Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning

CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease

The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on static risk variables, and the inability to adapt to new patient data, thereby necessitating the exploration of alternative strategies. In response, this study introduces CardioRiskNet, a hybrid AI-based model designed to transcend these limitations. The proposed CardioRiskNet consists of seven parts: data preprocessing, feature selection and encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, risk prediction and prognosis, evaluation and validation, and deployment and integration. At first, the patient data are preprocessed by cleaning the data, handling the missing values, applying a normalization process, and extracting the features. Next, the most informative features are selected and the categorical variables are converted into a numerical form. Distinctively, CardioRiskNet employs active learning to iteratively select informative samples, enhancing its learning efficacy, while its attention mechanism dynamically focuses on the relevant features for precise risk prediction. Additionally, the integration of XAI facilitates interpretability and transparency in the decision-making processes. According to the experimental results, CardioRiskNet demonstrates superior performance in terms of accuracy, sensitivity, specificity, and F1-Score, with values of 98.7%, 98.7%, 99%, and 98.7%, respectively. These findings show that CardioRiskNet can accurately assess and prognosticate the CVD risk, demonstrating the power of active learning and AI to surpass the conventional methods. Thus, CardioRiskNet's novel approach and high performance advance the management of CVDs and provide healthcare professionals a powerful tool for patient care.

A literature review: machine learning-based stem cell investigation

Background and Objective

Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement.

Methods

In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions.

Key Content and Findings

The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn't consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions.

Conclusions

The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

Bioprocessing considerations for generation of iPSCs intended for clinical application: perspectives from the ISCT Emerging Regenerative Medicine Technology working group

Approval of induced pluripotent stem cells (iPSCs) for the manufacture of cell therapies to support clinical trials is now becoming realized after 20 years of research and development. In 2022 the International Society for Cell and Gene Therapy (ISCT) established a Working Group on Emerging Regenerative Medicine Technologies, an area in which iPSCs-derived technologies are expected to play a key role. In this article, the Working Group surveys the steps that an end user should consider when generating iPSCs that are stable, well-characterised, pluripotent, and suitable for making differentiated cell types for allogeneic or autologous cell therapies. The objective is to provide the reader with a holistic view of how to achieve high-quality iPSCs from selection of the starting material through to cell banking. Key considerations include: (i) intellectual property licenses; (ii) selection of the raw materials and cell sources for creating iPSC intermediates and master cell banks; (iii) regulatory considerations for reprogramming methods; (iv) options for expansion in 2D vs. 3D cultures; and (v) available technologies and equipment for harvesting, washing, concentration, filling, cryopreservation, and storage. Some key process limitations are highlighted to help drive further improvement and innovation, and includes recommendations to close and automate current open and manual processes.

Unlocking the full potential of mesenchymal stromal cell therapy for osteoarthritis through machine learning-based in silico trials

Despite the potential of mesenchymal stromal cells (MSCs) in osteoarthritis (OA) treatment, the challenge lies in addressing their therapeutic inconsistency. Clinical trials revealed significantly varied therapeutic outcomes among patients receiving the same allogenic MSCs but different treatment regimens. Therefore, optimizing personalized treatment strategies is crucial to fully unlock MSCs' potential and enhance therapeutic consistency. We employed the XGBoost algorithm to train a self-collected database comprising 37 published clinical reports to create a model capable of predicting the probability of effective pain relief and Western Ontario and McMaster Universities (WOMAC) index improvement in OA patients undergoing MSC therapy. Leveraging this model, extensive in silico simulations were conducted to identify optimal personalized treatment strategies and ideal patient profiles. Our in silico trials predicted that the individually optimized MSC treatment strategies would substantially increase patients' chances of recovery compared to the strategies used in reported clinical trials, thereby potentially benefiting 78.1%, 47.8%, 94.4% and 36.4% of the patients with ineffective short-term pain relief, short-term WOMAC index improvement, long-term pain relief and long-term WOMAC index improvement, respectively. We further recommended guidelines on MSC number, concentration, and the patients' appropriate physical (body mass index, age, etc.) and disease states (Kellgren-Lawrence grade, etc.) for OA treatment. Additionally, we revealed the superior efficacy of MSC in providing short-term pain relief compared to platelet-rich plasma therapy for most OA patients. This study represents the pioneering effort to enhance the efficacy and consistency of MSC therapy through machine learning applied to clinical data. The in silico trial approach holds immense potential for diverse clinical applications.

