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+
+
+
+ 20241124204847-050ec0b8e3636e42bef6920ff0c1039b76b4ecf0
+ 20241124204847
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 11
+ 2024
+
+
+ 9
+
+ 103
+
+
+
+ BrightEyes-MCS: a control software for multichannel
+scanning microscopy
+
+
+
+ Mattia
+ Donato
+
+ Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, 16152, Italy
+
+ https://orcid.org/0000-0003-0026-747X
+
+
+ Eli
+ Slenders
+
+ Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, 16152, Italy
+
+ https://orcid.org/0000-0002-6757-1372
+
+
+ Alessandro
+ Zunino
+
+ Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, 16152, Italy
+
+ https://orcid.org/0000-0002-2512-8751
+
+
+ Luca
+ Bega
+
+ Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, 16152, Italy
+
+
+
+ Giuseppe
+ Vicidomini
+
+ Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, 16152, Italy
+
+ https://orcid.org/0000-0002-3085-730X
+
+
+
+ 11
+ 24
+ 2024
+
+
+ 7125
+
+
+ 10.21105/joss.07125
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.14012812
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/7125
+
+
+
+ 10.21105/joss.07125
+ https://joss.theoj.org/papers/10.21105/joss.07125
+
+
+ https://joss.theoj.org/papers/10.21105/joss.07125.pdf
+
+
+
+
+
+ A robust and versatile platform for image
+scanning microscopy enabling super-resolution FLIM
+ Castello
+ Nature Methods
+ 16
+ 10.1038/s41592-018-0291-9
+ 2019
+ Castello, M., Tortarolo, G.,
+Buttafava, M., Deguchi, T., Villa, F., Koho, S., Pesce, L., Oneto, M.,
+Pelicci, S., Lanzanó, L., Bianchini, P., Sheppard, C. J. R., Diaspro,
+A., Tosi, A., & Vicidomini, G. (2019). A robust and versatile
+platform for image scanning microscopy enabling super-resolution FLIM.
+Nature Methods, 16, 175–178.
+https://doi.org/10.1038/s41592-018-0291-9
+
+
+ Focus image scanning microscopy for sharp and
+gentle super-resolved microscopy
+ Tortarolo
+ Nature Communications
+ 13
+ 10.1038/s41467-022-35333-y
+ 2022
+ Tortarolo, G., Zunino, A., Fersini,
+F., Castello, M., Piazza, S., Sheppard, C. J. R., Bianchini, P.,
+Diaspro, A., Koho, S., & Vicidomini, G. (2022). Focus image scanning
+microscopy for sharp and gentle super-resolved microscopy. Nature
+Communications, 13.
+https://doi.org/10.1038/s41467-022-35333-y
+
+
+ Single-photon microscopy to study
+biomolecular condensates
+ Perego
+ Nature Communications
+ 14
+ 10.1038/s41467-023-43969-7
+ 2023
+ Perego, E., Zappone, S., Castagnetti,
+F., Mariani, D., Vitiello, E., Rupert, J., Zacco, E., Tartaglia, G. G.,
+Bozzoni, I., Slenders, E., & Vicidomini, G. (2023). Single-photon
+microscopy to study biomolecular condensates. Nature Communications, 14,
+8224. https://doi.org/10.1038/s41467-023-43969-7
+
+
+ Super-resolution in confocal
+imaging
+ Sheppard
+ Optik
+ 80
+ 1988
+ Sheppard, C. (1988). Super-resolution
+in confocal imaging. Optik, 80, 53–54.
+
+
+ Image scanning microscopy
+ Müller
+ Physical Review Letters
+ 104
+ 10.1103/PhysRevLett.104.198101
+ 2010
+ Müller, C. B., & Enderlein, J.
+(2010). Image scanning microscopy. Physical Review Letters, 104.
+https://doi.org/10.1103/PhysRevLett.104.198101
+
+
+ Reconstructing the image scanning microscopy
+dataset: An inverse problem
+ Zunino
+ Journal of Optics
+ 39
+ 10.1088/1361-6420/accdc5
+ 2022
+ Zunino, A., Castello, M., &
+Vicidomini, G. (2022). Reconstructing the image scanning microscopy
+dataset: An inverse problem. Journal of Optics, 39, 064004.
