SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification. The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in ‘Normalyzer’—a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1. Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally. This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Monitoring the development of resistance to the tyrosine kinase inhibitor (TKI) imatinib in chronic myeloid leukemia (CML) patients in the initial chronic phase (CP) is crucial for limiting the progression of unresponsive patients to terminal phase of blast crisis (BC). This study for the first time demonstrates the potential of Raman spectroscopy to sense the resistant phenotype. Currently recommended resistance screening strategy include detection of BCR-ABL1 transcripts, kinase domain mutations, complex chromosomal abnormalities and BCR-ABL1 gene amplification. The techniques used for these tests are expensive, technologically demanding and have limited availability in resource-poor countries. In India, this could be a reason for more patients reporting to clinics with advanced disease. A single method which can identify resistant cells irrespective of the underlying mechanism would be a practical screening strategy. During our analysis of imatinib-sensitive and -resistant K562 cells, by array comparative genomic hybridization (aCGH), copy number variations specific to resistant cells were detected. aCGH is technologically demanding, expensive and therefore not suitable to serve as a single economic test. We therefore explored whether DNA finger-print analysis of Raman hyperspectral data could capture these alterations in the genome, and demonstrated that it could indeed segregate imatinib-sensitive and -resistant cells. Raman spectroscopy, due to availability of portable instruments, ease of spectrum acquisition and possibility of centralized analysis of transmitted data, qualifies as a preliminary screening tool in resource-poor countries for imatinib resistance in CML. This study provides a proof of principle for a single assay for monitoring resistance to imatinib, available for scrutiny in clinics.

The molecular mechanism mediating expression of senescent cell antigen-aggregated or cleaved band 3 and externalized phosphatidylserine (PS) on the surface of aged erythrocytes and their premature expression in certain anemias is not completely elucidated. The erythrocytes with these surface modifications undergo macrophage-mediated phagocytosis. In this study, the role of protein kinase C (PKC) isoforms in the expression of these surface modifications was investigated. Inhibition of PKCαby 30 μM rottlerin (R30) and 2.3 nM Gö 6976 caused expression of both the senescent cell marker-externalized PS measured by FACS analysis and aggregated band 3 detected by western blotting. In contrast to this observation, but in keeping with literature, PKC activation by phorbol-12-myristate-13-acetate (PMA) also led to the expression of senescence markers. We explain this antithesis by demonstrating that PMA-treated cells show reduction in the activity of PKCα, thereby simulating inhibition. The reduction in PKCαactivity may be attributed to the known downregulation of PMA-activated PKCα, caused by its membrane translocation and proteolysis. We demonstrate membrane translocation of PKCαin PMA-treated cells to substantiate this inference. Thus loss of PKCαactivity either by inhibition or downregulation can cause surface modifications which can trigger erythrophagocytosis.