Automated multi-sample acquisition and analysis using atomic force microscopy for biomedical applications

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

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.

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines

Mechanobiological measurements have the potential to discriminate healthy cells from pathological cells. However, a technology frequently used to measure these properties, i.e., atomic force microscopy (AFM), suffers from its low output and lack of standardization. In this work, we have optimized AFM mechanical measurement on cell populations and developed a technology combining cell patterning and AFM automation that has the potential to record data on hundreds of cells (956 cells measured for publication). On each cell, 16 force curves (FCs) and seven features/FC, constituting the mechanome, were calculated. All of the FCs were then classified using machine learning tools with a statistical approach based on a fuzzy logic algorithm, trained to discriminate between nonmalignant and cancerous cells (training base, up to 120 cells/cell line). The proof of concept was first made on prostate nonmalignant (RWPE-1) and cancerous cell lines (PC3-GFP), then on nonmalignant (Hs 895.Sk) and cancerous (Hs 895.T) skin fibroblast cell lines, and demonstrated the ability of our method to classify correctly 73% of the cells (194 cells in the database/cell line) despite the very high degree of similarity of the whole set of measurements (79-100% similarity).

Atomic force microscopy in disease-related studies: Exploring tissue and cell mechanics

Despite significant progress in human medicine, certain diseases remain challenging to promptly diagnose and treat. Hence, the imperative lies in the development of more exhaustive criteria and tools. Tissue and cellular mechanics exhibit distinctive traits in both normal and pathological states, suggesting that "force" represents a promising and distinctive target for disease diagnosis and treatment. Atomic force microscopy (AFM) holds great promise as a prospective clinical medical device due to its capability to concurrently assess surface morphology and mechanical characteristics of biological specimens within a physiological setting. This review presents a comprehensive examination of the operational principles of AFM and diverse mechanical models, focusing on its applications in investigating tissue and cellular mechanics associated with prevalent diseases. The findings from these studies lay a solid groundwork for potential clinical implementations of AFM. RESEARCH HIGHLIGHTS: By examining the surface morphology and assessing tissue and cellular mechanics of biological specimens in a physiological setting, AFM shows promise as a clinical device to diagnose and treat challenging diseases.

Unraveling How Membrane Nanostructure Changes Impact the Eye Irritation of Nonionic Alkyl Ethoxylate Surfactants

Nonionic surfactants used in agri-spraying processes may cause varying degrees of corneal irritation when they come in direct contact with farmers' eyes, and the exact irritations are thought to be determined by how surfactants interact with corneal cell membranes. However, how nonionic surfactants interact with cell membranes at the molecular and nano levels remains largely unexplored. In this study, the interactions between nonionic surfactants (alkyl ethoxylate, C12Em) and lipid membranes were examined by membrane permeability measurement, quartz crystal microbalance with dissipation, dual polarization interferometry, confocal laser scanning microscopy, and neutron reflection, aiming to reveal complementary structural features at the molecular and nano levels. Apart from the extremely hydrophobic surfactant C12E2, all nonionic surfactants studied could penetrate the model cell membrane composed of a phosphocholine lipid bilayer. Nonionic surfactants with intermediate amphiphilicity (C12E6) rapidly fused into the lipid membrane and stimulated the formation of pores across the lipid bilayer, consistent with the cytoplasm leakage and fast cell necrosis observed from the cytotoxicity study of corneal cells. In comparison, while hydrophobic and hydrophilic surfactants [those with long and short ethoxylates (C12E4,12,23)] could cause mild structural alteration to the outer lipid layer of the membrane, these structural changes were insufficient to elicit large cytoplasmic leakage rapidly and instead cell death occurred over longer periods of time due to changes in the membrane permeability. These results reveal the strong link of surfactant-lipid membrane interactions to surfactant cytotoxicity and the association with amphiphilicity of nonionic surfactants.

