Sally Yunsun Kim^1,2, Dipesh Khanal^1,2, Bill Kalionis^3,4 and Wojciech Chrzanowski<sup>1,2*

1</sup> Faculty of Medicine and Health, Sydney Pharmacy School, The University of Sydney, Sydney, NSW, Australia. ² Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia. ³ Department of Maternal-Fetal Medicine, Pregnancy Research Centre and University of Melbourne, Parkville, VIC, Australia. ⁴ Department of Obstetrics and Gynaecology, Royal Women’s Hospital, Parkville, VIC, Australia

^*Correspondence to Wojciech Chrzanowski

Extracellular vesicles (EVs) are highly specialized nanoscale assemblies that deliver complex biological cargos to mediate intercellular communication. EVs are heterogeneous, and characterization of this heterogeneity is paramount to understanding EV biogenesis and activity, as well as to associating them with biological responses and pathologies. Traditional approaches to studying EV composition generally lack the resolution and/or sensitivity to characterize individual EVs, and therefore the assessment of EV heterogeneity has remained challenging. We have recently developed an atomic force microscope IR spectroscopy (AFM-IR) approach to probe the structural composition of single EVs with nanoscale resolution. Here, we provide a step-by-step procedure for our approach and show its power to reveal heterogeneity across individual EVs, within the same population of EVs and between different EV populations. Our approach is label free and able to detect lipids, proteins and nucleic acids within individual EVs. After isolation of EVs from cell culture medium, the protocol involves incubation of the EV sample on a suitable substrate, setup of the AFM-IR instrument and collection of nano-IR spectra and nano-IR images. Data acquisition and analyses can be completed within 24 h, and require only a basic knowledge of spectroscopy and chemistry. We anticipate that new understanding of EV composition and structure through AFM-IR will contribute to our biological understanding of EV biology and could find application in disease diagnosis and the development of EV therapies.