Prediction of antibody production performance change in Chinese hamster ovary cells using morphological profiling.

Monoclonal antibodies (mAbs) represent a significant segment of biopharmaceuticals, with the market for mAb therapeutics expected to reach $200 billion in 2021. Chinese Hamster Ovary (CHO) cells are the industry standard for large-scale mAb production owing to their adaptability and genetic engineering capabilities. However, maintaining consistent product quality is challenging, primarily because of the inherent genetic instability of CHO cells. In this study, we address the need for advanced technologies for quality monitoring of host cells in biopharmaceuticals. We highlight the limitations of traditional cell assessment techniques such as flow cytometry and propose a noninvasive, label-free image-based analysis method. By utilizing advanced image processing and machine learning, this technique aims to non-invasively and quantitatively evaluate subtle quality changes in suspension cells. The research aims to investigate the use of morphological analysis for identifying subtle alterations in mAb productivity of CHO cells, employing cells stimulated by compounds as a model for this study. Our results show that the mAb productivity of CHO cells (day 8) can be predicted only from their early morphological profile (day 3). Our study also discusses the importance of strategic methods for forecasting host cell mAb productivity using morphological profiles, as inferred from our machine learning models specialized in predictive score prediction and anomaly prediction.

Development of novel waxy bone haemostatic agents composed of biodegradable polymers with osteogenic-enhancing peptides in rabbit models.

OBJECTIVES: The use of bone wax (BW) is controversial for sternal haemostasis because it increases the risk of wound infection and inhibits bone healing. We developed new waxy bone haemostatic agents made from biodegradable polymers containing peptides and evaluated them using rabbit models. METHODS: We designed 2 types of waxy bone haemostatic agents: peptide wax (PW) and non-peptide wax (NPW), which used poly(ε-caprolactone)-based biodegradable polymers with or without an osteogenesis-enhancing peptide, respectively. Rabbits were randomly divided into 4 groups based on treatment with BW, NPW, PW or no treatment. In a tibial defect model, the bleeding amount was measured and bone healing was evaluated by micro-computed tomography over 16 weeks. Bone healing in a median sternotomy model was assessed for 2 weeks using X-ray, micro-computed tomography, histological examination and flexural strength testing. RESULTS: The textures of PW and NPW (n = 12 each) were similar to that of BW and achieved a comparable degree of haemostasis. The crevice area of the sternal fracture line in the BW group was significantly larger than that in other groups (n = 10 each). The PW group demonstrated the strongest sternal flexural strength (n = 10), with complete tibial healing at 16 weeks. No groups exhibited wound infection, including osteomyelitis. CONCLUSIONS: Waxy biodegradable haemostatic agents showed satisfactory results in haemostasis and bone healing in rabbit models and may be an effective alternative to BW.

Bioinformatics in bioscience and bioengineering: Recent advances, applications, and perspectives.

Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging analysis. These technologies have made it possible to obtain vast data from a single measurement. Subsequently, large datasets have pioneered the data-driven approach, an alternative to the traditional hypothesis-testing system, for researchers. However, processing, interpreting, and elucidating enormous datasets is no longer possible without computation. Bioinformatics is a field that has developed over long periods, intending to understand biological phenomena using methods collected from information science and statistics, thus solving this proposed research challenge. This review presents the latest methodologies and applications in sequencing, imaging, and mass spectrometry that were developed using bioinformatics. We presented the features of individual techniques and outlines in each part, avoiding the use of complex algorithms and formulas to allow beginning researchers to understand an overview. In the section on sequencing, we focused on comparative genomic, transcriptomic, and bacterial microbiome analyses, which are frequently used as applications of next-generation sequencing. Bioinformatic methods for handling sequence data and case studies were described. In the section on imaging, we introduced the analytical methods and microscopy imaging informatics techniques used in animal cell biology and plant physiology. We introduce informatics technologies for maximizing the value of measured data, including predicting the structure of unknown molecules and untargeted analysis in the section on mass spectrometry. Finally, we discuss the future outlook of this field. We anticipate that this review will assist biologists in using bioinformatics more effectively.

Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells.

