| Metabolism is the process through which an organism continuously exchanges matter and energy with the external environment and self-renewal in the body.Metabolites such as carbohydrates,lipids,amino acids and low molecular peptides play key regulatory roles in essential life activities,including signal release,energy transfer and cellular communication.When the metabolism of the proteins,lipids and carbohydrates are in disorder,diseases such as diabetes,obesity,high blood pressure,cardiovascular and cerebrovascular may take place,which can further cause cancer.At the same time,cancer is also known as a "metabolic dysregulation disease".Abnormal tricarboxylic acid cycle and lipid metabolism caused by oncogenes are also considered to be closely related to the occurrence of cancer.Therefore,metabolites are not only an important indicator of human health,but also can be seen as biomarkers for cancer diagnosis.It is of great significance to effectively monitor metabolites in human tissues and body fluids.Qualitative and quantitative analysis of large amounts of metabolites followed with multi-molecular pattern recognition is becoming a new type of accurate diagnostic strategy for high-risk diseases such as malignant tumors.Conventional clinical metabolite detection methods are mainly based on biochemical analysis and immunoassays,which are limited to several simple molecules such as glucose,urea,uric acid,bilirubin,and total triglycerides,and it is difficult to achieve parallel analysis of multiple metabolites.Nuclear magnetic resonance(NMR)and mass spectrometry(MS)are two emerging metabolomics analysis platforms that can accomplish multidimensional detection for different types of metabolites.In comparison,mass spectrometry shows advantages of high accuracy,high sensitivity,and high content.Thus,MS techniques have the capability to explore the relationship between the metabolic fingerprinte changes and diseases occurence.However,most existing MS-based strategies for metabolomes analysis requires complex extraction and sample pretreatment processes,which are time-consuming and have poor reproducibility,making them difficult to be applied in rapid clinical analysis and disease diagnosis.In this thesis,we are aiming to introduce silicon nanowires(SiNWs)-chips with excellent ability of extraction and matrix-free MS detection.A rapid tip-contact sampling/ionization mass spectrometry(TCSI-MS)strategy is also established.The thesis studies the tip-enhanced photo-induced electron transfer effect on SiNWs array,and proposes and validates the electron-transfer mechanism in surface-assisted laser desorption/ionization mass spectrometry(SALDI-MS)on nanostructured semi-conductor surface.The SiNWs chip is further applied to the fast contact-sampling and MS analysis of surface molecules in biological samples.The collected metabolic fingerprint can reflect the characteristics of different biomolecules and show spatial distribution.By means of statistical analysis,accurate identification and subtype discrimination of tumor tissues can be achieved and potential tumor-specific lipid biomarkers can be found.The main contents of this thesis are as follows:In the first part(chapter 1),we firstly mainly summarize the correlation between metabolism and diseases,and the major methods of extraction,detection and data process for metabolomics study.The application of novel mass spectrometry techniques in rapid detection of metabolites and disease diagnosis was reviewed.At the same time,the application of semiconductor nanomaterials in extraction/enrichment of complex biological samples and mass spectrometry as well as the main mechanism involved in SALDI-MS is introduced.Also,the significance of the topic in this thesis is proposed in this part.The second part(chapter 2)mainly discusses the tip-enhanced electron transfer effect on SiNWs chip when it is applied for SALDI-MS detection.On one hand,UV absorption,field ionization and time domain finite difference(FDTD)simulation are used to study the electric field enhancement at the top surface of SiNWs.On the other hand,the mass spectrometric behaviors of several model molecules including indigo,isatin,benzylpyridinium ion(BP+),phosphatidylethanolamine(PE)and phosphatidylcholine(PC)verify the enhanced electron transfer mechanism on the SiNWs surface.The results show that the sharpened silicon nanowires have stronger field strength and better electron transfer ability.When SiNWs surface is modified with reduced graphene oxide(rGO)to form SiNWs@rGO,these two nanomaterials exhibit synergistic effect.In negative-ion detection mode,electron transfer dominates the ionization process of the analyte,while desorption and proton transfer both play important roles in positive ion mode.Selection of an excellent substrate for matrix-free mass spectrometric detection is the primary prerequisite for SALDI-MS analysis.Analytes adsorbed at the SiNWs surface,including endogenous metabolites and exogenous substance on the skin and pesticide residues on fruit surfaces can be detected in situ due to