蛋白质双向电泳图谱的计算机处理和数量化分析

本文叙述了通过计算机硬件和软件的方法,对蛋白质的二维电泳图谱进行图像处理和数量化分析的工作.采用计算机图像处理的方法从电泳图谱的斑点图中抽取有用的信息,采用图像分析技术确定电泳图谱蛋白斑点的强度,比较几个凝胶的斑点图案.图谱的计算机处理和数量化分析技术,在许多生物物理和生物化学的研究中都可以应用,以给研究工作带来帮助.     

青海不同种群牦牛的血清蛋白电泳分析

本文首次报道了用双向电泳技术对青海果洛、天峻、海晏3个地区牦牛种群的血清蛋白的比较研究结果。各群体血清蛋白双向电泳图谱格局相似, 共有一百多个多肽斑点。果洛群体与天峻群体的电泳图谱更相近, 两者与海晏群体的电泳图谱在4个多肽斑点上差异很大。由电泳结果结合解剖学、生态学的研究成果讨论了造成高原型牦牛与环湖型牦牛差异的可能原因以及两者的分类学地位。为进一步探讨牦牛的分类和演化提供了生化资料。     

猪卵泡液样品制备方法对双向电泳图谱的影响

比较不同的蛋白制备方法,寻找适合猪卵泡液的双向电泳样品制备方法,提高2-DE图谱的分辨率和重复性。利用ProteoExtract Albumin/IgG Remove Kit去除卵泡液中的高峰度蛋白;然后分别用丙酮沉淀法、TCA/丙酮沉淀法和超滤法进行样品浓缩,比较分析对2-DE图谱的影响。结果表明,TCA/丙酮沉淀法制备的蛋白样品获得的电泳图谱质量较好,蛋白斑点数目较多,斑点较清晰、圆滑,分辨率较高,重复性较好。TCA-丙酮沉淀法更适合于制备猪卵泡液的双向电泳蛋白样品,为进一步对猪卵泡液2-DE图谱进行分析、鉴定及寻找生物标记物提供了保证。     

结肠癌蛋白质双向电泳技术的建立

目的:建立结肠癌13cm和24cm非线性分离系统的2-D图谱,分析比较两者的分辨率.方法:提取结肠癌总蛋白,用pH3-10非线性干胶条对样品进行等电聚焦分离,并分别使用13cm和24cm电泳系统进行双向电泳,考马斯亮蓝G250染色,图像分析,比较对比两组2-D图谱,量化分析两种系统的分辨率差异.结果:在等点电3-10,分子量20-170 kD范围内分别分离得到蛋白质斑点873个(13 cm电泳系统)和1349个(24cm电泳系统).对于24cm电泳系统,1 mg蛋白质上样量的电泳图谱清晰,分辨率较好.结论:成功建立了高分辨率、简便易控的结肠癌蛋白质组双向电泳技术平台.     

家蚕蛹期雌雄生殖腺蛋白质双向电泳比较分析

分析家蚕Bombyx mori雌雄生殖腺细胞蛋白质,有利于发现性别分化相关的功能蛋白质,探讨生殖腺发育相关基因的表达调控机理。本研究利用蛋白质双向凝胶电泳和图像分析技术,分析家蚕蛹期第2天的雌雄生殖腺细胞蛋白质。结果表明: 在雄蚕生殖腺蛋白质电泳图谱中共检测到435个蛋白斑点,其中特异性蛋白斑点73个,占总蛋白斑点数的16.8％;雌蚕生殖腺的电泳图谱中有417个蛋白斑点,其中特异性蛋白斑点55个,占总蛋白斑点数的13.2％。雌雄能匹配的蛋白斑点有362对,匹配率达85.0％。     

蛋白质电泳谱的同位素标记与分析

把放射性同位素应用于蛋白质电泳,不仅可提高分析灵敏度和准确性,还便于方法实现自动化.高分辨双向电泳技术问世以后,一块凝胶板上可显示上千个蛋白质斑点,但此种图谱分析十分困难.采用高灵敏度同位素检测技术可以解决这一问题.本文拟简述常用的蛋白质电泳谱同位素标记与分析技术.     