Advances in Hypertrophic Cardiomyopathy Disease Modelling Using hiPSC-Derived Cardiomyocytes

The advent of human induced pluripotent stem cells (hiPSCs) and their capacity to be differentiated into beating human cardiomyocytes (CMs) in vitro has revolutionized human disease modelling, genotype-phenotype predictions, and therapeutic testing. Hypertrophic cardiomyopathy (HCM) is a common inherited cardiomyopathy and the leading known cause of sudden cardiac arrest in young adults and athletes. On a molecular level, HCM is often driven by single pathogenic genetic variants, usually in sarcomeric proteins, that can alter the mechanical, electrical, signalling, and transcriptional properties of the cell. A deeper knowledge of these alterations is critical to better understanding HCM manifestation, progression, and treatment. Leveraging hiPSC-CMs to investigate the molecular mechanisms driving HCM presents a unique opportunity to dissect the consequences of genetic variants in a sophisticated and controlled manner. In this review, we summarize the molecular underpinnings of HCM and the role of hiPSC-CM studies in advancing our understanding, and we highlight the advances in hiPSC-CM-based modelling of HCM, including maturation, contractility, multiomics, and genome editing, with the notable exception of electrophysiology, which has been previously covered.

Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications

The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable to genetic engineering, and can be differentiated into most somatic cell types. iPSCs have been widely applied to model human development and diseases, perform drug screening, and develop cell therapies. In this review, we outline key developments in the iPSC field and highlight the immense versatility of the iPSC technology for in vitro modeling and therapeutic applications. We begin by discussing the pivotal discoveries that revealed the potential of a somatic cell nucleus for reprogramming and led to successful generation of iPSCs. We consider the molecular mechanisms and dynamics of somatic cell reprogramming as well as the numerous methods available to induce pluripotency. Subsequently, we discuss various iPSC-based cellular models, from mono-cultures of a single cell type to complex three-dimensional organoids, and how these models can be applied to elucidate the mechanisms of human development and diseases. We use examples of neurological disorders, coronavirus disease 2019 (COVID-19), and cancer to highlight the diversity of disease-specific phenotypes that can be modeled using iPSC-derived cells. We also consider how iPSC-derived cellular models can be used in high-throughput drug screening and drug toxicity studies. Finally, we discuss the process of developing autologous and allogeneic iPSC-based cell therapies and their potential to alleviate human diseases.

Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling

Purpose of Review

To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications.

Recent Findings

hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy.

Summary

Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.

Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to, that is, distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation toward hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Artificial intelligence and improvement of stem cell delivery in healthcare

Artificial intelligence (AI) is critical for improving the quality of stem cell manufacturing and delivery. AI can assist in determining the viability, effectiveness, efficacy, and safety of stem cells. Furthermore, in stem cell and regenerative medicine, AI is utilized to streamline simulation and model-building processes and find connections between cellular activities and their microenvironments. However, thoughtful consideration is required to minimize unwanted implications of AI incorporation for stem cell-based treatment.

Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.

