Shah Najaf A, Laws Richard J, Wardman Bradley, Zhao Lue Ping, Hartman John L
Department of Genetics, University of Alabama School of Medicine, Birmingham, Alabama 35294, USA.
BMC Syst Biol. 2007 Jan 8;1:3. doi: 10.1186/1752-0509-1-3.
Genome-wide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP). For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images.
YeastXtract software was developed for kinetic growth curve analysis of spotted agar cultures. The accuracy and precision for image analysis of agar culture arrays was comparable to OD measurements of liquid cultures. Using YeastXtract, image intensity vs. biomass of spot cultures was linearly correlated over two orders of magnitude. Thus cell proliferation could be measured over about seven generations, including four to five generations of relatively constant exponential phase growth. Spot area normalization reduced the variation in measurements of total growth efficiency. A growth model, based on the logistic function, increased precision and accuracy of maximum specific rate measurements, compared to empirical methods. The logistic function model was also more robust against data sparseness, meaning that less data was required to obtain accurate, precise, quantitative growth phenotypes.
Microbial cultures spotted onto agar media are widely used for genotype-phenotype analysis, however quantitative HTCP methods capable of measuring kinetic growth rates have not been available previously. YeastXtract provides objective, automated, quantitative, image analysis of agar cell culture arrays. Fitting the resulting data to a logistic equation-based growth model yields robust, accurate growth rate information. These methods allow the incorporation of imaging and automated image analysis of cell arrays, grown on solid agar media, into HTCP-driven experimental approaches, such as global, quantitative analysis of gene interaction networks.
全基因组突变菌株库增加了对高通量细胞表型分析(HTCP)的需求。例如,研究人员通过评估5000株酿酒酵母基因缺失菌株集合中细胞增殖的差异,利用HTCP来研究基因缺失突变与其他化学或基因扰动之间的相互作用。迄今为止,此类研究主要是定性的,使用琼脂细胞阵列主观地对生长差异进行评分。更精确的HTCP方法,如通过光密度对液体培养物中的细胞增殖进行动力学分析,将能够实现基因相互作用的定量系统水平分析。然而,与琼脂相比,处理液体培养物的要求使其相对繁琐且通量较低。为了提高HTCP性能并提升定量相互作用的能力,开发了YeastXtract软件用于细胞阵列图像的自动分析。
开发了YeastXtract软件用于斑点琼脂培养物的动力学生长曲线分析。琼脂培养阵列图像分析的准确性和精密度与液体培养物的光密度测量相当。使用YeastXtract,斑点培养物的图像强度与生物量在两个数量级上呈线性相关。因此,可以在大约七代内测量细胞增殖,包括四到五代相对恒定的指数期生长。斑点面积归一化减少了总生长效率测量的变化。与经验方法相比,基于逻辑函数的生长模型提高了最大比生长速率测量的精度和准确性。逻辑函数模型对数据稀疏性也更具鲁棒性,这意味着获得准确、精确的定量生长表型所需的数据更少。
接种在琼脂培养基上的微生物培养物广泛用于基因型-表型分析,然而以前尚无能够测量动力学生长速率的定量HTCP方法。YeastXtract提供了对琼脂细胞培养阵列的客观、自动、定量图像分析。将所得数据拟合到基于逻辑方程的生长模型可产生可靠、准确的生长速率信息。这些方法允许将在固体琼脂培养基上生长的细胞阵列的成像和自动图像分析纳入HTCP驱动的实验方法中,例如基因相互作用网络的全局定量分析。