Atomic force microscopy-based assessment of multimechanical cellular properties for classification of graded bladder cancer cells and cancer early diagnosis using machine learning analysis

Data-driven analysis for the evaluation of cortical mechanics of non-adherent cells

Atomic Force Microscopy (AFM) analysis of single cells, especially nonadherent, is inherently slow and analysis-heavy. To address the inherent difficulty of measuring individual cells, and to scale up toward a large number of cells, we take a two-fold approach; first, we introduce an easy-to-fabricate reusable poly(dimethylsiloxane)-based array that consists of micron-sized traps for single-cell trapping, second, we apply a deep-learning method directly on the extracted curves to facilitate and automate the analysis. Our approach is validated using suspended cells, and by applying a small compression with a tipless cantilever AFM probe, we investigate the effect of various cytoskeletal drugs on their deformability. We then apply deep learning models to extract the elasticity of the cell directly from the raw data (with a Coefficient of Determination of 0.47) as well as for binary (with an Area Under the Curve score of 0.91) and multi-class classification (with accuracy scores exceeding 0.9 for each drug). Overall, the versatility to fabricate the microwells in conjunction with the automated analysis and classification streamline the analysis process and demonstrate their ability to generalize to other tasks, such as drug detection.

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.

Exploring the role of graphene-metal hybrid nanomaterials as Raman signal enhancers in early stage cancer detection

Molecular diagnosis plays a significant role in detection of biomolecules linked to early stage cancer since it offers greater sensitivity and reliability for identification of biomarker level changes as the disease progresses. The application of vibrational spectroscopy in biomarker detection is defined by the fingerprint spectrum of a molecule originating from single-molecule vibrations. This characteristic makes surface enhanced Raman spectroscopy (SERS) a promising tool for identification of biomarkers. The performance of the SERS technique largely depends on the material being used as the SERS substrate. Graphene, with its large surface area and abundance of aromatic regions, is considered advantageous as SERS substrate. Combining graphene with metal nanomaterials considerably increases SERS signal intensity, thereby enhancing detection sensitivity. Therefore, this review emphasizes the significance of selecting graphene-metal nanohybrids as suitable SERS substrates for signal amplification. The detail understanding of the mechanism of graphene-metal hybrid in SERS based detection of early stage cancer is also presented. Furthermore, several examples demonstrated the application of graphene-metal hybrid nanomaterials in detecting biomarkers and cancer cell differentiation using SERS imaging.

Mechanobiology studies of bladder tumor cells using laterally squeezing microfluidic flow cytometry

The deformability and uptake capability of cells are critical indicators of their biomechanical properties and functional behaviors, particularly in tumor heterogeneity and cancer research. Here, we introduce a microfluidic flow cytometry platform integrated with a laterally adjustable squeezing structure for the characterization of bladder tumor cells (including 5637 and EJ cell lines) and uroepithelial cells (SV-HUC-1 cell line). The deformability of these cell types under varying channel width conditions was clearly assessed using this platform. The results demonstrated that tumor cells exhibited higher deformability compared to uroepithelial cells, with the EJ cell line exhibiting the greatest difference. Furthermore, the relationship between the malignancy, deformability, and uptake capability of bladder cells was explored through co-cultivation experiments with 2 μm particles. As the malignancy increased, the cells became more deformable and exhibited stronger phagocytic capability with particles. Subsequently, the heterogeneity of tumor cells was investigated by analyzing the deformability of phagocytic and non-phagocytic subpopulations within EJ cells. The developed microfluidic platform offers a promising high-throughput method to assess the biomechanical and phagocytic characteristics of cells, providing valuable insights into tumor cell biology, and potentially improving clinical status of urinary cytology examinations for bladder cancer.

Invited Review for 20th Anniversary Special Issue of PLRev "AI for Mechanomedicine"

Mechanomedicine is an interdisciplinary field that combines different areas including biomechanics, mechanobiology, and clinical applications like mechanodiagnosis and mechanotherapy. The emergence of artificial intelligence (AI) has revolutionized mechanomedicine, providing advanced tools to analyze the complex interactions between mechanics and biology. This review explores how AI impacts mechanomedicine across four key aspects, i.e., biomechanics, mechanobiology, mechanodiagnosis, and mechanotherapy. AI improves the accuracy of biomechanical characterizations and models, deepens the understanding of cellular mechanotransduction pathways, and enables early disease detection through mechanodiagnosis. In addition, AI optimizes mechanotherapy that targets biomechanical features and mechanobiological markers by personalizing treatment strategies based on real-time patient data. Even with these advancements, challenges still exist, particularly in data quality and the ethical integration into AI in clinical practice. The integration of AI with mechanomedicine offers transformative potential, enabling more accurate diagnostics and personalized treatments, and discovering novel mechanobiological pathways.

Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis

The influence of biomechanics on cell function has become increasingly defined over recent years. Biomechanical changes are known to affect oncogenesis; however, these effects are not yet fully understood. Atomic force microscopy (AFM) is the gold standard method for measuring tissue mechanics on the micro- or nano-scale. Due to its complexity, however, AFM has yet to become integrated in routine clinical diagnosis. Artificial intelligence (AI) and machine learning (ML) have the potential to make AFM more accessible, principally through automation of analysis. In this review, AFM and its use for the assessment of cell and tissue mechanics in cancer is described. Research relating to the application of artificial intelligence and machine learning in the analysis of AFM topography and force spectroscopy of cancer tissue and cells are reviewed. The application of machine learning and artificial intelligence to AFM has the potential to enable the widespread use of nanoscale morphologic and biomechanical features as diagnostic and prognostic biomarkers in cancer treatment.

Applying the Atomic Force Microscopy Technique in Medical Sciences-A Narrative Review

Penetrating deep into the cells of the human body in real time has become increasingly possible with the implementation of modern technologies in medicine. Atomic force microscopy (AFM) enables the effective live imaging of cellular and molecular structures of biological samples (such as cells surfaces, components of biological membranes, cell nuclei, actin networks, proteins, and DNA) and provides three-dimensional surface visualization (in X-, Y-, and Z-planes). Furthermore, the AFM technique enables the study of the mechanical, electrical, and magnetic properties of cells and cell organelles and the measurements of interaction forces between biomolecules. The technique has found wide application in cancer research. With the use of AFM, it is not only possible to differentiate between healthy and cancerous cells, but also to distinguish between the stages of cancerous conditions. For many years, AFM has been an important tool for the study of neurodegenerative diseases associated with the deposition of peptide amyloid plaques. In recent years, a significant amount of research has been conducted on the application of AFM in the evaluation of connective tissue cell mechanics. This review aims to provide the spectrum of the most important applications of the AFM technique in medicine to date.

Cell recognition based on features extracted by AFM and parameter optimization classifiers

Intelligent technology can assist in the diagnosis and treatment of disease, which would pave the way towards precision medicine in the coming decade. As a key focus of medical research, the diagnosis and prognosis of cancer play an important role in the future survival of patients. In this work, a diagnostic method based on nano-resolution imaging was proposed to meet the demand for precise detection methods in medicine and scientific research. The cell images scanned by AFM were recognized by cell feature engineering and machine learning classifiers. A feature ranking method based on the importance of features to responses was used to screen features closely related to categorization and optimization of feature combinations, which helps to understand the feature differences between cell types at the micro level. The results showed that the Bayesian optimized back propagation neural network has accuracy rates of 90.37% and 92.68% on two cell datasets (HL-7702 & SMMC-7721 and GES-1 & SGC-7901), respectively. This provides an automatic analysis method for identifying cancer cells or abnormal cells, which can help to reduce the burden of medical or scientific research, decrease misjudgment and promote precise medical care for the whole society.

Applications of Biosensors in Bladder Cancer

Bladder cancer (BC) is the tenth most common cancer globally, predominantly affecting men. Early detection and treatment are crucial due to high recurrence rates and poor prognosis for advanced stages. Traditional diagnostic methods like cystoscopy and imaging have limitations, leading to the exploration of noninvasive methods such as liquid biopsy. This review highlights the application of biosensors in BC, including electrochemical and optical sensors for detecting tumor markers like proteins, nucleic acids, and other biomolecules, noting their clinical relevance. Emerging therapeutic approaches, such as antibody-drug conjugates, targeted therapy, immunotherapy, and gene therapy, are also explored, the role of biosensors in detecting corresponding biomarkers to guide these treatments is examined. Finally, the review addresses the current challenges and future directions for biosensor applications in BC, highlighting the need for large-scale clinical trials and the integration of advanced technologies like deep learning to enhance diagnostic accuracy and treatment efficacy.

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.

Cell recognition based on atomic force microscopy and modified residual neural network

Cell recognition methods are in high demand in cell biology and medicine, and the method based on atomic force microscopy (AFM) shows a great value in application. The difference in mechanical properties or morphology of cells has been frequently used to detect whether cells are cancerous, but this detection method cannot be a general means for cancer cell detection, and the traditional artificial feature extraction method also has its limitations. In this work, we proposed an analytic method based on the physical properties of cells and deep learning method for recognizing cell types. The residual neural network used for recognition was modified by multi-scale convolutional fusion, attention mechanism and depthwise separable convolution, so as to optimize feature extraction and reduce operation costs. In the method, the collected cells were imaged by AFM, and the processed images were analyzed by the optimized convolutional neural network. The recognition results of two groups of cells (HL-7702 and SMMC-7721, SGC-7901 and GES-1) by this method show that the recognition rate of dataset with the combination of cell surface morphology, adhesion and Young's modulus is higher, and the recognition rate of the dataset with optimal resolution is higher. Our study indicated that the recognition of physical properties of cells using deep learning technology can serve as a universal and effective method for the automated analysis of cell information.