Surfactant-like Peptide Self-Assembled into Hybrid Nanostructures for Electronic Nose Applications

An electronic nose (e-nose) utilizes a multisensor array, which relies on the vector contrast of combinatorial responses, to effectively discriminate between volatile organic compounds (VOCs). In recent years, hierarchical structures made of nonbiological materials have been used to achieve the required sensor diversity. With the advent of self-assembling peptides, the ability to tune nanostructuration, surprisingly, has not been exploited for sensor array diversification. In this work, a designer surfactant-like peptide sequence, CG₇-NH₂, is used to fabricate morphologically and physicochemically heterogeneous "biohybrid" surfaces on Au-covered chips. These multistructural sensing surfaces, containing immobilized hierarchical nanostructures surrounded by self-assembled monolayers, are used for the detection and discrimination of VOCs. Through a simple and judicious design process, involving changes in pH and water content of peptide solutions, a five-element biohybrid sensor array coupled with a gas-phase surface plasmon resonance imaging system is shown to achieve sufficient discriminatory capabilities for four VOCs. Moreover, the limit of detection of the multiarray system is bench-marked at <1 and 6 ppbv for hexanoic acid and phenol (esophago-gastric biomarkers), respectively. Finally, the humidity effects are characterized, identifying the dissociation rate constant as a robust descriptor for classification, further exemplifying their efficacy as biomaterials in the field of artificial olfaction.

Towards a Fully Automated Scanning Probe Microscope for Biomedical Applications

The increase in capabilities of Scanning Probe Microscopy (SPM) has resulted in a parallel increase in complexity that limits the use of this technique outside of specialised research laboratories. SPM automation could substantially expand its application domain, improve reproducibility and increase throughput. Here, we present a bottom-up design in which the combination of positioning stages, orientation, and detection of the probe produces an SPM design compatible with full automation. The resulting probe microscope achieves sub-femtonewton force sensitivity whilst preserving low mechanical drift (2.0±0.2 nm/min in-plane and 1.0±0.1 nm/min vertically). The additional integration of total internal reflection microscopy, and the straightforward operations in liquid, make this instrument configuration particularly attractive to future biomedical applications.

Digital Polymerase Chain Reaction Paired with High-Speed Atomic Force Microscopy for Quantitation and Length Analysis of DNA Length Polymorphisms

DNA length polymorphisms are found in many serious diseases, and assessment of their length and abundance is often critical for accurate diagnosis. However, measuring their length and frequency in a mostly wild-type background, as occurs in many situations, remains challenging due to their variable and repetitive nature. To overcome these hurdles, we combined two powerful techniques, digital polymerase chain reaction (dPCR) and high-speed atomic force microscopy (HSAFM), to create a simple, rapid, and flexible method for quantifying both the size and proportion of DNA length polymorphisms. In our approach, individual amplicons from each dPCR partition are imaged and sized directly. We focused on internal tandem duplications (ITDs) located within the FLT3 gene, which are associated with acute myeloid leukemia and often indicative of a poor prognosis. In an analysis of over 1.5 million HSAFM-imaged amplicons from cell line and clinical samples containing FLT3-ITDs, dPCR-HSAFM returned the expected variant length and variant allele frequency, down to 5% variant samples. As a high-throughput method with single-molecule resolution, dPCR-HSAFM thus represents an advance in HSAFM analysis and a powerful tool for the diagnosis of length polymorphisms.

Structural Visualization of Septum Formation in Staphylococcus warneri Using Atomic Force Microscopy

Cell division of Staphylococcus adopts a "popping" mechanism that mediates extremely rapid separation of the septum. Elucidating the structure of the septum is crucial for understanding this exceptional bacterial cell division mechanism. Here, the septum structure of Staphylococcus warneri was extensively characterized using high-speed time-lapse confocal microscopy, atomic force microscopy, and electron microscopy. The cells of S. warneri divide in a fast popping manner on a millisecond timescale. Our results show that the septum is composed of two separable layers, providing a structural basis for the ultrafast daughter cell separation. The septum is formed progressively toward the center with nonuniform thickness of the septal disk in radial directions. The peptidoglycan on the inner surface of double-layered septa is organized into concentric rings, which are generated along with septum formation. Moreover, this study signifies the importance of new septum formation in initiating new cell cycles. This work unravels the structural basis underlying the popping mechanism that drives S. warneri cell division and reveals a generic structure of the bacterial cell.IMPORTANCE This work shows that the septum of Staphylococcus warneri is composed of two layers and that the peptidoglycan on the inner surface of the double-layered septum is organized into concentric rings. Moreover, new cell cycles of S. warneri can be initiated before the previous cell cycle is complete. This work advances our knowledge about a basic structure of bacterial cell and provides information on the double-layered structure of the septum for bacteria that divide with the "popping" mechanism.