Biology subjects

Athey, B. D.

Publications and source records attributed to Athey, B. D..

6 recordsLinked to original sources

bioRxiv2025

SEA CDM: Study-Experiment-Assay Common Data Model and Databases for Cross-Domain Data Integration and Analysis

With the increasing volume of biomedical experimental data, standardizing, sharing, and integrating heterogeneous experimental data across domains has become a major challenge. To address this challenge, we have developed an ontology-supported Study-Experiment-Assay (SEA) common data model (CDM), which includes 10 core and 3 auxiliary classes based on object-oriented modeling. SEA CDM uses interoperable ontologies for data standardization and knowledge inference. Building on the SEA CDM, we developed the Ontology-based SEA Network (OSEAN) relational database and knowledge graph, along with a set of ETL (Extract, Transform, Load) and query tools, and further applied them to represent 1,278 immune studies with over two million samples from three resources: VIGET, ImmPort, and CELLxGENE. Using simple, robust queries and analyses, our research identified multiple scientific insights into sex-specific immune responses, such as neutrophil degranulation and TNF binding to physiological receptors, following live attenuated and trivalent inactivated influenza vaccination. The novel SEA CDM system lays a foundation for establishing an integrative biodata ecosystem across biological and biomedical domains.

bioinformatics↗

bioRxiv2020

Valproic Acid-Induced Changes of 4D Nuclear Morphology in Astrocyte Cells

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin re-organization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin re-organization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

bioinformatics↗

bioRxiv2020

Ketamine's pharmacogenomic network in human brain contains sub-networks associated with glutamate neurotransmission and with neuroplasticity

The pharmacogenomic network responsible for the rapid antidepressant action of ketamine and concomitant adverse events in patients has been poorly defined. Integrative, multi-scale biological data analytics helps explain ketamines action. Using a validated computational pipeline, candidate ketamine-response genes and regulatory RNAs from published literature, binding affinity studies, and single nucleotide polymorphisms (SNPs) from genomewide association studies (GWAS), we identified 108 SNPs associated with 110 genes and regulatory RNAs. All of these SNPs are classified as enhancers, and additional chromatin interaction mapping in human neural cell lines and tissue shows enhancer-promoter interactions involving other network members. Pathway analysis and gene set optimization identified three composite sub-networks within the broader ketamine pharmacogenomic network. Expression patterns of ketamine network genes within the postmortem human brain are concordant with ketamine neurocircuitry based on the results of 24 published functional neuroimaging studies. The ketamine pharmacogenomic network is enriched in forebrain regions known to be rapidly activated by ketamine, including cingulate cortex and frontal cortex, and is significantly regulated by ketamine (p=6.26E-33; Fishers exact test). The ketamine pharmacogenomic network can be partitioned into distinct enhancer sub-networks associated with: (1) glutamate neurotransmission, chromatin remodeling, smoking behavior, schizophrenia, pain, nausea, vomiting, and post-operative delirium; (2) neuroplasticity, depression, and alcohol consumption; and (3) pharmacokinetics. The component sub-networks explain the diverse action mechanisms of ketamine and its analogs. These results may be useful for optimizing pharmacotherapy in patients diagnosed with depression, pain or related stress disorders. One Sentence SummaryThe ketamine network in the human brain consists of sub-networks associated with glutamate neurotransmission, neuroplasticity, and pharmacokinetics.

pharmacology and toxicology↗

bioRxiv2018

Evaluation of Methods for Cell Nuclear Structure Analysis from Microscopy Data

Changes in cell nuclear architecture are regulated by complex biological mechanisms that associated with the altered functional properties of a cell. Quantitative analyses of structural alterations of nuclei and their compartments are important for understanding such mechanisms. In this work we present a comparison of approaches for nuclear structure classification, evaluated on 2D per-channel representations from a 3D microscopy imaging dataset by maximum intensity projection. Specifically, we compare direct classification of pixel data from either raw intensity images or binary masks that contain only information about morphology of the object, but not intensity. We evaluate a number of widely used classification algorithms using 2 different cross-validation schemes to assess batch effects. We compare obtained results with the previously reported baselines and discuss novel findings.

bioinformatics↗

bioRxiv2018

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

bioinformatics↗

bioRxiv2017

3D cell nuclear morphology: microscopy imaging dataset and voxel-based morphometry classification results

Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AUROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project web-page: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.

bioinformatics↗