基于数据非依赖采集的无色谱数据处理软件系统

目的如何高效准确地定量蛋白质一直是蛋白质组学的主要关注点,基于液相色谱-数据依赖模式进行谱图采集的质谱方法是目前主流的蛋白质测定方式。但是,当面对复杂样本中蛋白质定量的对比实验,为了使肽段得到有效分离,使用较长时间色谱洗脱的方法占据了谱图生成的大量时间。为了解决此问题,并且能够高效、准确地定性定量肽段,提出一个基于数据非依赖采集(data-independent acquisition,DIA)的无色谱数据处理软件系统。方法基于以肽段为中心的蛋白质定量理念,利用现有解决混合图谱的方法对无色谱DIA质谱数据进行定性,随后仿照DIA方法下色谱面积的计算方法完成定量;最后基于分类模型,对最终结果给出统计分析控制。结果本系统能够处理生成无色谱的DIA质谱数据,并且在12 min内从海拉(Henrietta Lacks,Hela)蛋白质样本中定性定量出1954个肽段。结论使用本系统处理无色谱质谱数据,相比于DIA质谱数据,能够在更短的时间内准确定量出足够的肽段,对于在有限时间内测定大规模蛋白质样本有重要的意义。     

基质辅助激光解析电离飞行时间质谱在临床病原微生物鉴定中的应用进展

基质辅助激光解析电离飞行时间质谱(matrixassisted laser desorption/ionization-time of flight mass spectrometry,MALDI-TOF MS)是近年来发展起来的一种新型软电离质谱技术,现今其在微生物快速检测方面的应用得到广泛关注.相对于微生物实验室现有的传统鉴定方法和分子生物学方法,MALDITOF MS具有快速、准确、灵敏、分辨率好和高质量检测范围等优点,虽不能完全替代传统方法,但丰富了微生物检验方法的选择,克服传统方法操作繁琐、耗时的缺点,大大提高了微生物鉴定在临床中的参考价值.本文就MALDI-TOF MS在微生物鉴定中的应用及其进展作一综述.     

4819例遗传代谢病检测结果分析

目的 为了解遗传代谢病的发病率,以便推动遗传代谢病的全面筛查,应用液相色谱-串联质谱(liquid chromatography-tandem mass spectrometry,LC-MS/MS)检测血氨基酸和酰基肉碱,联合气相色谱-串联质谱(gas chromatography-tandem mass spectrometry,GC-MS)技术检测尿液中有机酸,对氨基酸、有机酸代谢病及脂肪酸β氧化障碍进行筛查和诊断.方法 收集4819例(包括1388例新生儿及3431例疑似遗传代谢病高危儿童)血氨基酸和酰基肉碱检测结果及尿有机酸检测结果,分别利用LC-MS/MS检测了4778例干滤纸片和GC-MS检测了3004例尿标本.结果 通过遗传代谢病筛查共确诊88例(占所检测样本的1.83%,这88例均行LC-MS/MS和GC-MS检测),其中氨基酸代谢病9种,37例;有机酸代谢病7种,40例;脂肪酸β氧化障碍5种,11例.结论 联合LC-MS/MS及GC-MS能快速对遗传代谢病进行筛查和诊断.     

奥曲肽对LPS处理的A549细胞代谢的影响

目的:探讨生长抑素衍生物奥曲肽对脂多糖(LPS)诱导A549细胞代谢组学变化的影响。方法:体外培养肺泡腺癌上皮细胞A549,分别经LPS和LPS+奥曲肽处理,气相色谱质谱联用技术(gas chromatography/mass spectrometry,GC/MS)和液相色谱质谱联用技术(liquid chromatography/mass spectrometry,LC/MS)检测A549细胞在不同处理条件下的代谢组变化,对结果进行色谱图可视化检查,将相应数据进行主成分分析(PCA),解析其差异表达代谢物,并构建互作网络图。结果:(1)在基于LC/MS方法的代谢组分析中,发现不同处理组之间有一定差异,进一步采用正交潜变量投影判别分析(OPLS-DA),获得差异性表达代谢物。差异代谢物以氨基酸类及磷脂类为主。(2)通过GC/MS技术分析不同处理A549细胞的代谢组,获得差异性表达代谢物,主要为有机酸,糖类及氨基酸类代谢产物。(3)构建互作网络图,主要涉及糖酵解/糖异生、色氨酸代谢、半乳糖代谢、尿素循环和柠檬酸代谢,并从中发现14个具有标志性代谢物作用的关键成分,包括5-羟色胺、吲哚、苏氨酸、丝氨酸、葡萄糖、苯丙氨酸、乳糖、延胡索酸盐、4-羟基苯乳酸、冬氨酸盐、天门冬素、腐胺、脯氨酸和琥珀酸盐。结论:(1)代谢组分析发现,在奥曲肽处理LPS刺激的A549细胞的过程中,有机酸,糖类、氨基酸类和脂类是其变化的主要成分。(2)构建了LPS致A549细胞炎症反应和奥曲肽干预作用的差异代谢物互作网络图,涉及5个代谢路径和14个关键代谢组分。     

