NGN2-driven induced pluripotent stem cell (iPSC)-to-neuron conversion is a popular method for human neurological disease modeling. In this study, we present a standardized approach for generating neurons utilizing clonal, targeted-engineered iPSC lines with defined reagents. We demonstrate consistent production of excitatory neurons at scale and long-term maintenance for at least 150 days. Temporal omics, electrophysiological, and morphological profiling indicate continued maturation to postnatal-like neurons. Quantitative characterizations through transcriptomic, imaging, and functional assays reveal coordinated actions of multiple pathways that drive neuronal maturation. We also show the expression of disease-related genes in these neurons to demonstrate the relevance of our protocol for modeling neurological disorders. Finally, we demonstrate efficient generation of NGN2-integrated iPSC lines. These workflows, profiling data, and functional characterizations enable the development of reproducible human in vitro models of neurological disorders.

BACKGROUND: Atrial fibrillation has an estimated prevalence of 1.5-2%, making it the most common cardiac arrhythmia. The processes that cause and sustain the disease are still not completely understood. An association between atrial fibrillation and systemic, as well as local, inflammatory processes has been reported. However, the exact mechanisms underlying this association have not been established. While it is understood that inflammatory macrophages can influence cardiac electrophysiology, a direct, causative relationship to atrial fibrillation has not been described. This study investigated the pro-arrhythmic effects of activated M1 macrophages on human induced pluripotent stem cell (hiPSC)-derived atrial cardiomyocytes, to propose a mechanistic link between inflammation and atrial fibrillation.
METHODS: Two hiPSC lines from healthy individuals were differentiated to atrial cardiomyocytes and M1 macrophages and integrated in an isogenic, pacing-free, atrial fibrillation-like coculture model. Electrophysiology characteristics of cocultures were analysed for beat rate irregularity, electrogram amplitude and conduction velocity using multi electrode arrays. Cocultures were additionally treated using glucocorticoids to suppress M1 inflammation. Bulk RNA sequencing was performed on coculture-isolated atrial cardiomyocytes and compared to meta-analyses of atrial fibrillation patient transcriptomes.
RESULTS: Multi electrode array recordings revealed M1 to cause irregular beating and reduced electrogram amplitude. Conduction analysis further showed significantly lowered conduction homogeneity in M1 cocultures. Transcriptome sequencing revealed reduced expression of key cardiac genes such as SCN5A, KCNA5, ATP1A1, and GJA5 in the atrial cardiomyocytes. Meta-analysis of atrial fibrillation patient transcriptomes showed high correlation to the in vitro model. Treatment of the coculture with glucocorticoids showed reversal of phenotypes, including reduced beat irregularity, improved conduction, and reversed RNA expression profiles.
CONCLUSIONS: This study establishes a causal relationship between M1 activation and the development of subsequent atrial arrhythmia, documented as irregularity in spontaneous electrical activation in atrial cardiomyocytes cocultured with activated macrophages. Further, beat rate irregularity could be alleviated using glucocorticoids. Overall, these results point at macrophage-mediated inflammation as a potential AF induction mechanism and offer new targets for therapeutic development. The findings strongly support the relevance of the proposed hiPSC-derived coculture model and present it as a first of its kind disease model.

Atherosclerosis, a leading cause of cardiovascular disease, necessitates advanced and innovative modeling techniques to better understand and predict plaque dynamics. The present work presents two distinct hypothetical models inspired by different research fields: the logistic map from chaos theory and Markov models from stochastic processes. The logistic map effectively models the nonlinear progression and sudden changes in plaque stability, reflecting the chaotic nature of atherosclerotic events. In contrast, Markov models, including traditional Markov chains, spatial Markov models, and Markov random fields, provide a probabilistic framework to assess plaque stability and transitions. Spatial Markov models, visualized through heatmaps, highlight the spatial distribution of transition probabilities, emphasizing local interactions and dependencies. Markov random fields incorporate complex spatial interactions, inspired by advances in physics and computational biology, but present challenges in parameter estimation and computational complexity. While these hypothetical models offer promising insights, they require rigorous validation with real-world data to confirm their accuracy and applicability. This study underscores the importance of interdisciplinary approaches in developing theoretical models for atherosclerotic plaques.

Cockayne Syndrome B (CSB) is a hereditary multiorgan syndrome which-through largely unknown mechanisms-can affect the brain where it clinically presents with microcephaly, intellectual disability and demyelination. Using human induced pluripotent stem cell (hiPSC)-derived neural 3D models generated from CSB patient-derived and isogenic control lines, we here provide explanations for these three major neuropathological phenotypes. In our models, CSB deficiency is associated with (i) impaired cellular migration due to defective autophagy as an explanation for clinical microcephaly; (ii) altered neuronal network functionality and neurotransmitter GABA levels, which is suggestive of a disturbed GABA switch that likely impairs brain circuit formation and ultimately causes intellectual disability; and (iii) impaired oligodendrocyte maturation as a possible cause of the demyelination observed in children with CSB. Of note, the impaired migration and oligodendrocyte maturation could both be partially rescued by pharmacological HDAC inhibition.

UNASSIGNED: This study sought to document and understand facilitators and barriers to producing, translating, and using modeled evidence in decision-making in Burkina Faso, Nigeria, India, and Kenya. We explored researcher-decision-maker engagement mechanisms as key facilitators of evidence use, with a focus on knowledge brokers and boundary organizations.
UNASSIGNED: The study used mixed methods drawing on analysis from key informant interviews and surveys, complemented by a rapid desk review to map modeling activities and actors. The survey was conducted online while the qualitative research entailed in-depth interviews with modelers, knowledge brokers, and decision-makers working in a representative variety of health fields, organizations, and levels of government. This study was approved by Health Media Lab IRB (Institutional Review Board) in the United States and a local IRB in each study country and conducted between September 2021 and June 2022.
UNASSIGNED: Informants interviewed for this study described a range of factors that facilitate and inhibit the use of modeled evidence in public health decision-making at the individual, organizational, and environmental levels. Key themes included the capacity to produce, translate, and use modeled evidence; the timing and relevance of modeling outputs; the existence of communications channels between modelers and decision-makers; the strength of underlying data systems; the role of sustained funding; and the impact of global crises.
UNASSIGNED: This study highlights the importance of taking an ecosystem approach to supporting modeling activities, considering individual, organizational, and environmental factors and how different actors and interact to inform the production, translation, and use of modeled evidence. Structured interaction that promotes dialogue, debate, and joint sense making between the producers and users of evidence is critical to informing and influencing the use of evidence in decision-making.

Recent advances in human pluripotent stem cell (hPSC) technologies have prompted the emergence of new research fields and applications for human neurons and brain organoids. Brain organoids have gained attention as an in vitro model system that recapitulates the higher structure, cellular diversity and function of the brain to explore brain development, disease modeling, drug screening, and regenerative medicine. This progress has been accelerated by abundant interactions of brain organoid technology with various research fields. A cross-disciplinary approach with human brain organoid technology offers a higher-ordered advance for more accurately understanding the human brain. In this review, we summarize the status of neural induction in two- and three-dimensional culture systems from hPSCs and the modeling of neurodegenerative diseases using brain organoids. We also highlight the latest bioengineered technologies for the assembly of spatially higher-ordered neural tissues and prospects of brain organoid technology toward the understanding of the potential and abilities of the human brain.

Despite improvements in patient outcomes, pediatric cancer remains a leading cause of non-accidental death in children. Recent genetic analysis of patients with pediatric cancers indicates an important role for both germline genetic predisposition and cancer-specific somatic driver mutations. Increasingly, evidence demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of vulnerability in pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers and how these models compare to other pediatric cancer model modalities.
Today, pediatric cancer is a leading cause of non-accidental death in children. In order to further improve outcomes, it is important for researchers and clinicians alike to recognize how pediatric cancers are distinct from adult cancers. Inherited risk of cancer may play a greater role in pediatric cancer risk, and subsequent tumor-specific acquired driver mutations initiate tumor formation. However, there is substantial interaction between inherited and acquired mutations, which supports consideration of both simultaneously. Recent advancements in biotechnology, have improved matching between early cells of development and pediatric cancer cells, although cell-of-origin for certain pediatric central nervous system tumors remain elusive. Increasingly, evidence, particularly in pediatric medulloblastoma, demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers, how human these models compare to other pediatric cancer model modalities, and how these models can be improved in the future.

BACKGROUND: Bladder cancer (BC) is a prevalent malignancy with high recurrence rates. Patient-derived bladder cancer organoids (BCO) pose as a promising approach in both, disease modeling and individualized treatment screening. The aim of this study was to investigate the transcriptomic plasticity in BCOs as a function of cultivation times to define ideal time periods for the applications envisioned.
METHODS: Tumor samples of three patients with pathologically confirmed non-muscle invasive and muscle-invasive bladder cancer were included in this study and expanded as BCOs. RNA expression was investigated at different time periods of cells in culture using differential gene expression for overall transcript expression and quantitative real-time PCR (qRT-PCR) for pathological relevant markers.
RESULTS: Differential gene expression of the BCO lines was investigated across passages 1-4, in passages 5-9 and above 9, respectively. Analysis of the entire transcriptome of the respective BCO lines revealed consistent profiles without significant alterations throughout the cultivation and expansion procedure. Notably, key transcripts like TP53, PIK3CA, BRCA1, among others, exhibited stable expression levels in the quantitative RNA analysis during the cultivation period.
CONCLUSIONS: The robust transcriptome during BCO cultivation advocates for the use of earlier passages of BCOs in personalized medicine providing a time-efficient drug screening option to accelerate the counseling of patients\' treatment options. Higher passages of BCOs still hold the potential in topics demanding for expanded cell masses such as medical device development and others.

Leigh syndrome (LS), a complex multisystemic disorder, poses significant challenges in genetic medicine due to its intricate pathogenesis and wide-ranging clinical manifestations. Notably, these arise from mutations in either nuclear genetic DNA or mitochondrial DNA, affecting ATP production and resulting in diverse clinical outcomes. The unpredictable trajectory of this disease, ranging from severe developmental delays to early mortality, underscores the need for improved therapeutic solutions. This research pivots toward the novel use of induced pluripotent stem cells (iPSCs) as a promising platform for understanding disease mechanisms and spearheading patient-specific drug discoveries. Given the past successes of iPSCs in delineating organ-specific disorders and the recent endorsement of human iPSC-derived cardiomyocytes (CMs) by the FDA for drug evaluation, our work seeks to bridge this innovation to Leigh syndrome research. We detail a methodological approach to generate iPSCs from LS patients and differentiate them into iPSCs-CMs. Using multi-electrode array (MEA) analyses, we evaluate the field potential of these cells, spotlighting the potential of hiPSC-CM in drug validation and disease modeling. This pioneering approach offers a glimpse into the future of patient-centric therapeutic interventions for Leigh/Leigh-like syndrome.

Since early 2020 the COVID-19 pandemic has paralyzed the world, resulting in more than half a billion infections and over 6 million deaths within a 28-month period. Knowledge about the disease remains largely disjointed, especially when considering the molecular mechanisms driving the diversity of clinical manifestations and symptoms. Despite the recent availability of vaccines, there remains an urgent need to develop effective treatments for cases of severe disease, especially in the face of novel virus variants. The complexity of the situation is exacerbated by the emergence of COVID-19 as a complex and multifaceted systemic disease affecting independent tissues and organs throughout the body. The development of effective treatment strategies is therefore predicated on an integrated understanding of the underlying disease mechanisms and their potentially causative link to the diversity of observed clinical phenotypes. To address this need, we utilized a computational technology (the Dataome platform) to build an integrated clinico-molecular view on the most important COVID-19 clinical phenotypes. Our results provide the first integrated, whole-patient model of COVID-19 symptomatology that connects the molecular lifecycle of SARS-CoV-2 with microvesicle-mediated intercellular communication and the contact activation and kallikrein-kinin systems. The model not only explains the clinical pleiotropy of COVID-19, but also provides an evidence-driven framework for drug development/repurposing and the identification of critical risk factors. The associated knowledge is provided in the form of the open source COVID-19 Explorer (https://covid19.molecularhealth.com), enabling the global community to explore and analyze the key molecular features of systemic COVID-19 and associated implications for research priorities and therapeutic strategies. Our work suggests that knowledge modeling solutions may offer important utility in expediting the global response to future health emergencies.