December’s Cold Spring Harbor meeting on “Automated Imaging and & High-Throughput Phenotyping” provided a very different take on the future of high content analysis field than is seen at other meetings that focus on using HCA for medium and high throughput screening. It was amusing how proud the geeks at this meeting were to be geeks!
The majority of the talks focused on four model systems, zebra fish, C. elegans, drosophlia, and arabodopsis. Perhaps because the models are all used to study development, the imaging, analysis and feature recognition problems and solutions were very similar. Huge 4-D data sets are acquired, often multi terabyte, requiring solutions to registration of adjacent files and planes that do not propagate errors across the image stack. Pavel Tamancak (MPI-CBG, Dresden) gave an amazing talk describing a gobal solution with examples from light and electron microscopy. Zhirong Bao (SKI) and John Murray (Penn) showed how microfludics can be used to allow high throughput imaging of living worms.
Investigators using off-the-shelf HCA systems are forced to use relatively simple analysis tools that measure minimal cell features, such as width and length or translocation of markers from one compartment to another. These measurements are blind to many features that are obvious to even the untrained eye. Examples include curved or straight cell processes or striped patterns of mRNAs or proteins in embryos. Many investigators at the meeting are using open source analysis packages like Cell Profiler (Broad), CellCognition (ETH), Fiji (MPI/ETH/EMBL), microManager (UCSF) and Bisque (UCSB) to identify more complex features.
The enormous data sets require some methods to reduce the analytical problems. One approach is to scan at low magnification, identify cells or features of interest and then automatically zoom in to acquire higher magnification images. On the analysis side, cell or feature classifiers, principal component analysis, and support vector machines are all being widely used to analyze HCA data and then variations on Tanimoto coefficients are used to help cluster data. One beautiful example was Yolanda Chong (Toronto) who used 69 SVM classifiers to define the morphology of budding yeast. Another example was Uwe Ohler (Duke) using a small set of classifiers to describe expression patterns in fly embryos.
Talks by Phil Keller (HHMI) and Thai Truong (Scott Fraser’s group at CalTech) provided spectacular examples of how light sheet microscopy can give very high-resolution 4D movies of developing embryos that cover very large volumes while minimizing bleaching and toxicity. These types of microscopes open a new quadrant of the time/resolution/image volume domain. This approach will likely revolutionize the analysis of organisms, organs and tissues for studies in development, mechanisms of disease and drug discovery.