Steno Diabetes Center, Niels Steensens Vej 1, 2820 Gentofte, Denmark.
Arch Physiol Biochem. 2010 Oct-Dec;116(4-5):227-32. doi: 10.3109/13813455.2010.501801. Epub 2010 Sep 24.
Combining samples from a national neonatal screening programme with the information from a national health registry allow for unique opportunities in analysing newborn blood for protein changes that could predict eventual disease development. A nested case-control cohort (n = 54 cases, 108 controls) was analysed by proteomics as a new way of looking for biomarkers that could bolster prediction of T1D risk in newborns. Protein extraction and haemoglobin depletion were automated and samples were processed and analysed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data set was reduced to the highest quality peaks and analysed using conditional logistic regression. A total of 25 protein peaks were found to differ between the two groups. The automated haemoglobin depletion provides a platform for further proteomics studies of individual patient material. The method opens a door to a wealth of patient material stored as dried blood spots.
将全国新生儿筛查计划的样本与国家健康登记处的信息相结合，为分析新生儿血液中的蛋白质变化提供了独特的机会，这些变化可能预测最终的疾病发展。通过蛋白质组学对嵌套病例对照队列（n=54 例病例，108 例对照）进行分析，寻找生物标志物，从而增强对新生儿 1 型糖尿病风险的预测。采用自动化方法进行蛋白质提取和血红蛋白耗竭，通过表面增强激光解吸/电离飞行时间质谱（SELDI-TOF-MS）对样品进行处理和分析。将数据集简化为最高质量的峰，并使用条件逻辑回归进行分析。发现两组之间有 25 个蛋白质峰存在差异。自动化血红蛋白耗竭为进一步研究个体患者材料的蛋白质组学提供了平台。该方法为大量以干血斑形式储存的患者材料开辟了道路。