斑点热群立克次体的PCR／SSCP分型技术研究

本文应用PCR／SSCP技术对斑点热群立克次体国际标准株、国内各参考株及黑龙江立克农作54株、36株进行了分型鉴定。结果显示各株之间SSCP图谱差异明显,国内各参考株的SSCP,图谱与西伯利亚立克次体的图谱完全一致,黑龙江立克次作的图谱与国内各株显著不同。分析表明,我国存在2种斑点热群立克次体,即西伯利亚立克农作种和斑点热群立克次体新种－黑龙江立克次体。同时证明应用PCP／SSCP技术分析斑点热群立克农作型别,具有方法简单、经济省时、结果准确等优点。该分型技术应用于立克次体分型国内外未见报道。     

鬼针草属六种植物的同工酶电泳实验研究

目的:建立鬼针草6个品种过氧化物酶同工酶(POD)电泳指纹图谱,探讨鬼针草种内变异规律.方法:聚丙烯酰胺凝胶电泳分析.结果:鬼针草6个品种过氧化物酶同工酶PAGE指纹图谱差异显著.结论:聚丙烯酰胺凝胶同工酶电泳图谱可用于鉴别鬼针草6个种质.     

成年和老年小鼠脑蛋白质组双向电泳图谱比较

使用双向电泳(2-DE)比较成年和老年小鼠脑蛋白质差异,从分子水平初步探索老年脑蛋白整体变化规律.以固相pH梯度等电聚焦为第一向,SDS-聚丙烯酰胺凝胶水平电泳(PAGE)为第二向进行2-DE.图象分析软件Imagemaster^® 2D Elite分析电泳图谱.重复性实验结果显示,同组样品在三次不同实验中所得蛋白质斑点数目的相对标准差（变异系数）为4.43%±0.25%;同一蛋白质斑点在三次实验中等电点、分子质量和蛋白质量的相对标准差分别为8.76%±5.14%, 13.00%±4.22%和10.84%±9.16%.成年和老年小鼠脑组织2-DE图谱分别获得996和1256个蛋白质斑点,其中8个蛋白质在老年脑组织中含量降低,20个蛋白质斑点含量增加.另至少有4个蛋白质斑点在老年脑组织中缺失,14个蛋白质点为老年脑特有. 以上差异点的发现为研究脑老化和退行性疾病机理提供了有益的线索.     

蛋白质电泳谱的同位素标记与分析

把放射性同位素应用于蛋白质电泳,不仅可提高分析灵敏度和准确性,还便于方法实现自动化。高分辨双向电泳技术问世以后,一块凝胶板上可显示上千个蛋白质斑点,但此种图谱分析十分困难。采用高灵敏度同位素检测技术可以解决这一问题。本文拟简述常用的蛋白质电泳谱同位素标记与分析技术。     

Using statistical image models for objective evaluation of spot detection in two-dimensional gels

Protein spot detection is central to the analysis of two-dimensional electrophoresis gel images. There are many commercially available packages, each implementing a protein spot detection algorithm. Despite this, there have been relatively few studies comparing the performance characteristics of the different packages. This is in part due to the fact that different packages employ different sets of user-adjustable parameters. It is also partly due to the fact that the images are complex. To carry out an evaluation, "ground truth" data specifying spot position, shape and intensities needs to be defined subjectively on selected test images. We address this problem by proposing a method of evaluation using synthetic images with unambiguous interpretation. The characteristics of the spots in the synthetic images are determined from statistical models of the shape, intensity, size, spread and location of real spot data. The distribution of parameters is described using a Gaussian mixture model obtained from training images. The synthetic images allow us to investigate the effects of individual image properties, such as signal-to-noise ratios and degree of spot overlap, by measuring quantifiable outcomes, e.g. accuracy of spot position, false positive and false negative detection. We illustrate the approach by carrying out quantitative evaluations of spot detection on a number of widely used analysis packages.     

