Method for evaluation of human induced pluripotent stem cell quality using image analysis based on the biological morphology of cells

Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellXTM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellXTM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods

Human pluripotent stem cells have the potential for unlimited proliferation and controlled differentiation into various somatic cells, making them a unique tool for regenerative and personalized medicine. Determining the best clone selection is a challenging problem in this field and requires new sensing instruments and methods able to automatically assess the state of a growing colony ('phenotype') and make decisions about its destiny. One possible solution for such label-free, non-invasive assessment is to make phase-contrast images and/or videos of growing stem cell colonies, process the morphological parameters ('morphological portrait', or signal), link this information to the colony phenotype, and initiate an automated protocol for the colony selection. As a step in implementing this strategy, we used machine learning methods to find an effective model for classifying the human pluripotent stem cell colonies of three lines according to their morphological phenotype ('good' or 'bad'), using morphological parameters from the previously published data as predictors. We found that the model using cellular morphological parameters as predictors and artificial neural networks as the classification method produced the best average accuracy of phenotype prediction (67%). When morphological parameters of colonies were used as predictors, logistic regression was the most effective classification method (75% average accuracy). Combining the morphological parameters of cells and colonies resulted in the most effective model, with a 99% average accuracy of phenotype prediction. Random forest was the most efficient classification method for the combined data. We applied feature selection methods and showed that different morphological parameters were important for phenotype recognition via either cellular or colonial parameters. Our results indicate a necessity for retaining both cellular and colonial morphological information for predicting the phenotype and provide an optimal choice for the machine learning method. The classification models reported in this study could be used as a basis for developing and/or improving automated solutions to control the quality of human pluripotent stem cells for medical purposes.

Generation of Human Blood Vessel and Vascularized Cerebral Organoids

Brain organoids have been widely used to study diseases and the development of the nervous system. Many reports have investigated the application of brain organoids, but most of these models lack vascular structures, which play essential roles in brain development and neurological diseases. The brain and blood vessels originate from two different germ layers, making it difficult to induce vascularized brain organoids in vitro. We developed this protocol to generate brain-specific blood vessel and cerebral organoids and then fused them at a specific developmental time point. The fused cerebral organoids exhibited robust vascular network-like structures, which allows simulating the in vivo developmental processes of the brain for further applications in various neurological diseases. Key Features • Culturing vascularized brain organoids using human embryonic stem cells (hESCs). • The new approach generates not only neural cells and vessel-like networks but also brain-resident microglia immune cells in a single organoid.

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.

Minimum criteria for defining induced mesenchymal stem cells

Mesenchymal stem cells (MSCs) are a promising cell type for cell-based therapies. The therapeutic potential of MSCs has been verified in preclinical and clinical studies, however; low cell number in adult tissues, restricted expansion and differentiation capacity, and donor-related heterogeneity limit their use. To address these issues, there has been considerable interest in induced pluripotent stem cells (iPSCs) derived MSCs (induced mesenchymal stem cells [iMSCs]). Investigators obtain iMSCs from iPSCs of different origins, with variable methods of generation and expansion. Results of current studies have suggested iMSCs as a unique alternative source of MSCs. However, iMSCs are defined using the same criteria (proposed previously for primary MSCs by the International Society for Cellular Therapy [ISCT]) without realizing the distinct nature of iMSCs as compared to primary MSCs. To rationally define iMSCs, additional characterization is proposed along with ISCT's minimum criteria for defining primary MSCs. Minimum criteria for defining iMSCs should include (1) spindle-shaped morphology, (2) plastic adherent growth, (3) positive expression of CD29, CD44, CD73, CD90, CD105, along with negative expression of hematopoietic markers (CD45, CD34, CD14 or CD11b, CD79α or CD19, HLA-DR), (4) lack of expression of iPSCs induction factors, (5) trilineage differentiation potential, (6) lack of ability to form teratoma, and (7) release of MSC relevant paracrine factors. Defining the minimum criteria for iMSCs will be of great interest in the field and will provide a uniform description and identification of iMSCs to expedite progress in the field. Furthermore, due to increased interest in the clinical use of iMSCs, the above-mentioned additional characterization before the clinical application is important to avoid unwanted complications for recipients.

