Super-resolution microscopy and deep learning methods: what can they bring to neuroscience: from neuron to 3D spine segmentation.

IF 2.5 4区医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Frontiers in Neuroinformatics Pub Date : 2025-09-29 eCollection Date: 2025-01-01 DOI:10.3389/fninf.2025.1630133

Paul Nazac, Shengyan Xu, Victor Breton, David Boulet, Lydia Danglot

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Abstract

In recent years, advances in microscopy and the development of novel fluorescent probes have significantly improved neuronal imaging. Many neuropsychiatric disorders are characterized by alterations in neuronal arborization, neuronal loss-as seen in Parkinson's disease-or synaptic loss, as in Alzheimer's disease. Neurodevelopmental disorders can also impact dendritic spine morphogenesis, as observed in autism spectrum disorders and schizophrenia. In this review, we provide an overview of the various labeling and microscopy techniques available to visualize neuronal structure, including dendritic spines and synapses. Particular attention is given to available fluorescent probes, recent technological advances in super-resolution microscopy (SIM, STED, STORM, MINFLUX), and segmentation methods. Aimed at biologists, this review presents both classical segmentation approaches and recent tools based on deep learning methods, with the goal of remaining accessible to readers without programming expertise.

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超分辨率显微镜和深度学习方法：它们能给神经科学带来什么：从神经元到3D脊柱分割。

近年来，显微技术的进步和新型荧光探针的发展显著改善了神经元成像。许多神经精神疾病的特点是神经元树突改变、神经元丧失（如帕金森病）或突触丧失（如阿尔茨海默病）。神经发育障碍也可以影响树突棘的形态发生，如在自闭症谱系障碍和精神分裂症中观察到的。在这篇综述中，我们提供了各种标记和显微镜技术的概述，可用于可视化神经元结构，包括树突棘和突触。特别关注可用的荧光探针，超分辨率显微镜（SIM， STED， STORM， MINFLUX）和分割方法的最新技术进展。针对生物学家，本文介绍了经典的分割方法和基于深度学习方法的最新工具，目标是让没有编程专业知识的读者也能访问。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES

CiteScore

4.80

自引率

5.70%

发文量

132

审稿时长

14 weeks

期刊介绍： Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.