CryoSIM: super resolution 3D structured illumination cryogenic fluorescence microscopy for correlated ultra-structural imaging

Pinto

Hall

et al. 2021

Preprint

Self Cite

We have developed “Microscope-Cockpit” (Cockpit), a highly adaptable open source user-friendly Python-based GUI environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near-instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses “Python-Microscope” (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging.HighlightsUser-friendly setup and use for simple to complex bespoke microscopes.Facilitates collaborations between biomedical scientists and microscope technologists.Touchscreen for near-instantaneous navigation of specimen landscape.Uses Python-Microscope, for abstracted open source hardware device control.Well-suited for user training of AI-algorithms for automated microscopy.

“…The CryoSIM system has previously been published in detail [28, 29]. It was used to image HeLa cells grown on carbon coated gold EM grids under standard tissue culture conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Cockpit has already been deployed in three systems reported in previous publications. The first is a cryo-super resolution microscope for correlative imaging, CryoSIM [28, 29](Figure 5(a)). The second demonstration was a laser-free spinning disk confocal microscope using adaptive optics (AO) for aberration correction [30] (Figure 5(b)).…”

Section: Use Casesmentioning

confidence: 99%

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

Pinto

Hall

et al. 2021

Preprint

Self Cite

“…CryoSIM ( Phillips et al , 2020) and Aurox AO ( Hussain et al , 2020) are two example setups that use the Microscope device-server for hardware control in different ways. CryoSIM incorporates four lasers and two filter wheels on one computer, a spatial light modulator on a second computer, two cameras on a third computer, and a fourth computer for overall system control, utilising the device-server to distribute many devices over several computers.…”

Section: Use Casesmentioning

confidence: 99%

Python-Microscope: A new open source Python library for the control of microscopes

Pinto

Hall

et al. 2021

Preprint

Self Cite

Bespoke microscopes often require control of multiple hardware devices and precise hardware coordination. It is also desirable to have a control solution that is scalable to more complex systems and translatable between components from different manufacturers. Here we report Python-Microscope, a free and open source Python library for high performance control of arbitrarily complex and scalable bespoke microscopes. Python-Microscope offers an elegant pythonic software platform to control microscopes, abstracting differences between physical devices by providing a defined interface for different device types. These include cameras, filter wheels, light sources, deformable mirrors, and stages. Concrete implementations are provided for a range of specific hardware and a framework is in place for further expansion. Python-Microscope supports the distribution of devices over multiple computers while maintaining synchronisation via highly precise hardware triggers. We discuss the architecture choices of Python-Microscope that overcome the performance problems often raised against Python and demonstrate the different use cases that drove its design: its integration in user facing projects, namely in the Microscope-Cockpit project; in controlling complex microscopes at high speed while using the Python programming language; and as a microscope simulation tool for software development.

“…To ensure data fidelity all samples need to be vitrified through snap‐freezing. Vitrification of biological samples 34 is the gold standard for sample preparation for SXT, 33,35 electron microscopy 36 and fluorescence microscopy 33,37,38 because it confers the following advantages: (a) cryopreservation of biological structures in their near‐native state without the need for chemical fixation, (b) protection of biological samples from radiation damage following exposure to soft X‐rays and (c) immobilisation of samples to capture a snapshot in time as well as preventing drift during data acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…At the UK synchrotron correlative cryo‐imaging beamline B24, cryoSXT is an established imaging mode accessible to both academia and industry 35 . To complement and further augment the capacity of cryoSXT, the beamline offers access to a newly developed 3D super resolution fluorescence microscope: cryo‐structured illumination microscopy (cryoSIM) 38 . Thus, fluorescently tagged features of interest within biological samples can be unambiguously correlated with ultrastructural features obtained by cryoSXT – a technique combination within the collective of correlative light/fluorescence and X‐ray tomography (CLXT).…”

Section: Introductionmentioning

confidence: 99%

Correlative imaging using super‐resolution fluorescence microscopy and soft X‐ray tomography at cryogenic temperatures provides a new way to assess virosome solutions for vaccine development

Okolo

Jadhav

et al. 2021

Journal of Microscopy

Self Cite

Active virosomes (AVs) are derivatives of viruses, broadly similar to ‘parent’ pathogens, with an outer envelope that contains a bespoke genome coding for four to five viral proteins capable of eliciting an antigenic response. AVs are essentially novel vaccine formulations that present on their surface selected viral proteins as antigens. Once administered, they elicit an initial ‘anti‐viral’ immune response. AVs are also internalised by host cells where their cargo viral genes are used to express viral antigen(s) intracellularly. These can then be transported to the host cell surface resulting in a second wave of antigen exposure and a more potent immuno‐stimulation. A new 3D correlative microscopy approach is used here to provide a robust analytical method for characterisation of Zika‐ and Chikungunya‐derivatised AV populations including vesicle size distribution and variations in antigen loading. Manufactured batches were compared to assess the extent and nature of batch‐to‐batch variations. We also show preliminary results that verify antigen expression on the surface of host cells. We present here a reliable and efficient high‐resolution 3D imaging regime that allows the evaluation of the microstructure and biochemistry of novel vaccine formulations such as AVs.