Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.10.22.465398

Retinal self-organization: a model of RGC and SAC mosaic formation

de Montigny, J.·Sernagor, E.·Bauer, R.
View original source ↗Export metadata ↓

Abstract

Individual retinal cell types exhibit semi-regular spatial patterns called retinal mosaics. These mosaics enable uniform sampling of visual information and are formed to varying degrees across cell types. Retinal ganglion cells (RGC) and amacrine cells (including starburst amacrine cells (SAC)) are notably known to exhibit such layouts. Mechanisms responsible for the formation of such organised structures and their requirements are still not well understood. Mosaic formation follows three main principles: (1) homotypic cells prevent nearby cells from adopting the same type, (2) cell tangential migration, with homotypic cell repulsion, (3) cell death (with RGCs exhibiting high rates of apoptosis). Here, we use BioDynaMo, an agent-based simulation framework, to build a detailed and mechanistic model of mosaic formation. In particular, we investigate the implications of the three theories for RGCs mosaic formation. We report that the cell migration mechanism yields the most regular mosaics and that cell death can create regular mosaics only if the death rate is kept below 30%, after which cell death has a negative impact on mosaic regularity. In addition, and in accordance with recent studies, we propose here that low density RGC type mosaics exhibit on average low regularities, and thus we question the relevance of regular spacing as a criterion for a group of RGCs to form a RGC type. We also investigate SAC mosaics formation and possible interactions between the ganglion cell layer (GCL) and inner nuclear layer (INL) populations. Investigations are conducted both experimentally and by applying our simulation model to the SAC population. We report that homotypic interactions between the GCL and INL populations during mosaics creation are required to reproduce the observed SAC mosaics characteristics. This suggests that the GCL and INL populations of SACs might not be independent during retinal development. Author SummaryRetinal function depends on cells self-organisation during early development. Understanding the mechanisms underlying this self-organisation could improve not only our comprehension of the retina and its development but also of the cortex. Ultimately, this could lead to novel therapeutic approaches for developmental diseases. Computational models can be of precious help to study this process of self-organisation, given that they are biologically plausible. In this sense, it is important that implemented developmental mechanisms follow the principle of locally available information, without any global knowledge or external supervisor. Here, we follow this principle to investigate mosaic formation during retinal development. In this work, we demonstrate that tangential migration is the only mechanism able to form regular mosaics and that the GCL/INL SAC populations might not be independent during their mosaic formation. More, we question the relevance of regular spacing for RGC types classification.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

de Montigny, J., Sernagor, E., Bauer, R.. 2021-10-24. Retinal self-organization: a model of RGC and SAC mosaic formation. https://doi.org/10.1101/2021.10.22.465398

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.

bioinformatics↗
bioRxiv2026

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗
bioRxiv2026

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