The Human Induced Pluripotent Stem Cell Test as an Alternative Method for Embryotoxicity Testing

The evaluation of substances for their potency to induce embryotoxicity is controlled by safety regulations. Test guidelines for reproductive and developmental toxicity rely mainly on animal studies, which make up the majority of animal usage in regulatory toxicology. Therefore, there is an urgent need for alternative in vitro methods to follow the 3R principles. To improve human safety, cell models based on human cells are of great interest to overcome species differences. Here, human induced pluripotent stem cells (hiPSCs) are an ideal cell source as they largely recapitulate embryonic stem cells without bearing ethical concerns and they are able to differentiate into most cell types of the human body. Here, we set up and characterized a fetal bovine serum (FBS)-free hiPSC-based in vitro test method, called the human induced pluripotent stem cell test (hiPS Test), to evaluate the embryotoxic potential of substances. After 10 days in culture, hiPSCs develop into beating cardiomyocytes. As terminal endpoint evaluations, cell viability, qPCR analyses as well as beating frequency and area of beating cardiomyocytes by video analyses are measured. The embryotoxic positive and non-embryotoxic negative controls, 5-Fluorouracil (5-FU) and Penicillin G (PenG), respectively, were correctly assessed in the hiPS Test. More compounds need to be screened in the future for defining the assay’s applicability domain, which will inform us of the suitability of the hiPS Test for detecting adverse effects of substances on embryonic development.

Bone Morphogenetic Protein 4 (BMP4) Enhances the Differentiation of Human Induced Pluripotent Stem Cells into Limbal Progenitor Cells

Corneal epithelium maintains visual acuity and is regenerated by the proliferation and differentiation of limbal progenitor cells. Transplantation of human limbal progenitor cells could restore the integrity and functionality of the corneal surface in patients with limbal stem cell deficiency. However, multiple protocols are employed to differentiate human induced pluripotent stem (iPS) cells into corneal epithelium or limbal progenitor cells. The aim of this study was to optimize a protocol that uses bone morphogenetic protein 4 (BMP4) and limbal cell-specific medium. Human dermal fibroblast-derived iPS cells were differentiated into limbal progenitor cells using limbal cell-specific (PI) medium and varying doses (1, 10, and 50 ng/mL) and durations (1, 3, and 10 days) of BMP4 treatment. Differentiated human iPS cells were analyzed by real-time polymerase chain reaction (RT-PCR), Western blotting, and immunocytochemical studies at 2 or 4 weeks after BMP4 treatment. Culturing human dermal fibroblast-derived iPS cells in limbal cell-specific medium and BMP4 gave rise to limbal progenitor and corneal epithelial-like cells. The optimal protocol of 10 ng/mL and three days of BMP4 treatment elicited significantly higher limbal progenitor marker (ABCG2, ∆Np63α) expression and less corneal epithelial cell marker (CK3, CK12) expression than the other combinations of BMP4 dose and duration. In conclusion, this study identified a successful reprogramming strategy to induce limbal progenitor cells from human iPS cells using limbal cell-specific medium and BMP4. Additionally, our experiments indicate that the optimal BMP4 dose and duration favor limbal progenitor cell differentiation over corneal epithelial cells and maintain the phenotype of limbal stem cells. These findings contribute to the development of therapies for limbal stem cell deficiency disorders.

Quantitative Craniofacial Analysis and Generation of Human Induced Pluripotent Stem Cells for Muenke Syndrome: A Case Report

