Biology subjects

Franco-Barranco, D.

Publications and source records attributed to Franco-Barranco, D..

8 recordsLinked to original sources

bioRxiv2026

The female larval fly CNS connectome: first draft

We release an enhanced FIB-SEM volume and associated neuron, mitochondria, synapse and glia automated segmentations for the complete, gap- and artifact-free central nervous system of the first instar female larval Drosophila melanogaster. Segmentation data is largely unproofread besides select neurons in the premotor and proprioceptive system and select subvolumes where synapses have been manually annotated, serving as training and evaluation data. The dataset represents the first complete central nervous system of the larval fly, and now awaits substantial improvement of machine vision approaches that effectively leverage domain knowledge for the automated yet data-guided proofreading, or substantial and costly human proofreading. All the data and methods are publicly available for further improvement and analysis.

neuroscience↗

bioRxiv2025

SpineDL: a Deep Learning-based approach for neuron andanatomical structure segmentation in immunofluorescenceimages of damaged spinal cords

In this study, we present SpineDL, an open-source deep learning (DL) approach for neurons and anatomical structure segmentation of the spinal cord in fluorescence images immunostained with NeuN and DAPI, within the context of murine models of spinal cord injury (SCI). SpineDL comprises two main modules: 1) SpineDL-Structure, for semantic segmentation of key spinal cord structures: gray matter, white matter, ependyma, and damaged tissue; and 2) SpineDL-Neuron, for instance-level identification of neuronal somas. To train the models, we developed the SpineDL dataset, a curated collection of 161 confocal images of mouse spinal cord, manually annotated by experts and organized into specific subsets. Both models are based on the HRNetV2-W48 architecture and were trained using state-of-the-art data augmentation and optimization techniques, implemented within the BiaPy framework, following an iterative refinement process driven by quantitative evaluation and expert feedback. Our results show that SpineDL achieves expert-level performance for both cases of structural segmentation and neuron identification. This work provides a robust, reproducible, and extensible platform for the spatial analysis of neurodegeneration following spinal cord injury, representing a step toward the automation of histopathological workflows in neuroscience.

bioinformatics↗

bioRxiv2025

3D Mapping of Intact Ovaries Reveals the Aging Dynamics of the Ovarian Reserve

Female fertility depends on a finite pool of oocytes that depletes during aging, yet the spatiotemporal dynamics of this depletion remain poorly understood. Traditional methods obscure the 3D architecture of the ovary, limiting quantitative insights. Here, we combine light-sheet microscopy, artificial intelligence (AI)-driven segmentation, and mathematical modeling to map over 85,000 oocytes in whole-ovaries across the reproductive lifespan in mouse. We find that newly activated oocytes represent a fixed fraction of the ovarian reserve, despite an age-related decline in total oocyte numbers. Spatial analysis revealed that oocytes are enriched along the lateral ovarian axis, and local oocyte density positively correlates with activation. We also uncover a bimodal distribution of oocyte sizes, suggesting a bottleneck during oogenesis. Finally, a differential equation-based model captures the kinetics of oocyte activation and loss. Our findings establish a quantitative framework for understanding ovarian aging and suggest that an organ-scale regulatory mechanism coordinates the age-related decline in oocyte numbers.

cell biology↗

bioRxiv2025

Beyond Agreement: Standardizing Crowdsourced Synapse Annotations through Proofreading in EM Connectomics

AO_SCPLOWBSTRACTC_SCPLOWReliable synapse identification in volumetric EM is hampered by subtle, 3D cues that yield variable human judgments. We present a standardized proofreading protocol that pairs explicit, operational criteria with machine-learning candidate generation and a two-stage calibration of annotators. In two larval Drosophila melanogaster volumes imaged at 8x8x8 nm, five raters (expert + 4 calibrated annotators) reviewed model-proposed candidates using efficient node-based labels. Multi-rater judgments were aggregated with a probabilistic Dawid-Skene (DS) model to produce consensus labels with calibrated uncertainty. Post-calibration, individual annotator accuracy versus the expert improved (McNemar p < 0.05 for all raters), DS-expert agreement increased, and DS posterior entropy decreased for true positives/negatives, indicating more decisive consensus; gains were modest and dataset-dependent in chance-corrected agreement (Krippendorffs ). By making uncertainty explicit, this protocol converts noisy judgments into auditable supervision suitable for training and evaluation, while honestly communicating residual ambiguity essential for reliable and robust connectomics at scale.

neuroscience↗

bioRxiv2025

Mapping the nervous system of the Idiosepius hallami pygmy squid: insights from whole-animal X-ray nanotomography imaging.