+https://doi.org/10.1088/1361-6420/accdc5
+
+
+ Open-source tools enable accessible and
+advanced image scanning microscopy data analysis
+ Zunino
+ Nature Photonics
+ 17
+ 10.1038/s41566-023-01216-x
+ 2023
+ Zunino, A., Slenders, E., Fersini,
+F., Bucci, A., Donato, M., & Vicidomini, G. (2023). Open-source
+tools enable accessible and advanced image scanning microscopy data
+analysis. Nature Photonics, 17, 457–458.
+https://doi.org/10.1038/s41566-023-01216-x
+
+
+ Structured detection for simultaneous
+super-resolution and optical sectioning in laser scanning
+microscopy
+ Zunino
+ 10.48550/arXiv.2406.12542
+ 2024
+ Zunino, A., Garrè, G., Perego, E.,
+Zappone, S., Donato, M., & Vicidomini, G. (2024). Structured
+detection for simultaneous super-resolution and optical sectioning in
+laser scanning microscopy.
+https://doi.org/10.48550/arXiv.2406.12542
+
+
+ SPAD-based asynchronous-readout array
+detectors for image-scanning microscopy
+ Buttafava
+ Optica
+ 7
+ 10.1364/optica.391726
+ 2020
+ Buttafava, M., Villa, F., Castello,
+M., Tortarolo, G., Conca, E., Sanzaro, M., Piazza, S., Bianchini, P.,
+Diaspro, A., Zappa, F., Vicidomini, G., & Tosi, A. (2020).
+SPAD-based asynchronous-readout array detectors for image-scanning
+microscopy. Optica, 7, 755.
+https://doi.org/10.1364/optica.391726
+
+
+ Cooled SPAD array detector for low light-dose
+fluorescence laser scanning microscopy
+ Slenders
+ Biophysical Reports
+ 1
+ 10.1016/j.bpr.2021.100025
+ 2021
+ Slenders, E., Perego, E., Buttafava,
+M., Tortarolo, G., Conca, E., Zappone, S., Pierzynska-Mach, A., Villa,
+F., Petrini, E. M., Barberis, A., Tosi, A., & Vicidomini, G. (2021).
+Cooled SPAD array detector for low light-dose fluorescence laser
+scanning microscopy. Biophysical Reports, 1, 100025.
+https://doi.org/10.1016/j.bpr.2021.100025
+
+
+ Compact and effective photon-resolved image
+scanning microscope
+ Tortarolo
+ Advanced Photonics
+ 6
+ 10.1117/1.ap.6.1.016003
+ 2024
+ Tortarolo, G., Zunino, A., Piazza,
+S., Donato, M., Zappone, S., Pierzyńska-Mach, A., Castello, M., &
+Vicidomini, G. (2024). Compact and effective photon-resolved image
+scanning microscope. Advanced Photonics, 6.
+https://doi.org/10.1117/1.ap.6.1.016003
+
+
+ The BrightEyes-TTM as an open-source
+time-tagging module for democratising single-photon
+microscopy
+ Rossetta
+ Nature Communications
+ 13
+ 10.1038/s41467-022-35064-0
+ 2022
+ Rossetta, A., Slenders, E., Donato,
+M., Zappone, S., Fersini, F., Bruno, M., Diotalevi, F., Lanzanò, L.,
+Koho, S., Tortarolo, G., Barberis, A., Crepaldi, M., Perego, E., &
+Vicidomini, G. (2022). The BrightEyes-TTM as an open-source time-tagging
+module for democratising single-photon microscopy. Nature
+Communications, 13, 7406.
+https://doi.org/10.1038/s41467-022-35064-0
+
+
+ ImSwitch: Generalizing microscope control in
+python
+ Moreno
+ Journal of Open Source
+Software
+ 6
+ 10.21105/joss.03394
+ 2021
+ Moreno, X., Al-Kadhimi, S., Alvelid,
+J., Bodén, A., & Testa, I. (2021). ImSwitch: Generalizing microscope
+control in python. Journal of Open Source Software, 6, 3394.
+https://doi.org/10.21105/joss.03394
+
+
+ Computer control of microscopes using
+µManager
+ Edelstein
+ Current Protocols in Molecular
+Biology
+ 92
+ 10.1002/0471142727.mb1420s92
+ 2010
+ Edelstein, A., Amodaj, N., Hoover,
+K., Vale, R., & Stuurman, N. (2010). Computer control of microscopes
+using µManager. Current Protocols in Molecular Biology, 92.
