The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

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Paget's disease of bone (PDB) is a focal disorder of bone remodeling that leads to overgrowth of affected bone, with rare progression to osteosarcoma. Extensive studies of familial PDB showed that a majority of cases harbor germline mutations in the Sequestosome1 gene (SQSTM1). In contrast, little is known about the mutational status of SQSTM1 in sporadic PDB. We hypothesized that somatic SQSTM1 mutations might occur in the affected tissues of sporadic PDB and pagetic osteosarcoma. We used laser capture microdissection to capture homogeneous populations of cells from the affected bone or tumor of patients with sporadic PDB or pagetic osteosarcoma, respectively. DNA from these samples and appropriate controls was used for sequence analysis and allelic discrimination analysis. Two of five patients with sporadic PDB had SQSTM1C1215T mutations detected in their affected bone but not in their blood samples, indicating a somatic origin of the mutations. Samples from three of five sporadic pagetic osteosarcoma patients had the SQSTM1 C1215T mutation, whereas the normal adjacent tissue from two of these tumors clearly lacked the mutation, again indicating an occurrence of somatic events. No SQSTM1 mutations were found in primary adolescent osteosarcomas. The discovery of somatic SQSTM1 mutations in sporadic PDB and pagetic osteosarcoma shows a role for SQSTM1 in both sporadic and inherited PDB. The discovery of somatically acquired mutations in both the diseased bone and tumor samples suggests a paradigm shift in our understanding of this disease.

We present an initial molecular characterization of a morphological transition between two early aging states. In previous work, an age score reflecting physiological age was developed using a machine classifier trained on images of worm populations at fixed chronological ages throughout their lifespan. The distribution of age scores identified three stable post-developmental states and transitions. The first transition occurs at day 5 post-hatching, where a significant percentage of the population exists in both state I and state II. The temperature dependence of the timing of this transition (Q 10~1 .17) is too low to be explained by a stepwise process with an enzymatic or chemical rate-limiting step, potentially implicating a more complex mechanism. Individual animals at day 5 were sorted into state I and state II groups using the machine classifier and analyzed by microarray expression profiling. Despite being isogenic, grown for the same amount of time, and indistinguishable by eye, these two morphological states were confirmed to be molecularly distinct by hierarchical clustering and principal component analysis of the microarray results. These molecular differences suggest that pharynx morphology reflects the aging state of the whole organism. Our expression profiling yielded a gene set that showed significant overlap with those from three previous age-related studies and identified several genes not previously implicated in aging. A highly represented group of genes unique to this study is involved in targeted ubiquitin-mediated proteolysis, including Skp1-related (SKR), F-box-containing, and BTB motif adaptors.