The study of a nervous system as big as the cephalopods requires multimodal imaging approaches capable of capturing neural architecture across scales. Here, we present a whole-animal volume of the pygmy squid hatchling Idiosepius hallami, acquired using X-ray holographic nanotomography at the beamline ID16A of the European Synchrotron. The reconstructed 3D volume comprises 40 tiled scans acquired at a voxel size of 125 nm. While individual neurons are not resolved at this resolution, we segmented major body regions and mapped the large-scale connectivity by tracing afferent and efferent nerve bundles, including projections from the olfactory organs, chromatophore lobes, and arm ganglia to the brain. The acquisition of this dataset represents a significant milestone for X-ray nanotomography, being the largest whole animal volume imaged at this spatial resolution. The volume serves as a resource for comparative neuroscience and cephalopod biology.

neuroscience↗

bioRxiv2024

Self-supervised Vision Transformers for image-to-image labeling: a BiaPy solution to the LightMyCells Challenge

Fluorescence microscopy plays a crucial role in cellular analysis but is often hindered by phototoxicity and limited spectral channels. Label-free transmitted light microscopy presents an attractive alternative, yet recovering fluorescence images from such inputs remains difficult. In this work, we address the Cell Painting problem within the LightMyCells challenge at the International Symposium on Biomedical Imaging (ISBI) 2024, aiming to predict optimally focused fluorescence images from label-free transmitted light inputs. Leveraging advancements self-supervised Vision Transformers, our method overcomes the constraints of scarce annotated biomedical data and fluorescence microscopys drawbacks. Four specialized models, each targeting a different organelle, are pretrained in a self-supervised manner to enhance model generalization. Our method, integrated within the open-source BiaPy library, contributes to the advancement of image-to-image deep-learning techniques in cellular analysis, offering a promising solution for robust and accurate fluorescence image prediction from label-free transmitted light inputs. Code and documentation can be found at https://github.com/danifranco/BiaPy and a custom tutorial to reproduce all results is available at https://biapy.readthedocs.io/en/latest/tutorials/image-to-image/lightmycells.html.

bioinformatics↗

bioRxiv2024

BiaPy: A unified framework for versatile bioimage analysis with deep learning

BiaPy is an open-source library and application that streamlines the use of common deep learning approaches for bioimage analysis. Designed to simplify technical complexities, it offers an intuitive interface, zero-code notebooks, and Docker integration, catering to both users and developers. While focused on deep learning workflows for 2D and 3D image data, it enhances performance with multi-GPU capabilities, memory optimization, and scalability for large datasets. Although BiaPy does not encompass all aspects of bioimage analysis, such as visualization and manual annotation tools, it empowers researchers by providing a ready-to-use environment with customizable templates that facilitate sophisticated bioimage analysis workflows.

bioinformatics↗

bioRxiv2023

CartoCell, a high-throughput pipeline for accurate 3D image analysis, unveils cell morphology patterns in epithelial cysts.

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our Single-cell Cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues. MOTIVATIONA major bottleneck in developing neural networks for cell segmentation is the need for labor-intensive manual curation in order to develop a training dataset. The present work addresses this limitation by developing an automated image analysis pipeline that utilizes small datasets to generate accurate labels of cells in complex, 3D epithelial contexts. The overall goal is to provide an automatic and feasible method to achieve high-quality epithelial reconstructions and to enable high-content analysis of morphological features, which can improve our understanding of how these tissues self-organize.

developmental biology↗