+https://doi.org/10.1002/0471142727.mb1420s92
+
+
+ Confocal-based fluorescence fluctuation
+spectroscopy with a SPAD array detector
+ Slenders
+ Light: Science &
+Applications
+ 10
+ 10.1038/s41377-021-00475-z
+ 2021
+ Slenders, E., Castello, M.,
+Buttafava, M., Villa, F., Tosi, A., Lanzanò, L., Koho, S. V., &
+Vicidomini, G. (2021). Confocal-based fluorescence fluctuation
+spectroscopy with a SPAD array detector. Light: Science &
+Applications, 10.
+https://doi.org/10.1038/s41377-021-00475-z
+
+
+ ISM-FLUX: MINFLUX with an array
+detector
+ Slenders
+ Physical Review Research
+ 5
+ 10.1103/PhysRevResearch.5.023033
+ 2023
+ Slenders, E., & Vicidomini, G.
+(2023). ISM-FLUX: MINFLUX with an array detector. Physical Review
+Research, 5, 023033.
+https://doi.org/10.1103/PhysRevResearch.5.023033
+
+
+ Arkitekt : An open-source framework for
+modern bioimage workflows
+ Roos
+ 2023
+ Roos, J. (2023). Arkitekt : An
+open-source framework for modern bioimage workflows [PhD thesis,
+Université de Bordeaux].
+https://theses.hal.science/tel-04341599
+
+
+
+
+
+
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+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+7125
+10.21105/joss.07125
+
+BrightEyes-MCS: a control software for multichannel
+scanning microscopy
+
+
+
+https://orcid.org/0000-0003-0026-747X
+
+Donato
+Mattia
+
+mattia.donato@iit.it
+
+*
+
+
+https://orcid.org/0000-0002-6757-1372
+
+Slenders
+Eli
+
+
+
+
+https://orcid.org/0000-0002-2512-8751
+
+Zunino
+Alessandro
+
+
+
+
+
+Bega
+Luca
+
+
+
+
+https://orcid.org/0000-0002-3085-730X
+
+Vicidomini
+Giuseppe
+
+
+
+
+
+Molecular Microscopy and Spectroscopy, Istituto Italiano di
+Tecnologia, Genoa, 16152, Italy
+
+
+
+
+* E-mail: mattia.donato@iit.it
+
+
+30
+10
+2024
+
+9
+103
+7125
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2024
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+Python
+imaging
+microscopy
+flim
+image scanning microscopy
+
+
+
+
+
+ Introduction
+
Fluorescence microscopy is an essential workhorse of biomedical
+ sciences, thanks to its capability to provide specific and
+ quantitative information on the observed specimens. The advent of
+ super-resolution techniques has further improved the quality of the
+ images produced by optical microscopes. Among the numerous
+ super-resolution techniques, image scanning microscopy (ISM) emerged
+ as a robust and reliable technique, being able to provide gentle
+ imaging at a high signal-to-noise ratio (SNR) with excellent optical
+ sectioning
+ (Castello
+ et al., 2019;
+ Perego
+ et al., 2023;
+ Tortarolo
+ et al., 2022). An ISM microscope shares the same architecture
+ as a confocal laser scanning microscope (CLSM) but exploits a pixeled
+ detector. Each detector element acts as a displaced small pinhole and
+ generates a confocal-like image after a full scan of the field of
+ view. As the detector is made by a matrix of pixels, the raw ISM
+ dataset is not a 2D image, it is a 4D dataset, which can be seen in
+ two ways: each single pixel scanned on the sample plane is associated
+ with a micro-image of the detector; vice-versa, for each physical
+ pixel on the detector plane is associated a full-scanned field of view
+ on the sample plane
+ (Müller
+ & Enderlein, 2010;
+ Sheppard,
+ 1988). Using tailored reconstruction algorithms, the images of
+ the raw ISM dataset can be fused to produce a single super-resolution
+ image using all the photons collected by the detector, guaranteeing an
+ excellent SNR
+ (Zunino
+ et al., 2022,
+ 2023,
+ 2024).
