Fenrich Keith K, Zhao Ethan Y, Wei Yuan, Garg Anirudh, Rose P Ken
CIHR Group in Sensory-Motor Integration, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada K7L 3N6; Center for Neuroscience Studies, Queen's University, Kingston, ON, Canada K7L 3N6; Aix Marseille University, Developmental Biology Institute of Marseille-Luminy (IBDML), CNRS 7288, Case 907 - Parc Scientifique de Luminy, 13009 Marseille, France; Faculty of Rehabilitation Medicine, University of Alberta, 3-88 Corbett Hall, Edmonton, AB, Canada T6G 2G4.
CIHR Group in Sensory-Motor Integration, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada K7L 3N6; Center for Neuroscience Studies, Queen's University, Kingston, ON, Canada K7L 3N6.
J Neurosci Methods. 2014 Apr 15;226:42-56. doi: 10.1016/j.jneumeth.2014.01.011. Epub 2014 Jan 30.
Isolating specific cellular and tissue compartments from 3D image stacks for quantitative distribution analysis is crucial for understanding cellular and tissue physiology under normal and pathological conditions. Current approaches are limited because they are designed to map the distributions of synapses onto the dendrites of stained neurons and/or require specific proprietary software packages for their implementation.
To overcome these obstacles, we developed algorithms to Grow and Shrink Volumes of Interest (GSVI) to isolate specific cellular and tissue compartments from 3D image stacks for quantitative analysis and incorporated these algorithms into a user-friendly computer program that is open source and downloadable at no cost.
The GSVI algorithm was used to isolate perivascular regions in the cortex of live animals and cell membrane regions of stained spinal motoneurons in histological sections. We tracked the real-time, intravital biodistribution of injected fluorophores with sub-cellular resolution from the vascular lumen to the perivascular and parenchymal space following a vascular microlesion, and mapped the precise distributions of membrane-associated KCC2 and gephyrin immunolabeling in dendritic and somatic regions of spinal motoneurons.
Compared to existing approaches, the GSVI approach is specifically designed for isolating perivascular regions and membrane-associated regions for quantitative analysis, is user-friendly, and free.
The GSVI algorithm is useful to quantify regional differences of stained biomarkers (e.g., cell membrane-associated channels) in relation to cell functions, and the effects of therapeutic strategies on the redistributions of biomolecules, drugs, and cells in diseased or injured tissues.
从三维图像堆栈中分离特定的细胞和组织区室以进行定量分布分析，对于理解正常和病理条件下的细胞和组织生理学至关重要。当前的方法存在局限性，因为它们旨在将突触分布映射到染色神经元的树突上，和/或需要特定的专有软件包来实施。
为了克服这些障碍，我们开发了用于生长和收缩感兴趣体积（GSVI）的算法，以从三维图像堆栈中分离特定的细胞和组织区室进行定量分析，并将这些算法整合到一个用户友好的计算机程序中，该程序是开源的且可免费下载。
GSVI算法用于分离活体动物皮质中的血管周围区域以及组织学切片中染色脊髓运动神经元的细胞膜区域。我们在血管微损伤后，以亚细胞分辨率跟踪了注射荧光团从血管腔到血管周围和实质空间的实时活体生物分布，并绘制了脊髓运动神经元树突和体细胞区域中与膜相关的KCC2和gephyrin免疫标记的精确分布。
与现有方法相比，GSVI方法专门设计用于分离血管周围区域和与膜相关的区域进行定量分析，用户友好且免费。
GSVI算法有助于量化与细胞功能相关的染色生物标志物（如与细胞膜相关的通道）的区域差异，以及治疗策略对患病或受伤组织中生物分子、药物和细胞重新分布的影响。
