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.

Precise quantification of cellular potential of stem cells, such as human bone marrow–derived mesenchymal stem cells (hBMSCs), is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1) the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2) predictions of potentials are generated before differentiation cultures are initiated; (3) prediction of multiple potentials can be provided simultaneously for each sample; and (4) predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic) and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21) and cytoskeleton-related genes (PTK2, CD146, and CD49) already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature conversion method for objective potential prediction, but should also allow clinicians to choose the most practical morphology-based prediction method for their own purposes.

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Effective surface modification with biocompatible molecules is known to be effective in reducing the life-threatening risks related to artificial cardiovascular implants. In recent strategies in regenerative medicine, the enhancement and support of natural repair systems at the site of injury by designed biocompatible molecules have succeeded in rapid and effective injury repair. Therefore, such a strategy could also be effective for rapid endothelialization of cardiovascular implants to lower the risk of thrombosis and stenosis. To achieve this enhancement of the natural repair system, a biomimetic molecule that mimics proper cellular organization at the implant location is required. In spite of the fact that many reported peptides have cell-attracting properties on material surfaces, there have been few peptides that could control cell-specific adhesion. For the advanced cardiovascular implants, peptides that can mimic the natural mechanism that controls cell-specific organization have been strongly anticipated. To obtain such peptides, we hypothesized the cellular bias toward certain varieties of amino acids and examined the cell preference (in terms of adhesion, proliferation, and protein attraction) of varieties and of repeat length on SPOT peptide arrays. To investigate the role of specific peptides in controlling the organization of various cardiovascular-related cells, we compared endothelial cells (ECs), smooth muscle cells (SMCs), and fibroblasts (FBs). A clear, cell-specific preference was found for amino acids (longer than 5-mer) using three types of cells, and the combinational effect of the physicochemical properties of the residues was analyzed to interpret the mechanism.

From the recent advances, there are growing expectations toward the mass production of induced pluripotent stem cells (iPSCs) for varieties of applications. For such type of industrial cell manufacturing, the technology which can stabilize the production efficiency is strongly required. Since the present iPSC culture is covered by delicate manual operations, there are still quality differences in produced cells from same culture protocols. To monitor the culture process of iPSCs with the quantified data to evaluate the culture status, we here introduce image-based visualization method of morphological diversity of iPSC colonies. We have set three types of experiments to evaluate the influential factors in iPSC culture technique that may disturb the undifferentiation status of iPSC colonies: (Exp. 1) technical differences in passage skills, (Exp. 2) technical differences in feeder cell preparation, and (Exp. 3) technical differences in maintenance skills (medium exchange frequency with the combination of manual removal of morphologically irregular colonies). By measuring the all existing colonies from real-time microscopic images, the heterogenous change of colony morphologies in the culture vessel was visualized. By such visualization with morphologically categorized Manhattan chart, the difference between technical skills could be compared for evaluating appropriate cell processing.

IntroductionAdvancing industrial-scale manufacture of cells as therapeutic products is an example of the wide applications of regenerative medicine. However, one bottleneck in establishing stable and efficient cell manufacture is quality control. Owing to the lack of effective in-process measurement technology, analyzing the time-consuming and complex cell culture process that essentially determines cellular quality is difficult and only performed by manual microscopic observation. Our group has been applying advanced image-processing and machine-learning modeling techniques to construct prediction models that support quality evaluations during cell culture. In this study, as a model of errors during the cell culture process, intentional errors were compared to the standard culture and analyzed based only on the time-course morphological information of the cells.MethodsTwenty-one lots of human mesenchymal stem cells (MSCs), including both bone-marrow-derived MSCs and adipose-derived MSCs, were cultured under 5 conditions (one standard and 4 types of intentional errors, such as clear failure of handlings and machinery malfunctions). Using time-course microscopic images, cell morphological profiles were quantitatively measured and utilized for visualization and prediction modeling. For visualization, modified principal component analysis (PCA) was used. For prediction modeling, linear regression analysis and the MT method were applied.ResultsBy modified PCA visualization, the differences in cellular lots and culture conditions were illustrated as traits on a morphological transition line plot and found to be effective descriptors for discriminating the deviated samples in a real-time manner. In prediction modeling, both the cell growth rate and error condition discrimination showed high accuracy (>80%), which required only 2 days of culture. Moreover, we demonstrated the applicability of different concepts of machine learning using the MT method, which is effective for manufacture processes that mostly collect standard data but not a large amount of failure data.ConclusionsMorphological information that can be quantitatively acquired during cell culture has great potential as an in-process measurement tool for quality control in cell manufacturing processes.

Peptides, especially intracellular functional peptides that can play a particular role inside a cell, have attracted attention as promising materials to control cell fate. However, hydrophilic materials like peptides are difficult for cells to internalize. Therefore, the screening and design of intracellular functional peptides are more difficult than that of extracellular ones. An effective high-throughput screening system for intracellular functional peptides has not been reported. Here, we demonstrate a novel peptide array system for screening intracellular functional peptides, in which both cell-penetrating peptide (CPP) domain and photo-cleavable linkers are used. By using this screening system, we determined how the cellular uptake properties of CPP-conjugated peptides varied depending on the properties of the conjugated peptides. We found that the internalization ability of CPP-conjugated peptides varied greatly depending on the property of the conjugated peptides, and anionic peptides drastically decreased the uptake ability. We summarized our data in a scatter diagram that plots hydrophobicity versus isoelectric point (pI) of conjugated peptides. These results define a peptide library suitable for screening of intracellular functional peptides. Thus, our system, including the diagram, is a promising tool for searching biological active molecules such as peptide-based drugs.