Tools for automated cell identification and cell lineage tracking

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.

The project will develop new software tools for the determination of cell lineage for the assessment of cell health or for the measurement of the effect of drug intervention. Researchers investigating cell mobility, the effect of cell cycle specific drug interventions will also benefit from our simple robust tracking algorithms.

Our protocols for cell tracking using quantum dot markers will allow cell tracking on low resolution, inexpensive microscopes which will significantly reduce the cost of this important measurement system. The reduced computation cost of tracking cells will also facilitate the possibility of real-time tracking where the tracking software will control the time interval to capture certain events such as mitosis and also the camera resolution. The development of the machine learning and evolutionary algorithms for cell identification will be interest to people identifying rare cells within a large population e.g. stem cell identification or cells at a precise point in the cell cycle for the investigation of specific drug interventions. These tools will be of interest to clincians.

Our existing UK and international collaborators will automatically benefit from the work as much of the data used for developing the software tools will be taken from their applications with their approval. As such the algorithms will be ideally suited to the lineage tracking and rare cell identification problems which they are investigating. Existing users of the CellProfile software for time-lapse microscopy lineage tracking and the IDEAS analysis software for imaging cytometry will automatically benefit from the inclusion of our new algorithms within these platforms.

Many clinical applications use cell tracking applications and flow cytometry is the main clinical tool for the identification of different cells types e.g. specific blood cells within a primary blood sample. Therefore the tools developed are well suited for clinical applications and the Cancer Research UK collaboration within the project will ensure their clinicians will benefit from access to our software and techniques.

The project will also ensure younger staff members and PhD students benefit from visiting the Broad Institute to experience research practices in this world leading research environment.