Label-free image analysis has several advantages with respect to the development of drug screening platforms. However, the evaluation of drug-responsive cells based exclusively on morphological information is challenging, especially in cases of morphologically heterogeneous cells or a small subset of drug-responsive cells. We developed a novel label-free cell sub-population analysis method called "in silico FOCUS (in silico analysis of featured-objects concentrated by anomaly discrimination from unit space)" to enable robust phenotypic screening of morphologically heterogeneous spinal and bulbar muscular atrophy (SBMA) model cells. This method with the anomaly discrimination concept can sensitively evaluate drug-responsive cells as morphologically anomalous cells through in silico cytometric analysis. As this algorithm requires only morphological information of control cells for training, no labeling or drug administration experiments are needed. The responses of SBMA model cells to dihydrotestosterone revealed that in silico FOCUS can identify the characteristics of a small sub-population with drug-responsive phenotypes to facilitate robust drug response profiling. The phenotype classification model confirmed with high accuracy the SBMA-rescuing effect of pioglitazone using morphological information alone. In silico FOCUS enables the evaluation of delicate quality transitions in cells that are difficult to profile experimentally, including primary cells or cells with no known markers.

Recent advances in animal cell technologies for industrial and medical applications.

The industrial use of living organisms for bioproduction of valued substances has been accomplished mostly using microorganisms. To produce high-value bioproducts such as antibodies that require glycosylation modification for better performance, animal cells have been recently gaining attention in bioengineering because microorganisms are unsuitable for producing such substances. Furthermore, animal cells are now classified as products because a large number of cells are required for use in regenerative medicine. In this article, we review animal cell technologies and the use of animal cells, focusing on useable cell generation and large-scale production of animal cells. We review recent advance in mammalian cell line development because this is the first step in the production of recombinant proteins, and it largely affects the efficacy of the production. We next review genetic engineering technology focusing on CRISPR-Cas system as well as surrounding technologies as these methods have been gaining increasing attention in areas that use animal cells. We further review technologies relating to bioreactors used in the context of animal cells because they are essential for the mass production of target products. We also review tissue engineering technology because tissue engineering is one of the main exits for mass-produced cells; in combination with genetic engineering technology, it can prove to be a promising treatment for patients with genetic diseases after the establishment of induced pluripotent stem cell technology. The technologies highlighted in this review cover brief outline of the recent animal cell technologies related to industrial and medical applications.

Bi-layered carboxymethyl cellulose-collagen vitrigel dual-surface adhesion-prevention membrane.

During wound regeneration, both cell adhesion and adhesion-inhibitory functions must be controlled in parallel. We developed a membrane with dual surfaces by merging the properties of carboxymethyl cellulose (CMC) and collagen using vitrification. A rigid membrane was formed by vitrification of a bi-layered CMC and collagen hydrogel without using cross-linking reagents, thus providing dual functions, strong cell adhesion-inhibition with the CMC layer, and cell adhesion with the collagen layer. We referred to this bi-layered CMC-collagen vitrigel membrane as "Bi-C-CVM" and optimized the process and materials. The introduction of the CMC layer conferred a "tough but stably wet" property to Bi-C-CVM. This enables Bi-C-CVM to cover wet tissue and make the membrane non-detachable while preventing tissue adhesion on the other side. The bi-layered vitrification procedure can expand the customizability of collagen vitrigel devices for wider medical applications.

Morphological heterogeneity description enabled early and parallel non-invasive prediction of T-cell proliferation inhibitory potency and growth rate for facilitating donor selection of human mesenchymal stem cells.