The statistical distribution of the intensity of pixels within spots of DNA microarrays: what is the appropriate single-value representative?

This paper opens a discussion about an important issue in the analysis of data from spotted DNA microarrays: how to summarise into a single value the distribution for the intensity values of the pixels within a spot. Although the most popular statistic used is the median, there is no clear study demonstrating why it is more appropriate than other measures of central tendency such as the mean or the mode. Here, we argue that the median intensity is not the most appropriate measure for many common cases and discuss a frequently encountered case of a 'doughnut'-shaped spot for which the mode is closest to the 'expected' spot intensity. For an 'ideal' spot with a clear boundary and uniformly hybridised, the intensity of its pixels should approximately be normally distributed. In practical situations, these two requirements are often not met due to the physical properties of pins and the particularities of the printing and hybridisation processes. As a consequence, the distribution of the intensity of the pixels is usually negatively skewed. This asymmetry results in a larger displacement for the mean and median than for the mode from the ideal situation mentioned above.     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis.Microarray data contains many sources of error.In order to glean information about mRNA expression levels,the true signal must first be segregated from noise.This research focuses on the variation that can be captured at the spot level in cDNA microarray images.Variation at other levels,due to differences at the array,dye,and block levels,can be corrected for by a variety of existing normalization procedures.Two signal quality estimates that capture the reliability of each spot printed on a microarray are described.A parametric estimate of within-spot vari ance,referred to here as σ s2pot,assumes that pixels follow a normal distribution and are spatially correlated.A non-parametric estimate of error,called the mean square prediction error(MSPE),assumes that spots of high quality possess pixels that are similar to their neighbors.This paper will provide a framework to use either spot quality measure in downstream analysis,specifically as weights in regression models.Using these spot quality estimates as weights can result in greater efficiency,in a statistical sense,when modeling microarray data.     

胚胎干细胞的研究进展

胚胎干细胞是来源于早期胚胎内细胞团的、具有多向分化潜能的细胞,已经广泛用于生命科学的许多领域,它在医学方面的应用成为医学领域的研究热点.本文对胚胎干细胞在诱导分化、基础研究和临床应用上的研究进展进行了综述,对今后的研究方向进行了展望.     

A novel strategy for microarray quality control using Bayesian networks

MOTIVATION: High-throughput microarray technologies enable measurements of the expression levels of thousands of genes in parallel. However, microarray printing, hybridization and washing may create substantial variability in the quality of the data. As erroneous measurements may have a drastic impact on the results by disturbing the normalization schemes and by introducing expression patterns that lead to incorrect conclusions, it is crucial to discard low quality observations in the early phases of a microarray experiment. A typical microarray experiment consists of tens of thousands of spots on a microarray, making manual extraction of poor quality spots impossible. Thus, there is a need for a reliable and general microarray spot quality control strategy. RESULTS: We suggest a novel strategy for spot quality control by using Bayesian networks, which contain many appealing properties in the spot quality control context. We illustrate how a non-linear least squares based Gaussian fitting procedure can be used in order to extract features for a spot on a microarray. The features we used in this study are: spot intensity, size of the spot, roundness of the spot, alignment error, background intensity, background noise, and bleeding. We conclude that Bayesian networks are a reliable and useful model for microarray spot quality assessment. SUPPLEMENTARY INFORMATION: http://sigwww.cs.tut.fi/TICSP/SpotQuality/.     