+
The capabilities of ISM can be further extended if coupled with a
+ fast detector, such as an array of single photon avalanche diodes
+ (SPAD)
+ (Buttafava
+ et al., 2020;
+ Slenders,
+ Perego, et al., 2021). This type of detector features the
+ readout of each pixel is independent and async, preserving the
+ temporal information of each photon arrival. In other words, it is
+ possible to tag each photon with its arrival time with respect to the
+ excitation laser, expanding the dataset to a 5D array. This data can
+ be used for estimating the fluorescence lifetime allowing the
+ fluorescent lifetime imaging scanning microscopy (FLISM), a microscopy
+ technique that merges the temporal information with the ISM advantages
+ mentioned above
+ (Rossetta
+ et al., 2022;
+ Tortarolo
+ et al., 2024).
+
+ Statement of need
+
In live-cell microscopy, the demand for high-resolution imaging
+ coexists with the necessity to protect sample integrity. Fast pixel
+ dwell times are instrumental in this pursuit, minimizing sample
+ damage while enhancing image quality. Leveraging the capabilities of
+ the SPAD array detector, which can achieve megahertz photon flux per
+ single pixel, requires an advanced data acquisition and control
+ system based on FPGA technology. Existing open-source microscope
+ control software, such as
+ ImSwitch (Moreno
+ et al., 2021) and
+ µManager (Edelstein
+ et al., 2010), while general-purpose, flexible, and
+ scriptable, are primarily designed for camera-based systems. They
+ are not optimized to handle the precise synchronization and
+ high-performance demands of ISM instruments, especially for
+ configuration with pixel dwell times as low as 1 µs and photon flux
+ rates in the megahertz range.
+
In contrast, BrightEyes-MCS (Microscope Control Suite) is an
+ open-source software specifically designed for controlling
+ laser-scanning microscopes equipped with SPAD detectors. It
+ overcomes the limitations of other software by providing real-time
+ synchronization, high-throughput data acquisition, and seamless
+ compatibility with the high-speed scanning demands of ISM
+ systems.
+
BrightEyes-MCS not only features a user-friendly graphical
+ interface (GUI) for microscope control, but also supports real-time
+ previews and efficient data saving in the HDF5 format, which is
+ ready for internal processing or integration with external analysis
+ tools. Although tailored for ISM, BrightEyes-MCS is versatile and
+ can be adapted for various applications, including fluorescence
+ correlation spectroscopy (FCS), spectroscopy, and other advanced
+ imaging techniques. This flexibility makes it a valuable resource
+ for researchers working with diverse microscopy setups that require
+ precise control and high-performance data acquisition.
+
BrightEyes is the name of the project founded by the ERC in 2018
+ (Consolidator Grant, N. 818699). In this context, other open-source
+ tools have been developed, like BrighEyes-TTM, an open-hardware
+ time-tagging module
+ (Rossetta
+ et al., 2022), BrightEyes-ISM
+ (Zunino
+ et al., 2023), a python library for ISM data analysis.
+
+
+
+ System Architecture
+
+
Scheme of BrightEyes-MCS architecture.
+
+
+
+
The BrightEyes-MCS controls and handles the data stream with the
+ underlying hardware, based on a field-programmable gate array (FPGA)
+ from National Instrument (NI). These boards provide the digital
+ inputs/outputs (I/O), and some models also provide analog I/Os.
+ [fig:fig1] shows a
+ sketch of the system architecture.
+
The system architecture features two parts: i) the low-level
+ firmware (BrightEyes-MCSLL) managing the hardware and the electronics,
+ running on the FPGA; ii) the high-level software (BrightEyes-MCS)
+ which includes the GUI and its libraries running on the PC.
+
The firmware was developed specifically for image scanning
+ microscopy, on NI LabView for FPGA. It is a separate project, free,
+ downloadable but it is not open source. The firmware can be controlled
+ by the high-level software through registers and the data are streamed
+ through dedicated FIFOs. Details are out of the scope of this paper
+ and together with its documentation are available in the
+ BrightEyes-MCSLL
+ repository.
+
Each time an acquisition starts, BrightEyes-MCS automatically loads
+ the firmware into the FPGA, sets the registers (for example for
+ configuring the number of pixels, and dwell-time), and waiting for the
+ data arriving through FIFO.
+
BrightEyes-MCS supports up to 25 digital channels (plus 2 extra
+ channels) and up-to 2 analog inputs. It controls the scanning and the
+ positioning with a maximum speed of 1 us for pixel dwell time and it
+ is possible to set a further time-subdivision of the same pixel down
+ to 0.5us per bin.
