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Dilliott, A. A.

Publications and source records attributed to Dilliott, A. A..

2 recordsLinked to original sources

ARPP21 defines a TDP-43-independent aggregation pathway in amyotrophic lateral sclerosis

Currently, pathological inclusions of ubiquitinated TDP-43 are considered central to the pathogenesis of amyotrophic lateral sclerosis (ALS). However, this view has yielded sparse attention to covert alternative pathways of aggregation in the disease. Here, we identified a pathological axis independent of TDP-43 led by ARPP21, a SUZ domain-containing RNA-binding protein encoded by a gene recently described in strong association with ALS. ARPP21 showed large fibrillar non-ubiquitinated aggregates in all the non-SOD1 ALS cases studied, consistently segregating from TDP-43 pathology. Biophysically, ARPP21 undergoes spontaneous phase separation, with a higher propensity for condensate formation than TDP-43 but without co-aggregating with it. ARPP21 also exhibited very slow soluble behavior and lower condensate dynamics compared to TDP-43, an effect that was more pronounced for the ARPP21-P529L disease-related variant. In human iPSC-derived motor neurons, expression of ARPP21 variants was sufficient to drive condensate formation and reduce cell viability. Mechanistically, ARPP21 promoted intercellular propagation by inducing tunnelling nanotube formation, enabling efficient cell-to-cell spreading of aggregates. Genetic analyses further identified multiple ARPP21 variants in ALS, supporting its clinical relevance. Together, our findings uncover ARPP21 as a previously unrecognized TDP-43-independent aggregation pathway in ALS, with implications for disease heterogeneity and therapeutic targeting.

neuroscience↗

Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling

Multiplexing samples from distinct individuals prior to sequencing is a promising step toward achieving population-scale single-cell RNA sequencing by reducing the restrictive costs of the technology. Individual genetic demultiplexing tools resolve the donor-of-origin identity of pooled cells using natural genetic variation but present diminished accuracy on highly multiplexed experiments, impeding the analytic potential of the dataset. In response, we introduce Ensemblex: an accuracy-weighted, ensemble genetic demultiplexing framework that integrates four distinct algorithms to identify the most probable subject labels. Using computationally and experimentally pooled samples, we demonstrate Ensemblexs superior accuracy and illustrate the implications of robust demultiplexing on biological analyses.

genomics↗