Multiplexed protein detection using an affinity aptamer amplification assay

Aptamer proteomics of serum exosomes from patients with Primary Raynaud's and patients with Raynaud's at risk of evolving into Systemic Sclerosis

BACKGROUND

A major unmet need for Systemic Sclerosis (SSc) clinical management is the lack of biomarkers for the early diagnosis of patients with Raynaud's Phenomenon at high risk of evolving into SSc.

OBJECTIVE

To identify proteins contained within serum exosomes employing an aptamer proteomic analysis that may serve to reveal patients with Raynaud's Phenomenon at risk of developing SSc.

METHODS

Exosomes were isolated from serum samples from patients with Primary Raynaud's Phenomenon and from patients with Raynaud's Phenomenon harbouring serum antinuclear antibodies (ANA) who may be at high risk of evolving into SSc. The expression of 1,305 proteins was quantified using SOMAscan aptamer proteomics, and associations of the differentially elevated or reduced proteins with the clinical subsets of Raynaud's Phenomenon were assessed.

RESULTS

Twenty one differentially elevated and one differentially reduced (absolute fold change >|1.3|) proteins were identified. Principal component analysis using these 22 most differentially expressed proteins resulted in excellent separation of the two Raynaud's Phenomenon clinical subsets. Remarkably, the most differentially elevated proteins are involved in enhanced inflammatory responses, immune cell activation and cell migration, and abnormal vascular functions.

CONCLUSION

Aptamer proteomic analysis of circulating exosomes identified differentially elevated or reduced proteins between Raynaud's Phenomenon at high risk of evolving into SSc and Primary Raynaud's Phenomenon patients. Some of these proteins are involved in relevant biological pathways that may play a role in SSc pathogenesis including enhanced inflammatory responses, immune cell activation, and endothelial cell and vascular abnormalities.

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.

Nucleic acids for ultra-sensitive protein detection

Major advancements in molecular biology and clinical diagnostics cannot be brought about strictly through the use of genomics based methods. Improved methods for protein detection and proteomic screening are an absolute necessity to complement to wealth of information offered by novel, high-throughput sequencing technologies. Only then will it be possible to advance insights into clinical processes and to characterize the importance of specific protein biomarkers for disease detection or the realization of "personalized medicine". Currently however, large-scale proteomic information is still not as easily obtained as its genomic counterpart, mainly because traditional antibody-based technologies struggle to meet the stringent sensitivity and throughput requirements that are required whereas mass-spectrometry based methods might be burdened by significant costs involved. However, recent years have seen the development of new biodetection strategies linking nucleic acids with existing antibody technology or replacing antibodies with oligonucleotide recognition elements altogether. These advancements have unlocked many new strategies to lower detection limits and dramatically increase throughput of protein detection assays. In this review, an overview of these new strategies will be given.