+
There is also the possibility of activating the so-called Digital
+ Frequency Domain (DFD) mode
+ (Tortarolo
+ et al., 2024). It is a heterodyne technique that allow to
+ obtain higher time resolution. In this mode, the laser pulsing is
+ driven by the FPGA, and for each detector element it is acquired the
+ histogram of the time-arrival of the photons with respect to the laser
+ pulse (known as Time-Correlated Single Photon Counting, TCSPC), with a
+ bin precision of 0.3 ns.
+
The software has been tested in a machine equipped with an Intel
+ Xeon CPU (2.2GHz) with 12 cores, and 32GByte of RAM. This system was
+ able to acquire datasets up to 2000x2000x25x81 (x, y, ch,t).
+
+
+ Specifications
+
The specifications of BrightEyes-MCS depend on the hardware
+ features of the NI FPGA used. To actuate the scanners, analog outputs
+ are needed. Since not all FPGA models provide them, they can be
+ replaced with an external independent board.
+
+
+
+
+
+
+
+
+
+
+
Channels
+
25
+
+
+
Extra Channels
+
2
+
+
+
Analog Inputs Channels
+
2 (selected out of 8), or not supported with
+ external DAC
+
+
+
Analog Output Channels
+
8, or 4 with external DAC
+
+
+
Pixel Dwell Time
+
1.0 µs
+
+
+
Minimum time bin
+
normal mode
+
0.5 µs
+
+
+
+
DFD mode
+
0.2 ns
+
+
+
Data storage
+
HDF5, data and metadata
+
+
+
Data array dimension
+
repetition, z, y, x, time-bin,
+ detector-channel
+
+
+
Boards tested
+
25 ch.
+
+
+
NI USB-7856R
+
+
+
+
+
+
+
+
+
NI USB-7856R OEM
+
+
+
+
+
+
+
+
+
NI PXIe-7856R
+
+
+
+
+
+
+
+
+
NI PXIe-7822R
+
+
+
(with external DAC*)
+
+
+
+
+
+
+
NI PCIe-7820R
+
+
+
(with external DAC*)
+
+
+
+
+
+
+
+
+
*As external DAC has been tested the
+ commercial evaluation board EVAL-AD5764 from Analog
+ Device.
+
+
+
+
+
+
+ Hardware and Electronics
+
+
Scheme of the hardware of BrightEyes-MCS
+ system.
+
+
+
This sketch shows the main electronics components controlled by
+ BrightEyes-MCS.
+
The sample positioning and the scanning are controlled by the
+ analog outputs connected to positioner. They can be either linear
+ galvo mirrors or piezo stages. The analog outputs supported are 8 and
+ the user can select for each channel to be used as X, Y, Z, or a
+ constant voltage. The possibility of setting a constant voltage is
+ useful for other types of elements such as Acousto-optic modulators
+ (AOM).
+
The support of photomultiplier tubes (PMTs) is given by reading out
+ two analog inputs at the same time. The user can select the two analog
+ inputs out of 8 channels.
+
In the case of the NI FPGA board used lacking the analog I/O, the
+ analog outputs are provided by an external DAC connected to the
+ digital I/O of the FPGA. Similarly to it, we are planning to support
+ for external ADC for providing the analog inputs.
+
The BrightEyes-MCS can control up to 4 lasers through 4 digital
+ output lines. They can be switched on and off easily at the start and
+ end of a measurement. In the case of DFD mode – i.e. time-of-arrival /
+ lifetime measurement – the system provides the synchronization clock
+ to the lasers for triggering the pulses.
+
+
+ Software Architecture
+
+
Scheme of the software
+ architecture
+
+
+
The Figure above shows the main parts of the BrightEyes-MCS Python
+ code. The main process runs the GUI (MainWindow). When an acquisition
+ is started the FPGAHandle instantiates the other three parallel
+ processes.
+
These processes are “infinite loops” (until the event
+ end-of-acquisition), implemented using the
+ multiprocessing
+ library. As they are independent Python instances, the communication
+ between needs “shared” objects such as mp.Dict, mp.Event and
+ MemorySharedArray (which uses mp.Array). Here below a short
+ description of what the three processes:
+
+
+
The FpgaHandleProcess uploads and runs the firmware on the
+ FPGA, listen to commands from the Main, and executes them. These
+ commands are mainly related to read/write registers. This process
+ continuously reads data available on the FIFOs from the FPGA,
+ sending them to the DataPreProcess via a mp.Queue. The
+ communication with the data is given by the
+ nifpga
+ library. The FIFO readout can performed either with nifpga library
+ or with
+ nifpga-fast-fifo-recv,
+ a Python library written in Rust which we developed for reaching
+ higher readout performances.