In this case report, we focus on Muenke syndrome (MS), a disease caused by the p.Pro250Arg variant in fibroblast growth factor receptor 3 (FGFR3) and characterized by uni- or bilateral coronal suture synostosis, macrocephaly without craniosynostosis, dysmorphic craniofacial features, and dental malocclusion. The clinical findings of MS are further complicated by variable expression of phenotypic traits and incomplete penetrance. As such, unraveling the mechanisms behind MS will require a comprehensive and systematic way of phenotyping patients to precisely identify the impact of the mutation variant on craniofacial development. To establish this framework, we quantitatively delineated the craniofacial phenotype of an individual with MS and compared this to his unaffected parents using three-dimensional cephalometric analysis of cone beam computed tomography scans and geometric morphometric analysis, in addition to an extensive clinical evaluation. Secondly, given the utility of human induced pluripotent stem cells (hiPSCs) as a patient-specific investigative tool, we also generated the first hiPSCs derived from a family trio, the proband and his unaffected parents as controls, with detailed characterization of all cell lines. This report provides a starting point for evaluating the mechanistic underpinning of the craniofacial development in MS with the goal of linking specific clinical manifestations to molecular insights gained from hiPSC-based disease modeling.

Quality evaluation of induced pluripotent stem cell colonies by fusing multi-source features

BACKGROUND AND OBJECTIVE

Induced pluripotent stem cells (iPSCs) have great potential as the basis of regenerative medicine. In this paper, we propose an automatic quality evaluation model based on multi-source feature ensemble learning to divide the iPSC colonies into three categories: good, medium and bad.

METHODS

First, we obtained iPSCs samples using a Sendai virus reprogramming method. Second, we collected the bright field-images of iPSC colonies and processed them with adaptive gamma transform and data enhancement. The evaluation for the iPSC colony quality was further verified with living cell fluorescent staining, currently accepted as the optimal biological method. Third, multi-source features were extracted using three deep convolutional neural networks (DCNNs) and four traditional feature descriptors. Finally, we utilized a support vector machine (SVM) to perform classification. Before feeding into the SVM, the features were processed by principal component analysis algorithm to save computational cost and training time.

RESULTS

Experimental results on the collected iPSC dataset (46,500 images) show that the proposed method could obtain 95.55% classification accuracy.

CONCLUSIONS

Our study could provide a method to efficiently and quickly judge the biological quality of a single iPSC colony or populations and facilitate the large-scale iPSC manufacturing.

Patient derived stem cells for discovery and validation of novel pathogenic variants in inherited retinal disease

Our understanding of inherited retinal disease has benefited immensely from molecular genetic analysis over the past several decades. New technologies that allow for increasingly detailed examination of a patient's DNA have expanded the catalog of genes and specific variants that cause retinal disease. In turn, the identification of pathogenic variants has allowed the development of gene therapies and low-cost, clinically focused genetic testing. Despite this progress, a relatively large fraction (at least 20%) of patients with clinical features suggestive of an inherited retinal disease still do not have a molecular diagnosis today. Variants that are not obviously disruptive to the codon sequence of exons can be difficult to distinguish from the background of benign human genetic variations. Some of these variants exert their pathogenic effect not by altering the primary amino acid sequence, but by modulating gene expression, isoform splicing, or other transcript-level mechanisms. While not discoverable by DNA sequencing methods alone, these variants are excellent targets for studies of the retinal transcriptome. In this review, we present an overview of the current state of pathogenic variant discovery in retinal disease and identify some of the remaining barriers. We also explore the utility of new technologies, specifically patient-derived induced pluripotent stem cell (iPSC)-based modeling, in further expanding the catalog of disease-causing variants using transcriptome-focused methods. Finally, we outline bioinformatic analysis techniques that will allow this new method of variant discovery in retinal disease. As the knowledge gleaned from previous technologies is informing targets for therapies today, we believe that integrating new technologies, such as iPSC-based modeling, into the molecular diagnosis pipeline will enable a new wave of variant discovery and expanded treatment of inherited retinal disease.

