At the very basis of all physiological research is the ability to image the anatomy of the tissue in question, facilitated by the tools available. Each organ system has its own defined structure corresponding to its functionality which gives rise to the need to image these systems in an unbiased and complete manner. Analysing large volumes of organ tissue, however, remains a palpable challenge within science. Despite the variety of sample preparation and imaging techniques, many require complex protocols and expensive equipment and reagents as well as being limited by the size of the sample able to be acquired as a whole. Organ systems and, specifically, neuronal circuits often extend beyond this limited data field. Here we present a pipeline that utilizes the resolution capabilities of fluorescent expansion microscopy techniques, in which the sample is physical and isotopically enlarged, and targets of interest are fluorescently labelled using assorted techniques, creating the data field. Adding to the pipeline is customized dual-sided light sheet microscope capable of imaging large samples, made from standard optical equipment, along with targeted data acquisition software which specifically targets the data field. The large sample chamber and the choice of different refractive index matching solutions provide the grounds for imaging at several resolutions, from the sub-micron to the mesoscale. Since the samples are able to undergo various rounds of testing under different expansion factors, a hierarchal approach can be employed: first imaging at low resolution in order to acquire the entire data field and proceeding to increase the resolution and reducing the data field to image finer structures at higher-resolution. The physical capabilities of the system facilitate the imaging of neuronal morphology and extended to the central nervous system. Moreover, this system has the potential to extend to other large tissue volumes, providing the basis for further anatomical research. The fundamental goal of this pipeline is to stay within the parameters accessible to most laboratories in terms of cost and the ability to be replicated, fulfilling the ultimate goal of expanding knowledge and enhancing opportunities for the research community. Also presented in this work is the contributions to larger-scale project which works to image, and subsequently map, the human brain using light-sheet fluorescent microscopy and a robust sample preparation technique. This section discusses the modalities that contribute in optimising the image quality while considering imaging time and the volume of data acquired when partaking in large-scale imaging. Among these methods, ways in which to trouble shoot challenges are discussed since human tissue is extremely precious and re-acquiring data sets can compromise the information within the sample.

Brain-wide mapping of the full, intact mouse brain with micrometre-scale resolution: Enhanced expansion microscopy and light-sheet microscopy for large tissue applications / Niamh Brady; Ludovico Silvestri;. - (2024).

Brain-wide mapping of the full, intact mouse brain with micrometre-scale resolution: Enhanced expansion microscopy and light-sheet microscopy for large tissue applications

Niamh Brady;Ludovico Silvestri
Supervision
2024

Abstract

At the very basis of all physiological research is the ability to image the anatomy of the tissue in question, facilitated by the tools available. Each organ system has its own defined structure corresponding to its functionality which gives rise to the need to image these systems in an unbiased and complete manner. Analysing large volumes of organ tissue, however, remains a palpable challenge within science. Despite the variety of sample preparation and imaging techniques, many require complex protocols and expensive equipment and reagents as well as being limited by the size of the sample able to be acquired as a whole. Organ systems and, specifically, neuronal circuits often extend beyond this limited data field. Here we present a pipeline that utilizes the resolution capabilities of fluorescent expansion microscopy techniques, in which the sample is physical and isotopically enlarged, and targets of interest are fluorescently labelled using assorted techniques, creating the data field. Adding to the pipeline is customized dual-sided light sheet microscope capable of imaging large samples, made from standard optical equipment, along with targeted data acquisition software which specifically targets the data field. The large sample chamber and the choice of different refractive index matching solutions provide the grounds for imaging at several resolutions, from the sub-micron to the mesoscale. Since the samples are able to undergo various rounds of testing under different expansion factors, a hierarchal approach can be employed: first imaging at low resolution in order to acquire the entire data field and proceeding to increase the resolution and reducing the data field to image finer structures at higher-resolution. The physical capabilities of the system facilitate the imaging of neuronal morphology and extended to the central nervous system. Moreover, this system has the potential to extend to other large tissue volumes, providing the basis for further anatomical research. The fundamental goal of this pipeline is to stay within the parameters accessible to most laboratories in terms of cost and the ability to be replicated, fulfilling the ultimate goal of expanding knowledge and enhancing opportunities for the research community. Also presented in this work is the contributions to larger-scale project which works to image, and subsequently map, the human brain using light-sheet fluorescent microscopy and a robust sample preparation technique. This section discusses the modalities that contribute in optimising the image quality while considering imaging time and the volume of data acquired when partaking in large-scale imaging. Among these methods, ways in which to trouble shoot challenges are discussed since human tissue is extremely precious and re-acquiring data sets can compromise the information within the sample.
2024
Ludovico Silvestri
REGNO UNITO DI GRAN BRETAGNA
Niamh Brady; Ludovico Silvestri;
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1359294
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