Peptide Fingerprint Detection And Latent Fingerprint Visualization Using Porous Silicon

Biological recognition technology includes molecular recognition in biosample, such as biofluids, tissues and cells, and external feature recognition. The internal molecular recognition is mainly based on the research of the bodyfluids, including genomics, proteomics, peptidomics and metabonomics to mining the disease information for diagnosis. Recently, mining the disease information from the proteomic profile in the low molecular weight range are becoming an alternative way in cancer research. The goal of this study was to create nanoporous silicon particles that allow us to capture, protect, and directly detect the low molecular weight peptides (LMWP) sieved from large pool of high abundant plasma proteins. The external feature recognition techniques mainly include face recognition and fingerprint recognition technology. In this paper, we developed an image-contrast technology based on the electrochemiluminescence of porous silicon. The visualization of oily latent fingerprints and explosive residues on the fingerprints has been demonstrated with this technology. In the future, we will get the endogenous molecular information of the fingerprint by mass spectrometry. The major contents of this thesis are as follows:In charpter 1, the development of bio-MS technology, cancer diagonosis based on the proteomic/peptidomic research, strategies that have been employed to image latent fingerprints and the mechanisms of porous Silicon based luminescence were summarized. In cancer diagonosis based on the proteomic/peptidomic research, we mainly reviewed the materials for peptide enrichment. In the next section, methods used to visualize the latent fingerprints were listed. Finally, the significance of this thesis was put forward.Chapter 2 introduce a nanoporous silicon microparticles applyed for capture, enrichment, and mass spectrometric detection of low-molecular-weight biomarkers. The pore size of porous silicon can be precisely tailored by well controlled electrical etching and surface modification technologies to selectively trap a subset of low molecular weight biomarkers and preclude the high abundance large protein such as serum albumin, immunoglobulin and protease in blood sample. Moreover, without need of elution step, the LMWP captured in the pore channel of NPSMF can be directly ionized by a UV laser with the assistance of CHCA matrix. Compared with nanoporous silica particles, mesoporous silica chip or film, smart hydrogel, the NPSMF show significant advantages. Ten serum samples from 5 colorectal cancer patients and 5 healthy people were pretreated and detected using our technology. The differences in LMWP profiles between cancer and the normal samples can be well discriminated by the hierarchical cluster method. The results demonstrate the possibility of applying the nanotechnology to disease diagnosis and discrimination based on LMWP profiling. This nanotechnology provides a powerful tool that will lead to the successful discovery of novel biomarkers with potential significant clinical utility to early diagnosis and manage various diseases.In chapter 3, a multi-dimensional on-particle detection technology was developed for multi-category disease classification. Human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Therefore, to reveal the complexity of molecule variation that occurs in disease progression, innovations in analytical technologies are urgently needed. Serum peptide profiles have been known as the "fingerprint library", which contains important bio-information. The motivation of this study is to take advantage of porous silicon microparticles with multiple surface chemistries to reduce the loss of peptide information and simplify the sample pretreatment. We developed a multi-dimensional on-particle MALDI-TOF technology to acquire high fidelity and cross-reactive molecular fingerprints for mining disease information. The peptide fingerprint of serum samples from 24 colorectal cancer patients,24 liver cancer patients and 24 healthy volunteers were measured with this technology. The featured mass spectral peaks can successfully discriminate and predict the multi-category disease. Data visualization for future clinical application was also demonstrated.Chapter 4 introduce a Storage stratagy of serum peptide information useing nanoporous silicon microparticles. Serum peptides are becoming a rich source of disease biomarkers, however, the possible change in composition and relative abundance of serum peptides after sample collection might be a big problem that may hinder the application of the peptidomic-based technology. It has been reported that serum proteins may be degraded very quickly by proteases present in the serum after the blood sample is collected. Therefore, preserving serum peptide information after sample collection is of great importance. This work has provided solid evidence that the porous nanostructure can successfully serve as a storage material for preserving peptide information. The result of cluster analysis using the mass spectral data obtained from serum samples (10 colorectal cancer patients and 10 healthy people) pre-treated with NPSMPs and stored for 7 days at-20℃. The statistical analyses show that the peptide fingerprints from colorectal cancer patients and those from the healthy controls can be fully separated and discriminated. However, without NPSMP protection, the peptide fingerprint between the cancer patients and healthy persons cannot be well classified, indicating the loss of disease information during sample storage even at a low temperature. If coupled with tandem MS technology, quantitation and identification of serum peptides with high fidelity can be achieved. The material may find wide application in the storage of various biofluid samples for disease diagnosis.Chapter 5 introduced an image-contrast technology based on the electrochemiluminescence of porous silicon and its application in fingerprint visualization. Biological recognition technology includes external feature recognition, includeing fingerprint recognition technology. Fingerprints result from the transfer of substances from the ridged epidermal skin of fingers to a surface during contact. Usually, fingerprints consist of perspiration, natural secretion residues, and exogenous components from the environment. However, current luminescence techniques usually require an additional luminophore and a coreactant. The electrochemiluminescence (ECL) of porous silicon (PSi) has attracted great interest for its potential application in display technology and chemical sensors. In this work, we found that the ECL emitted by PSi would undergo activation, strong emission and fading process. Meanwhile, PSi with different surface chemistry displayed an apparently different dynamic process of ECL. An image-contrast technology was established on the basis of the intrinsic mechanism of the ECL dynamic process. In the current approach, LFPs left on PSi can simply be visualized by ECL-based technology without the need for any molecular luminophore or coreactant. Impressions of fingerprints on a PSi surface may leave behind a unique spatially resolved chemical pattern. If a fingertip touches the PSi surface, the oily and tacky secretion on the ridge area will inevitably be transferred to the PSi surface to form an oil-based chemical pattern on PSi. As a proof of principle, the visualization of oily LFPs and explosive residues on the fingerprints has been demonstrated with this technology. As compared to ECL technology based on molecular luminescence, the technology proposed herein is simpler and does not need any coreactant. The application of this technology can potentially be extended to chemical-sensor arrays and the molecular imaging of tissue samples.