An automated system for measuring parameters of nematode sinusoidal movement.

An automated system for measuring parameters of nematode sinusoidal movement.
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DOI:
10.1186/1471-2156-6-5
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发表时间:
2005-02-07
期刊:
影响因子:
2.9
通讯作者:
Sternberg PW
Sternberg PW
中科院分区:
生物学3区
文献类型:
--
作者:
Cronin CJ;Mendel JE;Mukhtar S;Kim YM;Stirbl RC;Bruck J;Sternberg PW

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线虫的正弦运动在许多研究中被用作一种表型。elegans发展,behavior行为and physiology生理.对基因控制生物学这些方面的方式的透彻理解,部分取决于表型分析的准确性。虽然运动不良的蠕虫相对容易描述,但描述过度活跃的运动和运动调节则更具挑战性。因此,分析线虫运动的所有复杂性的增强能力将有助于我们理解基因如何控制行为。我们已经开发了一个用户友好的系统来分析线虫运动的自动化和定量的方式。在该系统中,线虫被自动识别,计算机控制的显微镜载物台确保线虫保持在摄像机的视野内,同时摄像机的视频图像存储在录像带上。第二步,对录像带中的图像进行处理,以识别蠕虫,并提取其随时间变化的位置和姿态。根据该信息,计算各种运动参数。这些参数包括蜗杆质心的速度、蜗杆沿着其轨道的速度、主体弯曲的程度和频率、正弦运动的振幅和波长以及收缩波沿着主体的传播。蜗杆的长度也被确定并用于归一化振幅和波长测量。为了证明该系统的实用性,我们在这里报告的一小部分突变体的运动参数的比较影响去/Gq介导的信号网络,控制乙酰胆碱释放在神经肌肉接头。该系统允许比较类似地影响运动的不同基因型(Gq-α的激活与Go-α功能的丧失),以及单个基因座处的不同突变等位基因(编码Go-α的果阿-1基因的无效和显性负等位基因)。我们还演示了使用该系统分析有毒物质的影响。浓度响应曲线的毒物亚砷酸盐和涕灭威,这两者都影响运动,确定了野生型和几个突变株,确定P-糖蛋白突变体作为不显着更敏感的化合物,而猫-4突变体更敏感亚砷酸盐,但不涕灭威。线虫运动的自动分析促进了广泛的实验。现在可以对调控网络中的多个等位基因和不同基因进行详细的遗传分析。这些研究将有助于C. elegans运动,以及基因功能的比较。浓度-反应曲线将允许对毒性剂以及药理剂进行严格的分析。因此,这种类型的系统代表了一个强大的分析工具,可以很容易地耦合到线虫的分子遗传学。
Nematode sinusoidal movement has been used as a phenotype in many studies of C. elegans development, behavior and physiology. A thorough understanding of the ways in which genes control these aspects of biology depends, in part, on the accuracy of phenotypic analysis. While worms that move poorly are relatively easy to describe, description of hyperactive movement and movement modulation presents more of a challenge. An enhanced capability to analyze all the complexities of nematode movement will thus help our understanding of how genes control behavior. We have developed a user-friendly system to analyze nematode movement in an automated and quantitative manner. In this system nematodes are automatically recognized and a computer-controlled microscope stage ensures that the nematode is kept within the camera field of view while video images from the camera are stored on videotape. In a second step, the images from the videotapes are processed to recognize the worm and to extract its changing position and posture over time. From this information, a variety of movement parameters are calculated. These parameters include the velocity of the worm's centroid, the velocity of the worm along its track, the extent and frequency of body bending, the amplitude and wavelength of the sinusoidal movement, and the propagation of the contraction wave along the body. The length of the worm is also determined and used to normalize the amplitude and wavelength measurements. To demonstrate the utility of this system, we report here a comparison of movement parameters for a small set of mutants affecting the Go/Gq mediated signaling network that controls acetylcholine release at the neuromuscular junction. The system allows comparison of distinct genotypes that affect movement similarly (activation of Gq-alpha versus loss of Go-alpha function), as well as of different mutant alleles at a single locus (null and dominant negative alleles of the goa-1 gene, which encodes Go-alpha). We also demonstrate the use of this system for analyzing the effects of toxic agents. Concentration-response curves for the toxicants arsenite and aldicarb, both of which affect motility, were determined for wild-type and several mutant strains, identifying P-glycoprotein mutants as not significantly more sensitive to either compound, while cat-4 mutants are more sensitive to arsenite but not aldicarb. Automated analysis of nematode movement facilitates a broad spectrum of experiments. Detailed genetic analysis of multiple alleles and of distinct genes in a regulatory network is now possible. These studies will facilitate quantitative modeling of C. elegans movement, as well as a comparison of gene function. Concentration-response curves will allow rigorous analysis of toxic agents as well as of pharmacological agents. This type of system thus represents a powerful analytical tool that can be readily coupled with the molecular genetics of nematodes.
DOI: 10.1186/1471-2105-5-115
发表时间: 2004-08-26
期刊: BMC bioinformatics
影响因子: 3
作者:
Feng Z;Cronin CJ;Wittig JH Jr;Sternberg PW;Schafer WR
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发表时间: 1996-01-12
期刊: CELL
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通讯作者: Horvitz, HR
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发表时间: 1996-05-01
期刊: NEURON
影响因子: 16.2
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发表时间: 1992-11-01
期刊: NEURON
影响因子: 16.2
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DOI: 10.1038/nature01135
发表时间: 2002-10-24
期刊: NATURE
影响因子: 64.8
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