Binder Steven R, Hixson Craig, Glossenger John
Clinical Diagnostics Group, Bio-Rad Laboratories, Hercules CA, USA.
Autoimmun Rev. 2006 Apr;5(4):234-41. doi: 10.1016/j.autrev.2005.07.007. Epub 2005 Aug 25.
The occurrence of antibody patterns in connective tissue diseases has been recognized for thirty years, but the generation of multiple antibody results relied on time-consuming immunodiffusion or electrophoretic techniques. Today it is possible to study the antibody repertoire using rapid multi-analyte technologies, generally referred to as protein arrays. These arrays may use planar surfaces similar to DNA arrays, or use microspheres in suspension ("liquid arrays"). Also, many high quality autoantigens are now commercially available, including recombinant antigens. The vast amount of information that can be generated by measuring multiple antibodies for multiple patients has created demand for data processing. Software programs to aid physicians in reviewing multiple inputs as an aid to disease diagnosis and classification have been available for twenty years. Initial work used the "expert systems" approach; more recently pattern recognition has been widely evaluated because of the improvements in software programs and computational speed. The use of antibody data, generated in protein arrays, may assist in establishing diagnosis, in identifying potentially significant antibody patterns in advance of clinical symptoms, and in classifying patients based on expected disease progression.
结缔组织病中抗体模式的出现已被认识三十年了,但多种抗体结果的产生依赖于耗时的免疫扩散或电泳技术。如今,使用快速多分析物技术(通常称为蛋白质阵列)来研究抗体库成为可能。这些阵列可以使用类似于DNA阵列的平面表面,或者使用悬浮微球(“液相阵列”)。此外,现在许多高质量的自身抗原在市场上都可以买到,包括重组抗原。通过为多名患者检测多种抗体所产生的大量信息引发了对数据处理的需求。帮助医生审查多种输入信息以辅助疾病诊断和分类的软件程序已经存在二十年了。最初的工作采用“专家系统”方法;最近,由于软件程序和计算速度的改进,模式识别得到了广泛评估。利用蛋白质阵列产生的抗体数据,可能有助于确立诊断,在临床症状出现之前识别潜在的重要抗体模式,并根据预期的疾病进展对患者进行分类。