Biology subjects

Woyshner, K.

Publications and source records attributed to Woyshner, K..

5 recordsLinked to original sources

bioRxiv2025

Intrinsic Gestational Timing Governs Human Cerebellar Development After Preterm Birth

Intrinsic programs shape brain maturation, yet which are perturbed by prematurity, and the molecular pathways involved remain unknown. The human cerebellum serves as a paradigm of extrauterine development; its accelerated growth and circuit formation align with the third trimester, a period disrupted by preterm delivery. Through multimodal datasets encompassing in vivo neuroimaging and neurodevelopmental outcomes with postmortem spatial-transcriptomic and histopathological profiling, we show that prematurity diverts the cerebellar developmental trajectory. Cerebellar growth and functional outcomes scaled with gestational age, despite modifying perinatal exposures. Spatially resolved developmental programs underlying macroscopic trajectories were marked by incomplete granule cell maturation and impaired Purkinje cell structural refinement, indicating lineage-specific developmental asynchrony. Thus, prematurity constitutes biologically displaced maturation, in which infants of equivalent post-menstrual age occupy divergent developmental states.

neuroscience↗

bioRxiv2025

Prematurity Reprograms Cerebellar Development and Long-Term Behavior

Preterm survivors often develop motor and socio-cognitive impairments that implicate altered cerebellar development, yet the underlying mechanisms remain poorly understood. A key challenge is that prematurity involves overlapping perinatal insults that converge on the developing brain, making their individual effects difficult to disentangle. Here, we model two major prematurity-associated insults, maternal immune activation (MIA) and neonatal hypoxia (Hx), in mice. By controlling timing and sequence, we define how these insults shape cerebellar assembly during rapid maturation. Comparative analysis with human tissue confirmed that these insults recapitulate key features of the human preterm cerebellum, establishing the translational validity of this model for dissecting insult-specific outcomes. Behavioral and kinematic profiling revealed divergent motor and social phenotypes, which we traced to insult-specific cerebellar remodelling. Hypoxia compromised both granule cell maturation in the internal granule layer and the principal excitatory afferents to Purkinje cells, a circuit state that manifested as impaired motor execution and stereotyped, sensory-disengaged social investigation. Maternal immune activation, by contrast, expanded the granule cell progenitor layer and reduced Purkinje cell dendritic complexity, a phenotype that preserved social preference but reorganized the kinematic structure of social investigation. When hypoxia followed maternal immune activation, it acted on this primed substrate to produce a distinct state in which the features established by prior inflammation were compounded by granule cell proliferative arrest, progressive mitochondrial dysfunction, and aberrant, hyper-interactive social investigation. Together, these findings reframe prematurity-associated insults as cerebellar reprogramming events shaped by both the identity and sequence of insults, linking distinct mechanistic substrates to divergent neurodevelopmental outcomes.

neuroscience↗

bioRxiv2024

Multi-sample non-negative spatial factorization

Analyzing multi-sample spatial transcriptomics data requires accounting for biological variation. We present multi-sample non-negative spatial factorization (mNSF), an alignment-free framework extending single-sample spatial factorization (NSF) to multi-sample datasets. mNSF incorporates sample-specific spatial correlation modeling and extracts low-dimensional data representations. Through simulations and real data analysis, we demonstrate mNSFs efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSFs performance is comparable to alignment-based methods when alignment is feasible, while enabling analysis in scenarios where spatial alignment is unfeasible. mNSF shows promise as a robust method for analyzing spatially resolved transcriptomics data across multiple samples.

genomics↗

bioRxiv2021

APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and control on the false discovery rate (FDR). To fill in this gap, we propose a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard shows that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies show that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

bioinformatics↗

bioRxiv2020

Clipper: p-value-free FDR control on high-throughput data from two conditions

High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely-used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.

bioinformatics↗