Unveiling the mechanism of micro-and-nano plastic phytotoxicity on terrestrial plants: A comprehensive review of omics approaches

Environment International, Journal Year: 2025, Volume and Issue: 195, P. 109257 - 109257

Published: Jan. 1, 2025

Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water nutrient uptake, reducing seed germination rates, impeding photosynthesis, resulting oxidative damage within system. The effects complex influenced by various factors size, shape, functional groups, concentration. Recent advancements omics technologies such as proteomics, metabolomics, transcriptomics, microbiomics, coupled with emerging like 4D omics, phenomics, spatial single-cell offer unprecedented insight into physiological, molecular, cellular responses exposure. This literature review synthesizes current findings regarding MNPs-induced phytotoxicity, emphasizing alterations gene expression, protein synthesis, metabolic pathways, physiological disruptions revealed analyses. We summarize how interact structures, disrupt processes, induce stress, ultimately affecting growth productivity. Furthermore, we have identified critical knowledge gaps proposed future research directions, highlighting necessity for integrative studies elucidate pathways toxicity plants. In conclusion, this underscores potential approaches MNPs-phytotoxicity develop strategies mitigating environmental impact on health.

Language: Английский

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Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial–Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma

Cancer Research, Journal Year: 2024, Volume and Issue: 84(9), P. 1517 - 1533

Published: April 8, 2024

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify cell and CAF interactions through the integration of single-cell data from human tumors organoid coculture experiments. Analysis comprehensive atlas PDAC RNA sequencing indicated that density associated increased inflammation epithelial–mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional patient-derived cocultures provided silico validation induction inflammatory EMT states. Further experimental demonstrated integrin beta 1 (ITGB1) vascular endothelial factor A (VEGFA) neuropilin-1 mediating CAF-epithelial cross-talk. Together, this introduces transfer analyses for discoveries cell–cell cross-talk identifies fibroblast-mediated regulation inflammation. Significance: Adaptation relate organoid-CAF facilitates discovery pancreatic cancer intercellular uncovers between CAFs cells VEGFA ITGB1.

Language: Английский

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Tumor microenvironment: recent advances in understanding and its role in modulating cancer therapies

Medical Oncology, Journal Year: 2025, Volume and Issue: 42(4)

Published: March 18, 2025

Language: Английский

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Cellular Dynamics of Tumor Microenvironment Driving Immunotherapy Resistance in Non-Small-Cell Lung Carcinoma

Cancer Letters, Journal Year: 2024, Volume and Issue: unknown, P. 217272 - 217272

Published: Sept. 1, 2024

Language: Английский

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Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(3)

Published: March 8, 2024

Understanding the intricate interactions of cancer cells with tumor microenvironment (TME) is a pre-requisite for optimization immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into TME dynamics and predict efficacy immunotherapy in virtual patient populations/digital twins but require vast amounts multimodal data parameterization. Large-scale datasets characterizing are available due to recent advances bioinformatics multi-omics data. Here, we discuss perspectives leveraging omics-derived estimates inform QSP circumvent challenges model calibration validation immuno-oncology.

Language: Английский

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Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 14, 2024

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for exploration cells their spatial context. A common element analysis delineating tissue domains or "niches" followed detecting differentially expressed genes to infer biological identity cell types. However, many studies approach differential expression using statistical approaches often applied non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting dependency observed ST data. In this study, we show that applying linear mixed models with correlation structures random effects effectively accounts autocorrelation and reduces inflation type-I error rate based testing. We also an exponential structure provide better fit as compared models, particularly spatially resolved technologies quantify at finer scales (i.e., single-cell resolution).

Language: Английский

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Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery

Cancer Research, Journal Year: 2024, Volume and Issue: 84(16), P. 2734 - 2748

Published: June 11, 2024

Due to the lack of treatment options, there remains a need advance new therapeutics in hepatocellular carcinoma (HCC). The traditional approach moves from initial molecular discovery through animal models human trials novel systemic therapies that improve outcomes for patients with cancer. Computational methods simulate tumors mathematically describe cellular and interactions are emerging as promising tools impact therapy entirely silico, potentially greatly accelerating delivery patients. To facilitate design dosing regimens identification potential biomarkers immunotherapy, we developed computational model track tumor progression at organ scale while capturing spatial heterogeneity HCC. This quantitative systems pharmacology was designed effects combination immunotherapy. initiated using literature-derived parameter values fitted specifics Model validation done comparison multiomics data neoadjuvant HCC clinical trial combining anti-PD1 immunotherapy multitargeted tyrosine kinase inhibitor cabozantinib. Validation proteomics imaging mass cytometry demonstrated closer proximity between CD8 T cells macrophages correlated nonresponse. We also compared output Visium transcriptomics profiling samples posttreatment resections another independent study monotherapy. Spatial confirmed simulation results, suggesting importance patterns vasculature TGFβ immune cell interactions. Our findings demonstrate incorporating mathematical modeling computer simulations high-throughput provides patient outcome prediction biomarker discovery. Significance: Incorporating an effective

Language: Английский

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Magnetic Resonance Imaging–Based Assessment of Pancreatic Fat Strongly Correlates With Histology-Based Assessment of Pancreas Composition

Pancreas, Journal Year: 2024, Volume and Issue: 53(2), P. e180 - e186

Published: Jan. 4, 2024

Objective The aim of the study is to assess relationship between magnetic resonance imaging (MRI)-based estimation pancreatic fat and histology-based measurement composition. Materials Methods In this retrospective study, MRI was used noninvasively estimate content in preoperative images from high-risk individuals disease controls having normal pancreata. A deep learning algorithm label 11 tissue components at micron resolution subsequent pancreatectomy histology. linear model determine correlation histologic composition estimation. Results Twenty-seven patients (mean age 64.0 ± 12.0 years [standard deviation], 15 women) were evaluated. measured by ranged 0% 36.9%. Intrapancreatic 0.8% 38.3%. positively correlated with microanatomical (r = 0.90, 0.83 0.95], P < 0.001); as well cancer precursor ( r 0.65, collagen 0.46, 0.001) content, negatively acinar −0.85, content. Conclusions Pancreatic measurable MRI, correlates stromal (fibrosis), presence neoplastic precursors cancer.

Language: Английский

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Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(5)

Published: July 25, 2024

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of data with unparalleled spatial resolution. This type collection, namely, proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms evolved manage the increasing dimensionality proteomics inherent this progress. Numerous imaging-based frameworks, such as pathology, been proposed for research and clinical applications. However, development these fields demands diverse domain expertise, creating barriers their integration further application. review seeks bridge divide by presenting a comprehensive guideline. We consolidate prevailing methods outline roadmap from image processing data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives field moves toward interfacing other quantitative domains, holding significant promise precision care immuno-oncology.

Language: Английский

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CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Abstract Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training algorithms requires advanced programming skills intricate workflows, limiting their accessibility many investigators. Here, we present a protocol software automatic images guided by graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies process microanatomical enabling users train highly customizable models without extensive coding expertise. The outlines best practices creating robust datasets, configuring model parameters, optimizing performance across diverse biomedical image modalities. enhances usability CODA algorithm ( Nature Methods , 2022) streamlining parameter configuration, training, evaluation, automatically generating quantitative results comprehensive reports. We expand beyond original implementation serial histology demonstrating numerous modalities biological questions. provide sample data types including histology, magnetic resonance imaging (MRI), computed tomography (CT). demonstrate use this tool applications metastatic burden vivo deconvolution spot-based transcriptomics datasets. is designed researchers interest rapid design basic understanding anatomy.

Language: Английский

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