Alvydas Mikulskis

A robust biomarker discovery pipeline for high-performance mass spectrometry data.

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.

Biomarker Discovery and Analysis Platform: Application to Alzheimer's Disease

Peptides and proteins have been associated with many disease states such as cancers, diabetes, neurological and cardiovascular diseases [1-4]. Despite the limited success of a handful of biomarkers, most diseases lack sensitive and specific biomarkers. One of the most successful biomarkers, Prostate Specific Antigen (PSA) has a fairly high false-positive rate and very low clinical sensitivity (~25%).

Proteomic analysis of mitochondrial proteins.

Publisher Summary The chapter discusses the proteomic analysis of mitochondrial proteins. Mitochondria play a central role in multiple cellular processes. In addition, mitochondrial dysfunction has been implicated in the cause of numerous diseases and disorders, including defects in energy metabolism, alzheimers and parkinsons diseases, cancer, type 2 diabetes, osteoarthritis, cardiovascular disease, and many drug side effects. A detailed map of the mitochondrial proteome, providing information on identity, function, and protein-protein interactions, would help to understand the complex mechanisms of the cellular function and disease. The rapid evolution of mass spectrometry (MS) based tools in conjunction with protein purification methods, such as 1-D gels, 2-D gels, fractionation techniques, and liquid chromatography allowed the development of mitochondrial protein maps. Numerous researchers have developed mouse models of human mitochondrial diseases by using homologous recombination. The descriptive proteomics techniques, such as mitochondrial protein maps and the functional proteomics techniques, such as (1) protein-protein interactions, (2) post-translational modifications, (3) proteomic expression profiling, and (4) differential protein expression studies using protein arrays, have helped in proteomic analysis. Differentially expressed mitochondrial and associated proteins can be identified by 2-D gel/orthogonal MALDI-TOF peptide mass fingerprinting. Future studies will undoubtedly correlate genomic, proteomic, and array data in an effort to more clearly elucidate the molecular mechanisms of mitochondria and ultimately, the whole cells.

Signal amplification on microarrays: techniques and advances in tyramide signal amplification (TSA)

Increased sensitivity for differential mRNA expression analysis on microarrays is rapidly becoming a serious need as the technology matures. Current techniques using direct cyanine labeled targets are effective for expression analysis of abundant mRNA sources but have limited utility for analysis where mRNA quantities are limited. Tyramide signal amplification (TSATM) applied to microarray detection provides dramatic improvements in sensitivity, allowing the reduction of sample sizes by as much as 200-fold. The technique includes hapten labeling of two separate RNA populations, microarray hybridization and detection of each hapten with sequential signal amplification steps. The system uses fluorescein and biotin nucleotide analogs as the hapten pair. Hybridized fluorescein and biotin labeled targets are sequentially reacted with horseradish peroxidase and cyanine 3 and cyanine 5 tyramides, resulting in the numerous depositions of these fluorophors on the array. Differential gene expression analysis of LNCaP and PC3 prostate cancer cell lines using one microgram of total RNA and TSA detection, indicates good correlation with results obtained starting with 100 micrograms ((mu) g) of total RNA in a conventional cyanine 3 and cyanine 5 nucleotide analog labeling and detection system (i.e., the direct method).