Mejia Maza, A. | Search bioRxiv

bioRxiv2026

TRACE: Open-Source Software for Quantifying Somatic Variation of Tandem Repeats by Capillary Electrophoresis

Expanded short tandem DNA repeats are implicated in over 60 human disorders. In many, somatic instability (SI) of the repeat plays a critical role in disease pathogenesis. For example, SI in vulnerable neurons is a key driver of clinical symptoms in Huntingtons disease. Quantifying SI has traditionally relied on PCR followed by capillary electrophoresis, with metrics describing the shape of repeat size distributions, such as the expansion index. However, current tools often require costly proprietary software, are time-consuming, and rely on custom pipelines that vary between labs. To address these challenges, we developed Tandem Repeats Analysis by Capillary Electrophoresis (TRACE), an open-source software that processes fragment analysis data end-to-end, from raw files to SI metrics. Additionally, we created an associated web app TRACE-shiny (https://traceshiny.mgh.harvard.edu/), for interactive usage. Outputs from TRACE benchmarked against published datasets confirm its utility for studying genetic and pharmacological modifiers of SI. TRACE eliminates the need for proprietary software or custom pipelines, making advanced tools for analysis of somatic repeat expansion widely accessible.

genetics↗

bioRxiv2020

NMJ-Analyser: high-throughput morphological screening of neuromuscular junctions identifies subtle changes in mouse neuromuscular disease models

The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with poor systematic criteria across studies, and separately from 3D morphological structure. Here, we describe the development of NMJ-Analyser, to comprehensively screen the morphology of NMJs and their corresponding innervation status automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses and applies machine learning to diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as a robust platform for systematic and structural screening of NMJs, and pave the way for transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.

neuroscience↗