BACKGROUND: Within the extensively developed therapeutic application of mesenchymal stem cells (MSCs), allogenic immunomodulatory therapy is among the promising categories. Although donor selection is a critical early process that can maximize the production yield, determining the promising candidate is challenging owing to the lack of effective biomarkers and variations of cell sources. In this study, we developed the morphology-based non-invasive prediction models for two quality attributes, the T-cell proliferation inhibitory potency and growth rate. METHODS: Eleven lots of mixing bone marrow-derived and adipose-derived MSCs were analyzed. Their morphological profiles and growth rates were quantified by image processing by acquiring 6 h interval time-course phase-contrast microscopic image acquisition. T-cell proliferation inhibitory potency was measured by employing flow cytometry for counting the proliferation rate of peripheral blood mononuclear cells (PBMCs) co-cultured with MSCs. Subsequently, the morphological profile comprising 32 parameters describing the time-course transition of cell population distribution was used for explanatory parameters to construct T-cell proliferation inhibitory potency classification and growth rate prediction models. For constructing prediction models, the effect of machine learning methods, parameter types, and time-course window size of morphological profiles were examined to identify those providing the best performance. RESULTS: Unsupervised morphology-based visualization enabled the identification of anomaly lots lacking T-cell proliferation inhibitory potencies. The best performing machine learning models exhibited high performances of predictions (accuracy > 0.95 for classifying risky lots, and RMSE < 1.50 for predicting growth rate) using only the first 4 days of morphological profiles. A comparison of morphological parameter types showed that the accumulated time-course information of morphological heterogeneity in cell populations is important for predicting the potencies. CONCLUSIONS: To enable more consistent cell manufacturing of allogenic MSC-based therapeutic products, this study indicated that early non-invasive morphology-based prediction can facilitate the lot selection process for effective cell bank establishment. It was also found that morphological heterogeneity description is important for such potency prediction. Furthermore, performances of the morphology-based prediction models trained with data consisting of origin-different MSCs demonstrated the effectiveness of sharing morphological data between different types of MSCs, thereby complementing the data limitation issue in the morphology-based quality prediction concept.

Predicting quality decay in continuously passaged mesenchymal stem cells by detecting morphological anomalies.

With rapid advances in cell therapy, technologies enabling both consistency and efficiency in cell manufacturing are becoming necessary. Morphological monitoring allows practical quality maintenance in cell manufacturing facilities, but relies heavily on human skill. For more reproducible and data-driven quality evaluation, image-based morphological analysis provides multiple advantages over manual observation. Our group has investigated the performance of multiple morphological parameters obtained from time-course images to non-invasively and quantitatively predict cellular quality using machine learning algorithms. Although such morphology-based computational models succeeded in early cell quality predictions, it was difficult to introduce our approach in cell manufacturing facilities owing to data variation issues. Since manufacturing facilities have fixed their protocol to minimize anomalies as much as possible, most accumulated data are normal, and anomalies are scarce. Thus, our morphological analysis had to adapt to such practical situation where it was difficult to observe a wide range of data variations, including both normal samples and anomalies, which is typically essential to improve most machine learning models' performance. In the present study, we introduce a practical morphological analysis concept by investigating the performance of anomalous quality decay discrimination during the continuous passaging of human mesenchymal stem cells (hMSCs). Combining the visualization method and asymmetric statistic discrimination, we describe an effective morphology-based, in-process quality monitoring concept to detect quality anomalies throughout cell culture process. Our results showed that the use of morphological parameters to reflect cellular population heterogeneity can predict hMSC quality decay within 6 h after seeding.

The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation.

Because of the growing demand for human cell spheroids as functional cellular components for both drug development and regenerative therapy, the technology to non-invasively evaluate their quality has emerged. Image-based morphology analysis of spheroids enables high-throughput screening of their quality. However, since spheroids are three-dimensional, their images can have poor contrast in their surface area, and therefore the total spheroid recognition by image processing is greatly dependent on human who design the filter-set to fit for their own definition of spheroid outline. As a result, the reproducibility of morphology measurement is critically affected by the performance of filter-set, and its fluctuation can disrupt the subsequent morphology-based analysis. Although the unexpected failure derived from the inconsistency of image processing result is a critical issue for analyzing large image data for quality screening, it has been tackled rarely. To achieve robust analysis performances using morphological features, we investigated the influence of filter-set's reproducibility for various types of spheroid data. We propose a new scoring index, the "recognition fitness deviation (RFD)," as a measure to quantitatively and comprehensively evaluate how reproductively a designed filter-set can work with data variations, such as the variations in replicate samples, in time-course samples, and in different types of cells (a total of six normal or cancer cell types). Our result shows that RFD scoring from 5000 images can automatically rank the best robust filter-set for obtaining the best 6-cell type classification model (94% accuracy). Moreover, the RFD score reflected the differences between the worst and the best classification models for morphologically similar spheroids, 60% and 89% accuracy respectively. In addition to RFD scoring, we found that using the time-course of morphological features can augment the fluctuations in spheroid recognitions leading to robust morphological analysis.

