石制品植物残留物分析的实验室方法———以水洞沟石制品处理为例

残留物分析是西方上世纪七十年代发展起来的考古标本功能分析技术,现今已经在我国得到了初步发展和应用,但由于考古标本年代、埋藏环境、器类等方面的差异,针对不同标本的残留物分析手段也不尽相同,旧石器时代考古标本因其年代和性质上的特殊性成为了残留物分析的难点。西方目前已有许多通过各种试验方法进行的旧石器石制品残留物分析实例,而我国至今对旧石器时代石制品的此方面研究还没有先例。本文以水洞沟遗址石制品植物残留物处理为例,简要介绍旧石器时代石制品的植物残留物实验室处理流程、观测方法及注意事项。     

旧石器时代残留物分析:回顾与展望

残留物分析是1970年代在西方发展起来的一项功能学分析技术, 即借助自然科学的多种手段, 对工具表面加工对象残存物的提取和鉴定分析。本文对西方石制品残留物分析研究史做了回顾和综述, 简要介绍此种方法的概念、原理及相关技术问题, 同时对中国旧石器时代石制品残留物分析的前景进行了展望, 希望对相关理论和方法的引进和应用起到铺垫和促进作用。我国旧石器时代考古资源非常丰富, 残留物分析在石器功能解读、遗址环境重建、古人类行为复原和食谱分析等方面都会起到重要的作用, 成为破解很多学术疑点和难题的钥匙。     

泥河湾盆地下马碑遗址2013年发掘简报

下马碑遗址位于广义泥河湾盆地南部的蔚县盆地中东部,是坐落于壶流河二级阶地上的一处旷野遗址。2013年,河北省文物考古研究院对该遗址进行了考古发掘,揭露出丰富的遗物和遗迹现象;2018年起,国内外多家单位共同完成了遗址地层年代、埋藏过程、古环境和出土文化遗存的系统性多学科研究工作。该遗址发掘揭露面积12 m²,地层剖面深度约为290 cm,自上而下分为7个地层单位,其中第6层为原地埋藏的主文化层,基于光释光与14C测年并经贝叶斯模型计算为距今4.1～3.9万年。本文对下马碑第6层出土的各类遗物与遗迹进行系统报道,主要包括382件石制品、445件动物化石碎片、1件骨器和1套赭石颜料加工遗存,以及1处火塘。该遗址的发现与研究再现了距今4万年前东亚早期现代人加工、使用赭石颜料和复合工具的生活图景,更新了国际学术界对东亚早期现代人行为适应的认识。     

甘肃苏苗塬头地点石制品特征与古环境分析

苏苗塬头地点位于甘肃省平凉市庄浪县东北部, 埋藏于章麻河二级阶地的典型马兰黄土中。经剖面清理和地表采集, 2002和2004年在该地点共获得石制品2318件, 本文以集中分布区出土(2166件)和地表采集(48件)共2214件石制品为主要研究对象。该批石制品以石片、碎片和碎屑为主, 有少量石核和工具。剥片技术以砸击法为主, 锤击法为辅, 鲜见第二步加工。石制品原料主要为来自现代河床或阶地底部砾石层的脉石英。炭屑加速器质谱(AMS 14C)年代测试和多环境代用指标分析显示, 苏苗塬头为一处旧石器时代晚期文化地点, 人类活动主要发生于气候干冷的末次盛冰期(约距今2.4—1.8万年), 反映了古人类较强的环境适应能力。     

泥河湾盆地板井子旧石器时代遗址的形成过程

板井子遗址是泥河湾盆地晚更新世早期的一处重要遗址,光释光年代为距今8～9万年.本文以2015年出土的考古材料为研究对象,从地层的沉积环境、考古材料本体的埋藏特点两个角度,对板井子遗址的形成过程进行分析.分析表明,主文化层第5层为近原地埋藏类型,水流作用对小尺寸标本的保存及标本的空间集聚特征影响较大,但石制品技术类型组合...     

河南南水北调水源地旧石器时代遗址的发现与研究

南水北调中线工程的水源地是位于河南、湖北交界处的丹江口水库,这一区域是南北方人类迁徙和文化交流的重要生态廊道。为配合南水北调工程建设,从上世纪90年代开始,文物部门组织对丹江口水库淹没区进行了系统考古调查,仅在河南淅川境内就发现了30多处旧石器时代中晚期遗址和化石地点。2009年以来,中国科学院古脊椎动物与古人类研究所与中国科学院大学等单位对其中20余处遗址和地点进行了抢救性发掘,发掘面积超1万平方米,发现石制品2万多件。本文重点介绍了丹江口水库(河南)淹没区旧石器时代遗址的发现和主要研究成果:1)该区域广域的、持续性的人类活动表明本地区是早期人类活动的密集区;2)出土石器特征同时具备南方砾石石器工业和北方石片石器工业的特点,有明显的南北方石器加工技术相互融合的现象;3)石器残留物分析,为了解遗址附近环境和先民植物利用提供了线索;4)遗址年代从距今约50万年到1万年左右,大致相当于旧石器时代早期晚段至旧石器时代晚期,是旧石器考古的关键时期;5)个别遗址发现的陶器残片、烧土类遗存以及燧石石叶等文化因素,为解决中国石叶技术源流和新、旧石器过渡提供了重要资料。     

