Tools for automated cell identification and cell lineage tracking

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This project is a collaboration between the Broad Institute, Amnis, Cancer Research UK and the Centre for Nanohealth in Swansea. The sabbatical will allow Professor Paul Rees to visit both the Broad Institute and Amnis in order to develop new tools which will be used to measure cell lineages and to automate the identification of specific cell types in large cell populations. These tools will be tailored to the needs of clinicians by collaborating with Cancer Research UK, Broad Institute affiliated hospitals and a range of collaborators in the UK. However the aim is to foster long term collaboration between the two US partners, the group at Swansea and CRUK, London Research Institute. Therefore we have developed a long term research programme which will be instigated by Paul Rees's visit and sustained by future planned visits for the other members of the team in Swansea and provision for the US partners to visit the Swansea.The function of an organism is determined by the evolution of a cell population all descended from a single progenitor cell. The lineage (relationship tree) of a cell population evolving from one progenitor cell is often used as a measure of that cell population or organism's health. The most appropriate method of determining lineage is to take time lapse images using microscopy (bright field) of the cell population. The cell movement is tracked and mitosis events identified and the appropriate relationship between parent and daughter cells noted. This can be done manually or recently researchers are developing automated cell tracking algorithms. However this process is computationally intensive and fails if the cells move out of focus or if the cell boundary has a low contrast compared with the background. For this project our idea is to simply use the florescent endosomes as a surrogate marker for the cell so by simply tracking the endosomes we track the cell. Many clinical and research applications rely on the identification of a particular cell type with a large cell population. One of the best techniques for this type of application is flow cytometry where cells flow past a laser and the scattered light is detected. This allows the cell size and structure to be measured together with the fluorescence from markers which can label cell structure and function. At Swansea we use the recently developed imaging flow cytometer which is a hybrid system that enables each individual cell within a cell population to be imaged at very high speeds by flowing the cells in a fluid between an exciting laser and a camera. This is an ideal platform identifying specific cell types by image analysis rather than the intensity of a fluorescent marker or scatter signal which provides no spatial information and is a more ambiguous indirect measure of the cell property. However the very nature of imaging cytometry means any measurement requires the user to effectively process the vast number of images to detect the traits of cells required. This is usually done manually using the basic image processing tools supplied with the cytometer and each idividual image is inspected to check for target cells which is incredibly time consuming with cell populations often in excess of 10^6. As a second project we aim to develop a tool which uses both evolutionary algorithms (or genetic programming) and machine learning to determine which image processing algorithms (and combinations of algorithms) best distinguish between the target cells and non target cells. The algorithms used will be compatible with the current IDEAS imagestream software provided freely by Amnis (which currently only allows simple user driven masking filters to assess cells) to allow the inclusion of advance machine learning and evolutionary algorithms into the IDEAS platform.