ImSwitch: Generalizing microscope control in Python

Moreno, Xavier Casas; Al-Kadhimi, Staffan; Alvelid, Jonatan; Bodén, Andreas; Testa, Ilaria

doi:10.21105/joss.03394

Cited by 31 publications

(25 citation statements)

References 10 publications

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“…The structure of rsGCaMP1.1 was solved with the MolRep 32 program from CCP4 (ref. 60 ). The closest homolog (PDB ID: 3ek4 ) served as a search model 61 .…”

Section: Methodsmentioning

confidence: 99%

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Genetically encoded photo-switchable molecular sensors for optoacoustic and super-resolution imaging

et al. 2021

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Reversibly photo-switchable proteins are essential for many super-resolution fluorescence microscopic and optoacoustic imaging methods. However, they have yet to be used as sensors that measure the distribution of specific analytes at the nanoscale or in the tissues of live animals. Here we constructed the prototype of a photo-switchable Ca2+ sensor based on GCaMP5G that can be switched with 405/488-nm light and describe its molecular mechanisms at the structural level, including the importance of the interaction of the core barrel structure of the fluorescent protein with the Ca2+ receptor moiety. We demonstrate super-resolution imaging of Ca2+ concentration in cultured cells and optoacoustic Ca2+ imaging in implanted tumor cells in mice under controlled Ca2+ conditions. Finally, we show the generalizability of the concept by constructing examples of photo-switching maltose and dopamine sensors based on periplasmatic binding protein and G-protein-coupled receptor-based sensors.

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“…The structure of rsGCaMP1.1 was solved with the MolRep 32 program from CCP4 (ref. 60 ). The closest homolog (PDB ID: 3ek4 ) served as a search model 61 .…”

Section: Methodsmentioning

confidence: 99%

“…11 for details). The software used for acquisition and final image reconstruction was ImSwitch 60 on Python 3.9.…”

Section: Methodsmentioning

confidence: 99%

Genetically encoded photo-switchable molecular sensors for optoacoustic and super-resolution imaging

et al. 2021

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“…μManager is a project for microscope control and data acquisition that uses ImageJ as its base [21] . There are several Python-based developments for microscope control like ImSwitch [22] , Microscope-Cockpit [23] . Some of them, like the Python microscope or PYME can perform both data acquisition and analysis or are close to that [22] , [24] , [25] , [26] .…”

Section: Hardware In Contextmentioning

confidence: 99%

The miEye: Bench-top super-resolution microscope with cost-effective equipment

Alsamsam¹,

Kopūstas²,

Jurevičiūtė³

et al. 2022

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“…Micropilot 6 ), including methods that adjust microscope settings (Micromator 7 ). Although similar methods do not yet exist for STED nanoscopy, an important step has been made by enabling microscope control through Python code (ImSwitch 8 for home-built systems and Imspector 9 for commercial Abberior microscopes). This basis allowed for the imaging of regions larger than the typical field of view (FOV) by STED 10 , and the implementation of event-triggered STED nanoscopy 5 .…”

Section: Mainmentioning

confidence: 99%

Automated STED nanoscopy for high-throughput imaging of cellular structures

Mol

Vlijm

2022

Preprint

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STimulated Emission Depletion (STED) nanoscopy uniquely combines a high spatial resolution (20-50nm in cells) with relatively fast imaging (frame rate of ~1-30Hz), straightforward sample preparation and direct image output (no postprocessing required). Although these characteristics in principle make STED very suitable for high-throughput imaging, only few steps towards automation have been made. Here, we have developed fully automated STED imaging, eliminating all manual steps including the selection and characterisation of the relevant (cellular) regions, sample focusing and positioning, and microscope adjustments. This automatic STED image acquisition increases the data output by roughly two orders of magnitude, resulting in a more efficient use of the high-end microscope, and the ability to detect and characterise objects that are only present in a small subset of the sample.

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ImSwitch: Generalizing microscope control in Python

Cited by 31 publications

References 10 publications

Genetically encoded photo-switchable molecular sensors for optoacoustic and super-resolution imaging

Genetically encoded photo-switchable molecular sensors for optoacoustic and super-resolution imaging

The miEye: Bench-top super-resolution microscope with cost-effective equipment

Automated STED nanoscopy for high-throughput imaging of cellular structures

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