Abstract
The geometrical confinement of small cell colonies gives differential cues to cells sitting at the periphery versus the core. To utilize this effect, for example to create spatially graded differentiation patterns of human mesenchymal stem cells (hMSCs) in vitro or to investigate underpinning mechanisms, the confinement needs to be robust for extended time periods. To create highly repeatable micro-fabricated structures for cellular patterning and high-throughput data mining, we employed here a simple casting method to fabricate more than 800 adhesive patches confined by agarose micro-walls. In addition, a machine learning based image processing software was developed (open code) to detect the differentiation patterns of the population of hMSCs automatically. Utilizing the agarose walls, the circular patterns of hMSCs were successfully maintained throughout 15 days of cell culture. After staining lipid droplets and alkaline phosphatase as the markers of adipogenic and osteogenic differentiation, respectively, the mega-pixels of RGB color images of hMSCs were processed by the software on a laptop PC within several minutes. The image analysis successfully showed that hMSCs sitting on the more central versus peripheral sections of the adhesive circles showed adipogenic versus osteogenic differentiation as reported previously, indicating the compatibility of patterned agarose walls to conventional microcontact printing. In addition, we found a considerable fraction of undifferentiated cells which are preferentially located at the peripheral part of the adhesive circles, even in differentiation-inducing culture media. In this study, we thus successfully demonstrated a simple framework for analyzing the patterned differentiation of hMSCs in confined microenvironments, which has a range of applications in biology, including stem cell biology.
Highlights
Learning how spatial confinement orchestrate the differentiation processes of cells is essential for the investigation of mechanisms that regulate morphogenesis of multicellular system and tissue regeneration processes [1,2,3]
While many of the mechanisms have been delineated from single cell studies, investigations of differentiation processes of multicellular systems under micro-confined conditions are required to close the gap of our understanding how single cell studies might relate to the tissue level
We show that it is well suited for stem cell differentiation experiments that run over extended time periods
Summary
Learning how spatial confinement orchestrate the differentiation processes of cells is essential for the investigation of mechanisms that regulate morphogenesis of multicellular system and tissue regeneration processes [1,2,3]. Because of the weak physisorption of adhesive and blocking agent onto the substrate surface, those patterns can be removed by cells in long-term cell culture This is a pressing problem for the study of stem cell differentiation processes. Utilizing the extremely inert property of agarose with respect to cellular adhesion [35] and biomolecular absorption [36], it was shown to successfully contain cellular patterns for more than 10 days [37] This method does not limit the adhesive types such as poly-L-lysine (PLL) or proteins, it is suitable for patterning of a variety of surface chemistries, proteins, and cells. SVM algorithm, which is much simpler than the cutting-edge machine learning methods such as deep learning [43], is applied here to classify spatially differentiated hMSCs
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