Performance of a Biodegradable Composite with Hydroxyapatite as a Scaffold in Pulp Tissue Repair.

Vital pulp therapy is an important endodontic treatment. Strategies using growth factors and biological molecules are effective in developing pulp capping materials based on wound healing by the dentin-pulp complex. Our group developed biodegradable viscoelastic polymer materials for tissue-engineered medical devices. The polymer contents help overcome the poor fracture toughness of hydroxyapatite (HAp)-facilitated osteogenic differentiation of pulp cells. However, the composition of this novel polymer remained unclear. This study evaluated a novel polymer composite, P(CL-co-DLLA) and HAp, as a direct pulp capping carrier for biological molecules. The biocompatibility of the novel polymer composite was evaluated by determining the cytotoxicity and proliferation of human dental stem cells in vitro. The novel polymer composite with BMP-2, which reportedly induced tertiary dentin, was tested as a direct pulp capping material in a rat model. Cytotoxicity and proliferation assays revealed that the biocompatibility of the novel polymer composite was similar to that of the control. The novel polymer composite with BMP-2-induced tertiary dentin, similar to hydraulic calcium-silicate cement, in the direct pulp capping model. The BMP-2 composite upregulated wound healing-related gene expression compared to the novel polymer composite alone. Therefore, we suggest that novel polymer composites could be effective carriers for pulp capping.

Machine Learning-Based Amino Acid Substitution of Short Peptides: Acquisition of Peptides with Enhanced Inhibitory Activities against α-Amylase and α-Glucosidase.

We developed a method for efficiently activating functional peptides with a large structural contribution using the peptide-searching method with machine learning. The physicochemical properties of the amino acids were employed as variables. As a model peptide, we used GHWYYRCW, which is a functional peptide that inhibits α-amylase derived from human pancreatic juice. First, training data were acquired. A total of 153 peptides were prepared in which 1 amino acid in GHWYYRCW was replaced to construct a 1-amino acid substitution coverage peptide library. The inhibitory activity of each peptide against α-amylase and α-glucosidase was evaluated. Second, random forest (RF) regression analysis was performed using 120 variables, and the enzyme inhibitory activity of the peptide was related to the physicochemical properties. The constructed model had many features describing the charge of the amino acid (isoelectric point and pK2). Then, high inhibitory (HI) peptides were predicted using a library of peptides with 2- or 3-amino acid substitution as test data, which were called HI2 and HI3 peptides. As results, the first or seventh amino acid of the HI2 peptide was replaced with Arg, Trp, or Tyr. We found that all 30 HI2 peptides had significantly higher activity than the original sequence (100%) and 26 of the 30 HI3 peptides were significantly active (86.7%). However, the actual inhibitory activity of the HI3 peptides was improved to a lesser extent. The docking simulation clarified that the CDOCKER energy decrease was roughly correlated with the inhibitory activity. The machine learning-based predictive model was a promising tool for design of substituted peptides with high activity values, and it was assumed that the advanced model that forecasts the interaction index such as the CDOCKER energy substituting for the inhibitory activity would be used to design HI peptides, even in the case of the HI3 peptides.

Time-course colony tracking analysis for evaluating induced pluripotent stem cell culture processes.

