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Open-Source Python Software in Super-Resolution Fluorescence Microscopy: from Instrument Control to Quantitative Data Analysis

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Open-Source Python Software in Super-Resolution Fluorescence Microscopy: from Instrument Control to Quantitative Data Analysis Open-source python software in super-resolution fluorescence microscopy: from instrument control to quantitative data analysis Federico M. Barabas1,2,3, Luciano A. Masullo1,2, 3, Andreas Bodén3, Fernando D. Stefani1,2, Ilaria Testa3 1 Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina. 2 Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón 1 Ciudad Universitaria, C1428EHA, Buenos Aires, Argentina. 3 Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden We present Tempesta and Gollum, two software for super-resolution fluorescence microscopy experiments coded in Python. Tempesta is an integral control software for optical microscopes. Gollum is an analysis software to automatically detect and quantify periodic structures in super-resolved images, which was implemented to study the actin/spectrin membrane structure in hippocampal neurons. These software are a practical and reliable alternative to commercial frameworks for experiment control and analysis, with the additional advantages of being free, open-source, more flexible, and supported by a large developer community. Tempesta[1] is a modular software to control microscopy experiments. It includes several key features for single-molecule localization such as large dataset handling, a focus stabilization module, two-color view and routines for background subtraction (specific for single-molecule data) and for the precise (nanometric) spatial overlap of multicolor acquisitions. New releases include support for the sequential triggering of illumination lasers and confocal scanning imaging. It is in constant development with inputs from its users at different setups and laboratories. In recent years, super-resolution imaging has allowed the visualization of new and previously unknown protein periodic structures[2]. Deciphering the biological functions of such protein nanostructures requires systematic and quantitative analysis of large number of images. Gollum[3] can automatically detect and quantify the protein periodic structures in super-resolved images. It analyzes subregions of the original image to test the presence of a predefined target pattern by computing the two-dimensional Pearson correlation. Here, we demonstrate the software capabilities by studying the spectrin membrane-associated periodic skeleton (MPS) in hippocampal neurons of 2 to 40 days in vitro (DIV). The super resolution images were acquired using two different nanoscopies: STED and STORM. The automated analysis reveals that both the abundance and the regularity of the MPS increase over time and reach maximum plateau values after 14 DIV. The analysis method is is highly versatile and can be applied to practically any periodic structure. We provide the open- source code and a user-friendly graphical interface in order to maximize the impact among experimental scientists with little or no expertise in programming. [1] F. M. Barabas, L. A. Masullo, F. D Stefani. “Tormenta: An open source Python-powered control software for camera based optical microscopy”. Rev. Sci. Instrum. 87, 126103 (2016). https://github.com/fedebarabas/tormenta, https://github.com/TestaLab/Tempesta/ [2] Xu, K., Zhong, G. & Zhuang, X. “Actin, spectrin, and associated proteins form a periodic cytoskeletal structure in axons”. Science 339, 452–6 (2013). [3] F. M. Barabas, L. A. Masullo, M. D. Bordenave, S. A. Giusti, N. Unsain, D. Refojo, A. Cáceres, F. D. Stefani. “Automated quantification of protein periodic nanostructures in fluorescence nanoscopy images: abundance and regularity of neuronal spectrin membrane-associated skeleton”. Scientific Reports, 7, 16029 (2017)- https://github.com/cibion-conicet/Gollum. .
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