Biology subjects

Healey, D.

Publications and source records attributed to Healey, D..

5 recordsLinked to original sources

bioRxiv2026

MRI-Trackable Mesenchymal Stem Cells for Oncolytic Therapy: A Clinically Feasible Dual-Labeling Protocol

Non-invasive imaging of oncolytic virus-loaded mesenchymal stem cells (MSCs) could help evaluate cell trafficking and delivery in cancer therapy. Labeling MSCs with iron oxide particles (IOPs) enables in vivo tracking using magnetic resonance imaging (MRI). This study evaluated a dual-labeling strategy that loads MSCs with MegaPro IOPs and the oncolytic virus Delta-24-RGD (DNX-2401; tasadenoturev) and assessed cellular function and MRI detectability. MSCs were labeled with IOPs alone or in combination with the oncolytic virus. We compared cell viability and intracellular iron content among IOP-labeled, dual-labeled, and naive MSCs, and evaluated differentiation, invasion, and immunophenotype in naive and IOP-labeled MSCs. We evaluated phantoms containing varying numbers of IOP-labeled MSCs (9 x 103-1.5 x 105 cells) using R2-, R2*-, and R1-weighted MRI, and generated corresponding relaxation rate maps to assess concentration dependent contrast and detection sensitivity. Iron content was comparable between IOP-labeled and dual-labeled MSCs (9.2 {+/-} 2.6 and 11.6 {+/-} 1.4 pg/cell, respectively), with >91% viability across all groups. IOP-labeled MSCs retained invasion and differentiation potential. At the highest labeled-cell concentration, R2*, R2, and R1 increased approximately 4, 2.5, and 1.5 fold, respectively, relative to the agarose control. The estimated detection limits were approximately 2 x 103 cells for R2* and 6 x 103 cells for R2 and R1. These findings demonstrate that dual labeling of MSCs with IOPs and oncolytic virus preserves key cellular characteristics while providing MRI-detectable contrast, supporting further investigation of this approach for image-guided tracking of cell-based cancer therapies.

bioengineering↗

bioRxiv2025

Defining the limits of plant chemical space: challenges and estimations

The plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species have been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches--a formula prediction model, a de novo prediction model, a combination of library search and de novo prediction, and MS2 clustering--to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, conservatively estimating that the total plant chemical space likely spans millions, if not more, with the vast majority still unexplored.

bioinformatics↗

bioRxiv2023

On the correspondence between the transcriptomic response of a compound and its effects on its targets

Better understanding the transcriptomic response produced by a compound perturbing its targets can shed light on the underlying biological processes regulated by the compound. However, establishing the relationship between the induced transcriptomic response and the target of a compound is non-trivial, partly because targets are rarely differentially expressed. Thus, connecting both modalities requires orthogonal information (e.g., pathway or functional information). Here, we present a comprehensive study aimed at exploring this relationship by leveraging thousands of transcriptomic experiments and target data for over 2,000 compounds. Firstly, we confirmed that compound-target information does not correlate as expected with the transcriptomic signatures induced by a compound. However, we demonstrate how the concordance between both modalities can be increased by connecting pathway and target information. Additionally, we investigated whether compounds that target the same proteins induce a similar transcriptomic response and conversely, whether compounds with similar transcriptomic responses share the same target proteins. While our findings suggest that this is generally not the case, we did observe that compounds with similar transcriptomic profiles are more likely to share at least one protein target, as well as common therapeutic applications. Lastly, we present a case scenario on a few compound pairs with high similarity to demonstrate how the relationship between both modalities can be exploited for mechanism of action deconvolution.

bioinformatics↗

bioRxiv2022

MS2Prop: A machine learning model that directly predicts chemical properties from mass spectrometry data for novel compounds

Mass spectrometry (MS) is a fundamental analytical tool for the study of complex molecular mixtures and in natural products drug discovery and metabolomics specifically, due to its high sensitivity, specificity, and throughput. A major challenge, however, is the lack of structurally annotated mass spectra for these applications. This deficiency is particularly acute for analyses conducted on extracts or fractions that are largely chemically undefined. This work describes the use of mass spectral data in a fundamentally different manner than structure determination; to predict properties or activities of structurally unknown compounds without the need for defined or deduced chemical structure using a machine learning (ML) model, MS2Prop. The models predictive accuracy and scalability is benchmarked against commonly used methods and its performance demonstrated in a natural products drug discovery setting. A new cheminformatic subdiscipline, quantitative spectra-activity relationships (QSpAR), using spectra rather than chemical structure as input, is proposed to describe this approach and to distinguish it from structure based quantitative methods.

bioinformatics↗

bioRxiv2021

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two applications showing how one can deconvolute the predictions made by RPath as well as predict novel targets. Finally, we have made the source code and data publicly available at https://github.com/enveda/RPath.

bioinformatics↗