蛋白质组学质谱数据预处理技术综述

基于质谱技术的蛋白质组学数据分析,是识别新型生物标记物模式的有效手段。质谱仪检测的数据含有大量潜在信息,但数据很容易被系统误差和噪声污染。蛋白质组学质谱数据预处理的目的在于抑制噪声、数据简约和增加谱可比性等,是增强生物学相关信息的至关重要步骤。只依赖质谱仪中的软件进行数据预处理存在一定局限,需要额外工具辅助。从数据简约、谱线平滑、基线校正、标准化、谱峰提取与量化、谱峰联配等方面介绍典型的预处理技术,对预处理方法存在的问题进行讨论,并就发展趋势进行展望。     

应用SELDI技术检测精索静脉曲张患者精浆中的差异表达蛋白

目的探讨用蛋白质芯片技术筛选精索静脉曲张患者精浆中蛋白质表达谱,寻找差异蛋白。方法采用表面增强激光解离飞行时间质谱技术（surface—enhanced laser desorption／ionization time of flight mass spectrometry,SELDI—TOF—MS）．运用CM10蛋白质芯片检测30例精索静脉曲张患者和30例正常对照精浆中蛋白质谱,获得的蛋白质谱采用Biomarker Wizard软件分析,初步筛选蛋白质峰,结合生物信息学的支持向量机（support vector machines,SVM）方法建立并测试精索静脉曲张患者精浆中的蛋白质指纹图谱。结果在芯片上捕获到163种蛋白质,用质谱仪筛选出精索静脉曲张患者与正常对照组相比的16种差异蛋白,其中有3个蛋存在显著性差异。结论精索静脉曲张患者与健康者精浆中存在较多差异蛋白质,本研究对进一步探索精索静脉曲张的病因及其临床治疗具有重要意义。     

串联质谱在遗传代谢性疾病筛查中的应用

目的研究串联质谱技术在儿科遗传代谢病筛查中的价值。方法对132例疑似遗传代谢病患儿采用串联质谱(Tandem mass spectrum,MS/MS)及气相色谱/质谱(Gas chromatography/Mass spectrometry,GC/MS)技术进行遗传代谢性疾病的筛检。结果 132例患儿中确诊为先天性遗传代谢病者分别为高苯丙氨酸血症35例,线粒体能量代谢障碍2例,二羧酸尿症2例,甲基丙二酸血症2例,丙酸血症1例,鸟氨酸氨甲酰基转移酶缺乏症(OTCD)1例。OTCD先证者及其父母作了基因测序,先证者检出c.626CT(p.A209V)突变基因,其母孕二胎时产前作了羊水及细胞培养基因分析未见突变基因,产后婴儿作了MS/MS、GC/MS分析未见异常。结论串联质谱技术可分析代谢物质浓度水平,可对遗传代谢病进行筛查,有利于临床确诊病因,减少医疗纠纷发生,对再生育者进行产前筛查有利于优生优育,值得推广应用。     

嘉兴地区2265例NICU新生儿串联质谱法遗传代谢病筛查的应用

目的研究串联质谱技术在NICU新生儿遗传性代谢病筛查中的价值。方法对2265例NICU新生儿,采用串联质谱技术(tandem mass spectrometry,MS/MS)进行遗传代谢性疾病的筛检。结果 2265例NICU新生儿中4例确诊为先天性遗传代谢病,阳性率为1.77‰。病种依次为:甲基丙二酸血症2例,尿素循环障碍1例,原发肉碱吸收障碍1例。结论串联质谱技术可分析代谢物质浓度水平,可对遗传性代谢病进行筛查。早期的筛查有利于医院确诊病因,减少医疗纠纷的发生,也对患儿及家庭早期干预有利,值得推广开展。     

