Navigation auf zora.uzh.ch

Search ZORA

ZORA (Zurich Open Repository and Archive)

Automated digital image quantification of histological staining for the analysis of the trilineage differentiation potential of mesenchymal stem cells

Eggerschwiler, Benjamin; Canepa, Daisy D; Pape, Hans-Christoph; Casanova, Elisa A; Cinelli, Paolo (2019). Automated digital image quantification of histological staining for the analysis of the trilineage differentiation potential of mesenchymal stem cells. Stem Cell Research & Therapy, 10:69.

Abstract

BACKGROUND Multipotent mesenchymal stem cells (MSCs) have the potential to repair and regenerate damaged tissues and are considered as attractive candidates for the development of cell-based regenerative therapies. Currently, there are more than 200 clinical trials involving the use of MSCs for a wide variety of indications. However, variations in their isolation, expansion, and particularly characterization have made the interpretation of study outcomes or the rigorous assessment of therapeutic efficacy difficult. An unbiased characterization of MSCs is of major importance and essential to guaranty that only the most suitable cells will be used. The development of standardized and reproducible assays to predict MSC potency is therefore mandatory. The currently used quantification methodologies for the determination of the trilineage potential of MSCs are usually based on absorbance measurements which are imprecise and prone to errors. We therefore aimed at developing a methodology first offering a standardized way to objectively quantify the trilineage potential of MSC preparations and second allowing to discriminate functional differences between clonally expanded cell populations.
METHOD MSCs originating from several patients were differentiated into osteoblasts, adipocytes, and chondroblasts for 14, 17, and 21 days. Differentiated cells were then stained with the classical dyes: Alizarin Red S for osteoblasts, Oil Red O for adipocytes, and Alcian Blue 8GX for chondroblasts. Quantification of differentiation was then performed with our newly developed digital image analysis (DIA) tool followed by the classical absorbance measurement. The results from the two techniques were then compared.
RESULT Quantification based on DIA allowed highly standardized and objective dye quantification with superior sensitivity compared to absorbance measurements. Furthermore, small differences between MSC lines in the differentiation potential were highlighted using DIA whereas no difference was detected using absorbance quantification.
CONCLUSION Our approach represents a novel method that simplifies the laboratory procedures not only for the quantification of histological dyes and the degree of differentiation of MSCs, but also due to its color independence, it can be easily adapted for the quantification of a wide range of staining procedures in histology. The method is easily applicable since it is based on open source software and standard light microscopy.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Department of Trauma Surgery
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Medicine (miscellaneous)
Life Sciences > Molecular Medicine
Life Sciences > Biochemistry, Genetics and Molecular Biology (miscellaneous)
Life Sciences > Cell Biology
Language:English
Date:26 February 2019
Deposited On:20 Mar 2019 15:46
Last Modified:20 Mar 2025 02:38
Publisher:BioMed Central
ISSN:1757-6512
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s13287-019-1170-8
PubMed ID:30808403
Download PDF  'Automated digital image quantification of histological staining for the analysis of the trilineage differentiation potential of mesenchymal stem cells'.
Preview
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

Metadata Export

Statistics

Citations

Dimensions.ai Metrics
27 citations in Web of Science®
26 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

71 downloads since deposited on 20 Mar 2019
8 downloads since 12 months
Detailed statistics

Authors, Affiliations, Collaborations

Similar Publications