Biology subjects

Theodoris, C. V.

Publications and source records attributed to Theodoris, C. V..

4 recordsLinked to original sources

bioRxiv2026

Temporal AI model predicts drivers of cell state trajectories across human aging

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

bioinformatics↗

bioRxiv2026

Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs oligodendrocyte functions and alters neuronal activity

Aging is the major risk factor for neurodegenerative disease, yet the mechanisms linking physiological aging to brain dysfunction remain unclear. Because telomere erosion is a hallmark of aging, we examined its impact on glial and neuronal physiology. Telomere shortened mice showed lipofuscinosis, hypomyelination, microglial atrophy, and cognitive deficits. Single nuclei RNA-seq revealed accelerated glial aging, elevated microglial senescence pathways, and impaired oligodendrocyte functions. Inducing senescence in human iPSC derived microglia with shortened telomeres identified soluble DLK1 as a novel senescence associated ligand. sDLK1 was increased in the cerebrospinal fluid of telomere shortened and naturally aged mice, and this increase was eliminated by microglial depletion. AAV delivery of sDLK1 in vivo caused hypomyelination and blocked oligodendrocyte lineage progression, demonstrating the detrimental nature of excessive sDLK1. In human iPSC systems, sDLK1 impaired oligodendrocyte maturation and altered calcium signaling in excitatory neurons. These findings identify microglial senescence as a core consequence of telomere shortening and reveal sDLK1 as a microglia-derived senescence ligand that drives oligodendrocyte and neuronal dysfunction in aging.

neuroscience↗

bioRxiv2024

Quantized multi-task learning for context-specific representations of gene network dynamics

While often represented as static entities, gene networks are highly context-dependent. Here, we developed a multi-task learning strategy to yield context-specific representations of gene network dynamics. We assembled a corpus comprising [~]103 million human single-cell transcriptomes from a broad range of tissues and diseases and performed a two stage pretraining, first with non-malignant cells to generate a foundational model and then with continual learning on cancer cells to tune the model to the cancer domain. We performed multi-task learning with the foundational model to learn context-specific representations of a broad range of cell types, tissues, developmental stages, and diseases. We then leveraged the cancer-tuned model to jointly learn cell states and predict tumor-restricting factors within the colorectal tumor microenvironment. Model quantization allowed resource-efficient fine-tuning and inference while preserving biological knowledge. Overall, multi-task learning enables context-specific disease modeling that can yield contextual predictions of candidate therapeutic targets for human disease.

bioinformatics↗

bioRxiv2021

MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here, we present MIRA: Probabilistic Multimodal Models for Integrated Regulatory Analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along developmental trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high fidelity lineage trees, determines key regulators of fate decisions at branch points, and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal maintenance differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

bioinformatics↗