Mass Spectrometry Imaging Reveals Early Metabolic Priming of Cell Lineage in Differentiating Human-Induced Pluripotent Stem Cells

Induced pluripotent stem cells (iPSCs) hold great promise in regenerative medicine; however, few algorithms of quality control at the earliest stages of differentiation have been established. Despite lipids having known functions in cell signaling, their role in pluripotency maintenance and lineage specification is underexplored. We investigated the changes in iPSC lipid profiles during the initial loss of pluripotency over the course of spontaneous differentiation using the co-registration of confocal microscopy and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging. We identified phosphatidylethanolamine (PE) and phosphatidylinositol (PI) species that are highly informative of the temporal stage of differentiation and can reveal iPS cell lineage bifurcation occurring metabolically. Several PI species emerged from the machine learning analysis of MS data as the early metabolic markers of pluripotency loss, preceding changes in the pluripotency transcription factor Oct4. The manipulation of phospholipids via PI 3-kinase inhibition during differentiation manifested in the spatial reorganization of the iPS cell colony and elevated expression of NCAM-1. In addition, the continuous inhibition of phosphatidylethanolamine N-methyltransferase during differentiation resulted in the enhanced maintenance of pluripotency. Our machine learning analysis highlights the predictive power of lipidomic metrics for evaluating the early lineage specification in the initial stages of spontaneous iPSC differentiation.

Label-free optical imaging and sensing for quality control of stem cell manufacturing

Human stem cells provide emerging methods for drug screening, disease modeling, and personalized patient therapies. To meet this growing demand for scale-up, stem cell manufacturing methods must be streamlined with continuous monitoring technologies and automated feedback to optimize growth conditions for high production and consistency. Label-free optical imaging and sensing, including multiphoton microscopy, Raman spectroscopy, and low-cost methods such as phase and transmitted light microscopy, can provide rapid, repeatable, and non-invasive monitoring of stem cells throughout cell differentiation and maturation. Machine learning algorithms trained on label-free optical imaging and sensing features could identify viable cells and predict optimal manufacturing conditions. These techniques have the potential to streamline stem cell manufacturing and accelerate their use in regenerative medicine.

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning

Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary to decrease the risk of cardiovascular adverse effects in the clinic. Recently, approaches leveraging calcium transients in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been developed to detect contractility changes, induced by a variety of mechanisms early during drug discovery projects. Although these approaches have been able to provide some predictive ability, we hypothesised that using additional waveform parameters could offer improved insights, as well as predictivity. In this study, we derived 25 parameters from each calcium transient waveform and developed a modified Random Forest method to predict the inotropic effects of the compounds. In total annotated data for 48 compounds were available for modelling, out of which 31 were inotropes. The results show that the Random Forest model with a modified purity criterion performed slightly better than an unmodified algorithm in terms of the Area Under the Curve, giving values of 0.84 vs 0.81 in a cross-validation, and outperformed the ToxCast Pipeline model, for which the highest value was 0.76 when using the best-performing parameter, PW10. Our study hence demonstrates that more advanced parameters derived from waveforms, in combination with additional machine learning methods, provide improved predictivity of cardiovascular risk associated with inotropic effects.

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.

Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks

Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with "good" and "bad" morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.

Bioengineering Strategies to Create 3D Cardiac Constructs from Human Induced Pluripotent Stem Cells

Human induced pluripotent stem cells (hiPSCs) can be used to generate various cell types in the human body. Hence, hiPSC-derived cardiomyocytes (hiPSC-CMs) represent a significant cell source for disease modeling, drug testing, and regenerative medicine. The immaturity of hiPSC-CMs in two-dimensional (2D) culture limit their applications. Cardiac tissue engineering provides a new promise for both basic and clinical research. Advanced bioengineered cardiac in vitro models can create contractile structures that serve as exquisite in vitro heart microtissues for drug testing and disease modeling, thereby promoting the identification of better treatments for cardiovascular disorders. In this review, we will introduce recent advances of bioengineering technologies to produce in vitro cardiac tissues derived from hiPSCs.

Data analytics for cardiac diseases

In the present research we tackled the classification of seven genetic cardiac diseases and control subjects by using an extensive set of machine learning algorithms with their variations from simple K-nearest neighbor searching method to support vector machines. The research was based on calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. All in all, 55 different machine learning alternatives were used to model eight classes by applying the principle of 10-fold crossvalidation with the peak data of 1626 signals. The best classification accuracy of approximately 69% was given by random forests, which can be seen high enough here to show machine learning to be potential for the differentiation of the eight disease classes.