From Pixels to Connections: Exploring in Vitro Neuron Reconstruction Software for Network Graph Generation

Overview

Journal Commun Biol

Specialty Biology

Date 2024 May 15

PMID 38750282

Authors

Cassandra Hoffmann

Ellie Cho

Andrew Zalesky

Maria A Di Biase

Affiliations

Soon will be listed here.

Abstract

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

References

Hyvarinen T, Hyysalo A, Kapucu F, Aarnos L, Vinogradov A, Eglen S . Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures. Sci Rep. 2019; 9(1):17125. PMC: 6868015. DOI: 10.1038/s41598-019-53647-8. View

Hickey S, Ung B, Bader C, Brooks R, Lazniewska J, Johnson I . Fluorescence Microscopy-An Outline of Hardware, Biological Handling, and Fluorophore Considerations. Cells. 2022; 11(1). PMC: 8750338. DOI: 10.3390/cells11010035. View

de Souza E, Costa E, Castellano G . Investigation of anisotropic fishing line-based phantom as tool in quality control of diffusion tensor imaging. Radiol Phys Technol. 2019; 12(2):161-171. DOI: 10.1007/s12194-019-00507-9. View

Ho S, Chao C, Huang H, Chiu T, Charoenkwan P, Hwang E . NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery. BMC Bioinformatics. 2011; 12:230. PMC: 3121649. DOI: 10.1186/1471-2105-12-230. View

Kayasandik C, Negi P, Laezza F, Papadakis M, Labate D . Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer. Sci Rep. 2018; 8(1):6450. PMC: 5915526. DOI: 10.1038/s41598-018-24753-w. View

Hynds D, Snow D . A semi-automated image analysis method to quantify neurite preference/axon guidance on a patterned substratum. J Neurosci Methods. 2002; 121(1):53-64. DOI: 10.1016/s0165-0270(02)00231-5. View

Shih C, Sporns O, Yuan S, Su T, Lin Y, Chuang C . Connectomics-based analysis of information flow in the Drosophila brain. Curr Biol. 2015; 25(10):1249-58. DOI: 10.1016/j.cub.2015.03.021. View

Mukherjee S, Condron B, Acton S . Tubularity flow field--a technique for automatic neuron segmentation. IEEE Trans Image Process. 2014; 24(1):374-89. DOI: 10.1109/TIP.2014.2378052. View

Kandel M, Kim E, Lee Y, Tracy G, Chung H, Popescu G . Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity. ACS Sens. 2021; 6(5):1864-1874. PMC: 8815662. DOI: 10.1021/acssensors.1c00100. View

10.

Haas A, Prigent S, Dutertre S, Le Drean Y, Le Page Y . Neurite analyzer: An original Fiji plugin for quantification of neuritogenesis in two-dimensional images. J Neurosci Methods. 2016; 271:86-91. DOI: 10.1016/j.jneumeth.2016.07.011. View

11.

Watts D, Strogatz S . Collective dynamics of 'small-world' networks. Nature. 1998; 393(6684):440-2. DOI: 10.1038/30918. View

12.

Gu H, Yu S, Gutekunst C, Gross R, Wei L . Inhibition of the Rho signaling pathway improves neurite outgrowth and neuronal differentiation of mouse neural stem cells. Int J Physiol Pathophysiol Pharmacol. 2013; 5(1):11-20. PMC: 3601458. View

13.

Vallotton P, Lagerstrom R, Sun C, Buckley M, Wang D, De Silva M . Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision. Cytometry A. 2007; 71(10):889-95. DOI: 10.1002/cyto.a.20462. View

14.

Ganmor E, Segev R, Schneidman E . Sparse low-order interaction network underlies a highly correlated and learnable neural population code. Proc Natl Acad Sci U S A. 2011; 108(23):9679-84. PMC: 3111274. DOI: 10.1073/pnas.1019641108. View

15.

Seguin C, Sporns O, Zalesky A . Brain network communication: concepts, models and applications. Nat Rev Neurosci. 2023; 24(9):557-574. DOI: 10.1038/s41583-023-00718-5. View

16.

Kim K, Kim S, Minxha J, Palmore G . A novel method for analyzing images of live nerve cells. J Neurosci Methods. 2011; 201(1):98-105. DOI: 10.1016/j.jneumeth.2011.07.017. View

17.

Rishal I, Golani O, Rajman M, Costa B, Ben-Yaakov K, Schoenmann Z . WIS-NeuroMath enables versatile high throughput analyses of neuronal processes. Dev Neurobiol. 2012; 73(3):247-56. DOI: 10.1002/dneu.22061. View

18.

Donohue D, Ascoli G . Automated reconstruction of neuronal morphology: an overview. Brain Res Rev. 2010; 67(1-2):94-102. PMC: 3086984. DOI: 10.1016/j.brainresrev.2010.11.003. View

19.

Glaser J, GLASER E . Neuron imaging with Neurolucida--a PC-based system for image combining microscopy. Comput Med Imaging Graph. 1990; 14(5):307-17. DOI: 10.1016/0895-6111(90)90105-k. View

20.

Al-Kofahi K, Can A, Lasek S, Szarowski D, Dowell-Mesfin N, Shain W . Median-based robust algorithms for tracing neurons from noisy confocal microscope images. IEEE Trans Inf Technol Biomed. 2004; 7(4):302-17. DOI: 10.1109/titb.2003.816564. View