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Automation of Full Organ Immunolabeling Protocol for Light Sheet Fluorescence Imaging

Palm, Ida LU and Schlemlein-Wenell, Henrik LU (2024) EEML05 20241
Department of Biomedical Engineering
Abstract
Visualization of the molecular and morphological complexity of organs can be achieved through advanced imaging techniques. Imaging has increased our understanding of disease, pathology, and mechanisms. Classic 2D-imaging modalities provide detailed information at a microscopic scale and while valuable, it lacks the ability to explore the spatial resolution of disease mechanisms such as the location of rare cell populations. Light sheet microscopy surpasses this limitation and allows for 3D imaging of organs. The sample processing and staining procedures can be lengthy and demanding. This project has automated these protocols in order to reduce human errors. The machine requires materials which are chemically compatible with the demanding... (More)
Visualization of the molecular and morphological complexity of organs can be achieved through advanced imaging techniques. Imaging has increased our understanding of disease, pathology, and mechanisms. Classic 2D-imaging modalities provide detailed information at a microscopic scale and while valuable, it lacks the ability to explore the spatial resolution of disease mechanisms such as the location of rare cell populations. Light sheet microscopy surpasses this limitation and allows for 3D imaging of organs. The sample processing and staining procedures can be lengthy and demanding. This project has automated these protocols in order to reduce human errors. The machine requires materials which are chemically compatible with the demanding protocols and can withstand mechanical stress for prolonged periods. The protocols also include elements of heating as well as cooling, while keeping a constant pressure and flow into the organs. A user interface has been designed to facilitate interaction with the machine with the goal to further develop and improve said protocols. The machine has successfully run a full protocol using coloured water. While this was without temperature control, it shows that this prototype works. (Less)
Please use this url to cite or link to this publication:
author
Palm, Ida LU and Schlemlein-Wenell, Henrik LU
supervisor
organization
alternative title
Swedish title: Automatisering av antikroppsfärgning av helt organ för light sheet flourescence imaging
course
EEML05 20241
year
type
M2 - Bachelor Degree
subject
language
English
id
9160559
date added to LUP
2024-06-20 13:06:20
date last changed
2024-06-20 13:06:20
@misc{9160559,
  abstract     = {{Visualization of the molecular and morphological complexity of organs can be achieved through advanced imaging techniques. Imaging has increased our understanding of disease, pathology, and mechanisms. Classic 2D-imaging modalities provide detailed information at a microscopic scale and while valuable, it lacks the ability to explore the spatial resolution of disease mechanisms such as the location of rare cell populations. Light sheet microscopy surpasses this limitation and allows for 3D imaging of organs. The sample processing and staining procedures can be lengthy and demanding. This project has automated these protocols in order to reduce human errors. The machine requires materials which are chemically compatible with the demanding protocols and can withstand mechanical stress for prolonged periods. The protocols also include elements of heating as well as cooling, while keeping a constant pressure and flow into the organs. A user interface has been designed to facilitate interaction with the machine with the goal to further develop and improve said protocols. The machine has successfully run a full protocol using coloured water. While this was without temperature control, it shows that this prototype works.}},
  author       = {{Palm, Ida and Schlemlein-Wenell, Henrik}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Automation of Full Organ Immunolabeling Protocol for Light Sheet Fluorescence Imaging}},
  year         = {{2024}},
}