tissues by Fluorescence Activated Cell Sorting FACS

Published on: January 14, 2013

Cancer-associated fibroblast classification in single-cell and spatial proteomics data.

Lena Cords^1,2,3, Sandra Tietscher^1,2,3, Tobias Anzeneder⁴

¹Department of Quantitative Biomedicine, University of Zurich, CH-8057, Zurich, Switzerland.

Nature Communications

|July 18, 2023

Summary

This summary is machine-generated.

Cancer-associated fibroblasts (CAFs) are key players in tumor growth. This study defines nine distinct CAF phenotypes using single-cell sequencing, aiding future cancer research and treatment strategies.

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Isolation of Primary Cancer-Associated Fibroblasts from a Syngeneic Murine Model of Breast Cancer for the Study of Targeted Nanoparticles

Isolation of Primary Cancer-Associated Fibroblasts from a Syngeneic Murine Model of Breast Cancer for the Study of Targeted Nanoparticles

Published on: May 14, 2021

Area of Science:

Oncology
Cell Biology
Bioinformatics

Background:

Cancer-associated fibroblasts (CAFs) are crucial components of the tumor microenvironment.
CAFs significantly influence tumor progression and patient outcomes.
Understanding CAF heterogeneity is essential for targeted cancer therapies.

Purpose of the Study:

To define and functionally annotate distinct cancer-associated fibroblast (CAF) phenotypes.
To establish a standardized classification system for CAFs across different cancer types.
To investigate the spatial distribution of CAF phenotypes within tumors.

Main Methods:

Analysis of a single-cell RNA sequencing (scRNA-seq) dataset comprising over 16,000 stromal cells from 14 breast cancer patients.
Functional annotation of identified CAF phenotypes.
Validation of the CAF classification system in four additional cancer types.
High-plex imaging mass cytometry for protein-level confirmation and spatial analysis of CAFs in breast cancer.

Main Results:

Identification and functional annotation of nine distinct CAF phenotypes and one pericyte class.
Validation of the CAF classification across multiple cancer types.
Confirmation of CAF phenotypes at the protein level and elucidation of their spatial organization within tumors.

Conclusions:

A comprehensive CAF classification scheme has been established, enabling cross-study comparisons.
This classification facilitates the analysis of CAF functional roles in cancer.
The findings may guide the development of novel therapeutic strategies targeting CAFs.