Feature-level MALDI-MS characterization of in situ-synthesized peptide microarrays

Scalable high-density peptide arrays for comprehensive health monitoring

There is an increasing awareness that health care must move from post-symptomatic treatment to presymptomatic intervention. An ideal system would allow regular inexpensive monitoring of health status using circulating antibodies to report on health fluctuations. Recently, we demonstrated that peptide microarrays can do this through antibody signatures (immunosignatures). Unfortunately, printed microarrays are not scalable. Here we demonstrate a platform based on fabricating microarrays (~10 M peptides per slide, 330,000 peptides per assay) on silicon wafers using equipment common to semiconductor manufacturing. The potential of these microarrays for comprehensive health monitoring is verified through the simultaneous detection and classification of six different infectious diseases and six different cancers. Besides diagnostics, these high-density peptide chips have numerous other applications both in health care and elsewhere.

Analysis of protein tyrosine phosphatase interactions with microarrayed phosphopeptide substrates using imaging mass spectrometry

Microarrays of peptide and recombinant protein libraries are routinely used for high-throughput studies of protein-protein interactions and enzymatic activities. Imaging mass spectrometry (IMS) is currently applied as a method to localize analytes on thin tissue sections and other surfaces. Here, we have applied IMS as a label-free means to analyze protein-peptide interactions in a microarray-based phosphatase assay. This IMS strategy visualizes the entire microarray in one composite image by collecting a predefined raster of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry spectra over the surface of the chip. Examining the bacterial tyrosine phosphatase YopH, we used IMS as a label-free means to visualize enzyme binding and activity with a microarrayed phosphopeptide library printed on chips coated with either gold or indium-tin oxide. Furthermore, we demonstrate that microarray-based IMS can be coupled with surface plasmon resonance imaging to add kinetic analyses to measured binding interactions. The method described here is within the capabilities of many modern MALDI-TOF instruments and has general utility for the label-free analysis of microarray assays.

Versatile method for the detection of covalently bound substrates on solid supports by DART mass spectrometry

Analysis of substrates directly on solid phase resins without the need for separate cleavage conditions remains an outstanding challenge in the field of solid phase synthesis. We now present the first example of simultaneous cleavage and mass spectrometric analysis of peptides from solid supports using direct analysis in real time (DART) mass spectrometry. We have shown that this method is compatible with a diverse array of solid phase resins and is suitable for analysis of both peptides and organic substrates.

High-throughput small molecule identification using MALDI-TOF and a nanolayered substrate

Encoderless combinatorial chemistry requires high-throughput product identification without the use of chemical or other tags. We developed a novel nanolayered substrate plate and combined it with a microarraying robot, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, and custom software to produce a high-throughput small molecule identification system. To optimize system performance, we spotted 5 different chemical entities, spanning a m/z range of 195 to 1338, in 20,304 spots for a total of 101,520 molecules. The initial spot identification rate was 99.85% (20,273 spots), and after a proofreading algorithm was added, 100% of 20,304 spots and 101,520 molecules were identified. An internal recalibration algorithm also significantly improved mass accuracy to as low as 45 ppm. Using this optimized system, 47 different chemical entities, spanning a m/z range of 138 to 1,592, were spotted over 5,076 spots and could be identified with 100% accuracy. Our study lays the foundation for improved encoderless combinatorial chemistry.