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Papers
61,005 resultsShowing papers similar to Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data
ClearSGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data
Researchers developed SGCRNA, a new computational tool for analyzing gene co-expression networks that addresses limitations of the widely used WGCNA method. The tool removes the assumption of scale-free topology, eliminates manual parameter tuning, and accounts for regression line slopes. While not specific to microplastics research, this bioinformatics tool can be applied to analyze multi-omic datasets from studies examining biological responses to environmental contaminants.
Innovative Multi-omic Strategies to Explore Micro- and Nanoplastic Effects
This conference abstract proposes a multi-omic integration framework — combining transcriptomics, proteomics, metabolomics, and lipidomics — as a more comprehensive approach to characterizing biological responses to micro- and nanoplastic exposures than single-level analyses.
DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species
This paper describes a new database for analyzing single-cell gene expression data across different animal species including fruit flies. This genomics database tool is unrelated to microplastic research.
Micro- and nanoplastic (MNPs) exposure at single-cell resolution impaired placental function and cellular dynamics
Researchers performed single-cell transcriptomic analysis of placentas from pregnant women exposed to micro- and nanoplastics, finding that MNP exposure altered trophoblast, macrophage, and fibroblast subpopulations, suggesting impaired placental function through disruption of cell communication and immune regulation.
Multidimensional analysis methods for flow cytometry : Pushing the boundaries
This thesis developed new methods for analyzing multidimensional flow cytometry data to better identify cell populations. While a bioinformatics and immunology paper, flow cytometry is also used in cutting-edge research to detect and quantify micro- and nanoplastics in biological fluids.
Learning Single‐Cell Distances from Cytometry Data
This computational paper developed machine learning methods to measure distances between individual cells in flow cytometry data. While a bioinformatics paper, the methodology is potentially applicable to automated classification of microplastic particles in environmental samples.
A computational framework for multi-scale data fusion in assessing the associations between micro- and nanoplastics and human hepatotoxicity
Researchers developed a computational toxicology framework integrating multi-source data and network analysis to map associations between micro- and nanoplastics and hepatotoxicity, identifying key molecular pathways through which MNPs may damage the liver, offering a scalable alternative to traditional in vivo testing.
Data mining of molecular data resulting from environmental exposure to xenobiotics
Researchers characterized the multi-layer gene expression response of human airway and liver cells exposed to polystyrene microplastics across multiple doses and time points. They found thousands of differentially expressed genes along with extensive reprogramming of gene isoforms, affecting protein coding capacity and RNA stability. The study demonstrates that microplastic exposure triggers a structured, dose- and time-dependent remodeling of cellular gene expression programs in human tissue models.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
4 Single cell RNA-seq samples exposed to nano plastic particles
Researchers used microfluidic chip-based single-cell RNA sequencing to profile the transcriptional responses of human peripheral blood immune cells exposed to carboxylated polystyrene nanoparticles of three sizes (40 nm, 200 nm, or a mixture), providing a cell-type-resolved view of nanoplastic effects on the immune system.
Single-cell transcriptomic analysis reveals heterogeneity of the patterns of responsive genes and cell communications in liver cell populations of zebrafish exposed to polystyrene nanoplastics
Researchers used single-cell gene analysis to examine how polystyrene nanoplastics affect different cell types in zebrafish livers. They discovered that various liver cell populations responded to nanoplastic exposure in distinctly different ways, with some cell types showing more disruption to fat metabolism and stress response genes than others. The study reveals that nanoplastic toxicity in the liver is not uniform and that certain cell populations may be more vulnerable than previously understood.
Exploring immune responses of microplastics exposure using high-dimensional spectral flow cytometry
Researchers used high-dimensional spectral flow cytometry to profile immune cell responses in animals exposed to micro- and nanoplastics, detecting changes across multiple immune cell populations simultaneously. The approach revealed complex immune alterations that conventional methods would not capture.
Metabolomics‑driven, data‑augmented machine learning for predicting toxicity of microplastic mixtures
Scientists developed a computer model that can predict how harmful mixtures of microplastics (tiny plastic particles) might be to our cells without testing each combination individually. The model works by analyzing how these plastic particles change the way cells produce energy, which helps explain why microplastics can be toxic. This breakthrough could help researchers quickly assess health risks from the complex mix of microplastics we're exposed to in real life through food, water, and air.
