Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer research and drug discovery. Efforts in stem cell research comparatively lag behind. We detail key paradigms in machine learning, with a focus on equipping stem cell biologists with the understanding necessary to begin conceptualizing and designing machine learning workflows within their own domain of expertise. Supervised approaches in both regression and classification as well as unsupervised clustering techniques are all covered, with examples from across the biological sciences. High-throughput, high-content, multiplex assays for data acquisition are also discussed in the form of single-cell RNA sequencing and image-based approaches. Lastly, potential applications in stem cell biology, including the development of novel cell types, and improving model maturation are also discussed. Machine learning approaches applied in stem cell biology show promise in accelerating progress in developmental biology, drug screening, disease modeling, and personalized medicine.

Introduction: A polygenic risk score (PRS) is derived from a genome-wide association study (GWAS) and represents an aggregate of thousands of single-nucleotide polymorphisms (SNPs) that provide a baseline estimate of an individual’s genetic risk for a specific disease or trait at birth. Cardiometabolic disease represents a set of disease processes that historically have disproportionally affected underrepresented racial and minority groups. Furthermore, these groups represent a population generally not well captured by traditional risk scores compared to European cohorts. Since the first GWAS studying myocardial infarction was published, PRSs have increasingly been seen as a promising tool to improve risk stratification of non-European populations. However, how PRSs can be best used in clinical practice remains unclear. Hypothesis: We provide an overview of the PRSs related to cardiometabolic disease, analyze the ancestral diversity of GWAS cohorts, and discuss the evidence supporting their clinical applications. Methods: The Preferred Reporting Items For Systematic Reviews and Meta-analysis extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. English studies that published a PRS related to atrial fibrillation (AF), cerebrovascular disease (CVD), coronary artery disease (CAD), dyslipidemia, heart failure, heritable cardiomyopathy, hypertension, and type 2 diabetes were reviewed. Results: Across the 4,863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to CAD, followed by hypertension and CVD. Limited ancestral diversity was observed as most studies (56) included only individuals of European ancestry. A smaller proportion of studies (16) published PRSs derived in multi-ancestry cohorts. Only ten studies published a PRS derived solely from a sample population of non-European ancestry (Chinese, East Asian, Japanese, and Korean). The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies (42) reported an integrated risk model combining a PRS with traditional risk factors or a clinical risk tool (FRS, PCE, CHADS2). The integrated risk model consistently improved predictive accuracy, but few studies investigated the performance in a non-European population. Conclusion: In conclusion, this scoping review reports strong evidence for the clinical use of PRSs in AF, CAD, CVD, and hypertension. However, most PRSs are derived in cohorts of European ancestry, which contributes to a lack of PRS transferability across different ancestral groups, likely exacerbating health inequities. Future prospective studies should focus on further establishing the clinical utility of PRSs. Additionally, diversity in future GWAS cohorts is essential to ensure that PRSs reflect the multi-ancestry society at large.

BackgroundProtein truncating mutations in the titin gene are associated with increased risk of atrial fibrillation (AF). However, little is known regarding the underlying pathophysiology.MethodsWe identified a heterozygous titin truncating variant in a patient with unexplained early-onset AF using whole exome sequencing. We used atrial and ventricular patient induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), CRISPR/Cas9 genetic correction, and engineered heart tissue (EHT) constructs to evaluate the impact of the titin truncating variant on electrophysiology, sarcomere structure, contractility, and gene expression.ResultsWe generated atrial and ventricular iPSC-CMs from the AF patient with the titin truncating variant and a CRISPR/Cas9 genome corrected isogenic control. We demonstrate that the titin truncating variant increases susceptibility to pacing-induced arrhythmia (prevalence of arrhythmogenic phenotypes, 85.7% versus 14.2%;P= 0.03), promotes sarcomere disorganization (mean ± SEM, 66.3 ± 6.8% versus 88.0 ± 2.9%;P= 0.04) in atrial iPSC-CMs, and reduces contractile force (0.013 ± 0.003 mN versus 0.027 ± 0.004 mN;P< 0.01) in atrial EHTs compared to isogenic controls. In ventricular iPSC-CMs, this variant led to altered electrophysiology (90.0% versus 33.3%;P= 0.02) and sarcomere organization (62.0 ± 3.9% versus 82.9 ± 2.9%;P< 0.01) with no change in EHT contractility compared to isogenic controls. RNA-sequencing revealed an upregulation of cell adhesion and extracellular matrix genes in the presence of the titin truncating variant for both atrial and ventricular EHTs.ConclusionsIn a patient with early-onset unexplained AF and normal ventricular function, iPSC-CMs with a titin truncating variant showed structural and electrophysiological abnormalities in both atrial and ventricular preparations, while only atrial EHTs demonstrated reduced contractility. Whole transcriptome sequencing showed upregulation of genes involved in cell-cell and cell-matrix interactions in both atrial and ventricular EHTs. Together, these findings suggest titin truncating variants promote the development of AF through remodeling of atrial cardiac tissue and provide insight into the chamber-specific effects of titin truncating variants.