Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses

Wu, You; Li, Qiao; Xie, Lei

doi:10.1016/j.crmeth.2023.100452

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…High-throughput proteomic approaches 194 have sharpened our understanding of cardiac gene function, as similar patterns of gene expression between tissues do not always predict similar function. 195 Indeed, differences in wiring between RNA and protein coexpression networks have revealed that functionally coherent RNA modules among tissues are more tightly linked by protein coexpression, 196 , 197 suggesting that multiomic approaches may ultimately provide a more accurate view of functional relationships in cells of the heart.…”

Section: Future Perspective and Discussionmentioning

confidence: 99%

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

Palmer,

Rosenthal,

Teichmann

et al. 2024

Circulation Research

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Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.

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Section: Future Perspective and Discussionmentioning

confidence: 99%

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

Palmer,

Rosenthal,

Teichmann

et al. 2024

Circulation Research

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An Ensemble Cascade Forest‐Based Framework for Multi‐Omics Drug Response and Synergy Prediction

Li,

Sui,

Leng

et al. 2024

Advanced Intelligent Systems

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The obscure drug response continues to be a limiting factor for accurate cures for cancer. Next generation sequencing technologies have propelled the pharmacogenomic studies with characterized large panels of cancer cell line at multi‐omics level. However, the sufficient integration of the multi‐omics data and the efficient prediction for drug response and synergy still remain a challenge. To address these problems, ECFD is designed, an ensemble cascade forest‐based framework that predicts drug response and synergy using five types of omics data. Experimental results show the significant advantages of the ECFD model over existing models. The best integration of feature extraction is determined and the superiorities of robust stability in the face of new and small samples are highlighted. In addition, the methodological framework highlights the explainability of the model, the mechanisms of drug resistance and drug combination treatment strategies based on explainable analyses and biological networks. In sum, ECFD may facilitate the evaluation of drug response and speculation of potential synergy therapies in personalized and precision treatment.

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Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis

Jochum,

Miccoli,

Sommersdorf

et al. 2024

Arch Toxicol

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Traditional risk assessment methodologies in toxicology have relied upon animal testing, despite concerns regarding interspecies consistency, reproducibility, costs, and ethics. New Approach Methodologies (NAMs), including cell culture and multi-level omics analyses, hold promise by providing mechanistic information rather than assessing organ pathology. However, NAMs face limitations, like lacking a whole organism and restricted toxicokinetic interactions. This is an inherent challenge when it comes to the use of omics data from in vitro studies for the prediction of organ toxicity in vivo. One solution in this context are comparative in vitro–in vivo studies as they allow for a more detailed assessment of the transferability of the respective NAM data. Hence, hepatotoxic and nephrotoxic pesticide active substances were tested in human cell lines and the results subsequently related to the biology underlying established effects in vivo. To this end, substances were tested in HepaRG and RPTEC/tERT1 cells at non-cytotoxic concentrations and analyzed for effects on the transcriptome and parts of the proteome using quantitative real-time PCR arrays and multiplexed microsphere-based sandwich immunoassays, respectively. Transcriptomics data were analyzed using three bioinformatics tools. Where possible, in vitro endpoints were connected to in vivo observations. Targeted protein analysis revealed various affected pathways, with generally fewer effects present in RPTEC/tERT1. The strongest transcriptional impact was observed for Chlorotoluron in HepaRG cells (increased CYP1A1 and CYP1A2 expression). A comprehensive comparison of early cellular responses with data from in vivo studies revealed that transcriptomics outperformed targeted protein analysis, correctly predicting up to 50% of in vivo effects.

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Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses

Cited by 5 publications

References 45 publications

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

An Ensemble Cascade Forest‐Based Framework for Multi‐Omics Drug Response and Synergy Prediction

Comparative case study on NAMs: towards enhancing specific target organ toxicity analysis

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