We propose to establish an Automation and Imaging Core to provide reagents, instruments, and expertise for protein analysis, image analysis, high throughput screening, and lab automation for the Projects in this program. The Core will leverage staff, lab automation, small molecule screening, RNAi screening, and computational resources from the Harvard Medical School (HMS) ICCB-Longwood Screening Facility. Core B will also provide education and outreach to the Boston scientific community on new assay technologies and best practices in running small molecule and RNAi screens.
Specific Aims for this Core are: 1. Develop standardized methods for quantitative analysis of proteins, including multiplexed bead-based detection methods, in-cell Western assays, and protein antibody arrays. 2. Support high throughput screening of compound libraries and of genome-scale siRNA libraries by the Projects. This includes liquid handling, assay readout, statistical analysis of data, and informatics support. 3. Provide expertise and resources for imaging and image analysis, including support for automated microscopy, obtaining/testing image analysis software, training investigators in the use of analysis software, and development of new image analysis algorithms. 4. Provide lab automation resources and expertise for cell-based and pure protein assays.
|Shi, Jue; Mitchison, Timothy J (2017) Cell death response to anti-mitotic drug treatment in cell culture, mouse tumor model and the clinic. Endocr Relat Cancer 24:T83-T96|
|Miller, Miles A; Askevold, Bjorn; Mikula, Hannes et al. (2017) Nano-palladium is a cellular catalyst for in vivo chemistry. Nat Commun 8:15906|
|Foijer, Floris; Albacker, Lee A; Bakker, Bjorn et al. (2017) Deletion of the MAD2L1 spindle assembly checkpoint gene is tolerated in mouse models of acute T-cell lymphoma and hepatocellular carcinoma. Elife 6:|
|Fallahi-Sichani, Mohammad; Becker, Verena; Izar, Benjamin et al. (2017) Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de-differentiated state. Mol Syst Biol 13:905|
|Giedt, Randy J; Fumene Feruglio, Paolo; Pathania, Divya et al. (2016) Computational imaging reveals mitochondrial morphology as a biomarker of cancer phenotype and drug response. Sci Rep 6:32985|
|Lee, Robin E C; Qasaimeh, Mohammad A; Xia, Xianfang et al. (2016) NF-?B signalling and cell fate decisions in response to a short pulse of tumour necrosis factor. Sci Rep 6:39519|
|Sarosiek, Kristopher A; Letai, Anthony (2016) Directly targeting the mitochondrial pathway of apoptosis for cancer therapy using BH3 mimetics - recent successes, current challenges and future promise. FEBS J 283:3523-3533|
|Bhola, Patrick D; Mar, Brenton G; Lindsley, R Coleman et al. (2016) Functionally identifiable apoptosis-insensitive subpopulations determine chemoresistance in acute myeloid leukemia. J Clin Invest 126:3827-3836|
|Chittajallu, Deepak R; Florian, Stefan; Kohler, Rainer H et al. (2015) In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy. Nat Methods 12:577-85|
|Krukenberg, Kristin A; Kim, Sujeong; Tan, Edwin S et al. (2015) Extracellular poly(ADP-ribose) is a pro-inflammatory signal for macrophages. Chem Biol 22:446-452|
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