An automated machine vision-based index for Rett Syndrome provides a generalizable framework for modeling rare disease therapeutics
- 1The Jackson Laboratory
Abstract
Rare diseases collectively affect millions of people, yet therapeutic development remains limited by preclinical phenotyping which is often dependent on subjective, low-resolution manual behavioral assessment. Rett syndrome (RTT), a rare disease affecting 1 in 10,000 female births, is caused by a MeCP2 deficiency. In mouse models for this disease, symptom severity is measured primarily with the observational Bird score. This subjective six-item ordinal scale was used to demonstrate that MeCP2 deficiency is reversible, but offers limited resolution for ranking candidate therapies or detecting partial rescue. We applied an automated machine learning (ML) pipeline to a humanized Mecp2 R270X mouse model. We conducted weekly Bird scoring and recorded one-hour open-field trials from 3 to 10 weeks of age in 24 hemizygous (Mecp2-/Y) males and 24 wild-type littermates, then extracted over 400 behavioral features spanning gait kinematics, open-field activity, body morphometrics, and trained behavior classifiers. Manual Bird scoring tracked overall disease trajectory, but most of its dynamic range derived from two of six items. In contrast, ML features separated genotypes as early as three weeks and identified disease-relevant behaviors not captured by the Bird protocol, including increased tail-tip amplitude, repetitive wall-directed jumping, and prolonged behavioral arrest. Sparse PLS-DA separated genotypes at every age tested. A composite score modeled from the ML features reproduced the temporal pattern of the total Bird score with substantially less variability. Subsampling analyses showed that the ML feature set reached 5% cross-validated genotype misclassification error with only 12 to 16 animals, whereas the Bird score did not reach the 5% threshold even with 44 animals before week 6, a reduction in animal use consistent with the 3Rs. Applied to a Mecp2 minigene rescue study, the composite score reproduced genotype separation in an independent cohort and detected both a partial shift of treated hemizygous survivors toward wild-type values and adverse effects of the construct in wild-type mice. These results establish an objective, high-resolution severity index for RTT and a generalizable framework for preclinical phenotyping in a rare disease.
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BibTeX
@misc{berger2026automated,
title = {{An automated machine vision-based index for Rett Syndrome provides a generalizable framework for modeling rare disease therapeutics}},
author = {M. L. Berger and M. Simon and G. S. Sabnis and C. M. Lutz and V. Kumar},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.753377},
url = {https://www.biorxiv.org/content/10.64898/2026.09.21.753377v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - An automated machine vision-based index for Rett Syndrome provides a generalizable framework for modeling rare disease therapeutics
AU - M. L. Berger
AU - M. Simon
AU - G. S. Sabnis
AU - C. M. Lutz
AU - V. Kumar
PY - 2026
DA - 2026/09/21
PB - bioRxiv
DO - 10.64898/2026.09.21.753377
UR - https://www.biorxiv.org/content/10.64898/2026.09.21.753377v1
AB - Rare diseases collectively affect millions of people, yet therapeutic development remains limited by preclinical phenotyping which is often dependent on subjective, low-resolution manual behavioral assessment. Rett syndrome (RTT), a rare disease affecting 1 in 10,000 female births, is caused by a MeCP2 deficiency. In mouse models for this disease, symptom severity is measured primarily with the observational Bird score. This subjective six-item ordinal scale was used to demonstrate that MeCP2 deficiency is reversible, but offers limited resolution for ranking candidate therapies or detecting partial rescue. We applied an automated machine learning (ML) pipeline to a humanized Mecp2 R270X mouse model. We conducted weekly Bird scoring and recorded one-hour open-field trials from 3 to 10 weeks of age in 24 hemizygous (Mecp2-/Y) males and 24 wild-type littermates, then extracted over 400 behavioral features spanning gait kinematics, open-field activity, body morphometrics, and trained behavior classifiers. Manual Bird scoring tracked overall disease trajectory, but most of its dynamic range derived from two of six items. In contrast, ML features separated genotypes as early as three weeks and identified disease-relevant behaviors not captured by the Bird protocol, including increased tail-tip amplitude, repetitive wall-directed jumping, and prolonged behavioral arrest. Sparse PLS-DA separated genotypes at every age tested. A composite score modeled from the ML features reproduced the temporal pattern of the total Bird score with substantially less variability. Subsampling analyses showed that the ML feature set reached 5% cross-validated genotype misclassification error with only 12 to 16 animals, whereas the Bird score did not reach the 5% threshold even with 44 animals before week 6, a reduction in animal use consistent with the 3Rs. Applied to a Mecp2 minigene rescue study, the composite score reproduced genotype separation in an independent cohort and detected both a partial shift of treated hemizygous survivors toward wild-type values and adverse effects of the construct in wild-type mice. These results establish an objective, high-resolution severity index for RTT and a generalizable framework for preclinical phenotyping in a rare disease.
ER -