Jenna M. Gregory

The paper maps cognitive impairments in ALS by integrating spatial and single-nucleus transcriptomics, revealing distinct cellular and molecular patterns in language and executive dysfunction.

This study uses machine learning to identify routine blood tests as predictive markers of pollution-related poor cognitive function in a cohort without neurological disease.

This study shows TDP-43 pathology occurs independently of stress granules in motor neurons, challenging existing models of TDP-43 pathogenesis.

This paper identifies distinct metabolic signatures in ALS subtypes defined by ferritin and TDP-43 pathology, supporting stratified therapeutic approaches.

The paper shows that amygdala TDP-43 pathology correlates with behavioural dysfunction and ferritin accumulation in sporadic ALS.

This study identifies brain region-specific TDP-43 pathology patterns in ageing, Alzheimer's disease, and amyotrophic lateral sclerosis, showing strong correlations with ferritin levels as a potential biomarker.

This study uses RNA aptamers to detect pathological FUS in ALS, showing improved specificity and sensitivity compared to antibodies.

This study identifies pre-symptomatic TDP-43 pathology in skin and other peripheral organs up to 26.5 years before ALS diagnosis, suggesting skin as a potential biomarker for early detection.

This study uses single molecule microscopy to fingerprint TDP-43 aggregates in brain tissue, distinguishing MND donors from controls and different MND cohorts.

The paper introduces NOVA, a deep learning model for analyzing organelle structures in neurons, revealing novel ALS mechanisms through phenotypic changes in TDP-43 and P-bodies.

This study uses an RNA aptamer to detect TDP-43 aggregation and shows that nuclear TDP-43 pathology and STMN-2 cryptic splicing occur before clinical symptoms in ALS.

The paper identifies two distinct neuroinflammatory signatures in ALS cohorts using gene expression analysis and validates their association with clinical metrics.

The paper uses random forest modelling to identify microglial activation as a key classifier for C9orf72-related ALS and shows spatial FUS staining changes predict disease status.

The paper identifies pTDP-43 aggregates in non-CNS tissues of ALS patients prior to symptom onset, suggesting systemic pathology beyond the central nervous system.