Human bone marrow mesenchymal stem cells (hBMSCs) are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP) activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions). The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient's own cell images to predict a new patient's cellular potential. The prediction accuracy was found to be greatly enhanced by incorporation of patients' own cell features in the modeling, indicating the practical strategy for clinical usage. Consequently, our results provide strong evidence for the feasibility of using a quantitative time series of phase-contrast cellular morphology for non-invasive cell quality prediction in regenerative medicine.

Peptide uptake systems that involve members of the proton-coupled oligopeptide transporter (POT) family are conserved across all organisms. POT proteins have characteristic substrate multispecificity, with which one transporter can recognize as many as 8,400 types of di/tripeptides and certain peptide-like drugs. Here we characterize the substrate multispecificity of Ptr2p, a major peptide transporter of Saccharomyces cerevisiae, using a dipeptide library. The affinities (Ki) of di/tripeptides toward Ptr2p show a wide distribution range from 48 mM to 0.020 mM. This substrate multispecificity indicates that POT family members have an important role in the preferential uptake of vital amino acids. In addition, we successfully establish high performance ligand affinity prediction models (97% accuracy) using our comprehensive dipeptide screening data in conjunction with simple property indices for describing ligand molecules. Our results provide an important clue to the development of highly absorbable peptides and their derivatives including peptide-like drugs.

Controlling the balance of endothelial cells (ECs) and smooth muscle cells (SMCs) in blood vessels is critically important to minimize the risk associated with vascular implants. Extracellular matrix (ECM) plays a key role in controlling the cellular balance, suggesting a promising source of cell-selective peptides. To obtain EC- or SMC-selective peptides, we start by highlighting sequence differences found among ECM molecules as enriched targets for cell-selective peptides. We explored the EC- or SMC-selective performance of tripeptides that are specifically enriched only in collagen type IV, but not in types I, II, III, and V. Collagen type IV was chosen since it is the major ECM in the basement membrane of blood vessels, which separates ECs and SMCs. Among 114 collagen type IV-derived tripeptides pre-screened from in silico analysis, 22 peptides (19%) were found to promote cell-selective adhesion, as determined by peptide array. One of the best performing EC-selective peptides (Cys-Ala-Gly (CAG)) was mixed into an electrospun fine-fiber, a vascular graft material, for practical application. Compared to unmodified fiber, the CAG containing fiber surface was found to enhance adhesion of ECs (+190%) while limiting SMCs (-20%). These results are not only consistent with the hypothesis of ECM as a source of cell selective peptides, but also suggest a new genre of EC- or SMC-selective peptides for tissue engineering applications. Collectively, these findings favorably support the screening approach used to discover new peptides for these purposes.

Given the difficulties inherent in maintaining human pluripotent stem cells (hPSCs) in a healthy state, hPSCs should be routinely characterized using several established standard criteria during expansion for research or therapeutic purposes. hPSC colony morphology is typically considered an important criterion, but it is not evaluated quantitatively. Thus, we designed an unbiased method to evaluate hPSC colony morphology. This method involves a combination of automated non-labelled live-cell imaging and the implementation of morphological colony analysis algorithms with multiple parameters. To validate the utility of the quantitative evaluation method, a parent cell line exhibiting typical embryonic stem cell (ESC)-like morphology and an aberrant hPSC subclone demonstrating unusual colony morphology were used as models. According to statistical colony classification based on morphological parameters, colonies containing readily discernible areas of differentiation constituted a major classification cluster and were distinguishable from typical ESC-like colonies; similar results were obtained via classification based on global gene expression profiles. Thus, the morphological features of hPSC colonies are closely associated with cellular characteristics. Our quantitative evaluation method provides a biological definition of ‘hPSC colony morphology’, permits the non-invasive monitoring of hPSC conditions and is particularly useful for detecting variations in hPSC heterogeneity.