Background

Amyloids, which are associated with numerous neurodegenerative diseases like Alzheimer's and Parkinson's, are characterized by their tendency to form insoluble fibrillar aggregates that impact neuronal functions by triggering neuroinflammation [1–3]. Gaining insights into the structural features of their fibril morphology is critical for elucidating not only the pathological mechanisms but also therapeutic development [4]. Atomic force microscopy (AFM) is a powerful technique for imaging amyloid fibril morphologies, utilizing advanced techniques such as tapping-mode and high-speed AFM [5–7]. Notably, studies have shown that tapping-mode AFM, which is frequently used, does not compromise fibrillar integrity because it minimizes the interaction between the tip and sample [8–11]. Several investigations have been conducted under different environmental conditions, ranging from low to extreme salt concentrations, to study their influence on amyloid morphologies [12–18]. However, caution must be taken during sample preparation, as it is a crucial aspect of AFM imaging. Improper or inadequate washing can lead to salt artifacts or protein loss, which may compromise image quality and affect data interpretation. For instance, the presence of excess salt in the sample can cause obstacles in AFM, thereby reducing the accuracy of amyloid fibril imaging [19,20]. Therefore, a standardized washing protocol must be followed in a manner that prevents salt artifacts while minimizing amyloid loss to maintain image quality [21]. The commonly used approach of pipette-based coating of amyloid fibrils onto mica sheets, followed by pipette-based rinsing and subsequent drying, may result in inconsistent fibril adsorption, limited reproducibility, and introduction of aggregation-related artifacts [22]. This realization has recently led to the development of a software tool based on a deep learning method to denoise salt artifacts from AFM images of amyloid proteins [23].