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

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          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.

          Electronic supplementary material

          The online version of this article (10.1186/s13287-019-1170-8) contains supplementary material, which is available to authorized users.

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          Most cited references19

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          AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

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            Effects of medium supplements on proliferation, differentiation potential, and in vitro expansion of mesenchymal stem cells.

            Mesenchymal stem cells (MSCs) possess great potential for use in regenerative medicine. However, their clinical application may be limited by the ability to expand their cell numbers in vitro while maintaining their differential potentials and stem cell properties. Thus the aim of this study was to test the effect of a range of medium supplements on MSC self-renewal and differentiation potential. Cells were cultured until confluent and subcultured continuously until reaching senescence. Medium supplementation with fibroblast growth factor (FGF)-2, platelet-derived growth factor (PDGF)-BB, ascorbic acid (AA), and epidermal growth factor (EGF) both increased proliferation rate and markedly increased number of cell doublings before reaching senescence, with a greater than 1,000-fold increase in total cell numbers for AA, FGF-2, and PDGF-BB compared with control cultures. Long-term culture was associated with loss of osteogenic/adipocytic differentiation potential, particularly with FGF-2 supplementation but also with AA, EGF, and PDGF-BB. In addition FGF-2 resulted in reduction in expression of CD146 and alkaline phosphatase, but this was partially reversible on removal of the supplement. Cells expressed surface markers including CD146, CD105, CD44, CD90, and CD71 by flow cytometry throughout, and expression of these putative stem cell markers persisted even after loss of differentiation potentials. Overall, medium supplementation with FGF-2, AA, EGF, and PDGF-BB greatly enhanced the total in vitro expansion capacity of MSC cultures, although differentiation potentials were lost prior to reaching senescence. Loss of differentiation potential was not reflected by changes in stem cell surface marker expression.
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              Medical image processing on the GPU - past, present and future.

              Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges. Copyright © 2013 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                benjamin.eggerschwiler@usz.ch
                daisy.canepa@usz.ch
                hans-christoph.pape@usz.ch
                elisa.casanovazimmermann@usz.ch
                paolo.cinelli@usz.ch
                Journal
                Stem Cell Res Ther
                Stem Cell Res Ther
                Stem Cell Research & Therapy
                BioMed Central (London )
                1757-6512
                26 February 2019
                26 February 2019
                2019
                : 10
                : 69
                Affiliations
                [1 ]ISNI 0000 0004 0478 9977, GRID grid.412004.3, Department of Trauma, , University Hospital Zurich, ; Sternwartstrasse 14, 8091 Zurich, Switzerland
                [2 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Life Science Zurich Graduate School, , University of Zurich, ; Winterthurerstrasse 190, 8057 Zurich, Switzerland
                [3 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Center for Applied Biotechnology and Molecular Medicine, , University of Zurich, ; Winterthurerstrasse 190, 8057 Zurich, Switzerland
                Author information
                http://orcid.org/0000-0002-0163-9055
                Article
                1170
                10.1186/s13287-019-1170-8
                6390603
                30808403
                a61fd157-579e-4713-b962-66ebb8c328aa
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 December 2018
                : 9 January 2019
                : 11 February 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100009396, UniversitätsSpital Zürich;
                Categories
                Method
                Custom metadata
                © The Author(s) 2019

                Molecular medicine
                digital image analysis,dye quantification,quantification of differentiation potential,histology,microscopy,mesenchymal stem cells,osteoblasts,adipocytes,chondroblasts

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