Fjelstrup Søren, Dupont Daniel M, Bus Claus, Enghild Jan J, Jensen Jørgen B, Birkenkamp-Demtröder Karin, Dyrskjøt Lars, Kjems Jørgen
Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Aarhus, Denmark.
Department of Molecular Biology and Genetics (MBG), Aarhus University, Aarhus, Denmark.
NAR Cancer. 2022 Aug 22;4(3):zcac025. doi: 10.1093/narcan/zcac025. eCollection 2022 Sep.
The molecular composition of blood is a signature of human health, reflected in the thousands of blood biomarkers known for human diseases. However, establishing robust disease markers is challenging due to the diversity of individual samples. New sequencing methods have simplified biomarker discovery for circulating DNA and RNA while protein profiling is still laborious and costly. To harness the power of high-throughput sequencing to profile the protein content of a biological sample, we developed a method termed APTASHAPE that uses oligonucleotide aptamers to recognize proteins in complex biofluids. We selected a large pool of 2'Fluoro protected RNA sequences to recognize proteins in human plasma and identified a set of 33 cancer-specific aptamers. Differential enrichment of these aptamers after selection against 1 μl of plasma from individual patients allowed us to differentiate between healthy controls and bladder cancer-diagnosed patients (91% accuracy) and between early non-invasive tumors and late stage tumors (83% accuracy). Affinity purification and mass spectrometry of proteins bound to the predictive aptamers showed the main target proteins to be C4b-binding protein, Complement C3, Fibrinogen, Complement factor H and IgG. The APTASHAPE method thus provides a general, automated and highly sensitive platform for discovering potential new disease biomarkers.
血液的分子组成是人类健康的标志，这体现在数千种已知的人类疾病血液生物标志物中。然而，由于个体样本的多样性，建立可靠的疾病标志物具有挑战性。新的测序方法简化了循环DNA和RNA的生物标志物发现过程，而蛋白质谱分析仍然费力且昂贵。为了利用高通量测序的能力来分析生物样本的蛋白质含量，我们开发了一种称为APTASHAPE的方法，该方法使用寡核苷酸适配体来识别复杂生物流体中的蛋白质。我们选择了一大组2'-氟保护的RNA序列来识别人类血浆中的蛋白质，并鉴定出一组33种癌症特异性适配体。在针对个体患者的1微升血浆进行筛选后，这些适配体的差异富集使我们能够区分健康对照和膀胱癌诊断患者（准确率91%）以及早期非侵袭性肿瘤和晚期肿瘤（准确率83%）。与预测性适配体结合的蛋白质的亲和纯化和质谱分析表明，主要靶蛋白为C4b结合蛋白、补体C3、纤维蛋白原、补体因子H和IgG。因此，APTASHAPE方法为发现潜在的新疾病生物标志物提供了一个通用、自动化且高度灵敏的平台。
