We constructed miniaturized autoantigen arrays to perform large-scale multiplex characterization of autoantibody responses directed against structurally diverse autoantigens, using submicroliter quantities of clinical samples. Autoantigen microarrays were produced by attaching hundreds of proteins, peptides and other biomolecules to the surface of derivatized glass slides using a robotic arrayer. Arrays were incubated with patient serum, and spectrally resolvable fluorescent labels were used to detect autoantibody binding to specific autoantigens on the array. We describe and characterize arrays containing the major autoantigens in eight distinct human autoimmune diseases, including systemic lupus erythematosus and rheumatoid arthritis. This represents the first report of application of such technology to multiple human disease sera, and will enable validated detection of antibodies recognizing autoantigens including proteins, peptides, enzyme complexes, ribonucleoprotein complexes, DNA and post-translationally modified antigens. Autoantigen microarrays represent a powerful tool to study the specificity and pathogenesis of autoantibody responses, and to identify and define relevant autoantigens in human autoimmune diseases.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anticoagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multi- We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches.This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

There is a substantial list of pre-analytical variables that can alter the analysis of blood-derived samples. We have undertaken studies on some of these issues including choice of sample type, stability during storage, use of protease inhibitors, and clinical standardization. As there is a wide range of sample variables and a broad spectrum of analytical techniques in the HUPO PPP effort, it is not possible to define a single list of pre-analytical standards for samples or their processing. We present here a compendium of observations, drawing on actual results and sound clinical theories and practices. Based on our data, we find that (1) platelet-depleted plasma is preferable to serum for certain peptidomic studies; (2) samples should be aliquoted and stored preferably in liquid nitrogen; (3) the addition of protease inhibitors is recommended, but should be incorporated early and used judiciously, as some form non specific protein adducts and others interfere with peptide studies. Further, (4) the diligent tracking of pre-analytical variables and (5) the use of reference materials for quality control and quality assurance, are recommended. These findings help provide guidance on sample handling issues, with the overall suggestion being to be conscious of all possible pre-analytical variables as a prerequisite of any proteomic study.

Carbohydrate post-translational modifications on proteins are important determinants of protein function in both normal and disease biology. We have developed a method to allow the efficient, multiplexed study of glycans on individual proteins from complex mixtures, using antibody microarray capture of multiple proteins followed by detection with lectins or glycan-binding antibodies. Chemical derivatization of the glycans on the spotted antibodies prevented lectin binding to those glycans. Multiple lectins could be used as detection probes, each targeting different glycan groups, to build up lectin binding profiles of captured proteins. By profiling both protein and glycan variation in multiple samples using parallel sandwich and glycan-detection assays, we found cancer-associated glycan alteration on the proteins MUC1 and CEA in the serum of pancreatic cancer patients. Antibody arrays for glycan detection are highly effective for profiling variation in specific glycans on multiple proteins and should be useful in diverse areas of glycobiology research.

To better understand the molecular mechanisms that underlie the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC), we studied the gene expression profiles of 29 ccRCC tumors obtained from patients with diverse clinical outcomes by using 21,632 cDNA microarrays. We identified gene expression alterations that were both common to most of the ccRCC studied and unique to clinical subsets. There was a significant distinction in gene expression profile between patients with a relatively nonaggressive form of the disease [100% survival after 5 years with the majority (15͞17 or 88%) having no clinical evidence of metastasis] versus patients with a relatively aggressive form of the disease (average survival time 25.4 months with a 0% 5-year survival rate). Approximately 40 genes most accurately make this distinction, some of which have previously been implicated in tumorigenesis and metastasis. To test the robustness and potential clinical usefulness of this molecular distinction, we simulated its use as a prognostic tool in the clinical setting. In 96% of the ccRCC cases tested, the prediction was compatible with the clinical outcome, exceeding the accuracy of prediction by staging. These results suggest that two molecularly distinct forms of ccRCC exist and that the integration of expression profile data with clinical parameters could serve to enhance the diagnosis and prognosis of ccRCC. Moreover, the identified genes provide insight into the molecular mechanisms of aggressive ccRCC and suggest intervention strategies.