Label-free multiphoton excitation imaging as a promising diagnostic tool for breast cancer.

Histopathological diagnosis is the ultimate method of attaining the final diagnosis; however, the observation range is limited to the two-dimensional plane, and it requires thin slicing of the tissue, which limits diagnostic information. To seek solutions for these problems, we proposed a novel imaging-based histopathological examination. We used the multiphoton excitation microscopy (MPM) technique to establish a method for visualizing unfixed/unstained human breast tissues. Under near-infrared ray excitation, fresh human breast tissues emitted fluorescent signals with three major peaks, which enabled visualizing the breast tissue morphology without any fixation or dye staining. Our study using human breast tissue samples from 32 patients indicated that experienced pathologists can estimate normal or cancerous lesions using only these MPM images with a kappa coefficient of 1.0. Moreover, we developed an image classification algorithm with artificial intelligence that enabled us to automatically define cancer cells in small areas with a high sensitivity of ≥0.942. Taken together, label-free MPM imaging is a promising method for the real-time automatic diagnosis of breast cancer.

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).

Automated algorithm development to assess survival of human neurons using longitudinal single-cell tracking: Application to synucleinopathy.

The development of phenotypic assays with appropriate analyses is an important step in the drug discovery process. Assays using induced pluripotent stem cell (iPSC)-derived human neurons are emerging as powerful tools for drug discovery in neurological disease. We have previously shown that longitudinal single cell tracking enabled the quantification of survival and death of neurons after overexpression of α-synuclein with a familial Parkinson's disease mutation (A53T). The reliance of this method on manual counting, however, rendered the process labor intensive, time consuming and error prone. To overcome these hurdles, we have developed automated detection algorithms for neurons using the BioStation CT live imaging system and CL-Quant software. In the current study, we use these algorithms to successfully measure the risk of neuronal death caused by overexpression of α-synuclein (A53T) with similar accuracy and improved consistency as compared to manual counting. This novel method also provides additional key readouts of neuronal fitness including total neurite length and the number of neurite nodes projecting from the cell body. Finally, the algorithm reveals the neuroprotective effects of brain-derived neurotrophic factor (BDNF) treatment in neurons overexpressing α-synuclein (A53T). These data show that an automated algorithm improves the consistency and considerably shortens the analysis time of assessing neuronal health, making this method advantageous for small molecule screening for inhibitors of synucleinopathy and other neurodegenerative diseases.

Pathogenic Pathways in Early-Onset Autosomal Recessive Parkinson's Disease Discovered Using Isogenic Human Dopaminergic Neurons.

Parkinson's disease (PD) is a complex and highly variable neurodegenerative disease. Familial PD is caused by mutations in several genes with diverse and mostly unknown functions. It is unclear how dysregulation of these genes results in the relatively selective death of nigral dopaminergic neurons (DNs). To address this question, we modeled PD by knocking out the PD genes PARKIN (PRKN), DJ-1 (PARK7), and ATP13A2 (PARK9) in independent isogenic human pluripotent stem cell (hPSC) lines. We found increased levels of oxidative stress in all PD lines. Increased death of DNs upon differentiation was found only in the PARKIN knockout line. Using quantitative proteomics, we observed dysregulation of mitochondrial and lysosomal function in all of the lines, as well as common and distinct molecular defects caused by the different PD genes. Our results suggest that precise delineation of PD subtypes will require evaluation of molecular and clinical data.

Nonlinear Optics with Near-Infrared Excitation Enable Real-Time Quantitative Diagnosis of Human Cervical Cancers.

Histopathologic analysis through biopsy has been one of the most useful methods for the assessment of malignant neoplasms. However, some aspects of the analysis such as invasiveness, evaluation range, and turnaround time from biopsy to report could be improved. Here, we report a novel method for visualizing human cervical tissue three-dimensionally, without biopsy, fixation, or staining, and with sufficient quality for histologic diagnosis. Near-infrared excitation and nonlinear optics were employed to visualize unstained human epithelial tissues of the cervix uteri by constructing images with third-harmonic generation (THG) and second-harmonic generation (SHG). THG images enabled evaluation of nuclear morphology in a quantitative manner with six parameters after image analysis using deep learning. It was also possible to quantitatively assess intraepithelial fibrotic changes based on SHG images and another deep learning analysis. Using each analytical procedure alone, normal and cancerous tissue were classified quantitatively with an AUC ≥0.92. Moreover, a combinatory analysis of THG and SHG images with a machine learning algorithm allowed accurate classification of three-dimensional image files of normal tissue, intraepithelial neoplasia, and invasive carcinoma with a weighted kappa coefficient of 0.86. Our method enables real-time noninvasive diagnosis of cervical lesions, thus constituting a potential tool to dramatically change early detection. SIGNIFICANCE: This study proposes a novel method for diagnosing cancer using nonlinear optics, which enables visualization of histologic features of living tissues without the need for any biopsy or staining dye.

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.

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

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.

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.

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.

Massive and Reproducible Production of Liver Buds Entirely from Human Pluripotent Stem Cells.

Organoid technology provides a revolutionary paradigm toward therapy but has yet to be applied in humans, mainly because of reproducibility and scalability challenges. Here, we overcome these limitations by evolving a scalable organ bud production platform entirely from human induced pluripotent stem cells (iPSC). By conducting massive "reverse" screen experiments, we identified three progenitor populations that can effectively generate liver buds in a highly reproducible manner: hepatic endoderm, endothelium, and septum mesenchyme. Furthermore, we achieved human scalability by developing an omni-well-array culture platform for mass producing homogeneous and miniaturized liver buds on a clinically relevant large scale (>108). Vascularized and functional liver tissues generated entirely from iPSCs significantly improved subsequent hepatic functionalization potentiated by stage-matched developmental progenitor interactions, enabling functional rescue against acute liver failure via transplantation. Overall, our study provides a stringent manufacturing platform for multicellular organoid supply, thus facilitating clinical and pharmaceutical applications especially for the treatment of liver diseases through multi-industrial collaborations.

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.

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

UNLABELLED: : 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. SIGNIFICANCE: This is the first study to use a noninvasive method using images to systemically determine the growth of human pluripotent stem cells (hPSCs) without damaging or wasting cells. This method would be useful for quality control during cell culture of clinical hPSCs.

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.

Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells.

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.