应用SELDI-TOF-MS技术分析VKC泪液的差异表达蛋白

目的:探讨用蛋白质芯片技术筛选春季卡他性结膜炎(vernal keratoconjunctivis,VKC)患者泪液中蛋白质表达谱,寻找泪液中的标志性蛋白。方法:采用表面增强激光解离飞行时间质谱技术(surface-enhanced la-ser desorption/ionization time of flight mass spectrometry,SELDI-TOF-MS),运用CM10蛋白质芯片检测66例VKC患者和62例正常对照组泪液中蛋白质谱,获得的蛋白质谱采用Biomarker Wizard软件分析,初步筛选蛋白质峰,结合生物信息学的支持向量机(support vector machines,SVM)方法建立并测试VKC患者泪液中的蛋白质指纹图谱模型。结果:在芯片上捕获到145种蛋白质,用质谱仪筛选出VKC患者与正常对照组相比的23种差异蛋白,从中再次筛选出3种蛋白质组成VKC的蛋白质谱最优化模型,VKC患者泪液中质荷比(m/z)分别为2024.3,6630.2和8598.9的3种蛋白质表达上调。模型经三倍交叉验证后用盲法测定,其敏感性和特异性分别为90.91%和93.55%,阳性预测值为93.75%。结论:蛋白质芯片技术可快速、有效地筛选出VKC患者泪液差异蛋白,结合SVM可建立一个由3种蛋白质组成的蛋白质指纹图谱模型,可对VKC做很好的诊断预测,对这3种蛋白质尤其是m/z为2024.8的蛋白质进行研究,有助于VKC病因学进展及诊断标记物的发现。     

基于iTRAQ技术的人感染H7N9禽流感病例血浆蛋白质组学特征分析

目的 分析人感染H7N9禽流感病例与正常人之间血浆蛋白质组学差异.方法 收集华北地区首例人感染H7N9禽流感病例发病期和恢复期血浆标本,以及同时期病例父亲(未感染病毒)血浆标本,分离提取血浆蛋白,通过同位素标记相对与绝对定量(isotopically labeled relative and absolute quantification,iTRAQ)技术联合液相色谱串联质谱(liquid chromatography tandem mass spectrometry,LC-MS/MS)技术对样本进行蛋白质质谱检测.利用PANTHER平台对差异蛋白质组进行Gene Ontology(GO)注释以及基因路径(pathway)分析.结果 本研究鉴定得到与人感染H7N9禽流感病毒感染相关的差异表达蛋白共250个,其中表达上调蛋白合计159个,表达下调蛋白合计91个.这些差异蛋白共参与了11个生物学过程、共参与了7个分子通路.结论 入感染H7N9禽流感病毒可能导致7个分子通路发生改变.     

Data‐independent acquisition‐based quantitative proteomic analysis reveals differences in host immune response of peripheral blood mononuclear cells to sepsis

This study was aimed to uncover proteins that are differentially expressed in sepsis. Data‐independent acquisition (DIA) was used for analysis to identify differentially expressed proteins in peripheral blood mononuclear cells (PBMCs) of patients. A total of 24 non‐septic intensive care unit (ICU) patients, 11 septic shock patients and 27 patients diagnosed with sepsis were recruited for the mass spectrometry (MS) discovery. PBMCs were isolated from routine blood samples and digested into peptides. A DIA workflow was developed using a quadrupole‐Orbitrap liquid chromatography LC‐MS system, and mass spectra peaks were extracted by Skyline software. Orthogonal partial least‐squares discriminant analysis (OPLS‐DA) and partial least‐squares discriminant analysis (PLS‐DA) were applied to distinguish the patient groups at the level of fragment ion and peptide. Differentially expressed proteins in the patient groups were verified by enzyme‐linked immunosorbent assay (ELISA). Receiver‐operating characteristic (ROC) curves were used to evaluate the protein expression. A total of 1062 fragment ions and 122 proteins were identified in the MS‐DIA analysis conducted by Skyline software. Using gene ontology clustering analysis, we discovered that 51 of the 122 identified proteins were associated with biological processes, including carbon metabolism, biosynthesis of antibiotics, platelet activation, bacterial invasion of epithelial cells and complement, and coagulation cascades. Among them, five proteins (high‐mobility group box1 [HMGB1], matrix metalloproteinase 8 [MMP8], neutrophil gelatinase‐associated lipocalin [NGAL], lactotransferrin [LTF] and grancalcin [GCA]) were identified by ELISA as closely related to the development of sepsis. The ROC curves displayed good sensitivity and specificity.     

