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Precision
Published in Lawrence S. Chan, William C. Tang, Engineering-Medicine, 2019
Being one of the most commonly diagnosed male neoplasms in the developed nations and the second most common cancer in men of the US, prostate cancer, is known to occur in 170,000 new patients annually in the US, according to the latest available statistics of 2014 (CDC 2017). However, currently there is significantly inefficiency in the care of these patients. The key point of this inefficiency is that these patients as a group were massively over-treated. Due to the slow-growing nature of prostate tumors, many of these patients may not require aggressive treatment and suffer the significant treatment side-effects but were treated nevertheless due to our inability to identify this subgroup of patients (Schroder et al. 2009). Thus, we need to delineate biomarkers for patient stratification based on prognostic risk and for more precise treatment options (Flores-Morales and Iglesias-Gato 2017). Here is where precision medicine can be very beneficial. While the recent large scale of genomic studies on prostate cancer have provided advanced understanding of the molecular mechanism driving the tumor development, our understanding of the progression of prostate cancer remains restricted (Grasso et al. 2012, CGARN 2015, Robinson et al. 2015, Fraser et al. 2017, Flores-Morales and Iglesias-Gato 2017). The recent development of the method of using mass spectrometry enables the detection and quantification of thousands of proteins and post-translational modifications from small amounts of biological materials such as fresh frozen biopsy, formalin-fixed paraffin-embedded tissues, blood, and urine (Flores-Morales and Iglesias-Gato 2017). This mass spectrometry-based proteomic profiling thus forms the basis for the prostate cancer precision medicine. One of the new tools that facilitates this proteomic profiling is ITRAQ, abbreviations for isobaric tag for relative and absolute quantitation. The ITRAQ method is based on the derivatization of primary amino acid groups in intact proteins or peptides and utilization of isobaric tag for quantitation. Since the isobaric mass design reagents do not alter the mass of the tagged proteins or peptides, the samples (tagged or untagged) would appear as single peaks in mass spectrometry analysis, thus allowing accurate protein quantitation (Wiese et al. 2007). Figure 1 schematically depicts the common approaches for this proteomic profiling methodology. This technology will significantly facilitate the determination of protein profiles in given diseases much speedier than the traditional antibody-based testing method, and would provide healthcare providers with much more information they needed to make medical decisions. Advance mass spectrometry, in addition to proteomic profiling, is now able to perform intraoperative cancer margin identification in nearly real-time and this book has devoted an independent chapter on that development.
Omics to address the opportunities and challenges of nanotechnology in agriculture
Published in Critical Reviews in Environmental Science and Technology, 2021
Sanghamitra Majumdar, Arturo A. Keller
Electrospray ionization (ESI) and Matrix-Assisted Laser Desorption Ionization (MALDI) are the most common platforms used to ionize peptides and proteins. The precise molecular mass of the resulting ions is then analyzed using mass analyzers, such as ion trap, quadrupole, Orbitrap, time-of-flight (TOF), and Fourier transform ion cyclotron resonance (FTICR), which are often used in tandem to achieve higher degrees of ion separation (Lai et al., 2012). In untargeted proteomics using LC-MS/MS, data dependent acquisition is performed, where the highest abundance peptide ions from full MS scans are selected for MS/MS. However, this may generate datasets skewed toward the identification of relatively high abundance proteins, thereby masking and excluding the low abundance proteins from quantification (Hart-Smith et al., 2017). Several labeling techniques such as isobaric Tags for Relative and Absolute Quantitation (iTRAQ) and Stable Isotope Labeling by Amino acids in Cell culture (SILAC) are also available which can help to reduce errors introduced during measurement conditions. However, untargeted proteomic analysis requires extensive data processing and is currently challenged by incomplete and limited nature of plant genomic and proteomic databases. In recent times, the use of label-free shotgun proteomic techniques have become increasingly popular, as they do not restrict the number of proteins identified compared to gel-based methods (Majumdar et al., 2015, 2019; Mirzajani et al., 2014; Vannini et al., 2014; Verano-Braga et al., 2014). However, gel-free methods have several drawbacks, such as masking of low abundant peptides and unavailability of protein database for all species.