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Neural stem cells (NSCs) are multipotent and are considered ideal source for regenerating damaged neural cells for neurological disorders. During culture of NSCs, both the measurement and the evaluation of their differentiation potential are important to maintain stable quality-assured NSCs for regenerative treatments since the rate of differentiation into certain lineages from NSCs is still not fully controllable. However, conventional cell evaluation techniques using biological molecular are still invasive, costly, and time-consuming. Therefore, a non-invasive, low-cost, and rapid cell evaluation method is required to expand the possibilities of regenerative therapy, especially in the facilities that produce cells for therapy. To address these such technological limitations in non-invasive cell evaluation, we propose the efficacy of computer-aided morphology-based prediction of potentials of stem cells by using multiple and time-course morphological parameters from phase-contrast microscopic images combined with experimentally determined differentiation potentials. In this work, we quantified the morphological parameters of NSCs during three types of differentiation culture and investigated two applications with NSCs: (i) evaluation of their differentiation type and (ii) early prediction of neural differentiation rate. Our data demonstrate that it is possible to non-invasively evaluate neural differentiation types and quantitatively predict future differentiation rates by using morphological information from the first 4 days. Our findings indicate the potential application of morphology-based non-invasive evaluation for optimizing effective differentiation protocols, screening of compounds to mediate NSC differentiation, and quality maintenance of regenerative medicine products.

To investigate the binding properties of a peptide sequence, we conducted principal component analysis (PCA) of the physicochemical features of a tetramer peptide library comprised of 512 peptides, and the variables were reduced to two principal components. We selected IL-2 and IgG as model proteins and the binding affinity to these proteins was assayed using the 512 peptides mentioned above. PCA of binding affinity data showed that 16 and 18 variables were suitable for localizing IL-2 and IgG high-affinity binding peptides, respectively, into a restricted region of the PCA plot. We then investigated whether the binding affinity of octamer peptide libraries could be predicted using the identified region in the tetramer PCA. The results show that octamer high-affinity binding peptides were also concentrated in the tetramer high-affinity binding region of both IL-2 and IgG. The average fluorescence intensity of high-affinity binding peptides was 3.3- and 2.1-fold higher than that of low-affinity binding peptides for IL-2 and IgG, respectively. We conclude that PCA may be used to identify octamer peptides with high- or low-affinity binding properties from data from a tetramer peptide library.

We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

The success of drug development is greatly influenced by the efficiency of drug screening methods. Recently, phenotype-based screens have raised expectations, based on their proven record of identifying first-in-class drugs at a higher rate. Although fluorescence images are the data most commonly used in phenotype-based cell-based assays, nonstained cellular images have the potential to provide new descriptive information about cellular responses. In this study, we applied morphology-based evaluation of nonlabeled microscopic images to a phenotype-based assay. As a study case, we attempted to increase the efficiency of a cell-based assay for chemical compounds that induce production of nerve growth factor (NGF), using lyconadin B as a model compound. Because the total synthesis of lyconadin B was accomplished very recently, there is no well-established cell-based assay scheme for further drug screening. The conventional cell-based assay for evaluating NGF induction requires two types of cells and a total of 5 days of cell culture. The complexity and length of this assay increase both the risk of screening errors and the cost of screening. Our findings show that analysis of cellular morphology enables evaluation of NGF induction by lyconadin B within only 9 h.

Immobilization of functional peptides on polymer material is necessary to produce cell-selective scaffolds. However, the expected effects of peptide immobilization differ considerably according to the properties of selected polymers. To understand such combinational effects of peptides and polymers, varieties of scaffolds including a combination of six types of poly(e-caprolactone-co-D,L-lactide) and four types of cell-selective adhesion peptides were fabricated and compared. On each scaffold, the scaffold properties (i.e. mechanical) and their biological functions (i.e. fibroblast-/endothelial cell-/smooth muscle cell-selective adhesion) were measured and compared. The results showed that the cell adhesion performances of the peptides were considerably enhanced or inhibited by the combination of peptide and polymer properties. In the present study, we illustrated the combinational property effects of peptides and polymers using multi-parametric analyses. We provided an example of determining the best scaffold performance for tissue-engineered medical devices based on quantitative data-driven analyses.