Biology subjects

Bhatti, N.

Publications and source records attributed to Bhatti, N..

2 recordsLinked to original sources

bioRxiv2026

A Scalable, High-Throughput Optomotor Response Pipeline for Quantitative Analysis of Vision Using Infinity-Pools

The optomotor response (OMR) provides a robust readout assay for evaluating visually guided locomotion and has been widely applied to various animal models. We present a scalable, high-throughput OMR platform with tuneable and moving stripe patterns simultaneously projected to the side of 50 petri dishes. The setup is optimized for hatchling-stage medaka (Oryzias latipes and Oryzias sakaizumii) and larval stage zebrafish (Danio rerio) and integrates custom software for automated generation of visual stimuli and specimen detection, enabling precise and automated quantification of optomotor behaviour across thousands of individuals. Using this platform, we systematically assess the visual performance of zebrafish and five medaka strains. By varying stripe width, direction, speed, contrast, and colour, we address spatial resolution, contrast, and colour sensitivity. Albino mutant medaka exhibited the highest sensitivity, responding at lower stripe widths and across a broader range of contrasts and chromatic conditions. Interestingly, some Cab and HO5 hatchlings displayed uni-directional response, revealing strain-specific visuomotor differences. Our platform enables reliable detection of OMR behaviour already at hatching and supports robust analysis of visuomotor function. The modular design, automated detection, analysis pipeline, and capacity for large sample numbers make it a powerful tool for comparative vision research, high-throughput genetic screening, and systematic behavioural profiling.

animal behavior and cognition↗

bioRxiv2025

Deep learning predicts tissue outcomes in retinal organoids

Retinal organoids have become important models for studying development and disease, yet stochastic heterogeneity in the formation of cell types, tissues, and phenotypes remains a major challenge. This limits our ability to precisely experimentally address the early developmental trajectories towards these outcomes. Here, we utilize deep learning to predict the differentiation path and resulting tissues in retinal organoids well before they become visually discernible. Our approach effectively bypasses the challenge of organoid-related heterogeneity in tissue formation. For this, we acquired a high-resolution time-lapse imaging dataset comprising about 1,000 organoids and over 100,000 images enabling precise temporal tracking of organoid development. By combining expert annotations with advanced image analysis of organoid morphology, we characterized the heterogeneity of the retinal pigmented epithelium (RPE) and lens tissues, as well as global organoid morphologies over time. Using this training set, our deep learning approach accurately predicts the emergence and size of RPE and lens tissue formation on an organoid-by-organoid basis at early developmental stages, refining our understanding of when early lineage decisions are made. This approach advances knowledge of tissue and phenotype decision-making in organoid development and can inform the design of similar predictive platforms for other organoid systems, paving the way for more standardized and reproducible organoid research. Finally, it provides a direct focus on early developmental time points for in-depth molecular analyses, alleviated from confounding effects of heterogeneity.

developmental biology↗