Biology subjects

Avino Esteban, L.

Publications and source records attributed to Avino Esteban, L..

4 recordsLinked to original sources

bioRxiv2025

LimbLab: Pipeline for 3D Analysis and Visualisation of Limb Bud Gene Expression

MotivationAlthough some aspects of limb development can be treated as a 2D problem, a true understanding of the morphogenesis and patterning requires 3D analysis. Since the data on gene expression patterns are largely static 3D image stacks, a major challenge is an efficeint pipeline for staging each data-set, and then aligning and warping the data into a standard atlas for convenient visualisation. ResultsWe present a novel bioinformatic pipeline tailored for 3D visualization and analysis of developing limb buds. The pipeline integrates key steps such as data acquisition, volume cleaning, surface extraction, staging, alignment, and advanced visualization techniques. Its modular design allows researchers to customize workflows while maintaining compatibility with tools such as Fiji and Vedo. Our results highlight the pipelines effectiveness in managing complex 3D gene expression data, enhancing the accuracy and reproducibility of limb development studies. Availability and ImplementationLimbLab is released under the MIT license. The pipeline is implemented in Python and is available as an open-source package. It can be easily installed via pip and is accompanied by comprehensive documentation (https://limblab.embl.es/) to support users at various levels of expertise. The pipeline can be accessed and downloaded at https://github.com/LauAvinyo/limblab.

bioinformatics↗

bioRxiv2024

WNT signaling coordinately controls mouse limb bud outgrowth and establishment of the digit-interdigit pattern

Self-organization, such as the emergence of a pattern from a homogenous state, is a fascinating property of biological systems. Early limb bud outgrowth and patterning in mice are controlled by a robust and self-regulatory signaling system, and initiation of the periodic digit-interdigit pattern appears under control of a self-regulatory Turing system. Previous studies established the requirement of WNT and BMP signaling for both early limb bud and digit-interdigit morphogenesis, but the molecular changes underlying the transition from early limb bud signaling to the digit-interdigit patterning system remained unknown. Here, we use small molecule inhibitors to rapidly but transiently block WNT signaling to identify the early transcriptional targets that are altered during disruption and recovery of limb bud and digit development. Together, this study highlights the overarching role of WNT signaling in controlling early limb bud outgrowth and patterning, and establishment of the periodic digit-interdigit pattern. Finally, the transient WNT signaling disruption approach reveals the plasticity and robustness of these self-organizing limb bud and digit patterning systems.

developmental biology↗

bioRxiv2024

Spatio-temporal Reconstruction of Gene Expression Patterns in Developing Mice

Understanding gene regulation in organism development is crucial in biology. Techniques like whole-mount in situ hybridization can reveal spatial gene expression in organs and tissues. However, capturing time-lapse images of gene expression dynamics in embryos developing in utero, such as mice, remains technically challenging beyond the early stages. To address this, we present a method to integrate static snapshots of gene expression patterns across limb developmental stages, creating a continuous 2D reconstruction of gene expression patterns over time. This method interpolates small tissue regions over time to create smooth temporal trajectories of gene expression. We successfully applied it to a number of key genes in limb development, including Sox9, Hand2, and Bmp2. This approach enables a detailed spatio-temporal mapping of gene expression, providing insights into developmental mechanisms. By estimating Gene Expression Patterns at previously unobserved time points, it facilitates the comparison of these patterns across samples. The reconstructed trajectories offer high-quality data that will be useful to guide computational modeling and machine learning, advancing the study of developmental biology in systems where real-time imaging is technically difficult or impossible.

developmental biology↗

bioRxiv2019

HypercubeME: two hundred million combinatorially complete datasets from a single experiment

MotivationEpistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a \"combinatorially complete dataset\". So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets.\n\nResultsWe present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data.\n\nAvailabilityhttps://github.com/ivankovlab/HypercubeME.git.

evolutionary biology↗