LAUSR

Simple Summary Aptamers represent an emerging technology that enables researchers to screen biological matrices such as blood and urine for thousands of different proteins at a rapid pace with high precision and accuracy. However, the sheer data volume generated by this high-capacity screening technique also creates a fundamental challenge towards efficiently analyzing these complex datasets and translating findings for the clinic. We address the new analytical considerations brought forth by aptamers, explore the necessary statistical analysis needed, and create a baseline to analyze these large-scale databases more comprehensively. In addition, we explore how aptamers can co-exist with current proteomic platforms to produce more robust findings in an evolving, multi-faceted approach towards the field. Unlocking the underlying signals masquerading behind these large datasets will ultimately empower clinicians and researchers to better understand diseases of interest and to curate more robust findings for patient care. Abstract The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.