Yasujiro Kiyota

Label-Free Morphology-Based Prediction of Multiple Differentiation Potentials of Human Mesenchymal Stem Cells for Early Evaluation of Intact Cells

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.

Parametric analysis of colony morphology of non-labelled live human pluripotent stem cells for cell quality control

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.

Characterization of time‐course morphological features for efficient prediction of osteogenic potential in human mesenchymal stem cells

Human bone marrow mesenchymal stem cells (hBMSCs) represents one of the most frequently applied cell sources for clinical bone regeneration. To achieve the greatest therapeutic effect, it is crucial to evaluate the osteogenic differentiation potential of the stem cells during their culture before the implantation. However, the practical evaluation of stem cell osteogenicity has been limited to invasive biological marker analysis that only enables assaying a single end‐point. To innovate around invasive quality assessments in clinical cell therapy, we previously explored and demonstrated the positive predictive value of using time‐course images taken during differentiation culture for hBMSC bone differentiation potential. This initial method establishes proof of concept for a morphology‐based cell evaluation approach, but reveals a practical limitation when considering the need to handle large amounts of image data. In this report, we aimed to scale‐down our proposed method into a more practical, efficient modeling scheme that can be more broadly implemented by physicians on the frontiers of clinical cell therapy. We investigated which morphological features are critical during the osteogenic differentiation period to assure the performance of prediction models with reduced burden on image acquisition. To our knowledge, this is the first detailed characterization that describes both the critical observation period and the critical number of time‐points needed for morphological features to adequately model osteogenic potential. Our results revealed three important observations: (i) the morphological features from the first 3 days of differentiation are sufficiently informative to predict bone differentiation potential, both activities of alkaline phosphatase and calcium deposition, after 3 weeks of continuous culture; (ii) intervals of 48 h are sufficient for measuring critical morphological features; and (iii) morphological features are most accurately predictive when early morphological features from the first 3 days of differentiation are combined with later features (after 10 days of differentiation). Biotechnol. Bioeng. 2014;111: 1430–1439.

Development of a Monitoring Method for Nonlabeled Human Pluripotent Stem Cell Growth by Time-Lapse Image Analysis

Cell growth is an important criterion for determining healthy cell conditions. When somatic cells or cancer cells are dissociated into single cells for passaging, the cell numbers can be counted at each passage, providing information on cell growth as an indicator of the health conditions of these cells. In the case of human pluripotent stem cells (hPSCs), because the cells are usually dissociated into cell clumps of ∼50–100 cells for passaging, cell counting is time‐consuming. In the present study, using a time‐lapse imaging system, we developed a method to determine the growth of hPSCs from nonlabeled live cell phase‐contrast images without damaging these cells. Next, the hPSC colony areas and number of nuclei were determined and used to derive equations to calculate the cell number in hPSC colonies, which were assessed on time‐lapse images acquired using a culture observation system. The relationships between the colony areas and nuclei numbers were linear, although the equation coefficients were dependent on the cell line used, colony size, colony morphology, and culture conditions. When the culture conditions became improper, the change in cell growth conditions could be detected by analysis of the phase‐contrast images. This method provided real‐time information on colony growth and cell growth rates without using treatments that can damage cells and could be useful for basic research on hPSCs and cell processing for hPSC‐based therapy.

Visualization of morphological categories of colonies for monitoring of effect on induced pluripotent stem cell culture status

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.

Morphology-based non-invasive quantitative prediction of the differentiation status of neural stem cells

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.

New applications of the infrared CCD camera

In Japan, two dimensional IRCCD cameras such as Nikons LAIRD 3 have been sold by various manufacturers. In addition to the staring type IR cameras, there are mechanical scanning type IR cameras which are also receiving much attention in the market. The primary applications of the IR camera market have been night-time security surveillance, non-destructive testing and/or process control in the industrial environment. In such applications, infrared radiation emitted from the observed object is seen as heat. Thus, many IR cameras have been designed to maximize the amount of IR radiation collected from an observed object. One new application for the LAIRD3 camera is to observe the spectral signature of an object over a specific spectral band in order to determine the presence and location of a specific gas. This paper described the results of using this method to detect the presence and location of methane in an observed environment.

Using Automated Live Cell Imaging to Reveal Early Changes during Human Motor Neuron Degeneration

Visual Abstract Human neurons expressing mutations associated with neurodegenerative disease are becoming more widely available. Hence, developing assays capable of accurately detecting changes that occur early in the disease process and identifying therapeutics able to slow these changes should become ever more important. Using automated live-cell imaging, we studied human motor neurons in the process of dying following neurotrophic factor withdrawal. We tracked different neuronal features, including cell body size, neurite length, and number of nodes. In particular, measuring the number of nodes in individual neurons proved to be an accurate predictor of relative health. Importantly, intermediate phenotypes were defined and could be used to distinguish between agents that could fully restore neurons and neurites and those only capable of maintaining neuronal cell bodies. Application of live-cell imaging to disease modeling has the potential to uncover new classes of therapeutic molecules that intervene early in disease progression.

A morphology-based assay platform for neuroepithelial-like cells differentiated from human pluripotent stem cells

Cell morphology is recognized as an important hallmark of neural cells. During the differentiation of human pluripotent stem cells (hPSCs) into neural cells, cell morphology changes dynamically. Therefore, characterization of the morphology of cells during this period is important to improve our understanding of the differentiation and development of neural cells. General methods for the directed induction of hPSCs include the steps of multi-cellular aggregation or high-density cell culture, particularly at the early phase of neural differentiation, and therefore, the morphology of each differentiating cell is difficult to recognize. Here, we have developed a new method for the directed differentiation of neuroepithelial-like cells (NELCs) from hPSCs at a low cell density in an adherent monolayer culture, as well as an image-processing algorithm to evaluate the cell morphology of differentiating NELCs, in order to follow cell morphology during the differentiation of hPSCs into NELCs. Using these methods, the morphological transition of differentiating cells was observed in real time using phase contrast imaging and then quantified. Because cell morphology is also considered an inherent biological marker of neural cells cultured in vitro, this method is potentially useful to study the mechanisms underlying neural cell differentiation.

Operating temperature compensation for LAIRD S-270

Thermal vision system with 2D sensor such as PtSi, InSb, MCT and any others in the commercial market is generally required to correct non-uniformity of response of every pixels in order to display satisfactory IR images. We have studied the non-uniformity correction methods for our thermal vision systems and in a previous study we proposed the most suitable non-uniformity correction method for PtSi SBD sensor. This method was optimized at a certain operating temperature, which was a room temperature. However thermal vision systems could be used at a various operating temperature. It is also obvious that his change of operating temperature causes input IR flux change to a sensor, this affects the accuracy of the measured results. Therefore, it is necessary not only to correct non-uniformity of each pixel but also to compensate this influence by the change of temperature to have a satisfactory IR image. In this paper, this compensation method is described. Besides, the result of this method is presented.