Profiling of endogenous peptides by multidimensional liquid chromatography: On-line automated sample cleanup for biomarker discovery in human urine

[Preparation of multi-functional magnetic nanoparticles for harvesting low-molecular-weight glycoproteins]

低分子量糖蛋白被认为是发现疾病生物标志物的宝库。特异性的萃取吸附剂对这一类化合物的萃取和富集是必不可少的。硼亲和材料在近年来取得了很大的发展,但专门用于选择性富集低分子量糖蛋白的硼亲和材料目前鲜有报道。该文提出了具有多种功能的磁性纳米颗粒(MNPs),用于低分子量糖蛋白的选择性捕获。该多功能磁性纳米颗粒是用硼酸功能化聚合物网络包裹的磁性纳米复合物。该多功能磁性纳米材料是利用磁性的纳米颗粒内核通过在其表面修饰苯硼酸功能团的聚丙烯酸高分子网络链制备得到。该材料不仅具有常规磁性材料在磁分离方面的基本优势,还能提供三重预先设计的先进功能:1)尺寸排阻效应,去除高分子量蛋白质的干扰;2)对低分子量糖蛋白的选择性萃取;3)保护捕获到的低分子量糖蛋白不被降解和污染。该材料的选择性萃取功能来自于硼酸配基与糖蛋白的顺式二醇部分的亲和性,而尺寸限制效应和保护功能则依赖于磁性纳米颗粒表面修饰的聚合物网络,允许低分子量化合物选择性通过。通过实验验证了这些预设的功能,且通过改变聚合物链长可以调节限径效应的阈值。这种多功能磁性纳米复合物可以进一步发展成有前景的纳米探针,不仅可以选择性捕获低分子量糖蛋白,还可以选择性捕获核苷和聚糖等其他具有重要生物学意义的顺式二醇分子。因此,该文报道的材料制备策略为从复杂样品中选择性萃取靶标化合物的多功能吸附剂的设计和合成提供了新思路。

Methodology for Urine Peptidome Analysis Based on Nano-HPLC Coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Urine is a sample of choice for noninvasive biomarkers search because it is easily available in large amounts and its molecular composition provides information on processes in the organism. The high potential of urine peptidomics has been demonstrated for clinical purpose. Several mass spectrometry based approaches have been successfully applied for urine peptidome analysis and potential biomarkers search. Summarizing literature data and our own experience we developed a protocol for comprehensive urine peptidome analysis. The technology includes several stages and consists of urine sample preparation by size exclusion chromatography and identification of featured peptides by nano-HPLC coupled to Fourier transform ion cyclotron resonance mass spectrometry, semiquantitative and statistical data analysis.

Optimization for Peptide Sample Preparation for Urine Peptidomics

Analysis of native or endogenous peptides in biofluids can provide valuable insight into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for noninvasive monitoring of human diseases. The noninvasive nature of urine collection and the abundance of peptides in the urine make analysis by high-throughput "peptidomics" methods an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regard to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements, in that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS-based peptidome analysis. We report on a novel adaptation of the standard solid-phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS-based peptidomics, in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. The mSPE method provides significantly improved efficiencies for the preparation of samples from urine. The mSPE method is found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis, due to optimized sample cleanup that provides improved experimental inference from confidently identified peptides.

A protein deep sequencing evaluation of metastatic melanoma tissues

Malignant melanoma has the highest increase of incidence of malignancies in the western world. In early stages, front line therapy is surgical excision of the primary tumor. Metastatic disease has very limited possibilities for cure. Recently, several protein kinase inhibitors and immune modifiers have shown promising clinical results but drug resistance in metastasized melanoma remains a major problem. The need for routine clinical biomarkers to follow disease progression and treatment efficacy is high. The aim of the present study was to build a protein sequence database in metastatic melanoma, searching for novel, relevant biomarkers. Ten lymph node metastases (South-Swedish Malignant Melanoma Biobank) were subjected to global protein expression analysis using two proteomics approaches (with/without orthogonal fractionation). Fractionation produced higher numbers of protein identifications (4284). Combining both methods, 5326 unique proteins were identified (2641 proteins overlapping). Deep mining proteomics may contribute to the discovery of novel biomarkers for metastatic melanoma, for example dividing the samples into two metastatic melanoma "genomic subtypes", ("pigmentation" and "high immune") revealed several proteins showing differential levels of expression. In conclusion, the present study provides an initial version of a metastatic melanoma protein sequence database producing a total of more than 5000 unique protein identifications. The raw data have been deposited to the ProteomeXchange with identifiers PXD001724 and PXD001725.

