Use of SELDI-TOF mass spectrometry for identification of new biomarkers: potential and limitations

Discovery of a new biomarker for the mucopolysaccharidoses (MPS), dipeptidyl peptidase IV (DPP-IV; CD26), by SELDI-TOF mass spectrometry

Surface enhanced laser desorption/ionisation time of flight (SELDI-TOF) mass spectrometry has been used to search for new protein biomarkers in the plasma of patients with mucopolysacharidoses (MPS). Differences in the levels of some plasma proteins, particularly the apolipoprotein ApoCI, were observed between MPS patients and normal controls, using the different chromatographic surfaces (ProteinChips). ApoCI was identified by both its mass and by immunological techniques. In plasma, it exists in two forms, ApoCI and a truncated form which lacks two N-terminal amino acids, ApoCI'. In controls, the ratio of ApoCI':ApoCI observed using the cation-exchange surface (CM10) was approximately 1:2 whereas in most MPS patients it varied from 1:1 to 1:0.8. The ratio of ApoCI':ApoCI in plasma is determined by the activity of dipeptidyl peptidase IV, DPP-IV (also known as the leucocyte antigen CD26), which was found to be elevated up to 3-fold in MPS patients. The DPP-IV activity decreased in MPS I patients undergoing enzyme replacement therapy, indicating that it could be a useful biomarker for monitoring the efficacy of treatment in MPS disease. As DPP-IV has an important regulatory role in metabolism, it is possible that its elevation could cause some of the secondary pathology in MPS, and inhibition of DPP-IV might have a role in MPS therapy.

An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

Separation of peptides with polyionic nanosponges for MALDI-MS analysis

A polymer brush consisting of 70% poly(N-isopropylacrylamide) (PNIPAAM) and 30% polymethacrylic acid (PMAA) was synthesized from gold substrates with a "grafting from" AIBN-type free-radical initiator. Fractionation of two peptides, bradykinin and buccalin, was accomplished in less than 120 s by placing a 30 pM (pH approximately 6.2) droplet onto the polymer brush substrate. The eluant containing the anionic buccalin is pipetted away for MALDI analysis while the cationic bradykinin adsorbed to the swollen anionic brush and was subsequently released by adding a droplet of formic acid to the substrate. This caused the brush to collapse and release the bradykinin, much like squeezing a sponge; these nanosponge substrates exhibited very high loading capacity (>2.0 mg/mL) compared to plasma-polymer-modified MALDI substrates. Ellipsometric measurements showed that complementary peptides adsorb rapidly while those of the same charge do not, and MALDI-MS analysis of the two fractions showed separation of both peptides. The adsorption of bradykinin was monitored over time, and 85% of the peptide had been adsorbed to the nanosponge in 1 min from a 0.5 mg/mL aqueous solution.

Optimization of SELDI-TOF protein profiling for analysis of cervical mucous

Cervical mucous, produced in the region where cervical neoplasia occurs, is thought to be a good choice for discovery of biomarkers to improve cervical cancer screening. In this study, SELDI-TOF MS analysis was used to evaluate parameters for protein profiling of mucous. Proteins were extracted from mucous collected with Weck-Cel sponges. Several parameters like extraction reagent, loading protein concentration, matrix type, bind/wash conditions and sample fractionation, on different protein chip surfaces were evaluated. SELDI peak number and consistency in the resulting spectra were used to evaluate each condition. Analysis of spectra generated by different protein chips revealed an average of 30 peaks in the 2.5-30 kDa mass range using sinnapinic acid in the unfractionated sample. Sample concentration and buffer conditions evaluated did not lead to large alterations in the profiles. Quality control spectra were reproducible with intra- and inter-assay intensity CV for CM10, H50 and Q10 arrays being less than 20% and 30% respectively. IMAC30-Cu chips had higher intra- and inter-assay CV's at 25% and 35%. Current data showed that optimizing pre-analytical parameters can help in standardization and reproducibility of protein profiles produced by cervical mucous, and thus can be used for protein biomarker discovery with the SELDI platform.

The use of gene array technology and proteomics in the search of new targets of diseases for therapeutics

The advent of functional genomics has been greatly broadening our view and accelerating our way in numerous medical research fields. The complete genomic data acquired from the human genome project and the desperate clinical need of comprehensive analytical tools to study complex diseases, has allowed rapid evolution of genomic and proteomic technologies, speeding the rate and number of discoveries in new biomarkers. By jointly using genomics, proteomics and bioinformatics there is a great potential to make considerable contribution to biomarker identification and to revolutionize both the development of new therapies and drug development process.

Biomarker discovery in neurodegenerative diseases: a proteomic approach

Biomarkers for neurodegenerative disorders are essential to facilitate disease diagnosis, ideally at early stages, monitor disease progression, and assess response to existing and future treatments. Application of proteomics to the human brain, cerebrospinal fluid and plasma has greatly hastened the unbiased and high-throughput searches for novel biomarkers. There are many steps critical to biomarker discovery, whether for neurodegenerative or other diseases, including sample preparation, protein/peptide separation and identification, as well as independent confirmation and validation. In this review we have summarized current proteomics technologies involved in discovery of biomarkers for neurodegenerative diseases, practical considerations and limitations of several major aspects, as well as the current status of candidate biomarkers revealed by proteomics for Alzheimer and Parkinson diseases.

Urine in clinical proteomics

Urine has become one of the most attractive biofluids in clinical proteomics as it can be obtained non-invasively in large quantities and is stable compared with other biofluids. The urinary proteome has been studied by almost any proteomics technology, but mass spectrometry-based urinary protein and peptide profiling has emerged as most suitable for clinical application. After a period of descriptive urinary proteomics the field is moving out of the discovery phase into an era of validation of urinary biomarkers in larger prospective studies. Although mainly due to the site of production of urine, the majority of these studies apply to the kidney and the urinary tract, but recent data show that analysis of the urinary proteome can also be highly informative on non-urogenital diseases and used in their classification. Despite this progress in urinary biomarker discovery, the contribution of urinary proteomics to the understanding of the pathophysiology of disease upon analysis of the urinary proteome is still modest mainly because of problems associated to sequence identification of the biomarkers. Until now, research has focused on the highly abundant urinary proteins and peptides, but analysis of the less abundant and naturally existing urinary proteins and peptides still remains a challenge. In conclusion, urine has evolved as one of the most attractive body fluids in clinical proteomics with potentially a rapid application in the clinic.