iTRAQ结合2D LC-MS/MS技术在草菇不同生长发育时期蛋白质组分析中的应用

【目的】旨在采用iTRAQ标记结合二维液相色谱串联质谱技术对草菇不同生长发育阶段的差异蛋白质组进行研究。【方法】首先将提取的草菇不同生长阶段蛋白样品进行SDS-PAGE分析,其次将经二维液相色谱串联质谱技术获取的串联质谱数据通过MASCOT软件搜库,之后对鉴定蛋白质数据进行了主成分分析(Principal componentanalysis,PCA)、层次聚类(Hierarchy clustering)分析、K-均值(K-means)聚类和GeneOntology(GO)注释分析。【结果】试验结果显示,共计获得2 335个不同肽段,鉴定到1 039个蛋白质,其中1 030个蛋白质具有定量信息。在子实体阶段中显著上调蛋白质64个,下调蛋白质150个。生物信息学分析表明,iTRAQ标记技术结合二维液相色谱串联质谱可对不同生长发育时期的草菇蛋白样品进行有效地分离和鉴定。【结论】这一研究结果为深入研究草菇乃至其他大型担子菌子实体形成和发育的分子机制提供借鉴。     

生物大分子质谱电离技术的突破及核磁共振三维结构测定方法的建立——2002年诺贝尔化学奖简介

2002年诺贝尔化学奖授予了质谱和核磁共振领域的三位科学家以表彰他们对生物大分子鉴定及结构分析方法做出的贡献．其中两位科学家J．B．Fenn和K．Tanaka分别发展了生物大分子质谱分析的软解吸电离方法;另一科学家K．Wüthrich则将核磁共振技术成功地应用于生物大分子如蛋白质的溶液三维结构测定．他们的研究成果已使质谱和核磁共振技术成为生物大分子强有力的研究手段,极大地促进了生物大分子的研究进程,必将对整个生命科学研究产生深远的影响．     

陕西西头遗址十六国时期墓葬出土枕头的材质研究

枕头是睡眠时保持人体头颈部正常位置的一种日常卧具,对维护人类身体健康具有重要作用。由于古代有机质组成的软质枕头易降解,难以在考古遗迹中保存,影响了人们对枕头成分的认识。本文选取陕西旬邑西头遗址尖子地点十六国时期(公元304-439年)两座墓葬内的枕头残留物作为研究对象,对其分别进行扫描电镜显微观察、能谱分析和植硅体分析,目的是观察枕头的显微结构和分析枕头材质的组成成分。扫描电镜的显微观察发现,枕头残留物包含大量的禾本科植物表皮细胞和植硅体、少量植物纤维;能谱分析显示,枕头残留物的主要构成元素为氧、硅、碳,还包括少量的钾、镁、磷、钙、硫、氮、锰、铁等元素;植硅体分析表明,枕头残留物主要由禾本科(Poaceae)植物表皮细胞、黍亚科(Panicoideae)植物茎叶哑铃型、竖排哑铃型以及粟(Setaria italica)和黍(Panicum miliaceum)的稃壳组成。以上结果显示两件枕头主要由禾本科茎叶类植物材料和粟、黍等小米类稃壳组成,还可能夹杂少量蛋白质类物质。本研究为探讨十六国时期枕头的制作材料及其组成提供了重要信息,对于进一步探索卧具的发展历程、了解当时社会不同阶层的丧葬习...     

蛋白质组学样品消化条件的优化

随着质谱的飞速发展,基于质谱的"鸟枪法"技术广泛的应用于大规模的蛋白质组学分析。化学反应保护效率过低或者酶切效率过低则会降低鉴定效率,并且在现有的计算方式下会丢失很多肽段信息。因此,蛋白质样品的前处理在现有的蛋白质组学研究中发挥重要作用。本研究对蛋白质的烷基化试剂的反应条件进行优化以提高烷基化效率,同时优化酶解Buffer提高酶切效率以及增加酶量和引入多种酶以提高酶切效率等,最终确定了蛋白质前处理的最优条件,最终使用1μg样品在一次质谱分析鉴定到(2 425±7)个蛋白质,较未优化的方法提高了31%。优化后的蛋白质前处理方法可有效提高现有蛋白质组学的研究效率,可进一步应用于蛋白质的定量及动态分析研究。     

A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard

The comparison of two-dimensional (2-D) gel images from different samples is an established method used to study differences in protein expression. Conventional methods rely on comparing images from at least 2 different gels. Due to the high variation between gels, detection and quantification of protein differences can be problematic. Two-dimensional difference gel electrophoresis (Ettan trade mark DIGE) is an emerging technique for comparative proteomics, which improves the reproducibility and reliability of differential protein expression analysis between samples. In the application of DIGE different samples are labelled with mass and charge matched spectrally resolvable fluorescent dyes and are then separated on the same 2-D gel. Using an Escherichia coli lysate "spiked" with varying amounts of four different known proteins, we have tested a novel experimental design that exploits the sample multiplexing capabilities of DIGE, by including a standard sample in each gel. The standard sample comprises equal amounts of each sample to be compared and was found to improve the accuracy of protein quantification between samples from different gels allowing accurate detection of small differences in protein levels between samples.     