Increasing the yield and maintaining a high quality of induced pluripotent stem cells (iPSCs) is necessary for manufacturing iPSCs at the industrial scale. However, because iPSCs are delicate, it is important to evaluate their quality during processing. To examine the status of cultured iPSCs non-invasively, morphology-based iPSC colony evaluation may be an efficient technology for cellular status monitoring and analysis. In this study, we examined the effectiveness of time-course colony tracking analysis for evaluating the iPSC culture process. Particularly, we obtained detailed time-course data to evaluate the effect of the pipetting technique on cell dissociation before seeding. Although the pipetting process causes severe shear stress to cells, which affects their quality, these effects have not been quantitatively analyzed because of their complex and uncontrollable parameters. By analyzing the heterogeneity and time-course responses of individual colonies, our colony tracking analysis revealed a critically damaged population caused by pipetting stress which could not be detected in conventional bulk analysis. Moreover, by comprehensively analyzing colony tracking data, which links the time-course morphology and marker staining results with each colony, we found that colony morphology is only highly correlated with the undifferentiated marker in the final stage, with a lower correlation in the early stages. Thus, colony tracking analysis provides a way to quantify cellular morphological information when evaluating complex iPSC manufacturing processes.

Morphology-Based Analysis of Myoblasts for Prediction of Myotube Formation.

The development of new drugs depends on the efficiency of drug screening. Phenotype-based screening has attracted interest due to its considerable potency for the discovery of first-in-class drugs. In general, fluorescently labeled imagery is the leading technique for phenotype-based screening; however, there are growing requirements to understand total culture profiles, which are unclear after end-point assays. In this study, we demonstrate that morphology-based cellular evaluation of unlabeled cells is an efficient approach to evaluate myotube formation assays. One of our aims was to study the myogenic differentiation process in C2C12 cells to discern the differences between cellular responses to different medium conditions (serum concentrations and insulin dosages). Our results show that predictive morphological profiles that strongly correlate with myogenic differentiation can be generated from myotube images, even in the confluent stage. The differentiation rate after 14 days can be quantitatively predicted with the highest accuracy by means of images taken on days 0-11.5. In addition, for the application of our morphology-based cellular evaluation, the effect of cyclic guanosine monophosphate (cGMP) on myogenic differentiation was analyzed. Our results show that the quantitated morphological profile from these images can be an effective descriptor for analysis of the myotube-recovering effect of cGMP.

Effect of mechanical vibration stress in cell culture on human induced pluripotent stem cells.

The development of induced pluripotent stem cell (iPSC) techniques has solved various limitations in cell culture including cellular proliferation and potency. Hence, the expectations on wider applications and the quality of manufactured iPSCs are rapidly increasing. To answer such growing expectations, enhancement of technologies to improve cell-manufacturing efficiency is now a challenge for the bioengineering field. Mechanization of conventional manual operations, aimed at automation of cell manufacturing, is quickly advancing. However, as more processes are being automated in cell manufacturing, it is need to be more critical about influential parameters that may not be as important in manual operations. As a model of such parameters, we focused on the effect of mechanical vibration, which transmits through the vessel to the cultured iPSCs. We designed 7 types of vertical vibration conditions in cell culture vessels using a vibration calibrator. These conditions cover a wide range of potential situations in cell culture, such as tapping or closing an incubator door, and examined their effects by continuous passaging (P3 to P5). Detailed evaluation of cells by time-course image analysis revealed that vibrations can enhance cell growth as an early effect but can negatively affect cell adhesion and growth profile after several passages as a delayed effect. Such unexpected reductions in cell quality are potentially critical issues in maintaining consistency in cell manufacturing. Therefore, this work reveals the importance of continuous examination across several passages with detailed, temporal, quantitative measurements obtained by non-invasive image analysis to examine when and how the unknown parameters will affect the cell culture processes.

Quantitative analysis of operators' flow line in the cell culture for controlled manual operation.

INTRODUCTION: Although cell culture has been widely used in the life sciences, there are still many aspects of this technique that are unclear. In this study, we have focused on the manual operations in the cell culture process and try to analyze the operators' flow line. METHODS: During a course of approximately 6 years, we obtained the operators' flow line data from two places (three layouts) and 38 operators (93 subcultures) using two network cameras and a motion detection software (Vitracom SiteView). RESULTS: Our investigation succeeded in quantifying the flow line of the subculture process and analyzed the time taken to carry out the process, to travel around the workplace. For the subculture process, the total time of the process being rerated the time of the operation in the place where the main operation is performed; the total distance of travel and the counts of travel not being related to the total time of the process. Based on these results, we propose a new way of evaluating the efficiency of cell culture process in terms of time and traveling. We believe that the results of this study can guide cell culture operators in handling cells more efficiently in cell manufacturing processes. CONCLUSIONS: The flow line analysis method suggested by us can record the operators involved and improve the efficiency and consistency of the process; it can, therefore, be introduced in cell manufacturing processes. In addition, this method only requires network cameras and motion detection software, which are inexpensive and can be set up easily.

