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Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface
- Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface
- Park, JW[Park, Ji-Won]; Jeong, H[Jeong, Hyobin]; Kang, B[Kang, Byeongsoo]; Kim, SJ[Kim, Su Jin]; Park, SY[Park, Sang Yoon]; Kang, S[Kang, Sokbom]; Kim, HK[Kim, Hark Kyun]; Choi, JS[Choi, Joon Sig]; Hwang, D[Hwang, Daehee]; Lee, TG[Lee, Tae Geol]
- DGIST Authors
- Hwang, D[Hwang, Daehee]
- Issue Date
- Scientific Reports, 5
- Article Type
- Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states.
- Nature Publishing Group
- Related Researcher
Hwang, Dae Hee
Systems Biology and Medicine Lab
Multilayered spatiotemporal networks; Regulatory motifs or pathways; Metabolite-protein networks; Network stochasticity; Proteomics and informatics
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