Advances in imaging-based digital sensing for biomedical analysis

Abstract

Traditional biomedical analysis faces significant limitations in accurately quantifying trace biomolecules in complex matrices due to its reliance on signal averaging. The advent of imaging-based digital sensing technologies has revolutionized this field by transforming averaged analog signals into discrete, countable digital events, thereby enabling ultra-sensitive detection, absolute quantification, and enhanced tolerance to background noise. In this review, we focus on the recent advances in imaging-based digital sensing techniques and systematically categorize them into two major categories: endpoint static quantification and real-time dynamic monitoring. For static quantification, we examine sample digitization methods (e.g., microchamber, microdroplet compartmentalization) and signal digitization strategies (e.g., patterned interfaces, discrete microcarriers). For dynamic monitoring, we explore techniques employing far-field, near-field imaging, and external field modulation to resolve single-molecule interactions and dynamic biochemical processes digitally. We present the fundamental principles, representative technologies, and key biomedical applications of these methods, and critically discuss current limitations and future directions.