In vitro high-content tissue models to address precision medicine challenges

Afewerki, Samson; Stocco, Thiago Domingues; Silva, André Diniz Rosa da; Furtado, André Sales Aguiar; Sousa, Gustavo Fernandes de; Ruiz–Esparza, Guillermo U.; Webster, Thomas J.; Marciano, Fernanda R.; Strömme, Maria; Zhang, Yu Shrike; Lobo, Anderson Oliveira

doi:10.1016/j.mam.2022.101108

Cited by 4 publications

(3 citation statements)

References 391 publications

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“…Finally, the approach of integrating advanced AI tools for the encoding and decoding high-content phenotypes with multivariate statistical analysis of latent spaces is applicable to a wide array of high-content phenotypic modalities and use cases. For example, the approach implemented in HistoGWAS can facilitate the study of genetic effects on morphological changes visible through non-invasive medical imaging [8][9][10][11][12][13][14] , and enable the assessment of the impacts of genetic and chemical perturbations in high-content screens of in vitro systems 62 . This is particularly relevant in advanced systems like organoids, enabling a detailed description and visualization of complex phenotypic changes resulting from induced perturbations.…”

Section: Discussionmentioning

confidence: 99%

HistoGWAS: An AI-enabled Framework for Automated Genetic Analysis of Tissue Phenotypes in Histology Cohorts

Chaudhary,

Voigts,

Bereket

et al. 2024

Preprint

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Understanding how genetic variation affects tissue structure and function is crucial for deciphering disease mechanisms, yet comprehensive methods for genetic analysis of tissue histology are currently lacking. We address this gap with HistoGWAS, a framework that merges AI-driven tissue characterization with fast variance component models for scalable genetic association testing. This integration enables automated, genome-wide assessments of variant effects on tissue histology and facilitates the visualization of phenotypes linked to significant genetic loci. Applying HistoGWAS to eleven tissue types from the GTEx cohort, we identified four genome-wide significant loci, which we linked to distinct tissue histological and gene expression changes. Ultimately, a power analysis confirms HistoGWAS’s effectiveness in large-scale histology cohorts, underscoring its transformative potential in studying the effects of genetic variations on tissue and their role in health and disease.

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Section: Discussionmentioning

confidence: 99%

HistoGWAS: An AI-enabled Framework for Automated Genetic Analysis of Tissue Phenotypes in Histology Cohorts

Chaudhary,

Voigts,

Bereket

et al. 2024

Preprint

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“…The OOC model is a microfluidic system that can reproduce a 3D microenvironment with constant fluid flow and shear stress by depicting precise flow dynamics. − , The OOC model can be an elegant system to predict the preclinical target efficacy, toxicity, and metabolic conversion rate. Thus, different OOC models can be used to study reliable assessments of the efficacy of plant-based extracts and compounds vs animal-based in vivo studies .…”

Section: Types Of Ooc Models For Nutraceutical Testingmentioning

confidence: 99%

“…For instance, biomarkers associated with organ-specific toxicity or disease conditions can be measured in real time using biosensors integrated into the OOC system. This allows for the early detection of adverse effects or disease-related changes induced by nutraceutical compounds aiding in developing effective interventions. − …”

Section: Types Of Ooc Models For Nutraceutical Testingmentioning

confidence: 99%

Organ-on-Chip: Advancing Nutraceutical Testing for Improved Health Outcomes

Farooqi,

Kang,

Choi

2023

ACS Omega

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The recent global wave of organic food consumption and the vitality of nutraceuticals for human health benefits has driven the need for applying scientific methods for phytochemical testing. Advanced in vitro models with greater physiological relevance than conventional in vitro models are required to evaluate the potential benefits and toxicity of nutraceuticals. Organ-onchip (OOC) models have emerged as a promising alternative to traditional in vitro models and animal testing due to their ability to mimic organ pathophysiology. Numerous studies have demonstrated the effectiveness of OOC models in identifying pharmaceutically relevant compounds and accurately assessing compound-induced toxicity. This review examines the utility of traditional in vitro nutraceutical testing models and discusses the potential of OOC technology as a preclinical testing tool to examine the biomedical potential of nutraceuticals by reducing the need for animal testing. Exploring the capabilities of OOC models in carrying out plant-based bioactive compounds can significantly contribute to the authentication of nutraceuticals and drug discovery and validate phytochemicals medicinal characteristics. Overall, OOC models can facilitate a more systematic and efficient assessment of nutraceutical compounds while overcoming the limitations of current traditional in vitro models.

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