Biology subjects

McDonald, T. O.

Publications and source records attributed to McDonald, T. O..

6 recordsLinked to original sources

bioRxiv2026

Multi-region biopsies and patient-derived neurosphere cultures reveal spatial divergence in glioblastoma.

Intratumor heterogeneity (ITH) is one of the main reasons for the lack of effective targeted therapies for glioblastoma (GBM). Imaging-guided surgical navigation allows for tumor-wide sampling to account for variation across distant regions of the tumor, but typical drug screening is performed on cell lines derived from a single biopsy and does not account for GBM heterogeneity. Here we profiled matching MRI-guided multi-region primary tumor biopsies from 6 GBM cases (n=40 biopsies) and corresponding neurosphere cultures (n=30) derived from these spatially distinct tumor samples. We found that in vitro cultures derived from distinct regions of the same tumor display divergent phenotypes, proliferative capacity and ability to accumulate 5-aminolevulinic acid, used to visualize cancer cells during surgery. The differential drug response of the multi-region neurospheres remains linked to the gene expression of the original tumor biopsies. Thus, studies with multiregion-derived neurospheres are essential to faithfully model GBM ITH for therapeutic testing. KEY POINTSO_LIMulti-region biopsy-derived neurospheres represent distinct spatial locations in the GBM tumor. C_LIO_LICultures derived from different regions of the tumor retain phenotypic diversity. C_LIO_LIParental biopsy phenotype predicts drug response better than to in vitro phenotype. C_LI IMPORTANCE OF THE STUDYCell lines developed from spatially distinct regions of glioblastoma capture its intratumor heterogeneity. We show that while the transcriptional output of these cell lines is not connected to their spatial origin, their drug response can be linked to it. Thus, spatial heterogeneity reflected in our neurosphere collection provides a new paradigm for drug screening in these highly heterogenous and difficult to treat tumors.

cancer biology↗

bioRxiv2025

PROFET Predicts Continuous Gene Expression Dynamics from scRNA-seq Data to Elucidate Heterogeneity of Cancer Treatment Responses

Single-cell RNA sequencing profiles cellular heterogeneity but captures only static snapshots, limiting inference of gene expression dynamics. We developed PROFET (Particle-based Reconstruction Of generative Force-matched Expression Trajectories), a framework that reconstructs continuous, nonlinear single-cell trajectories from sparsely sampled scRNA-seq time series. PROFET combines a particle-based gradient-flow algorithm with simulation-free force matching to accurately infer cellular dynamics. Across mouse and human in vitro datasets and an in vivo axolotl regeneration dataset, PROFET achieved 2.6-12.5X lower prediction error than ten state-of-the-art trajectory inference methods. Applying PROFET to newly generated scRNA-seq data from a palbociclib-treated MCF7 cell line and three published breast cancer patient datasets, we reconstructed treatment-response trajectories and identified a resistant cell subpopulation exhibiting large phenotypic shifts and enrichment of the surface markers UNC5B, TLR3, PCDH19, PROCR, SLITRK6, and SEMA6B. PROFET provides a biologically grounded framework for reconstructing cell-state dynamics from static single-cell data across development, regeneration, and therapeutic response.

bioinformatics↗

bioRxiv2025

BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture

Understanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. Traditional dose-response models rely on simplified metrics, limiting their ability to quantify parameters like cell division, death, and transition rates between cell states. To address these limitations, we developed Bayesian Estimation of STochastic processes for Dose-Response (BESTDR), a novel framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR quantifies rates in multi-state systems across multiple cell lines using hierarchical modeling to support high-throughput screening. We validated BESTDR with synthetic and experimental datasets, demonstrating its robustness and accuracy in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic and other effects, BESTDR enhances dose-response studies, facilitating robust drug comparisons and mechanism-specific analyses. BESTDR offers a versatile tool for early-stage preclinical research, paving the way for drug discovery and informed experimental design.

systems biology↗

bioRxiv2023

Reconstruction of single cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and intricate mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport (OT) analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the latter group of genes represents newly identified regulators, shedding light on potential targets for modulating EMT trajectories. Significance StatementIn our study, we investigated cellular trajectories during EMT using a time-series scRNAseq dataset. OT analysis was used to infer cell-to-cell connections from scRNAseq data, allowing us to predict cell linkages and overcome limitations of sequencing such as the need to sacrifice cells for each measurement. This approach allowed us to identify diverse EMT responses under uniform treatment, a significant advancement over previous studies limited by the static nature of scRNAseq data. Our analysis identified a broad set of genes involved in the EMT process, uncovering novel insights such as the upregulation of cell cycle genes in cells predisposed to a high EMT state and the enhancement of cell adhesion marker genes in cells veering towards a partial EMT state. This work enriches our understanding of the dynamic processes of EMT, showcasing the varied cellular fates within the same experimental setup.

systems biology↗

bioRxiv2022

evosim: fast and scalable stochastic simulations of evolutionary dynamics

The simulation of clonal dynamics with branching processes can provide valuable insights into disease progression and treatment optimization, but exact simulation of branching processes via the Stochastic Simulation Algorithm (SSA) is computationally prohibitive at the large population sizes associated with therapeutically-relevant scenarios. evosim is a versatile and flexible Python implementation of a fast and unbiased tau-leaping algorithm for the simulation of birth-death-mutation branching processes that is scalable to any population size. Package functionalities support the incorporation and tracking of a sequence of evolutionary changes such as therapeutic interventions as well as the analysis of population diversity. We show that runtimes scale logarithmically with population size, by contrast to the linear scaling of the SSA, and simulations exhibit strong agreement with SSA simulation results. These findings are also supported by mathematical results (Supplementary information). AvailabilityPackage, documentation, and tutorials / usage examples are available on GitHub (https://github.com/daliten/evosim). Mathematical details of the algorithm and the pseudocode are provided in the included Supplementary information.

evolutionary biology↗

bioRxiv2021

Single-cell genetic heterogeneity linked to immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most aggressive brain tumor with a median survival of ~15 months. Targeted approaches have not been successful in this tumor type due to the large extent of intratumor heterogeneity. Mosaic amplification of oncogenes suggests that multiple genetically distinct clones are present in each tumor. To uncover the relationships between genetically diverse subpopulations of GBM cells and their native tumor microenvironment, we employed highly multiplexed spatial protein profiling, coupled with single-cell spatial mapping of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA. Single-cell FISH analysis of a total of 35,843 single nuclei (~2,100 per tumor) revealed that tumors in which amplifications of EGFR and CDK4 more frequently co-occur in the same cell exhibit higher infiltration of CD163+ immunosuppressive macrophages. Our results suggest that high throughput assessment of genomic alterations at the single cell level could provide a measure for predicting the immune state of GBM.

cancer biology↗