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SILMAS Structured Illumination Light-sheet Microscopy with Axial Sweeping: 3D imaging of brain tissue

Frantz, David LU (2024)
Abstract
Modern healthcare allows people to live longer and healthier lives. However, as the average lifespan increases, age-related diseases are becoming more prevalent. Neuroscientists are therefore increasingly focused on understanding and developing treatments for neurodegenerative disorders, such as Alzheimer's and Parkinson's disease. These conditions are characterized by progressive neuronal degeneration resulting in cognitive decline, including memory loss, personality changes, and motor dysfunction, and are ultimately fatal.

A key aspect of studying these diseases involves imaging pathology in animal models, which provides insights into disease progression. Owing to the advances of tissue clearing, it is now possible to... (More)
Modern healthcare allows people to live longer and healthier lives. However, as the average lifespan increases, age-related diseases are becoming more prevalent. Neuroscientists are therefore increasingly focused on understanding and developing treatments for neurodegenerative disorders, such as Alzheimer's and Parkinson's disease. These conditions are characterized by progressive neuronal degeneration resulting in cognitive decline, including memory loss, personality changes, and motor dysfunction, and are ultimately fatal.

A key aspect of studying these diseases involves imaging pathology in animal models, which provides insights into disease progression. Owing to the advances of tissue clearing, it is now possible to volumetrically image brain tissue from rodents using visible light and fluorescent protein labeling. Unfortunately, such imaging still faces limitations in resolution and contrast due to tissue opacity in larger samples, such as whole rodent brains.

This thesis addresses those limitations by presenting a 3D imaging technique called Structured Illumination Light-sheet Microscopy with Axial Sweeping (SILMAS). SILMAS improves upon conventional light-sheet microscopy by introducing a structured light sheet with recognizable stripes, allowing differentiation between direct light from the sheet and scattered light. Additionally, the sweeping motion of the light sheet focus enhances the resolution of the 3D volume constructed from stacked 2D images. The effect of tissue opacity is first investigated using a Monte Carlo simulation software which is validated in Paper I. While highly dependent on sample, in Paper III, SILMAS is shown to increase contrast up to 370\% and improve optical section widening by 74\% in a cleared mouse brain.

The thesis also presents post-processing schemes for SILMAS, including attenuation compensation and shadow line suppression by bandpass-wavelet-Fourier filtering, which yield comparable intensities throughout large and varied volumes. Finally, pathology is segmented in SILMAS data to quantify the impact of neurodegenerative diseases on brain samples. This is done using conventional methods and machine learning, where the efficiency is increased thanks to the improved uniformity of data. This could ultimately facilitate research and aid neuroscientists in the fight against neurodegenerative disorders. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Prof. Kner, Peter, University of Georgia, USA.
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Structured illumination, Microscopy Fluorescence, Image analysis, Light sheet fluorescence microscopy, Light scattering
publisher
Department of Physics, Lund University
defense location
Lecture Hall Rydbergsalen, Department of Physics, Professorsgatan 1, Faculty of Engineering LTH, Lund University, Lund.
defense date
2024-10-18 09:15:00
ISBN
978-91-8104-196-5
978-91-8104-195-8
language
English
LU publication?
yes
id
d2457dd8-2421-4629-aee4-7031df39ce16
date added to LUP
2024-09-24 10:22:35
date last changed
2024-09-25 12:39:43
@phdthesis{d2457dd8-2421-4629-aee4-7031df39ce16,
  abstract     = {{Modern healthcare allows people to live longer and healthier lives. However, as the average lifespan increases, age-related diseases are becoming more prevalent. Neuroscientists are therefore increasingly focused on understanding and developing treatments for neurodegenerative disorders, such as Alzheimer's and Parkinson's disease. These conditions are characterized by progressive neuronal degeneration resulting in cognitive decline, including memory loss, personality changes, and motor dysfunction, and are ultimately fatal. <br/><br/>A key aspect of studying these diseases involves imaging pathology in animal models, which provides insights into disease progression. Owing to the advances of tissue clearing, it is now possible to volumetrically image brain tissue from rodents using visible light and fluorescent protein labeling. Unfortunately, such imaging still faces limitations in resolution and contrast due to tissue opacity in larger samples, such as whole rodent brains. <br/><br/>This thesis addresses those limitations by presenting a 3D imaging technique called Structured Illumination Light-sheet Microscopy with Axial Sweeping (SILMAS). SILMAS improves upon conventional light-sheet microscopy by introducing a structured light sheet with recognizable stripes, allowing differentiation between direct light from the sheet and scattered light. Additionally, the sweeping motion of the light sheet focus enhances the resolution of the 3D volume constructed from stacked 2D images. The effect of tissue opacity is first investigated using a Monte Carlo simulation software which is validated in Paper I. While highly dependent on sample, in Paper III, SILMAS is shown to increase contrast up to 370\% and improve optical section widening by 74\% in a cleared mouse brain.<br/><br/>The thesis also presents post-processing schemes for SILMAS, including attenuation compensation and shadow line suppression by bandpass-wavelet-Fourier filtering, which yield comparable intensities throughout large and varied volumes. Finally, pathology is segmented in SILMAS data to quantify the impact of neurodegenerative diseases on brain samples. This is done using conventional methods and machine learning, where the efficiency is increased thanks to the improved uniformity of data. This could ultimately facilitate research and aid neuroscientists in the fight against neurodegenerative disorders.}},
  author       = {{Frantz, David}},
  isbn         = {{978-91-8104-196-5}},
  keywords     = {{Structured illumination; Microscopy Fluorescence; Image analysis; Light sheet fluorescence microscopy; Light scattering}},
  language     = {{eng}},
  publisher    = {{Department of Physics, Lund University}},
  school       = {{Lund University}},
  title        = {{SILMAS Structured Illumination Light-sheet Microscopy with Axial Sweeping: 3D imaging of brain tissue}},
  url          = {{https://lup.lub.lu.se/search/files/195760499/e-spik_ex_david.pdf}},
  year         = {{2024}},
}