Dataset Information

Upgraded D22 SEC-SANS setup dedicated to the biology community.

ABSTRACT: Described here is the current status of the upgraded in situ size-exclusion chromatography (SEC) system implemented with the D22 small-angle neutron scattering (SANS) instrument at the Institut Laue-Langevin. Since its initial proof of principle in 2016, this SEC-SANS arrangement has been continuously requested by the user community, leading to the design of an upgraded version. A detailed description of the setup and its control is provided, and a few examples of protein structural investigations are presented, which will highlight the various possibilities and limitations of the setup to optimize experimental success.

SUBMITTER: Martel A

PROVIDER: S-EPMC10405598 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Upgraded D22 SEC-SANS setup dedicated to the biology community.

Martel Anne A Cocho Cristina C Caporaletti Francesca F Jacques Mark M El Aazzouzi Abdelali A Lapeyre Franck F Porcar Lionel L

Journal of applied crystallography 20230612 Pt 4

Described here is the current status of the upgraded <i>in situ</i> size-exclusion chromatography (SEC) system implemented with the D22 small-angle neutron scattering (SANS) instrument at the Institut Laue-Langevin. Since its initial proof of principle in 2016, this SEC-SANS arrangement has been continuously requested by the user community, leading to the design of an upgraded version. A detailed description of the setup and its control is provided, and a few examples of protein structural inves ...[more]

PMID: 37555207

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Project description:BackgroundRecently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers.ResultsThe Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We demonstrate it's usefulness using the example of a signaling cascade leading to formation of a morphological gradient of Fus3 within the cytoplasm of the mating yeast cell Saccharomyces cerevisiae.ConclusionsSTSE is an efficient and powerful novel platform, designed for computational handling and evaluation of microscopic images. It allows for an uninterrupted workflow including digitization, representation, analysis, and mathematical modeling. By providing the means to relate the simulation to the image data it allows for systematic, image driven model validation or rejection. STSE can be scripted and extended using the Python language. STSE should be considered rather as an API together with workflow guidelines and a collection of GUI tools than a stand alone application. The priority of the project is to provide an easy and intuitive way of extending and customizing software using the Python language.

Dataset Information

Upgraded D22 SEC-SANS setup dedicated to the biology community.

Publications

Upgraded D22 SEC-SANS setup dedicated to the biology community.

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