+
+
+
The DataPreProcess is just waiting for data from the input
+ Queue and cumulate them up to a given value and sends them to the
+ AcquisitionLoopProcess with an mp.Queue.
+
+
+
The AcquisitionLoopProcess converts the raw data to a numpy
+ array. This is performed by FastConverter, a function developed in
+ Cython due to performance reasons. The converted data are reshaped
+ and stored into a buffer used for the live preview and for saving
+ the data to the HDF5 file
+ h5py
+ library.
+
+
+
The GUI is implemented with
+ PySide2
+ library. The images and the other plots are drawn by
+ PyQtGraph
+ library. The GUI also provides an integrated Python console
+ (which uses the library
+ QtConsole
+ ) which exposes all running objects. This means that on the console,
+ the user can modify parts of the running software, in-live. This
+ allows to run scripts for automatized operation, moreover, it allows
+ to easily run scripts for example a quick data analysis at the end of
+ the measurement.
+
+
Screenshot of BrightEyes-MCS
+ GUI
+
+
+
+
+ Conclusion
+
The BrightEyes-MCS is a new open-source tool for controlling image
+ scanning microscopes. It provides a real-time preview and supports up
+ to 25 channels. It is designed for image-scanning microscopy with a
+ SPAD array but allows easily to be used on different scenarios. For
+ example, it can be used on a single-detector confocal microscope
+ equipped with a single-pixel SPAD or PMT. It can be used also outside
+ the context of scanning microscopy as for example it is possible to
+ use it in the context of Fluorescence correlation spectroscopy (FCS)
+ (Slenders,
+ Castello, et al., 2021). Furthermore, the software can support
+ single-molecule localization microscopy (SMLM) with non-conventional
+ scanning patterns, such as MINFLUX and ISMFLUX
+ (Slenders
+ & Vicidomini, 2023). Data acquired by BrightEyes-MCS are
+ stored in HDF5 format, which can be easily opened with popular data
+ analysis frameworks like Python notebooks or MATLAB. Additionally, a
+ Python library called BrightEyes-ISM
+ (Zunino
+ et al., 2023) has been developed, enabling users to open these
+ files and apply enhancement techniques such as APR
+ (Castello
+ et al., 2019) and Focus-ISM
+ (Tortarolo
+ et al., 2022). For those using
+ Napari,
+ a plugin named
+ Napari-ISM
+ is available, allowing the same enhancements to be performed within
+ its graphical user interface.
+
As Python open-source tool can be easily adapted to other systems.
+ For example, BrightEyes-MCS has been integrated with BrightEyes-TTM,
+ an open-source time-tagging module that allows to time-tag single
+ photons with a resolution of about
+ 30ps (Rossetta
+ et al., 2022). It can be controlled by BrightEyes-MCS: every
+ time an acquisition is starting the TTM remotely starts acquiring data
+ in another machine. Moreover, BrightEyes-MCS can be controlled by
+ external tools via HTTP REST APIs, which allow access to the GUI
+ parameters, main commands (such as start acquisition, preview, and
+ stop), and the preview image. This facilitates integration into a
+ larger control framework, such as ImSwitch
+ (Moreno
+ et al., 2021) or Arkitekt
+ (Roos,
+ 2023).
+
In conclusion, BrightEyes-MCS presents a promising open-source
+ solution for controlling image-scanning microscopes. It features
+ real-time preview and multi-channel support, and offers great
+ versatility for various microscopy setups. We envision that our
+ open-source and free tool will be widely adopted by the scientific
+ community, contributing to the dissemination of open science culture
+ in microscopy.
+
+
+ Disclosures & Acknowledgements
+
G.V. has a personal financial interest (co-founder) in Genoa
+ Instruments, Italy. The remaining authors declare no competing
+ interests. We acknowledge our former colleagues, Marco Castello and
+ Simonluca Piazza, now founder and respectively CTO and CEO of Genoa
+ Instruments, for their important contributions to the firmware in the
+ early stages of the project. BrightEyes-MCS is designed to be an
+ open-source software for research purposes and does not reflect the
+ performance of the commercial products offered by Genoa
+ Instruments.
+
The repository of BrightEyes-MCS is
+ https://github.com/VicidominiLab/BrightEyes-MCS.