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mmc2021 Abstract Database

Automating Correlative Microscopy with Python: Removing the Frustrations

Automating Correlative Microscopy with Python: Removing the Frustrations

Session

Stream 2: Software and Smart Microscopy

Authors

Mr Thomas Fish (1), Dr Victoria Beilsten-Edmands (1), Dr Peter Chang (1), Dr Maria Harkiolaki (1)

Affiliations

1. Diamond Light Source

Keywords

X-ray, Tomography, SXT, SIM, cryo, CLXT, Python, automation

Abstract text

Summary

Rapid developments in high resolution biological 3D imaging and the budding correlative imaging schemes that harness naturally complementing, but radically different, techniques require the development of a diverse range of in silico protocols to allow their use in an effective and efficient way. At the correlative cryo-imaging beamline at the UK synchrotron, such a pairing of cutting-edge cryo-imaging technologies (including structured illumination fluorescence imaging and soft X-ray tomography) required the development of a rational, step-by-step processing pipeline that can harness the data and metadata from each modality, as well as the correlation of identified regions of interest from samples as they are transferred from one microscope to the next. Data collected from a variety of platforms under different development environments was collated and standardised to allow further analyses and in-depth correlation. Here we present the workflow developed at beamline B24 and the steps that have been taken to streamline data handling, acquisition and analysis and enable the user community to take full advantage of our methods.

Introduction

Major developments in cryo-imaging in recent years have brought about a greater understanding of biological systems through the capture of processes and structures to nanometre resolution. Correlative imaging schemes have now become a necessary next step to allow us to harvest even more information from a given sample, with correlative light and electron microscopy (CLEM) and correlative light and X-ray tomography (CLXT) being the most current developments. At the correlative cryo-imaging beamline, B24, at the UK synchrotron, a new CLXT platform has been developed using two high resolution 3D imaging systems for same sample imaging: (1) cryo-fluorescence Structured Illumination Microscopy (cryo-SIM), which provides 3D localisation of chemical information within cells, and (2) cryo-Soft X-ray Tomography (cryo-SXT), which uses the natural absorption contrast of vitrified biological mater to deliver high resolution 3D data on cells and their internal architecture.

A typical sample at beamline B24 presents as a population of cells or other biological structures supported by a perforated carbon film which is attached to a 3mm gold EM grid. The sample is kept at liquid nitrogen temperatures at all times and data is collected on cells that reside in the gaps between the metal bars that comprise the grid pattern (commonly decorated with positional markers to aid image alignment and relocation of regions of interest). The data acquisition workflow at the beamline for such a sample includes: (a) mapping of the grid surface using diffraction limited bright light and fluorescence cryo-imaging to evaluate sample suitability; (b) at the SIM microscope: white light 2D mapping of the sample followed by imaging (brightfield and fluorescence) to extract super-resolution fluorescence localisation and feature capture at regions of interest, which is followed by data reconstruction and wavelength alignment; (c) at the SXT microscope: white light 2D mapping of the sample and localisation of previously identified regions of interest before X-ray light 2D mapping and SXT data collection in those areas, which is followed by data processing and reconstruction.

There were essentially three major challenges in bringing together these microscopes as far as data acquisition and management were concerned. First, for each sample, positional information needed to be captured and relayed to allow data collection in the same areas in each microscope. Second, each output needed to contain sufficient and accurate metadata that was readable by all conventional and non-commercial reconstruction and imaging software to make it truly accessible and amenable to further data correlation and analyses. Third data processing should be archived and reconstructed with the least amount of user-feedback to ensure data fidelity and method usability. 

Methods

At Diamond Light Source beamline B24, a bespoke biological imaging platform has been developed offering SIM in conjunction to SXT. 

Both primary imaging modalities at B24 became a part of this correlative platform with pre-existing set of variables that defined it. SIM data is collected through Python-based Cockpit, which delivers MRC image stacks for both 2D mosaics (which are accompanied by relative positional information) and 3D volumes. The microscope and control software were developed by an academic institution (Micron), which resulted in a tailored, highly adaptable environment that required further standardisation. SXT data is collected using a proprietary software (Zeiss XRMDataExplorer) that saves data in OLE type files. These are data-rich files but aren’t readable by standard image software.

To provide uninterrupted CLXT data flow at beamline B24 we have developed a workflow that harnesses and harmonises CLXT imaging. Individual components of this linked workflow address distinct requirements each of which bring the beamline one step closer to full automation. Software developed neatly associates with key points in the data flow. These include: (a) StitchM which repackages Cockpit’s 2D mosaic output into OME-TIFF; (b) GridSNAP which automates the 2D correlation of grid mosaics for region of interest relocation between microscopes; (c) XMBatch which uses Zeiss’ API to queue up and automate SXM data collections; (d) txrm2tiff which converts OLE type files into OME-TIFF; (e) DataPath which detects new SXT data, submits it for automated reconstruction and reports progress.

Results

We have produced software to address the challenges that hindered efficient CLXT data collection and processing, from mosaic stitching and sample alignment to data acquisition and reconstruction. We have developed in-house scripts and created freely available software to streamline processes and democratise access to data tools for CLXT users. Our pipeline has delivered a degree of automation that is unprecedented for a CLXT facility but remains constantly under responsive development. As such it acts as an exemplar or the next step of guided automation in correlative imaging. 

Conclusion

Here, we present a unique and fully commissioned correlative microscopy platform that allows the investigation of cell populations at near-physiological condition and to tens of nanometre resolution using both laser and X-ray radiation. While software development is ongoing, these applications are now commissioned and accessible to everyone.

References

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