ABSTRACT
A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.
Subject(s)
Microscopy , Software , Humans , Community SupportABSTRACT
We present a computational framework to simultaneously perform image acquisition, reconstruction, and analysis in the context of open-source microscopy automation. The setup features multiple computer units intersecting software with hardware devices and achieves automation using python scripts. In practice, script files are executed in the acquisition computer and can perform any experiment by modifying the state of the hardware devices and accessing experimental data. The presented framework achieves concurrency by using multiple instances of ImSwitch and napari working simultaneously. ImSwitch is a flexible and modular open-source software package for microscope control, and napari is a multidimensional image viewer for scientific image analysis. The presented framework implements a system based on file watching, where multiple units monitor a filesystem that acts as the synchronization primitive. The proposed solution is valid for any microscope setup, supporting various biological applications. The only necessary element is a shared filesystem, common in any standard laboratory, even in resource-constrained settings. The file watcher functionality in Python can be easily integrated into other python-based software. We demonstrate the proposed solution by performing tiling experiments using the molecular nanoscale live imaging with sectioning ability (MoNaLISA) microscope, a high-throughput super-resolution microscope based on reversible saturable optical fluorescence transitions (RESOLFT).
ABSTRACT
Live-cell imaging of biological structures at high resolution poses challenges in the microscope throughput regarding area and speed. For this reason, different parallelisation strategies have been implemented in coordinate- and stochastic-targeted switching super-resolution microscopy techniques. In this line, the molecular nanoscale live imaging with sectioning ability (MoNaLISA), based on reversible saturable optical fluorescence transitions (RESOLFT), offers 45 - 65 nm $45 - 65\;{\rm{nm}}$ resolution of large fields of view in a few seconds. In MoNaLISA, engineered light patterns strategically confine the fluorescence to sub-diffracted volumes in a large area and provide optical sectioning, thus enabling volumetric imaging at high speeds. The optical setup presented in this paper extends the degree of parallelisation of the MoNaLISA microscope by more than four times, reaching a field-of-view of ( 100 - 130 µ m ) 2 ${( {100 - 130\;{\rm{\mu m}}} )^2}$ . We set up the periodicity and the optical scheme of the illumination patterns to be power-efficient and homogeneous. In a single recording, this new configuration enables super-resolution imaging of an extended population of the post-synaptic density protein Homer1c in living hippocampal neurons.
ABSTRACT
The performance of fluorescence microscopy and nanoscopy is often discussed by the effective point spread function and the optical transfer function. However, due to the complexity of the fluorophore properties such as photobleaching or other forms of photoswitching, which introduce a variance in photon emission, it is not trivial to choose optimal imaging parameters and to predict the spatial resolution. In this paper, we analytically derive a theoretical framework for estimating the achievable resolution of a microscope depending on parameters such as photoswitching, labeling densities, exposure time and sampling. We developed a numerical simulation software to analyze the impact of reversibly switchable probes in RESOLFT imaging.
