Dataset Information

Publicly available ex vivo transcriptomics datasets to explore CNS physiology and neurodegeneration: state of the art and perspectives.

ABSTRACT: The central nervous system (CNS) is characterized by an intricate composition of diverse cell types, including neurons and glia cells (astrocytes, oligodendrocytes, and microglia), whose functions may differ along time, between sexes and upon pathology. The advancements in high-throughput transcriptomics are providing fundamental insights on cell phenotypes, so that molecular codes and instructions are ever more described for CNS physiology and neurodegeneration. To facilitate the search of relevant information, this review provides an overview of key CNS transcriptomics studies ranging from CNS development to ageing and from physiology to pathology as defined for five neurodegenerative disorders and their relative animal models, with a focus on molecular descriptions whose raw data were publicly available. Accurate phenotypic descriptions of cellular states correlate with functional changes and this knowledge may support research devoted to the development of therapeutic strategies supporting CNS repair and function.

SUBMITTER: Abdullatef S

PROVIDER: S-EPMC10481165 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Publicly available <i>ex vivo</i> transcriptomics datasets to explore CNS physiology and neurodegeneration: state of the art and perspectives.

Abdullatef Sandra S Farina Cinthia C

Frontiers in neuroscience 20230823

The central nervous system (CNS) is characterized by an intricate composition of diverse cell types, including neurons and glia cells (astrocytes, oligodendrocytes, and microglia), whose functions may differ along time, between sexes and upon pathology. The advancements in high-throughput transcriptomics are providing fundamental insights on cell phenotypes, so that molecular codes and instructions are ever more described for CNS physiology and neurodegeneration. To facilitate the search of rele ...[more]

PMID: 37680966

Similar Datasets

Project description:The development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly available dental imaging datasets to address this gap and support AI development. This observational study searched all publicly available dataset resources (academic databases, preprints, and AI challenges), focusing on datasets/articles from 2020 to 2023, with PubMed searches extending back to 2011. We comprehensively searched for dental AI datasets containing images (intraoral photos, scans, radiographs, etc.) using relevant keywords. We included datasets of >50 images obtained from publicly available sources. We extracted dataset characteristics, patient demographics, country of origin, dataset size, ethical clearance, image details, FAIRness metrics, and metadata completeness. We screened 131,028 records and extracted 16 unique dental imaging datasets. The datasets were obtained from Kaggle (18.8%), GitHub, Google, Mendeley, PubMed, Zenodo (each 12.5%), Grand-Challenge, OSF, and arXiv (each 6.25%). The primary focus was tooth segmentation (62.5%) and labeling (56.2%). Panoramic radiography was the most common imaging modality (58.8%). Of the 13 countries, China contributed the most images (2,413). Of the datasets, 75% contained annotations, whereas the methods used to establish labels were often unclear and inconsistent. Only 31.2% of the datasets reported ethical approval, and 56.25% did not specify a license. Most data were obtained from dental clinics (50%). Intraoral radiographs had the highest findability score in the FAIR assessment, whereas cone-beam computed tomography datasets scored the lowest in all categories. These findings revealed a scarcity of publicly available imaging dental data and inconsistent metadata reporting. To promote the development of robust, equitable, and generalizable AI tools for dental diagnostics, treatment, and research, efforts are needed to address data scarcity, increase diversity, mandate metadata completeness, and ensure FAIRness in AI dental imaging research.

Project description:Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.

Project description:Scientific and technological advances within the life sciences have enabled the generation of very large datasets that must be processed, stored, and managed computationally. Researchers increasingly require data science skills to work with these datasets at scale in order to convert information into actionable insights, and undergraduate educators have started to adapt pedagogies to fulfill this need. Course-based undergraduate research experiences (CUREs) have emerged as a leading model for providing large numbers of students with authentic research experiences including data science. Originally designed around wet-lab research experiences, CURE models have proliferated and diversified globally to accommodate a broad range of academic disciplines. Within microbiology, diversity metrics derived from microbiome sequence information have become standard data products in research. In some cases, researchers have deposited data in publicly accessible repositories, providing opportunities for reproducibility and comparative analysis. In 2020, with the onset of the COVID-19 pandemic and concomitant shift to remote learning, the University of British Columbia set out to develop an online data science CURE in microbiology. A team of faculty with collective domain expertise in microbiome research and CUREs developed and implemented a data science CURE in which teams of students learn to work with large publicly available datasets, develop and execute a novel scientific research project, and disseminate their findings in the online Undergraduate Journal of Experimental Microbiology and Immunology. Analysis of the resulting student-authored research articles, including comments from peer reviews conducted by subject matter experts, demonstrate high levels of learning effectiveness. Here, we describe core insights from course development and implementation based on a reverse course design model. Our approach to course design may be applicable to the development of other data science CUREs.

Project description:Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data.

Project description:The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Dataset Information

Publicly available ex vivo transcriptomics datasets to explore CNS physiology and neurodegeneration: state of the art and perspectives.

Publications

Publicly available <i>ex vivo</i> transcriptomics datasets to explore CNS physiology and neurodegeneration: state of the art and perspectives.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets