Biology subjects

Luna, D.

Publications and source records attributed to Luna, D..

3 recordsLinked to original sources

bioRxiv2025

Modeling Organoid Population Electrophysiology Dynamics

Improving models to investigate neurodegenerative disease, neurodevelopmental disease, neuro-toxicology and neuropharmacology is critical to improve our basic understanding of the human nervous system, as well as to accelerate discovery of interventions and drugs. Improved models of the human central nervous system could enable critical discoveries related to functional changes induced by sensory stimulation or toxic exposures. Neural organoids, complex three-dimensional cell cultures derived from adult human stem cells, have been grown with complex connectivity and neuroanatomy. Moreover, these cultures have been interfaced with bi-directional electrical stimulation and recording, as well as chemical stimulation. This effort sought to develop new computational techniques which could be applied to comparative studies using neural organoids. In particular, we adapted CEBRA, a state of the art model from the in vivo modeling literature, to generate 2D and 3D embeddings (projections into structured low-dimensional spaces) of high dimensional neural organoid electrophysiology data. This can be done in an unsupervised or semi-supervised manner. Results indicate these embeddings can be quickly and reliably generated and serve as a low-dimensional, interpretable embeddings for characterizing changes in neural organoid activity over time, as well as clustering results around known bursting phenomena. Moreover, we demonstrate that mixtures of von Mises-Fisher distributions can be used as a parametric model for these embedding spaces to enable statistics hypothesis testing. This technique may enable new types of comparative studies using neural organoids, and may be critical for creating a representation for quantitative comparison and validation of neural organoid models against human and animal data. Looking ahead, this work could allow the formulation of a new class of experiments investigating the functional impact of toxins, genetic manipulations, or pharmacological interventions on human neurons.

neuroscience↗

bioRxiv2024

DeepPaint: A deep-learning package for Cell Painting Image Classification

Recent developments in the high-content imaging (HCI) space have allowed for the production of large Cell Painting datasets. These datasets are typically derived from cells exposed to a set of biological perturbants including proteins, small molecules, or even pathogens. While the method of Cell Painting has shown utility for drug discovery and hazard evaluation purposes, traditional analyses pipelines applied Cell Painting datasets typically require the segmentation of single cells from thousands to millions of images, a process that is time-consuming and subject to noise and experimental variability. Here we present DeepPaint, a Python package that uses a deep learning framework to perform image analysis of cell painting images including treatment classification and latent space analysis, circumventing the need for image segmentation. DeepPaint is easily tunable to different HCI setups and datasets and can be applied to classify broad types of biological perturbations. Here we demonstrate that DeepPaint can generate highly accurate neural networks for binary and multiclass classification of cell painting images. The DeepPaint package and example notebooks are freely available at https://github.com/jhuapl-bio/DeepPaint.

bioinformatics↗

bioRxiv2022

NeuVue: A Framework and Workflows for High-Throughput Electron Microscopy Connectomics Proofreading

Connectomic reconstruction from large image volumes produces segmentation and synaptic-assignment errors that must be resolved to support downstream analyses. As datasets have grown larger and teams more distributed, proofreading has become a critical operational bottleneck. Workflows for proofreading and error correction have not scaled commensurately with connectomic data production and may not accommodate heterogeneous proofreader expertise and machine-generated candidate edits. New tools are therefore needed to organize, prioritize, and coordinate proofreading at volume scale. Here we present NeuVue, a task-management and prioritization framework that operationalizes proofreading through atomic, auditable tasks for individual and team review, multistage routing across proofreader cohorts, performance and volume-state tracking, and integration with community annotation, visualization, and analysis services. We report the use of NeuVue across two volumetric datasets, supporting scalable proofreading by over forty proofreaders and producing over fifty thousand edits. NeuVue provides a reproducible human-in-the-loop framework for generating, validating, and maintaining large connectomic datasets.

neuroscience↗