the tip-enhanced electron transfer of SiNWs.The mechanism study and material optimization in this part lay the foundation for the proposal of TCSI-MS.The SiNWs array structure not only has excellent ability of energy absorption and charge transfer,but also can be used as micro-extraction heads for molecular extraction and imprinting from soft surfaces,such as tissue samples.Contact-sampling and MS detection can be combined to accomplish rapid detection of complex tissue samples.In the third part(chapter 3),we mainly focus on the extraction-transfer and mass spectrometric detection of metabolites by TCSI-MS on the tissue sruface.SiNWs with different surface chemistry and presence or absence of rGO modification exhibit different extraction and detection properties.The kinetics of TCSI-MS in the molecular transfer of tissue surface has been explored by adjusting the tip-contact sampling(TCS)time.Through multi-tissue.TCSI-MS detection and analysis,tissue from different organs and distribution of different micro-regions on the same tissue can be identified,indicating that metabolites in specific tissue microenvironments can be obtained by TCSI-MS.TCSI-MS not only can be applied ex vivo,but also can be used in vivo,showing its great potential for clinical identification and diagnosis in surgery.Most molecules acquired by TCSI-MS on the surface of tissues are lipids,whose dysregulation in synthesis and metabolism have been found to be closely related to the occurrence,development and metastasis of cancer.Therefore,lipids can be used as biomarkers for precise identification of tumor tissues.Based on the second and third parts of the work,we focus on the rapid discrimination of renal cell carcinoma(RCC)and hepatocellular carcinoma(HCC)by TCSI-MS in the fourth part(Chapters 4 and 5).Lipidomic profiles of 30 specimens of RCC tissues along with their adjacent para-tumor samples and 20 specimens of HCC along with para-tumor tissues,normal liver tissues were acquired by TCSI-MS.SiNWs chips were used for RCC tissues extraction and detection.It can be seen that the negative-ion mode MS fingerprints of RCC cancer tissues and non-cancerous tissues show significant differences.As for HCC tissues,the composite of rGO and SiNWs can increase the signal intensities of glycerophospholipids in negative ion mode and can extend the information capacity of phosphatidylcholine(PC),sphingomyelin(SM)and triglyceride(TG)in positive ion mode.The spectra in both detection modes are complemented to each other to increase the dimension of effective information.At the same time,we propose a method for discrimination of HCC with the ratio information of adjacent "dual-peaks",which canhelp avoid signal supression between different classes of lipids,reduce the effect of in-source degradation of phospholipids and further improve the information stability.Multiple ratio values of such "dual-peaks" show signifcant difference between HCC tumor tissue and non-tumor tissues.Discrimination of RCC and HCC tissues can be accomplished by multivariate statistical analysis,including principal component analysis(PCA),orthogonal partial least squares discriminant analysis(OPLS-DA)and linear discriminant analysis(LDA).Multiple predictive models all show high sensitivity,high specificity and high accuracy.Also,the three major subtypes included in RCC and primary liver cancers with different origins can also be distinguished according to their characteristic lipid molecules.The close relationship between RCC subtype tumors can be indicated through cluster analysis,which is also related to clinical prognosis and reported gene expression similarity of different subtype RCC tumor tissues.The tumor-specific composition of sulfatide molecules play an important role in renal dysfunction and malignancy.Feature fatty acid dual-peaks are not only able to clearly distinguish HCC tumor boundaries in imaging experiments,but also can indicate the abnormal fatty acid metabolism associated with linoleic acid and arachidonic acid in the cancerous liver,which were not found in benign liver tumors.From this part of work,it is indicated that the TCSI-MS results can not only serve as a basis for determining the property of intraoperative margin,but also improve the accurate diagnosis of tumors and accomplish tumor margin imaging.Molecules with similar structures and similar molecular weights in tumor cells include not only lipid molecules,but also peptides and proteins.In the fifth part of work(chapter 6),we develop a method for the identification and relative quantification of various types of HCC cells based on the ratiometric MALDI-MS information.According to their intensity ratios in MALDI-MS,qualitative and quantitative identification for different types of HCC cells can be achieved in cell mixture and heterogeneous tissues.Compared with single peaks,the ratiometric information in cells is more stable and highly conserved.Also,novel "bimolecular markers" is defined here for the application of tumor heterogeneity. |
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