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

BackgroundMultipotent 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.MethodMSCs 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.ResultQuantification 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.ConclusionOur 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.

[1]  M. Hanefeld,et al.  The adipocyte volume in human adipose tissue: 1. Lipid space, normal and maximum values, and the relation to body weight index. , 1978, International journal of obesity.

[2]  R. Albin Regeneration , 1993, Neurology.

[3]  S. Björnsson Quantitation of proteoglycans as glycosaminoglycans in biological fluids using an alcian blue dot blot analysis. , 1998, Analytical biochemistry.

[4]  M. Pittenger,et al.  Multilineage potential of adult human mesenchymal stem cells. , 1999, Science.

[5]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[6]  H. Lorenz,et al.  Multilineage cells from human adipose tissue: implications for cell-based therapies. , 2001, Tissue engineering.

[7]  F. Claas,et al.  Amniotic fluid as a novel source of mesenchymal stem cells for therapeutic transplantation. , 2003, Blood.

[8]  D. Prockop,et al.  Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement. , 2006, Cytotherapy.

[9]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[10]  J. Kiernan Dyes and other colorants in microtechnique and biomedical research , 2006 .

[11]  C. M. van der Loos Multiple immunoenzyme staining: methods and visualizations for the observation with spectral imaging. , 2008, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[12]  D. Hourcade,et al.  The Quantification of Glycosaminoglycans: A Comparison of HPLC, Carbazole, and Alcian Blue Methods. , 2008, Open glycoscience.

[13]  C. Loos Multiple Immunoenzyme Staining: Methods and Visualizations for the Observation With Spectral Imaging , 2008 .

[14]  F. Silvestris,et al.  Umbilical cord stroma as source of mesenchymal stem cells for bone regeneration , 2009 .

[15]  A. Cselenyák,et al.  Mesenchymal stem cells rescue cardiomyoblasts from cell death in an in vitro ischemia model via direct cell-to-cell connections , 2010, BMC Cell Biology.

[16]  Jin An,et al.  Prosaposin in the secretome of marrow stroma‐derived neural progenitor cells protects neural cells from apoptotic death , 2010, Journal of neurochemistry.

[17]  Keerthana Prasad,et al.  Image Analysis Tools for Evaluation of Microscopic Views of Immunohistochemically Stained Specimen in Medical Research–a Review , 2012, Journal of Medical Systems.

[18]  B. Geramizadeh,et al.  Human Bone Marrow-derived Mesenchymal Stem Cell: A Source for Cell-Based Therapy , 2012, International journal of organ transplantation medicine.

[19]  Y. Oh,et al.  Mesenchymal stem cell-conditioned media recovers lung fibroblasts from cigarette smoke-induced damage. , 2012, American journal of physiology. Lung cellular and molecular physiology.

[20]  A. Boskey,et al.  Chondrogenic ATDC5 cells: An optimised model for rapid and physiological matrix mineralisation , 2012, International journal of molecular medicine.

[21]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[22]  B. Gharibi,et al.  Effects of Medium Supplements on Proliferation, Differentiation Potential, and In Vitro Expansion of Mesenchymal Stem Cells , 2012, Stem cells translational medicine.

[23]  Rikke Riber-Hansen,et al.  Digital image analysis: a review of reproducibility, stability and basic requirements for optimal results , 2012, APMIS : acta pathologica, microbiologica, et immunologica Scandinavica.

[24]  D. Bhartiya Are Mesenchymal Cells Indeed Pluripotent Stem Cells or Just Stromal Cells? OCT-4 and VSELs Biology Has Led to Better Understanding , 2013, Stem cells international.

[25]  G. Wanner,et al.  Yield and proliferation rate of adipose-derived stromal cells as a function of age, body mass index and harvest site-increasing the yield by use of adherent and supernatant fractions? , 2013, Cytotherapy.

[26]  Anders Eklund,et al.  Medical image processing on the GPU - Past, present and future , 2013, Medical Image Anal..

[27]  P. Stanko,et al.  Comparison of human mesenchymal stem cells derived from dental pulp, bone marrow, adipose tissue, and umbilical cord tissue by gene expression. , 2013, Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia.

[28]  N. Câmara,et al.  Increased Infllammation and Fibrosis Caused by Hyperoaluria in na Experimental Model of Renal Ischemia and Reperfusion , 2014 .

[29]  Claire Yu,et al.  Comparison of Human Adipose‐Derived Stem Cells Isolated from Subcutaneous, Omental, and Intrathoracic Adipose Tissue Depots for Regenerative Applications , 2014, Stem cells translational medicine.

[30]  T. Meckel,et al.  A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy: Error Estimation and Spectral Consideration , 2015, Scientific Reports.

[31]  Jihua Chen,et al.  Effect of an Experimental Direct Pulp-capping Material on the Properties and Osteogenic Differentiation of Human Dental Pulp Stem Cells , 2016, Scientific Reports.

[32]  Shadi Albarqouni,et al.  AggNet : Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016 .

[33]  Yaojiong Wu,et al.  Mesenchymal stem cell subpopulations: phenotype, property and therapeutic potential , 2016, Cellular and Molecular Life Sciences.

[34]  Nassir Navab,et al.  AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[35]  Nasir M. Rajpoot,et al.  Handcrafted features with convolutional neural networks for detection of tumor cells in histology images , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[36]  Xudong Jiang,et al.  Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images , 2017, IEEE Trans. Medical Imaging.

[37]  Jie-Zhi Cheng,et al.  Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images , 2017, IEEE Transactions on Medical Imaging.

[38]  N. Aghdami,et al.  Isolation, Characterization and Osteogenic Potential of Mouse Digit Tip Blastema Cells in Comparison with Bone Marrow-Derived Mesenchymal Stem Cells In Vitro , 2017, Cell journal.

[39]  Mélanie Desancé,et al.  Chondrogenic Differentiation of Defined Equine Mesenchymal Stem Cells Derived from Umbilical Cord Blood for Use in Cartilage Repair Therapy , 2018, International journal of molecular sciences.

[40]  Y. Tabata,et al.  Coupling of bone resorption and formation by RANKL reverse signalling , 2018, Nature.

[41]  Rachel E. Brewer,et al.  Identification of the Human Skeletal Stem Cell , 2018, Cell.