An official website of the United States government
The ubiquitous pollution of the environment with microplastics, a diverse suite of contaminants, is of growing concern for science and currently receives considerable public, political, and academic attention. The potential impact of microplastics in the environment has prompted a great deal of research in recent years. Many diverse methods have been developed to answer different questions about microplastic pollution, from sources, transport, and fate in the environment, and about effects on humans and wildlife. These methods are often insufficiently described, making studies neither comparable nor reproducible. The proliferation of new microplastic investigations and cross-study syntheses to answer larger scale questions are hampered. This diverse group of 23 researchers think these issues can begin to be overcome through the adoption of a set of reporting guidelines. This collaboration was created using an open science framework that we detail for future use. Here, we suggest harmonized reporting guidelines for microplastic studies in environmental and laboratory settings through all steps of a typical study, including best practices for reporting materials, quality assurance/quality control, data, field sampling, sample preparation, microplastic identification, microplastic categorization, microplastic quantification, and considerations for toxicology studies. We developed three easy to use documents, a detailed document, a checklist, and a mind map, that can be used to reference the reporting guidelines quickly. We intend that these reporting guidelines support the annotation, dissemination, interpretation, reviewing, and synthesis of microplastic research. Through open access licensing (CC BY 4.0), these documents aim to increase the validity, reproducibility, and comparability of studies in this field for the benefit of the global community.
more »
« less
Experimental reporting of fish transcriptomic responses in environmental toxicology and ecotoxicology
Abstract Due to its increasing affordability and efforts to understand transcriptional responses of organisms to biotic and abiotic stimuli, transcriptomics has become an important tool with significant impact on toxicological investigations and hazard and risk assessments, especially during development and application of new approach methodologies (NAMs). Data generated using transcriptomic methodologies have directly informed adverse outcome pathway frameworks, chemical and biological read across, and aided in the identification of points of departure. Using data reporting frameworks for transcriptomics data offers improved transparency and reproducibility of research and an opportunity to identify barriers to adoption of these NAMs, especially in environmental toxicology and ecotoxicology with aquatic models. Improved reporting also allows for reexamination of existing data, limiting needs for experiment replication and further reducing animal experimentation. Here, we use a standardized form of data reporting for omics-based studies, the Organisation for Economic Co-operation and Development omics reporting framework, which specifically reports on a list of parameters that should be included in transcriptomics studies used in a regulatory context. We focused specifically on fish studies using RNA- Sequencing (Seq)/microarray technologies within a toxicology context. Inconsistencies in reporting and methodologies among the experimental designs (toxicology vs. molecular characterization) were observed in addition to foundational differences in reporting of sample concentration or preparation or quality assessments, which can affect reproducibility and read across, confidence in results, and contribute substantially to understanding molecular mechanisms of toxicants and toxins. Our findings present an opportunity for improved research reporting. We also provide several recommendations as logical steps to reduce barriers to adoption of transcriptomics within environmental toxicology and ecotoxicology.
more »
« less
- Award ID(s):
- 2042035
- PAR ID:
- 10631610
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Environmental Toxicology and Chemistry
- Volume:
- 44
- Issue:
- 9
- ISSN:
- 0730-7268
- Format(s):
- Medium: X Size: p. 2580-2598
- Size(s):
- p. 2580-2598
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.more » « less
-
Synopsis Understanding wildlife immune responses is crucial for assessing disease risks, environmental stress effects, and conservation challenges. Traditional ecoimmunology approaches rely on targeted assays, which, while informative, often provide a fragmented and species-limited view of immune function. Proteomics offers a powerful alternative by enabling the high-throughput, system-wide quantification of immune-related proteins, providing a functional perspective on immunity that overcomes many limitations of conventional methods. However, proteomics remains underutilized in ecoimmunology despite its potential to enhance biomarker discovery, host–pathogen interaction studies, and environmental health assessments. This perspective highlights proteomics as a transformative tool for ecoimmunology, disease ecology, and conservation biology. We discuss its unique advantages over other -omics approaches, including its ability to capture realized immune function rather than inferred gene expression, its applicability to diverse wildlife taxa, and its potential for longitudinal immune monitoring of individuals using minimally invasive sampling. We also address key challenges, including limited genomic reference resources, sample constraints, reproducibility issues, and the need for standardized protocols. To overcome these barriers, we propose practical solutions, such as leveraging proteomes of closely related species for annotation and using their annotated genomes as search spaces for peptide mapping. Additionally, we highlight the importance of alternative quality control strategies and improved data-sharing practices to enhance the utility of proteomics in wildlife research. To fully integrate proteomics into ecoimmunology, we recommend expanding public reference databases for non-model species, refining field-adapted workflows, and fostering interdisciplinary collaboration between ecologists, immunologists, and bioinformaticians. By embracing these advancements, the field can leverage proteomics to bridge the gap between molecular mechanisms and ecological processes, ultimately improving our ability to monitor wildlife health, predict disease risks, and inform conservation strategies in the face of environmental change.more » « less
-
Blockchain has emerged as a solution for ensuring accurate and truthful environmental variable monitoring needed for the management of pollutants and natural resources. The immutability property of blockchain helps protect the measured data on pollution and natural resources to enable truthful reporting and effective management and control of polluting agents. However, specifics on what to measure, how to use blockchain, and highlighting which blockchain frameworks have been adopted need to be explored to fill the research gaps. Therefore, we review existing works on the use of blockchain for monitoring and managing environmental variables in this paper. Specifically, we examine existing blockchain applications on greenhouse gas emissions, solid and plastic waste, food waste, food security, water usage, and the circular economy and identify what motivates the adoption of blockchain, features sought, used blockchain frameworks and consensus algorithms, and the adopted supporting technologies to complement data sensing and reporting. We conclude the review by identifying practical works that provide implementation details for rapid adoption and remaining challenges that merit future research.more » « less
-
Elucidation of complex molecular networks requires integrative analysis of molecular features and changes at different levels of information flow and regulation. Accordingly, high throughput functional genomics tools such as transcriptomics, proteomics, metabolomics, and lipidomics have emerged to provide system-wide investigations. Unfortunately, analysis of different types of biomolecules requires specific sample extraction procedures in combination with specific analytical instrumentation. The most efficient extraction protocols often only cover a restricted type of biomolecules due to their different physicochemical properties. Therefore, several sets/aliquots of samples are needed for extracting different molecules. Here we adapted a biphasic fractionation method to extract proteins, metabolites, and lipids from the same sample (3-in-1) for liquid chromatography-tandem mass spectrometry (LC-MS/MS) multi-omics. To demonstrate utility of the improved method, we used bacteria-primed Arabidopsis leaves to generate multi-omics datasets from the same sample. In total, we were able to analyze 1849 proteins, 1967 metabolites, and 424 lipid species in single samples. The molecules cover a wide range of biological and molecular processes, and allow quantitative analyses of different molecules and pathways. Our results have shown the clear advantages of the multi-omics method, including sample conservation, high reproducibility, and tight correlation between different types of biomolecules.more » « less
