We have continued to develop our high-density RNAi screening technology, and we have characterized image data from small-scale pilot screens. The ability to quantify image similarity has allowed us to demonstrate that knock-down of genes known to have strong genetic or physical interactions leads to highly similar phenotypes. One of the key objectives was the ability to directly compare the phenotypes resulting from independent screens. Our preliminary data indicates that combining independent screens into a common classifier enables it to more accurately represent more broadly defined phenotypic classes (e.g. blocks in cell cycle stages), while maintaining the very close similarity between phenotypes resulting from knockdown of genes encoding proteins known to physically interact. Work continues on further refinement of this screening platform to achieve a greater degree of consistency and a more universal classifier. We have begun work on a collaboration with Dr. Minoru Ko (LG-NIA) to characterize developmental pathways induced in mouse embryonic stem cells using transcription factor manipulation. The first set of pilot experiments was to determine if cell lineage can be consistently identified using a selected set of cell lines expressing transcription factors with known cell lineage associations. An important outcome of these studies is identifying the earliest stages of lineage commitment, potentially allowing for the identification of the mechanisms governing these cell fate decisions. This work has allowed us to demonstrate that new phenotypic classes can be identified by WND-CHARM without them being defined a priori. Our work on distinguishing cell lineages by cellular morphology without the use of lineage markers is nearing publication. Recently, we have obtained evidence for the first time that phenotype classes identified by WND-CHARM alone have a molecular basis. In previous published work we used WND-CHARM to identify distinct morphological aging states in C. elegans. We used this technique to sort worms based on their age state during a transition period where an aging population is evenly divided between individuals in Stage I and Stage II. The worms were identical genetically, by chronological age, by growth conditions, and by visual appearance, and could only be sorted into age-states using WND-CHARM. Micro-array experiments performed on these two sub-populations revealed several hundred genes with significantly altered expression profiles. These results will be followed up in the coming year by selecting up to 100 candidate genes and measuring their effects on stage-transitions using the worm screening platform described above.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Scientific Cores Intramural Research (ZIC)
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National Institute on Aging
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Shamir, Lior; Rahimi, Salim; Orlov, Nikita et al. (2010) Progression analysis and stage discovery in continuous physiological processes using image computing. EURASIP J Bioinform Syst Biol 2010:107036
Shamir, Lior; Wolkow, Catherine A; Goldberg, Ilya G (2009) Quantitative measurement of aging using image texture entropy. Bioinformatics 25:3060-3
Shamir, Lior; Ling, Shari; Rahimi, Salim et al. (2009) Biometric identification using knee X-rays. Int J Biom 1:365-370
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Shamir, Lior; Eckley, D Mark; Delaney, John et al. (2009) An Image Informatics Method for Automated Quantitative Analysis of Phenotype Visual Similarities. IEEE NIH Life Sci Syst Appl Workshop 2009:96-99
Nishiyama, Akira; Xin, Li; Sharov, Alexei A et al. (2009) Uncovering early response of gene regulatory networks in ESCs by systematic induction of transcription factors. Cell Stem Cell 5:420-33
Shamir, Lior; Ling, Shari M; Scott Jr, William W et al. (2009) Knee x-ray image analysis method for automated detection of osteoarthritis. IEEE Trans Biomed Eng 56:407-15
Tadeu, Ana Mafalda Baptista; Ribeiro, Susana; Johnston, Josiah et al. (2008) CENP-V is required for centromere organization, chromosome alignment and cytokinesis. EMBO J 27:2510-22