Biology subjects

Lynch, A. W.

Publications and source records attributed to Lynch, A. W..

4 recordsLinked to original sources

bioRxiv2026

Topographical archetypes of somatic mutagenesis in cancer

The genome of every cancer cell carries a record of the mutational processes that have acted throughout its history. Mutational signature analysis, which infers the activity of mutagenic processes from their characteristic base-change patterns, has become an indispensable tool for interpreting somatic mutations. However, this framework captures only which types of mutations a process generates and not where in the genome they occur -- a distribution influenced by replication timing, chromatin organization, transcription, DNA secondary structure, and other genomic features. Here, we present a generative probabilistic framework (MuTopia) that jointly infers mutational spectra and their genome-wide topography as nonlinear functions of genomic and epigenomic state. Applied to whole-genome sequencing data from 15 tumor types, MuTopia reveals that mutational processes fall into eight conserved topographic archetypes, or topotypes, shaped primarily by replication timing and chromatin state. Diverse mutational processes converge upon this limited repertoire, indicating that the genomic distribution of mutagenesis is constrained less by the source of damage than by how that damage is processed. Individual mutational processes exhibit state-dependent variation in their genomic distributions: the same signature can adopt distinct topotypes depending on repair proficiency and replication stress. For instance, SBS8 shifts from a canonical late-replicating profile in homologous recombination-proficient tumors to an early-replicating, stress-associated topotype in HR-deficient tumors, and replication stress similarly reshapes the genomic distribution of APOBEC editing. Topotypes, therefore, provide a classification of mutagenesis distinct from spectral signatures, capturing aspects of tumor biology that spectra alone cannot resolve.

bioinformatics↗

bioRxiv2024

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation

Enteroendocrine cells (EECs), which secrete serotonin (enterochromaffin cells, EC) or a dominant peptide hormone, serve vital physiologic functions. As with any adult human lineage, the basis for terminal cell diversity remains obscure. We replicated human EEC differentiation in vitro, mapped transcriptional and chromatin dynamics that culminate in discrete cell types, and studied abundant EEC precursors expressing selected transcription factors (TFs) and gene programs. Before expressing the pre-terminal factor NEUROD1, non-replicating precursors oscillated between epigenetically similar but transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor substantially accelerated EEC differentiation and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and hormone-producing cell features. Expressed late in EEC differentiation, the latter TFs mainly bind cis-elements that are accessible in undifferentiated stem cells and tailor the subsequent expression of TF combinations that specify EEC types. Thus, TF oscillations retard EEC maturation to enable accurate EEC diversification.

molecular biology↗

bioRxiv2023

scATAnno: Automated Cell Type Annotation for single-cell ATAC-seq Data

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

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↗