A wavelet-based data pre-processing analysis approach in mass spectrometry

Recently, mass spectrometry analysis has a become an effective and rapid approach in detecting early-stage cancer. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, machine-learning methods, such as feature selection and classification, have already been involved in the analysis of mass spectrometry (MS) data with some success. However, the performance of existing machine learning methods for MS data analysis still needs improving. The study in this paper proposes a wavelet-based pre-processing approach to MS data analysis. The approach applies wavelet-based transforms to MS data with the aim of de-noising the data that are potentially contaminated in acquisition. The effects of the selection of wavelet function and decomposition level on the de-noising performance have also been investigated in this study. Our comparative experimental results demonstrate that the proposed de-noising pre-processing approach has potentials to remove possible noise embedded in MS data, which can lead to improved performance for existing machine learning methods in cancer detection.     

Efficiency of database search for identification of mutated and modified proteins via mass spectrometry

Although protein identification by matching tandem mass spectra (MS/MS) against protein databases is a widespread tool in mass spectrometry, the question about reliability of such searches remains open. Absence of rigorous significance scores in MS/MS database search makes it difficult to discard random database hits and may lead to erroneous protein identification, particularly in the case of mutated or post-translationally modified peptides. This problem is especially important for high-throughput MS/MS projects when the possibility of expert analysis is limited. Thus, algorithms that sort out reliable database hits from unreliable ones and identify mutated and modified peptides are sought. Most MS/MS database search algorithms rely on variations of the Shared Peaks Count approach that scores pairs of spectra by the peaks (masses) they have in common. Although this approach proved to be useful, it has a high error rate in identification of mutated and modified peptides. We describe new MS/MS database search tools, MS-CONVOLUTION and MS-ALIGNMENT, which implement the spectral convolution and spectral alignment approaches to peptide identification. We further analyze these approaches to identification of modified peptides and demonstrate their advantages over the Shared Peaks Count. We also use the spectral alignment approach as a filter in a new database search algorithm that reliably identifies peptides differing by up to two mutations/modifications from a peptide in a database.     

基于卷积神经网络的超声图像散斑去噪算法

为了提高超声图像质量,解决传统去噪算法在抑制散斑噪声和保留超声图像纹理特征方面的难题,提出一种基于卷积神经网络的超声图像散斑去噪算法DSCNN（De-speckling CNN）。本文提出的算法利用卷积神经网络强大的拟合能力来学习从超声图像到其相应的高质量图像的复杂映射,同时,通过改进损失函数的方式来减少去噪过程中纹理信息的损失和细节的模糊。不同于以往简单地假设超声散斑噪声为乘性噪声,本文利用基于超声图像采集模型和散斑噪声形成模型的模拟超声成像技术为去噪模型生成更贴合真实超声图像的训练数据,解决深度学习方法训练数据匮乏以及在临床上无法获得与超声图像空间配准作为标签的无噪声图像的难题。通过与其他具有代表性的超声图像去噪算法比较,经DSCNN去噪后的超声图像无论在视觉效果还是图像质量评价指标上都取得了更好的结果,其中SSIM达到0.856 9,在文中所有方法中最高。     

Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients

The ability to identify patterns of diagnostic signatures in proteomic data generated by high throughput mass spectrometry (MS) based serum analysis has recently generated much excitement and interest from the scientific community. These data sets can be very large, with high-resolution MS instrumentation producing 1-2 million data points per sample. Approaches to analyze mass spectral data using unsupervised and supervised data mining operations would greatly benefit from tools that effectively allow for data reduction without losing important diagnostic information. In the past, investigators have proposed approaches where data reduction is performed by a priori "peak picking" and alignment/warping/smoothing components using rule-based signal-to-noise measurements. Unfortunately, while this type of system has been employed for gene microarray analysis, it is unclear whether it will be effective in the analysis of mass spectral data, which unlike microarray data, is comprised of continuous measurement operations. Moreover, it is unclear where true signal begins and noise ends. Therefore, we have developed an approach to MS data analysis using new types of data visualization and mining operations in which data reduction is accomplished by culling via the intensity of the peaks themselves instead of by location. Applying this new analysis method on a large study set of high resolution mass spectra from healthy and ovarian cancer patients, shows that all of the diagnostic information is contained within the very lowest amplitude regions of the mass spectra. This region can then be selected and studied to identify the exact location and amplitude of the diagnostic biomarkers.     

