Venkataraman S, Allison D P, Qi H, Morrell-Falvey J L, Kallewaard N L, Crowe J E, Doktycz M J
Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
Ultramicroscopy. 2006 Jun-Jul;106(8-9):829-37. doi: 10.1016/j.ultramic.2006.01.014. Epub 2006 May 5.
A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM.
在从真空到环境再到液体的各种环境下，原子力显微镜（AFM）都可以对多种生物样本进行成像。一般来说，进行成像的目的是评估样本的结构特征，或者识别研究人员诱导的样本中的一些结构变化。在许多情况下，样本特征和诱导结构变化的AFM图像通常用一般定性术语来解释，如明显更小或更大、更粗糙、高度不规则或光滑。可以使用各种手动工具来分析图像并提取更多定量数据，但这通常是一个繁琐的过程。为了便于进行定量AFM成像，正在开发自动图像分析程序。以在水中成像的病毒颗粒作为测试案例，开发了一种算法，该算法可从大量单个颗粒中自动提取平均尺寸信息。提取的信息允许对颗粒的尺寸特征进行统计分析，并有助于解释与颗粒与表面结合相关的问题。该算法正在扩展，用于分析通过AFM成像的其他生物样本和物理对象。
