Nature Methods
2, 691 - 697 (2005)
Published online: 23 August 2005; | doi:10.1038/nmeth778
A streamlined platform for high-content functional proteomics of primary human specimensNadim Jessani1, 4, 5, Sherry Niessen1, 5, BinQing Q Wei1, 5, Monica Nicolau2, Mark Humphrey1, Youngran Ji2, Wonshik Han3, Dong-Young Noh3, John R Yates III1, Stefanie S Jeffrey2
& Benjamin F Cravatt11
The Skaggs Institute for Chemical Biology and Departments of Cell Biology and Chemistry, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, California 92037, USA. 2
Department of Surgery, Stanford University School of Medicine, MSLS Building, Room P214, 1201 Welch Road, Stanford, California 94305, USA. 3
Cancer Research Institute and Department of Surgery, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744, South Korea. 4
Present address: Celera, 180 Kimball Way, South San Francisco, California 94080, USA. 5
These authors contributed equally to this work.
Correspondence should be addressed to Benjamin F Cravatt cravatt@scripps.edu Achieving information content of satisfactory breadth and depth remains a formidable challenge for proteomics. This problem is particularly relevant to the study of primary human specimens, such as tumor biopsies, which are heterogeneous and of finite quantity. Here we present a functional proteomics strategy that unites the activity-based protein profiling and multidimensional protein identification technologies (ABPP-MudPIT) for the streamlined analysis of human samples. This convergent platform involves a rapid initial phase, in which enzyme activity signatures are generated for functional classification of samples, followed by in-depth analysis of representative members from each class. Using this two-tiered approach, we identified more than 50 enzyme activities in human breast tumors, nearly a third of which represent previously uncharacterized proteins. Comparison with cDNA microarrays revealed enzymes whose activity, but not mRNA expression, depicted tumor class, underscoring the power of ABPP-MudPIT for the discovery of new markers of human disease that may evade detection by other molecular profiling methods.
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