Mass spectrum sequential subtraction speeds up searching large peptide MS/MS spectra datasets against large nucleotide databases for proteogenomics

We have developed a novel bioinformatics method called mass spectrum sequential subtraction (MSSS) to search large peptide spectra datasets produced by liquid chromatography/mass spectrometry (LC-MS/MS) against protein and large-sized nucleotide sequence databases. The main principle in MSSS is to search the peptide spectra set against the protein database, followed by removal of the spectra corresponding to the identified peptides to create a smaller set of the remaining peptide spectra for searching against the nucleotide sequences database. Therefore, we reduce the number of spectra to be searched to limit the peptide search space. Comparing MSSS and conventional search approach using a dataset of 27 LC-MS/MS runs of rice culture cells indicated that MSSS reduced the search queries to 50% and the search time to 75% on average. In addition, MSSS had no effect on the identification false-positive rate (FPR) or the novel peptide sequences identification ability. We used MSSS to analyze another dataset of 34 LC-MS/MS runs, resulting in identifying additional 74 novel peptides. Proteogenomic analysis with these additional peptides yielded 47 new genomic features in 24 rice genes plus 24 intergenic peptides. These results show that the utility of MSSS in searching large databases with large MS/MS datasets for proteogenomics.     

Human catalytic RNA- and DNA-hydrolyzing antibodies

Sequencing of anti-vancomycin monoclonal antibody (mAb) Fab region (48,000 Da) was carried out using liquid chromatography–electrospray ionization ion trap mass spectrometry (LC/ESI-MS). Comprehensive strategies were employed to ensure complete sequence coverage. The sequence information was obtained from the spectra of collision-induced dissociation (CID) (MS/MS) of the protonated proteolytic peptides resulting from multiple enzymatic digestions of reduced/S-carboxymethylated (RCM) light chain and Fd fragment. Database searching of the spectra against the published immunoglobulin G (IgG) sequences allowed the identification of all the peptides in constant domains as well as partial sequences in variable domains. The rest of the sequences were deduced by manual interpretation of the peptide tandem mass spectrometry (MS/MS) spectra. The analysis showed that the N-terminus of the heavy chain was modified by the conversion of a glutamine residue to pyroglutamic acid.     

X-ray spectral reconstruction from attenuation data using neural networks

An artificial neural network using input data derived from attenuation measurements was trained to generate spectral profiles (relative number of photons versus energy). Once the relative spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure versus energy) can be calculated. A neural network was trained on spectra generated mathematically using the Birch-Marshall model, combined with attenuation data, calculated from the spectra by numerical integration. Whereas attenuation data can be calculated in a straightforward manner from the x-ray spectra, the reverse is not true. Several neural networks were successfully taught to reconstruct the spectra, given the attenuation data. The networks were tested using kV/inherent filtration combinations that were not in the training set, and the performance of the reconstruction was excellent. Noise in the attenuation data was simulated to test the effects of noise propagation in the reconstruction. The effects of network architecture and data averaging on noise propagation were investigated. Experimentally determined spectral data complied by Fewell were also used to train a neural network, and the results of the reconstruction were also found to be excellent.     

先天性心脏病筛查系统设计

为提高医生筛查先天性心脏病的效率,设计一款基于卷积神经网络的先天性心脏病筛查系统。系统以软硬协同的方式实现心音、心电等生理参数的实时同步采集以及可视化和定量化分析。系统包含上下位机,下位机以FPGA为核心实现心音心电数据采集以及小波阈值去噪等预处理,上位机在Windows系统环境下以Python编程语言实现二阶谱特征提取、卷积神经网络二分类识别以及用户界面可视化显示。最终,系统对200名志愿者进行测试,准确率达到94.5%,特异度为95.9%,敏感度为93.2%。结果表明系统具有良好的表现,可以为临床先心病筛查提供有效的辅助。