Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence

Abstract

Atomic force microscopy (AFM) has reached a significant level of maturity in biology, demonstrated by the diversity of modes for obtaining not only topographical images but also insightful mechanical and adhesion data by performing force measurements on delicate samples with a controlled environment (e.g., liquid, temperature, pH). Numerous studies have applied AFM to describe biological phenomena at the molecular and cellular scales, and even on tissues. Despite these advances, AFM is not established as a diagnostic tool in the biomedical field. This article describes the reasons for this gap, focusing on one of the main weaknesses of bio-AFM: its low data throughput. We review current efforts to improve the automation of AFM measurements in particular on living cells, as well as the developments in automating data analysis. For the latter, artificial intelligence (AI) is progressively employed to classify data to distinguish healthy and diseased cells or tissues. Finally, we propose a roadmap to foster the application of bio-AFM into medical diagnostics.