Numerical investigation of particle dispersion in the preprocessing stage for a static cell cultivation.

In order to produce high-quality cells in a static culture, the initial placement of the cells is one of the most important factors. Dense distribution of the cells increases the risk of cell death. Thus, the cells need to be uniformly distributed during the preprocessing of a static culture. This process depends on the operator's experience and has not been standardized. In this study, we have performed numerical simulations to investigate the efficiency of cell dispersion by using OpenFOAM. The numerical domain is a square-shaped dish. Two shaking methods, one-direction and multi-direction reciprocal shaking, were considered and calculations were conducted under five oscillation frequencies. The cell colony was assumed as a solid spherical particle. The initial particles were densely positioned at the center. The numerical result showed that the multi-direction reciprocal shaking was more effective to disperse the particles than the one-direction reciprocal shaking. In addition, at a low frequency, almost all particles sank to the bottom and hardly dispersed. These results indicate that strong fluctuations can lift particles from the bottom and that frequent changes in the flow direction make for more even distribution.

In-process evaluation of culture errors using morphology-based image analysis.

INTRODUCTION: Advancing 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. METHODS: Twenty-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. RESULTS: By 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. CONCLUSIONS: Morphological 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.

Tschimganine and its derivatives extend the chronological life span of yeast via activation of the Sty1 pathway.

Most antiaging factors or life span extenders are associated with calorie restriction (CR). Very few of these factors function independently of, or additively with, CR. In this study, we focused on tschimganine, a compound that was reported to extend chronological life span (CLS). Although tschimganine led to the extension of CLS, it also inhibited yeast cell growth. We acquired a Schizosaccharomyces pombe mutant with a tolerance for tschimganine due to the gene crm1. The resulting Crm1 protein appears to export the stress-activated protein kinase Sty1 from the nucleus to the cytosol even under stressful conditions. Furthermore, we synthesized two derivative compounds of tschimganine, α-hibitakanine and ß-hibitakanine; these derivatives did not inhibit cell growth, as seen with tschimganine. α-hibitakanine extended the CLS, not only in S. pombe but also in Saccharomyces cerevisiae, indicating the possibility that life span regulation by tschimganine derivative may be conserved across various yeast species. We found that the longevity induced by tschimganine was dependent on the Sty1 pathway. Based on our results, we propose that tschimganine and its derivatives extend CLS by activating the Sty1 pathway in fission yeast, and CR extends CLS via two distinct pathways, one Sty1-dependent and the other Sty1-independent. These findings provide the potential for creating an additive life span extension effect when combined with CR, as well as a better understanding of the mechanism of CLS.

Imaging cell picker: A morphology-based automated cell separation system on a photodegradable hydrogel culture platform.

Cellular morphology on and in a scaffold composed of extracellular matrix generally represents the cellular phenotype. Therefore, morphology-based cell separation should be interesting method that is applicable to cell separation without staining surface markers in contrast to conventional cell separation methods (e.g., fluorescence activated cell sorting and magnetic activated cell sorting). In our previous study, we have proposed a cloning technology using a photodegradable gelatin hydrogel to separate the individual cells on and in hydrogels. To further expand the applicability of this photodegradable hydrogel culture platform, we here report an image-based cell separation system imaging cell picker for the morphology-based cell separation on a photodegradable hydrogel. We have developed the platform which enables the automated workflow of image acquisition, image processing and morphology analysis, and collection of a target cells. We have shown the performance of the morphology-based cell separation through the optimization of the critical parameters that determine the system's performance, such as (i) culture conditions, (ii) imaging conditions, and (iii) the image analysis scheme, to actually clone the cells of interest. Furthermore, we demonstrated the morphology-based cloning performance of cancer cells in the mixture of cells by automated hydrogel degradation by light irradiation and pipetting.

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.