Quantitative and Real-Time Image Analysis for Advanced Light Microscopy.

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In microscopy based research it is common to acquire and analyze microscopy images of cells sampled from populations which have undergone differential treatments. A non-exhaustive number of samples (i.e. images of cells) are often acquired due to experimental limitations that include: restricted acquisition periods, provision of expensive equipment or pressures to move onto the next experiment. Random sampling is a methodology that is used to sample representatively. There are some good reasons however for not sampling randomly. For example, cells maybe expressing a reporter protein and the copy number (quantity) of this molecule maybe too high or too low for subsequent analysis and so preference is shown toward those examples where the expression levels are within a certain range. Or, alternatively, the cells in an area maybe compromised by an artifact, be unhealthy, or too dense or sparse for a particular analysis. There are many factors that will influence the choices of an imaging researcher that are fully justifiable, but it is a problem as to how this information is documented and shared with other scientists. Removing the scientist from the pipeline of acquisition and analysis is counter-productive, a better solution is to provide tools that allow them to document and describe any experimental subjectivity as well as boosting reproducibility through automation. The objectives of this first part of the project (1-4) revolve around solving these issues of documenting, describing and automating image acquisition so that experimental design can be better communicated between users and laboratories. Furthermore, we would like to develop tools and approaches, which allow better visualization and real-time feedback for advanced approaches so as to better inform imaging scientists as they perform their research and make experimental decisions. The second part of the project relates to this goal, objectives 5-6.