Rice Resistance to Brown Spot Mediated by Silicon and its Interaction with Manganese

Brown spot is one of the most devasting and prevalent disease of rice and its control is mainly dependent on fungicide application. Therefore, this study aimed to examine the effects of Si and Mn on the development of brown spot on rice plants grown in hydroponic culture. Rice plants (cv. ‘Metica‐1’) were grown in plastic pots containing 0 or 2 mm Si (?Si and +Si treatments, respectively) with three Mn rates (0.5, 2.5 and 10 μm ). Plants were inoculated with B. oryzae 39 days after emergence. The following components of resistance were evaluated: incubation period (IP), number of lesions (NL) per cm² of leaf area, real disease severity (RDS) and area under brown spot progress curve (AUBSPC). The content of Si and Mn in the plant tissues was also determined. Si content was significantly higher in rice tissue of plants of the +Si treatment than of the ?Si treatment regardless of the Mn rates used. The Mn rates did not affect the Si content of the rice plants. The Mn content of the rice tissues was significantly higher in the ?Si treatment than on +Si treatment, regardless of the Mn rate used. The Mn content was significantly lower at 0.5 μm Mn in comparison to the other rates for both ?Si and +Si treatments. The IP of brown spot on rice leaves significantly increased in the +Si treatment; but the Mn rates in the presence of Si had no effect on IP. In the ?Si treatment, the IP was significantly higher only at the rate of 2.5 μm . The NL, RDS and AUBSPC were significantly reduced in the +Si treatment regardless of the Mn rates. The Mn rates in the presence of Si had no effect on these components of resistance. Overall, Si dramatically impacted the development of brown spot regardless of the presence of Mn at either low or high rates. This may be useful in regions where the soil has either toxic or deficient levels of Mn and cultivars with brown spot resistance are not commercially available.     

A machine learning-based framework for delivery error prediction in proton pencil beam scanning using irradiation log-files

PurposeThis study aims to investigate the use of machine learning models for delivery error prediction in proton pencil beam scanning (PBS) delivery.MethodsA dataset of planned and delivered PBS spot parameters was generated from a set of 20 prostate patient treatments. Planned spot parameters (spot position, MU and energy) were extracted from the treatment planning system (TPS) for each beam. Delivered spot parameters were extracted from irradiation log-files for each beam delivery following treatment. The dataset was used as a training dataset for three machine learning models which were trained to predict delivered spot parameters based on planned parameters. K-fold cross validation was employed for hyper-parameter tuning and model selection where the mean absolute error (MAE) was used as the model evaluation metric. The model with lowest MAE was then selected to generate a predicted dose distribution for a test prostate patient within a commercial TPS.ResultsAnalysis of the spot position delivery error between planned and delivered values resulted in standard deviations of 0.39 mm and 0.44 mm for x and y spot positions respectively. Prediction error standard deviation values of spot positions using the selected model were 0.22 mm and 0.11 mm for x and y spot positions respectively. Finally, a three-way comparison of dose distributions and DVH values for select OARs indicates that the random-forest-predicted dose distribution within the test prostate patient was in closer agreement to the delivered dose distribution than the planned distribution.ConclusionsPBS delivery error can be accurately predicted using machine learning techniques.     

Fabrication of high quality microarrays

Fabrication of DNA microarray demands that between ten (diagnostic microarrays) and many hundred thousands of probes (research or screening microarrays) are efficiently immobilised to a glass or plastic surface using a suitable chemistry. DNA microarray performance is measured by parameters like array geometry, spot density, spot characteristics (morphology, probe density and hybridised density), background, specificity and sensitivity. At least 13 factors affect these parameters and factors affecting fabrication of microarrays are used in this review to compare different fabrication methods (spotted microarrays and in situ synthesis of microarrays) and immobilisation chemistries.     

胚胎干细胞诱导分化的研究进展

胚胎干细胞(embryonic stem cell,ESC)因其具有自我更新能力和发育的多能性,成为当前医学研究的热点。ESC不但可以自发分化,而且在诱导因素作用下可以定向分化为某一种特定的成熟细胞。因此,ESC在移植医学、发育生物学等领域有着广阔的应用前景。本文对几种定向诱导ESC分化的策略进行了综述。