Efficient Modulation of TP53 Expression in Human Induced Pluripotent Stem Cells

TP53 point mutations are found in 50% of all cancers and seem to play an important role in cancer pathogenesis. Thus, human induced pluripotent stem cells (hiPSCs) overexpressing mutant TP53 are a valuable tool for the generation of in vitro models of cancer stem cells or for in vivo xenograft models. Here, we describe a protocol for the alteration of gene expression in hiPSCs via overexpression of a mutant form of the TP53 (R249S) gene using lentiviral transduction. A high amount of TP53 protein is detected 1 week after transduction and antibiotic selection. Differentiation of transduced hiPSCs gives insight into better understanding cancer formation in different tissues and may be a useful tool for genetic or pharmacologic screening assays. © 2019 The Authors. Basic Protocol 1: Production and concentration of third-generation lentivirus Support Protocol 1: Cloning of gene of interest into modulation vector Support Protocol 2: Preparation of DMEM GlutaMAX™ with 10% fetal bovine serum and 1% penicillin-streptomycin Basic Protocol 2: Transduction of human induced pluripotent stem cells and selection of positively transfected cells Support Protocol 3: Preparation of Matrigel^® -coated plates Support Protocol 4: Preparation of mTeSR™1 medium.

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status

Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an automated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.

The Role of Brain Microvascular Endothelial Cell and Blood-Brain Barrier Dysfunction in Schizophrenia

BACKGROUND

Despite decades of research, little clarity exists regarding pathogenic mechanisms related to schizophrenia. Investigations on the disease biology of schizophrenia have primarily focused on neuronal alterations. However, there is substantial evidence pointing to a significant role for the brain's microvasculature in mediating neuroinflammation in schizophrenia.

SUMMARY

Brain microvascular endothelial cells (BMEC) are a central element of the microvasculature that forms the blood-brain barrier (BBB) and shields the brain against toxins and immune cells via paracellular, transcellular, transporter, and extracellular matrix proteins. While evidence for BBB dysfunction exists in brain disorders, including schizophrenia, it is not known if BMEC themselves are functionally compromised and lead to BBB dysfunction.

KEY MESSAGES

Genome-wide association studies, postmortem investigations, and gene expression analyses have provided some insights into the role of the BBB in schizophrenia pathophysiology. However, there is a significant gap in our understanding of the role that BMEC play in BBB dysfunction. Recent advances differentiating human BMEC from induced pluripotent stem cells (iPSC) provide new avenues to examine the role of BMEC in BBB dysfunction in schizophrenia.

Microengineered systems with iPSC-derived cardiac and hepatic cells to evaluate drug adverse effects

Hepatic and cardiac drug adverse effects are among the leading causes of attrition in drug development programs, in part due to predictive failures of current animal or in vitro models. Hepatocytes and cardiomyocytes differentiated from human induced pluripotent stem cells (iPSCs) hold promise for predicting clinical drug effects, given their human-specific properties and their ability to harbor genetically determined characteristics that underlie inter-individual variations in drug response. Currently, the fetal-like properties and heterogeneity of hepatocytes and cardiomyocytes differentiated from iPSCs make them physiologically different from their counterparts isolated from primary tissues and limit their use for predicting clinical drug effects. To address this hurdle, there have been ongoing advances in differentiation and maturation protocols to improve the quality and use of iPSC-differentiated lineages. Among these are in vitro hepatic and cardiac cellular microsystems that can further enhance the physiology of cultured cells, can be used to better predict drug adverse effects, and investigate drug metabolism, pharmacokinetics, and pharmacodynamics to facilitate successful drug development. In this article, we discuss how cellular microsystems can establish microenvironments for these applications and propose how they could be used for potentially controlling the differentiation of hepatocytes or cardiomyocytes. The physiological relevance of cells is enhanced in cellular microsystems by simulating properties of tissue microenvironments, such as structural dimensionality, media flow, microfluidic control of media composition, and co-cultures with interacting cell types. Recent studies demonstrated that these properties also affect iPSC differentiations and we further elaborate on how they could control differentiation efficiency in microengineered devices. In summary, we describe recent advances in the field of cellular microsystems that can control the differentiation and maturation of hepatocytes and cardiomyocytes for drug evaluation. We also propose how future research with iPSCs within engineered microenvironments could enable their differentiation for scalable evaluations of drug effects.

