Biology subjects

Agar, N.

Publications and source records attributed to Agar, N..

5 recordsLinked to original sources

bioRxiv2026

Systemic control and inter-organ communication of ovarian aging

Whether ovarian aging is intrinsic and unidirectional or dynamically regulated by systemic context remains an open question for fertility and women's healthspan. To address it, we developed a mouse ovarian transcriptomic aging clock (OvaAge) and applied it, together with published transcriptomic, epigenetic and pathway-resolved DNA methylation (PathAge) clocks, multi-omic analyses and reproductive outcomes, to new reciprocal heterochronic ovary transplantation and parabiosis experiments. Ovarian molecular age shifted in both directions: aged ovaries became younger in young hosts and young ovaries became older in aged hosts, and in each case the shift was partial across transcriptomic, epigenetic and pathway-resolved measures. Ovaries from 13-month-old C57BL/6J donors supported full-term pregnancies and live births at 17-18 months of age when transplanted into young hosts, but not when transplanted into old hosts, and young ovaries supported live births in 17-18-month-old recipients. Donor ovary age altered the transcriptomic age of the uterus in both directions, while young ovaries also lowered the transcriptomic age of aged liver and brain, identifying the ovary as an active node within an inter-organ aging network. Across unilateral and bilateral transplantation and parabiosis, an older environment increased inflammatory gene expression in the ovary, whereas a younger environment reduced it and increased mitochondrial and biosynthetic programs. Integration of methylation, expression and cell-type markers linked these changes to declining oocyte-associated programs and an expanding stromal compartment enriched for multinucleated giant cells (MNGCs). MNGC-rich regions formed a reversibly remodeled, focally aged niche with distinct spatial metabolic features, and a related macrophage expression signature was detected in human ovaries. Together, these findings identify ovarian aging as a modular, bidirectionally plastic process that is regulated by the systemic environment and point to tractable intervention points.

systems biology↗

bioRxiv2025

Predicting High-Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging (MSI) have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time- of-flight matrix-assisted laser desorption/ionization (MALDI-ToF), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging techniques. By leveraging FT-ICRs unparalleled spectral resolution and ToF-SIMSs exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

neuroscience↗

bioRxiv2024

Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

The spatial-omic analysis of biomolecules such as nucleic acids, lipids, metabolites, and proteins is advancing the study of biological systems and processes in a physio-pathological context. Here, we describe an innovative matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) method to detect metals within biological tissues using instrumentation that is widely available in research and clinical laboratories. We characterize the spatial distribution of metals in diverse settings including mouse embryogenesis, genetic disorders leading to abnormal metal accumulation, and preclinical testing for improved platinum-based chemotherapy delivery through focused ultrasound across the blood-brain barrier. Spatial metal profiling will advance research studies and the clinical analysis of metal-related diseases, enabling more precise use of metal-based therapies and advances in diverse scientific fields beyond biomedicine. One-Sentence SummarySpatial metallomic profiling maps native metals or those coordinated to xenobiotics, antibodies, and biomolecules in tissues.

cancer biology↗

bioRxiv2022

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the H&E-stained biopsy data with MALDI mass-spectrometric imaging of the corresponding tissue to determine the cancerous regions and their unique chemical signatures, and variation of the predicted regions with original pathological annotations. We spatially register features obtained through deep learning from high-resolution optical micrographs of whole slide H&E stained data with MSI data to correlate the chemical signature with the tissue anatomy of the data, and then use the learned correlation to predict prostate cancer from observed H&E images using trained co-registered MSI data. We found that this system is more robust than predicting from a single imaging modality and can predict cancerous regions with[~] 80% accuracy. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This will improve on generating patient treatment trajectories by more accurately predicting prostate cancer directly from H&E-stained biopsy images.

cancer biology↗

bioRxiv2021

A druggable addiction to de novo pyrimidine biosynthesis in diffuse midline glioma

Diffuse midline glioma (DMG) is a uniformly fatal pediatric cancer driven by oncohistones that do not readily lend themselves to drug development. To identify druggable targets for DMG, we conducted a genome-wide CRISPR screen that reveals a DMG selective dependency on the de novo pathway for pyrimidine biosynthesis. This metabolic vulnerability reflects an elevated rate of uridine/uracil degradation that depletes DMG cells of substrates for the alternate salvage pathway for pyrimidine biosynthesis. A clinical stage inhibitor of DHODH (rate limiting enzyme in the de novo pathway) diminishes UMP pools, generates DNA damage, and induces apoptosis through suppression of replication forks--an "on target" effect, as shown by uridine rescue. MALDI mass spectroscopy imaging demonstrates that this DHODH inhibitor (BAY2402234) accumulates in brain at therapeutically relevant concentrations, suppresses de novo pyrimidine biosynthesis in vivo, and prolongs survival of mice bearing intracranial DMG xenografts, highlighting BAY2402234 as a promising therapy against DMGs.

cancer biology↗