Real-time prediction of cellular states in 3D lattice light sheet microscopy
Lead Research Organisation:
University of Warwick
Department Name: Warwick Medical School
Abstract
Programme overview:
This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice.
Project overview:
Lattice light sheet microscopy (LLSM) is a new technology to visualise fast cellular processes at the time scale of 1 second, in 3D. LLSM is very low through-put however, limiting its use for studying rare events, such as cell divisions. In close collaboration with industrial partner Intelligent Imaging Innovations Ltd. (3i), suppliers of LLSM, we will develop an integrated imaging pipeline to classify and anticipate physiologically meaningful events during the cell cycle using state of the art machine learning.
The main goal is to 1) enable automated control of the image acquisition and increase its throughput, and 2) make it possible to analyse statistically significant numbers of well-defined cellular events and their progression from an early stage, which often go unnoticed by even the most expert human experimenter. Enabling detailed spatio-temporal analysis of the 3D imaging data will help to better understand the timing and control of different stages of cell division and recognise more subtle defects in cell division which can affect development or diseases such as cancer where divisions occur uncontrolled.
This is an interdisciplinary project at the interface of cell biology, computer science and engineering, enabling fundamental science to improve human health through world-class biomedical research. Health focus is enabling biological research into genetic risk and disease mechanisms, aiming at new strategies for early diagnosis and treatment.
The specific training the student will receive is geared towards quantitative and interdisciplinary skills and understanding of whole organism physiology in addition to that of single cells in the main project. The training in advanced machine learning and computing addresses the demand for team scientists and technology specialists and will help to build new software technologies and imaging instruments that will become available to the biomedical community in the future.
This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice.
Project overview:
Lattice light sheet microscopy (LLSM) is a new technology to visualise fast cellular processes at the time scale of 1 second, in 3D. LLSM is very low through-put however, limiting its use for studying rare events, such as cell divisions. In close collaboration with industrial partner Intelligent Imaging Innovations Ltd. (3i), suppliers of LLSM, we will develop an integrated imaging pipeline to classify and anticipate physiologically meaningful events during the cell cycle using state of the art machine learning.
The main goal is to 1) enable automated control of the image acquisition and increase its throughput, and 2) make it possible to analyse statistically significant numbers of well-defined cellular events and their progression from an early stage, which often go unnoticed by even the most expert human experimenter. Enabling detailed spatio-temporal analysis of the 3D imaging data will help to better understand the timing and control of different stages of cell division and recognise more subtle defects in cell division which can affect development or diseases such as cancer where divisions occur uncontrolled.
This is an interdisciplinary project at the interface of cell biology, computer science and engineering, enabling fundamental science to improve human health through world-class biomedical research. Health focus is enabling biological research into genetic risk and disease mechanisms, aiming at new strategies for early diagnosis and treatment.
The specific training the student will receive is geared towards quantitative and interdisciplinary skills and understanding of whole organism physiology in addition to that of single cells in the main project. The training in advanced machine learning and computing addresses the demand for team scientists and technology specialists and will help to build new software technologies and imaging instruments that will become available to the biomedical community in the future.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/R015910/1 | 30/09/2018 | 29/09/2026 | |||
| 2430216 | Studentship | MR/R015910/1 | 04/10/2020 | 30/12/2024 |