Back

SCAMP - an open-source tool for the quantification of calcification in fish larvae

Czimer, D.; Kaluzsa, P.; Kövendi, J.; Li, K. L.; Kapusi, B.; Pomozi, V.; Fülöp, K.; Nagy, B.; Benedek, C.; Varadi, A.; Varga, M.

2026-07-27 developmental biology
10.64898/2026.07.25.740697 bioRxiv
Show abstract

Quantifying skeletal mineralization phenotypes in larval fish is complicated by the natural curvature of the notochord and by sample-to-sample variability in orientation, staining and imaging. Consequently, many studies rely on summary measures such as vertebral counts or total stain intensity. Here we present SCAMP (Spinal Calcification & Mineralization Profiler), an open-source, GUI-based Python tool that computationally straightens the curved notochord of Alizarin Red S-stained fish larvae and generates standardized mineralization profiles along the spinal axis. This approach reduces positional and shape variability, allowing direct, quantitative comparison of calcification patterns within and between experimental cohorts, without requiring programming expertise. We validate SCAMP using a zebrafish model of Pseudoxanthoma elasticum (abcc6aelu15/elu15), recovering genotype-specific differences in the intensity, extent and spatial distribution of ectopic calcification. Using SCAMP, we further show that inorganic pyrophosphate (PPi) supplementation of the medium suppresses ectopic notochord calcification, alters the anterior-posterior distribution of mineralized regions in homozygous mutants, and promotes mineralization at physiological vertebral sites. We also show that methylene blue, a routine antifungal additive in fish medium, reduces baseline calcification, with the most pronounced effects observed in heterozygous controls. SCAMP is freely available and has the potential to be adapted to other fish species used in skeletal and mineralization research.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.