A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry

Bonnie Nijhof, Anna Castells-Nobau, Louis Wolf, Jolanda M Scheffer- de Gooyert, Ignacio Monedero, Laura Torroja, Lluis Coromina, Jeroen A W M van der Laak and Annette Schenck

PLOS Computational Biology, 2016, vol. 12, issue 3, 1-25

Abstract: The morphology of synapses is of central interest in neuroscience because of the intimate relation with synaptic efficacy. Two decades of gene manipulation studies in different animal models have revealed a repertoire of molecules that contribute to synapse development. However, since such studies often assessed only one, or at best a few, morphological features at a given synapse, it remained unaddressed how different structural aspects relate to one another. Furthermore, such focused and sometimes only qualitative approaches likely left many of the more subtle players unnoticed. Here, we present the image analysis algorithm ‘Drosophila_NMJ_Morphometrics’, available as a Fiji-compatible macro, for quantitative, accurate and objective synapse morphometry of the Drosophila larval neuromuscular junction (NMJ), a well-established glutamatergic model synapse. We developed this methodology for semi-automated multiparametric analyses of NMJ terminals immunolabeled for the commonly used markers Dlg1 and Brp and showed that it also works for Hrp, Csp and Syt. We demonstrate that gender, genetic background and identity of abdominal body segment consistently and significantly contribute to variability in our data, suggesting that controlling for these parameters is important to minimize variability in quantitative analyses. Correlation and principal component analyses (PCA) were performed to investigate which morphometric parameters are inter-dependent and which ones are regulated rather independently. Based on nine acquired parameters, we identified five morphometric groups: NMJ size, geometry, muscle size, number of NMJ islands and number of active zones. Based on our finding that the parameters of the first two principal components hardly correlated with each other, we suggest that different molecular processes underlie these two morphometric groups. Our study sets the stage for systems morphometry approaches at the well-studied Drosophila NMJ.Author Summary: Altered synapse function underlies cognitive disorders such as intellectual disability, autism and schizophrenia. The morphology of synapses is crucial for their function but is often described using only a small number of parameters or categories. As a consequence, it is still unknown how different aspects of synapse morphology relate to each other and whether they respond in a coordinated or independent manner. Here, we report a sensitive and multiparametric method for systematic synapse morphometry at the Drosophila Neuromuscular Junction (NMJ), a popular model for mammalian synapse biology. Surveying a large NMJ image repository, we provide insights in the natural variation of NMJ morphology as a result of differences in gender, genetic background and abdominal body segment. We show which synapse parameters correlate and find that parameters fall into five groups. Based on our findings, we propose that two of them, NMJ size and geometry, are controlled by different molecular mechanisms. Our study provides insights into the design principles of a model synapse and tools that can be applied in future studies to identify genes that modulate or co-orchestrate different aspects of synapse morphology.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004823 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 04823&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004823

DOI: 10.1371/journal.pcbi.1004823

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().