Optimization for peptide sample preparation for urine peptidomics

Analysis of native or endogenous peptides in biofluids can provide valuable insights into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for non-invasive monitoring of human diseases. The non-invasive nature of urine collection and the abundance of peptides in the urine makes analysis by high-throughput ‘peptidomics’ methods , an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regards to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS based peptidome analysis. We report on a novel adaptation of the standard solid phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS based peptidomics in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. Expense and time requirements were comparable for both SPE and mSPE, but more interfering contaminants from the urine matrix were evident in the SPE preparations (e.g., clogging of the LC-MS columns, yellowish background coloration of prepared samples due to retained urobilin, lower peptide yields) when compared to the mSPE method. When we compared data from technical replicates of 4 runs, the mSPE method provided significantly improved efficiencies for the preparation of samples from urine (e.g., mSPE peptide identification 82% versus 18% with SPE; p = 8.92E-05). Additionally, peptide identifications, when applying the mSPE method, highlighted the biology of differential activation of urine peptidases during acute renal transplant rejection with distinct laddering of specific peptides, which was obscured for most proteins when utilizing the conventional SPE method. In conclusion, the mSPE method was found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis due to the optimized sample clean up that provided improved experimental inference from the confidently identified peptides.

Applicability of 2D gel electrophoresis and liquid chromatography in proteomic analysis of urine using mass spectrometry MALDI-TOF

Proteomics including the studies of the structure, function and dependences between proteins is more and more extensively applied in human medicine and veterinary medicine. The analysis of protein profiles of tissues and body fluid from healthy and ill individuals allows to identify diagnostic, prognostic and predictive markers in various pathological states in people and animals. This paper presents preparation of urine samples for analysis in the mass spectrometer MALDI-TOF (Ultraflextreme, Bruker, Bremen, Germany) by means of two methods: liquid chromatography based on the system Nano-LC (PROTEINER FC II, Bruker Daltonics, Bremen Germany). and two-direction electrophoresis 2DE (GE Healthcare, United Kingdom). Both methods enable separation of the mixture under consideration into individual fractions of high purity indispensable for obtaining readable mass spectra. The purpose of this paper is to determine applicability of these methods in analysis of protein composition of urine samples.

Direct bioanalytical sample injection with 2D LC–MS

New analytical platforms have been developed in response to the need for attaining increased peak capacity for multicomponent complex analysis with higher sensitivity and characterization of the analytes, and high-throughput capabilities. This review outlines the fundamental principles of target and comprehensive 2D LC method development and encompasses applications of LC–LC and LC × LC coupled to MS in bioanalysis using a variety of online analytical procedures. It also provides a rationale for the usage of the most employed mass analyzers and ionization sources on these platforms.

Sample preparation techniques for the untargeted LC-MS-based discovery of peptides in complex biological matrices

Although big progress has been made in sample pretreatment over the last years, there are still considerable limitations when it comes to overcoming complexity and dynamic range problems associated with peptide analyses from biological matrices. Being the little brother of proteomics, peptidomics is a relatively new field of research aiming at the direct analysis of the small proteins, called peptides, many of which are not amenable for typical trypsin-based analytics. In this paper, we present an overview of different techniques and methods currently used for reducing a sample's complexity and for concentrating low abundant compounds to enable successful peptidome analysis. We focus on techniques which can be employed prior to liquid chromatography coupled to mass spectrometry for peptide detection and identification and indicate their advantages as well as their shortcomings when it comes to the untargeted analysis of native peptides from complex biological matrices.

Analyzing the cryptome: uncovering secret sequences

The mammalian cryptome consists of bioactive peptides generated by the proteolysis of precursor proteins. It is speculated that the cryptide repertoire increases the complexity of the proteome by an order of magnitude. Cryptides have been found to function in a wide range of processes including neuronal signaling, antigen presentation, and the inflammatory response. Due to their potential as therapeutic agents, there has been an increasing interest in studying cryptides. In this review, we discuss different approaches for discovering these hidden peptides and how proteomic tools can be utilized to aid in their identification and characterization.

Reversed-phase HPLC and hyphenated analytical strategies for peptidomics

Peptide study and analysis widely involve liquid chromatography. Among the different strategies available, reversed-phase liquid chromatography (RP-HPLC) is one of the methods of choice to separate species in a nontargeted approach. The compounds are sorted according to their hydrophobicity, even though the experimental order of elution could change according to the nature of the mobile phase and the stationary phase. In our work, we have developed protocols to resolve hundred of peptidic species. To overcome the limitations of peak capacity of RP-HPLC alone, it has been coupled downstream to tandem mass spectrometry using two different ionization modes. To overcome the limitations of peak capacity of RP-HPLC MS/MS, it has been coupled upstream to strong cation exchange liquid chromatography. Multidimensional analysis allows for a deeper description of a sample because the limit of detection is often due to a lack of dynamic range of the detection itself rather than due to a lack of sensitivity. In this chapter, different protocols are presented. They should be considered as examples that could be used as starting point for new protocols optimization. Even if RP-HPLC is a universal peptide separation method, it should be optimized according to the specific characteristics of the peptide(s) of interest.