Automation, Monitoring, and Standardization of Cell Product Manufacturing

Although regenerative medicine products are at the forefront of scientific research, technological innovation, and clinical translation, their reproducibility and large-scale production are compromised by automation, monitoring, and standardization issues. To overcome these limitations, new technologies at software (e.g., algorithms and artificial intelligence models, combined with imaging software and machine learning techniques) and hardware (e.g., automated liquid handling, automated cell expansion bioreactor systems, automated colony-forming unit counting and characterization units, and scalable cell culture plates) level are under intense investigation. Automation, monitoring and standardization should be considered at the early stages of the developmental cycle of cell products to deliver more robust and effective therapies and treatment plans to the bedside, reducing healthcare expenditure and improving services and patient care.

Noninvasive measurement of cell/colony motion using image analysis methods to evaluate the proliferative capacity of oral keratinocytes as a tool for quality control in regenerative medicine

Image-based cell/colony analyses offer promising solutions to compensate for the lack of quality control (QC) tools for noninvasive monitoring of cultured cells, a regulatory challenge in regenerative medicine. Here, the feasibility of two image analysis algorithms, optical flow and normalised cross-correlation, to noninvasively measure cell/colony motion in human primary oral keratinocytes for screening the proliferative capacity of cells in the early phases of cell culture were examined. We applied our software to movies converted from 96 consecutive time-lapse phase-contrast images of an oral keratinocyte culture. After segmenting the growing colonies, two indices were calculated based on each algorithm. The correlation between each index of the colonies and their proliferative capacity was evaluated. The software was able to assess cell/colony motion noninvasively, and each index reflected the observed cell kinetics. A positive linear correlation was found between cell/colony motion and proliferative capacity, indicating that both algorithms are potential tools for QC.

Automated sensing and splitting of stem cell colonies on microraft arrays

Human induced pluripotent stem cells (hiPSCs) are widely used for disease modeling, tissue engineering, and clinical applications. Although the development of new disease-relevant or customized hiPSC lines is of high importance, current automated hiPSC isolation technologies rely largely on the fluorescent labeling of cells, thus limiting the cell line development from many applications. The objective of this research was to develop a platform for high-throughput hiPSC cytometry and splitting that utilized a label-free cell sensing approach. An image analysis pipeline utilizing background subtraction and standard deviation projections was implemented to detect hiPSC colonies from bright-field microscopy data. The pipeline was incorporated into an automated microscopy system coupling quad microraft cell-isolation arrays, computer-based vision, and algorithms for smart decision making and cell sorting. The pipeline exhibited a hiPSC detection specificity of 98% and a sensitivity of 88%, allowing for the successful tracking of growth for hundreds of microcolonies over 7 days. The automated platform split 170 mother colonies from a microarray within 80 min, and the harvested daughter biopsies were expanded into viable hiPSC colonies suitable for downstream assays, such as polymerase chain reaction (PCR) or continued culture. Transmitted light microscopy offers an alternative, label-free modality for isolating hiPSCs, yet its low contrast and specificity for adherent cells remain a challenge for automation. This novel approach to label-free sensing and microcolony subsampling with the preservation of the mother colony holds the potential for hiPSC colony screening based on a wide range of properties including those measurable only by a cell destructive assay.

Media composition modulates human embryonic stem cell morphology and may influence preferential lineage differentiation potential

Undifferentiated human embryonic stem cells have a distinct morphology (hESC). Changes in cell morphology during culture can be indicative of differentiation. hESC, maintained in diverse medias, demonstrated alterations in morphological parameters and subsequent alterations in underlying transcript expression and lineage differentiation. Analysis of morphological parameters showed distinct and significant differences between the undefined, less defined and Xeno-free medias while still maintaining pluripotency markers. This suggested that the less defined media may be creating dynamic instability in the cytoskeleton, with the cytoskeleton becoming more stabilised in the Xeno-free media as demonstrated by smaller and rounder cells. Examination of early lineage markers during undirected differentiation using d5 embryoid bodies demonstrated increased mesodermal lineage preference as compared to endodermal or ectoderm in cells originally cultured in Xeno-free media. Undefined media showed preference for mesoderm and ectoderm lineages, while less defined media (BSA present) demonstrated no preference. These data reveal that culture media may produce fundamental changes in cell morphology which are reflected in early lineage differentiation choice.