Biology subjects

O'Connor, S. A.

Publications and source records attributed to O'Connor, S. A..

4 recordsLinked to original sources

bioRxiv2026

Resolving Immune Lineage and Cell-State Heterogeneity in Human PBMCs via Mass Spectrometry-Based Single-Cell Proteomics

Single-cell proteomics (SCP) currently lacks validated benchmarking standards, and cell annotation often relies on transcriptomic proxies. Unsupervised clustering offers a proxy-free alternative, but its success depends on biological signal outweighing technical variation. In homogeneous samples this is achievable, but in heterogeneous populations, where closely related cell types differ only subtly, technical variation can dominate the clustering and obscure the biology needed for annotation. To address this, we developed an integrated experimental and computational pipeline for protein-level cell annotation and applied it to human PBMCs as an immune-cell test case. We isolated T cells, B cells, monocytes, and NK cells by negative-selection sorting to build a high-fidelity reference. In parallel, unsorted PBMCs from the same donor were processed on a cellenONE and acquired using label-free DIA on an Orbitrap Astral Zoom. Using the labeled reference dataset, we systematically benchmarked normalization, imputation, and clustering methods to assess their effect on cell-type separation. Unsupervised analysis resolved functional subpopulations within each lineage, and a probabilistic SCP classifier trained on these annotations identified the corresponding cell types and states in the unsorted PBMC fraction, validating the pipeline on unenriched, heterogeneous samples. Together, this work delivers an analytically benchmarked SCP workflow that resolves immune lineage and cell-state heterogeneity in human PBMCs and provides a classifier-ready, protein-level reference for immune-cell assignment.

bioinformatics↗

bioRxiv2026

Circuit-selective cognitive vulnerability to environmental stress: multi-domain assessment of space radiation in both sexes reveals countermeasure trade-offs

Environmental stressors rarely affect just one brain circuit. Most studies assess single cognitive endpoints, obscuring whether vulnerabilities are global or circuit-selective and how effects distribute across interconnected systems. To address this, we used galactic cosmic radiation (GCR), a Mars mission-relevant stressor that disrupts the hippocampal-nucleus accumbens-prefrontal circuit. C57BL/6J mice received 33-ion GCR simulation (33-GCR, 0.75 Gy) or sham radiation with the Nrf2-activating compound CDDO-EA or vehicle, followed by multi-domain behavioral testing in both sexes. Under very high memory load, male Veh/33-GCR mice showed enhanced pattern separation compared to Veh/Sham males, an effect normalized by CDDO-EA. Female mice showed no radiation-induced changes in pattern separation but weighed 9-18% more than Veh/Sham females and had reduced locomotor activity. Reward-based learning differed by sex: males showed no changes, while female Veh/33-GCR mice displayed enhanced reward anticipation that was further increased by CDDO-EA alone, with both treatments contributing to elevated goal-tracking. For behavioral flexibility, CDDO-EA impaired reversal learning in males regardless of radiation, while 33-GCR impaired reversal learning in females regardless of CDDO-EA. Principal component analysis revealed that treatments disrupted specific circuit relationships while leaving others intact, consistent with selective rather than global cognitive effects. Fiber photometry showed enhanced dentate gyrus encoding activity in irradiated males under high memory load. Combined CDDO-EA/33-GCR selectively reduced dentate gyrus progenitors in females. Males and females showed distinct, circuit-selective vulnerability patterns, demonstrating that multi-domain, both-sex assessment is necessary to capture how stressors and interventions affect integrated brain function. CDDO-EA proved to be a double-edged sword: protecting one cognitive domain while impairing another, a trade-off invisible to single-endpoint assessment. This framework has immediate relevance for astronaut risk assessment and extends to any context where neuroprotective interventions are evaluated against environmental stressors.

animal behavior and cognition↗

bioRxiv2024

Breaking out of the cell cycle: Including quiescence in cell cycle classification

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state (Neural G0), and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We demonstrate that the ccAFv2 classifier effectively generalizes the S, S/G2, G2/M, and M/Early G1 states across cell types derived from all three germ layers. While the G0, G1, and Late G1 states perform well in neuroepithelial cell types, their accuracy is lower in other cell types. However, misclassifications are confined to the G0, G1, and Late G1 states. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat or a PyPI package integrated with SCANPY. We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

cell biology↗

bioRxiv2022

Systematic integration of protein affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile

Somatic mutations occur as random genetic changes in genes through protein-affecting mutations (PAMs), gene fusions, or copy number alterations (CNAs). Mutations of different types can have a similar phenotypic effect (i.e., allelic heterogeneity) and should be integrated into a unified gene mutation profile. We developed OncoMerge to fill this niche of integrating somatic mutations to capture allelic heterogeneity, assign a function to mutations, and overcome known obstacles in cancer genetics. Application of OncoMerge to the TCGA Pan-Cancer Atlas increased the number and frequency of somatically mutated genes and improved the prediction of the somatic mutation role as either activating or loss of function. Using integrated somatic mutation matrices increased the power to infer gene regulatory networks and uncovered the enrichment of switch-like feedback motifs and delay-inducing feed-forward loops. These studies demonstrate that OncoMerge efficiently integrates PAMs, fusions, and CNAs and strengthens downstream analyses linking somatic mutations to cancer phenotypes. MotivationGenes are mutated in tumors through either PAMs, CNAs, or gene fusions, thereby splitting the signal of the somatic mutation effect across these three mutation types. We developed OncoMerge to systematically integrate the three mutation types into a single mutation profile that better captures the impact of somatic mutations on cancer phenotypes. As a tool OncoMerge fills the gap between the sophisticated variant calling pipelines and downstream analyses.

systems biology↗