Exploring immune responses of microplastics exposure using high-dimensional spectral flow cytometry
Researchers used high-dimensional spectral flow cytometry to characterize immune responses to microplastic and nanoplastic exposure in human cells, examining changes in intracellular signaling following uptake of plastic particles. The multi-parameter approach revealed complex immune alterations in cells exposed to micro- and nanoplastics, highlighting potential immunotoxicity pathways.
Distinguish the toxic differentiations between acute exposure of micro- and nano-plastics on bivalves: An integrated study based on transcriptomic sequencing
Researchers found that nanoplastics are more toxic than microplastics in mussels, causing severe inflammatory responses and greater oxidative stress, with transcriptomic analysis revealing contrasting gene expression patterns between the two particle sizes.
An In Vitro Assay to Quantify Effects of Micro- and Nano-Plastics on Human Gene Transcription
Researchers developed an in vitro assay to quantify how micro- and nano-plastics affect human gene transcription, demonstrating that internalized plastic particles can alter gene expression patterns in human cells, providing a standardized tool for assessing plastic particle toxicity.
Multi-Omics Approach on the Ecotoxicological Assessment of Microplastics
This review examines the application of multi-omics approaches — including genomics, transcriptomics, proteomics, and metabolomics — to the ecotoxicological assessment of microplastics in living organisms. The authors synthesize how these integrated molecular tools are advancing understanding of the mechanistic pathways by which microplastics disrupt biological systems, offering a more comprehensive picture than single-endpoint toxicity studies.
Single-Cell RNA Sequencing Profiling Cellular Heterogeneity and Specific Responses of Fish Gills to Microplastics and Nanoplastics
Using advanced single-cell sequencing, researchers mapped how individual cell types in fish gills respond differently to micro- and nanoplastic exposure. Microplastics mainly affected immune cells called macrophages, while nanoplastics primarily targeted T cells, and a structural cell type called fibroblasts was especially sensitive to microplastics. This detailed cell-level view reveals that plastic particles of different sizes can trigger distinct immune and tissue responses.
Molecular LandscapeRemodeling Unravels the Cross-Linksof Microplastics-Induced Lipidomic Fluctuations,Nutrient Disorders and Energy Disarrangements
Researchers used combined lipidomic and transcriptomic analysis to demonstrate that polypropylene microplastics accumulated in mouse liver and disrupted key metabolic pathways including lipid biosynthesis, cholesterol metabolism, and energy homeostasis.
Single-cell transcriptome analysis of liver immune microenvironment changes induced by microplastics in mice with non-alcoholic fatty liver
Using advanced single-cell analysis, researchers showed that microplastics worsened non-alcoholic fatty liver disease in mice fed a high-fat diet by changing how immune cells behaved in the liver. Microplastic exposure amplified inflammatory responses and altered the communication between different liver cell types. This study is important because it reveals specific immune mechanisms by which microplastics could worsen liver disease, a condition already affecting roughly one in four adults worldwide.
Molecular LandscapeRemodeling Unravels the Cross-Linksof Microplastics-Induced Lipidomic Fluctuations,Nutrient Disorders and Energy Disarrangements
This study examined how polypropylene microplastics accumulate in and damage the mouse liver, using integrated lipidomics and transcriptomics to map the molecular landscape of microplastic-induced lipid disruption and metabolic dysfunction.
Characterization of Microplastics in Human Gastric Cancer and Control Tissues and Analysis of Associated Genetic Features
Researchers detected and characterized microplastics in human gastric cancer tissue and adjacent healthy tissue, finding significantly higher microplastic concentrations in cancer tissue, and used transcriptome sequencing to explore potential molecular mechanisms linking microplastic exposure to gastric cancer development.
Combining machine learning with meta-analysis for predicting cytotoxicity of micro- and nanoplastics
This meta-analysis used machine learning to predict how toxic different types of micro- and nanoplastics are to cells. By analyzing data from many studies, it identified that particle size, concentration, and exposure time are key factors determining toxicity — smaller particles and longer exposures tend to cause more cell damage.
Linking the physical and chemical characteristics of single small microplastics or nanoplastics via photolithographic silicon substrates
Researchers developed photolithographic silicon substrates as a platform to co-locate individual small microplastics and nanoplastics, enabling simultaneous morphological and chemical characterization of the same single particles using multiple analytical instruments.