The implications of proteolytic background for shotgun proteomics

The analysis by liquid chromatography coupled to tandem mass spectrometry of complex peptide mixtures, generated by proteolysis of protein samples, is the main proteomics method used today. The approach is based on the assumption that each protein present in a sample reproducibly and predictably generates a relatively small number of peptides that can be identified by mass spectrometry. In this study this assumption was examined by a targeted peptide sequencing strategy using inclusion lists to trigger peptide fragmentation attempts. It was found that the number of peptides observed from a single protein is at least one order of magnitude greater than previously assumed. This unexpected complexity of proteomics samples implies substantial technical challenges, explains some perplexing results in the proteomics literature, and prompts the need for developing alternative experimental strategies for the rapid and comprehensive analysis of proteomes.     

Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide‐based quantitative proteomics

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide‐based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an “empirical rule for linearly correlated peptide selection (ERLPS)” in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label‐free to O18/O16‐labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide‐based quantitative proteomics over a large dynamic range.     

Detecting the immune system response of a 500 year-old inca mummy

Disease detection in historical samples currently relies on DNA extraction and amplification, or immunoassays. These techniques only establish pathogen presence rather than active disease. We report the first use of shotgun proteomics to detect the protein expression profile of buccal swabs and cloth samples from two 500-year-old Andean mummies. The profile of one of the mummies is consistent with immune system response to severe pulmonary bacterial infection at the time of death. Presence of a probably pathogenic Mycobacterium sp. in one buccal swab was confirmed by DNA amplification, sequencing, and phylogenetic analyses. Our study provides positive evidence of active pathogenic infection in an ancient sample for the first time. The protocol introduced here is less susceptible to contamination than DNA-based or immunoassay-based studies. In scarce forensic samples, shotgun proteomics narrows the range of pathogens to detect using DNA assays, reducing cost. This analytical technique can be broadly applied for detecting infection in ancient samples to answer questions on the historical ecology of specific pathogens, as well as in medico-legal cases when active pathogenic infection is suspected.     

The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 study: relative protein quantitation

The determination of differences in relative protein abundance is a critical aspect of proteomics research that is increasingly used to answer diverse biological questions. The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 study was a quantitative proteomics project in which the aim was to determine the identity and the relative amounts of eight proteins in two mixtures. There are numerous methodologies available to study the relative abundance of proteins between samples, but to date, there are few examples of studies that have compared these different approaches. For the 2006 Proteomics Research Group study, there were 52 participants who used a wide variety of gel electrophoresis-, HPLC-, and mass spectrometry-based methods for relative quantitation. The quantitative data arising from this study were evaluated along with several other experimental details relevant to the methodologies used.     

Use of DNA ladders for reproducible protein fractionation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) for quantitative proteomics

In proteomics, one-dimensional (1D) sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is widely used for protein fractionation prior to mass spectrometric analysis to enhance the dynamic range of analysis and to improve the identification of low-abundance proteins. Such protein prefractionation works well for quantitation strategies if the proteins are labeled prior to separation. However, because of the poor reproducibility of cutting gel slices, especially when small amounts of samples are analyzed, its application in label-free and peptide-labeling quantitative proteomics methods has been greatly limited. To overcome this limitation, we developed a new strategy in which a DNA ladder is mixed with the protein sample before PAGE separation. After PAGE separation, the DNA ladder is stained to allow for easy, precise, and reproducible gel cutting. To this end, a novel visible DNA-staining method was developed. This staining method is fast, sensitive, and compatible with mass spectrometry. To evaluate the reproducibility of DNA-ladder-assisted gel cutting for quantitative protein fractionation, we used stable isotope labeling with amino acids in cell culture (SILAC). Our results show that the quantitative error associated with fractionation can be minimized using the DNA-assisted fractionation and multiple replicates of gel cutting. In conclusion, 1D PAGE fractionation in combination with DNA ladders can be used for label-free comparative proteomics without compromising quantitation.     

Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the “resolution revolution,” their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.     

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.     

Proteomics for Protein Expression Profiling in Neuroscience

As the technology of proteomics moves from a theoretical approach to a practical reality, neuroscientists will have to determine the most appropriate applications for this technology. Neuroscientists will have to surmount difficulties particular to their research, such as limited sample amounts, heterogeneous cellular compositions in samples, and the fact that many proteins of interest are rare, hydrophobic proteins. This review examines protein isolation and protein fractionation and separation using two-dimensional electrophoresis (2-DE) and mass spectrometry proteomic methods. Methods for quantifying relative protein expression between samples (e.g., 2-DIGE, and ICAT) are also described. The coverage of the proteome, ability to detect membrane proteins, resource requirements, and quantitative reliability of different approaches is also discussed. Although there are many challenges in proteomic neuroscience, this field promises many rewards in the future.     

A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics.