BACKGROUND AND OBJECTIVE:Cell mechanics, elasticity and viscoelasticity, are key markers of biological states like cancer. Atomic force microscopy (AFM) is ideal for such studies, but its low throughput limits large-scale use. Two solutions exist: automation for higher throughput, or high-density measurements for richer data. The latter enables machine learning (ML)-based classification, with viscoelastic parameters offering unique insights beyond static measures like Young's modulus. METHODS:This study used dynamic mechanical analysis (DMA) to classify cells, focusing on viscoelastic descriptors (storage/loss moduli) across frequencies. Normal (RWPE-1) and grade IV cancerous (PC3-GFP) prostate cells were probed at 1-200Hz, generating 304 features per cell. The fuzzy logic-based LAMDA algorithm, trained on 19 selected features, classified cells using 40 samples per line. RESULTS:PC3-GFP cells showed higher deformability and heterogeneity, behaving more like viscous fluids at low frequencies. The model achieved 79% classification accuracy. Adding features improved performance, suggesting fewer training samples may suffice with rich datasets. A sensitivity-optimized threshold reduced false negatives in cancer detection. CONCLUSIONS:Combining viscoelastic analysis with ML effectively discriminates normal and malignant cells. Future work could refine training and integrate new features, though acquisition time remains a challenge. This approach offers a promising framework for mechanome-based diagnostics, with applications in cancer and stem cell research.

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.

Aims: Doxorubicin (DOX) is a highly effective chemotherapeutic agent whose clinical use is limited by cumulative cardiotoxicity. The subcellular origins of early cardiac injury remain unclear but cardiomyocyte (CM) mitochondrial dysfunction is implicated. However, vulnerability of specific CM mitochondrial subpopulations is unknown. Building on our previous work linking the postnatal maturation of crest-associated subsarcolemmal mitochondria (SSM) at the CM surface to diastolic function, we investigated the spatial and temporal susceptibility of SSM during DOX exposure and their contribution to early diastolic dysfunction. Methods and results: Adult male mice received chronic DOX treatment (5 mg/kg/week for 5 weeks) to mimic cumulative clinical exposure. Cardiac function was monitored longitudinally, during treatment and after protocol completion, using echocardiography-Doppler imaging, and global longitudinal strain (GLS). Subcellular mitochondrial remodeling was assessed using atomic force microscopy (AFM) and transmission electron microscopy (TEM). A tamoxifen-inducible, CM-specific Ephrin-B1 knockout model was used to probe the functional role of SSM in DOX-induced injury. DOX induced a progressive and selective loss of crest/SSM at the CM surface very early within 3 days of exposure, while the architecture of interfibrillar mitochondria IFM remained preserved. This early SSM depletion paralleled impaired myocardial relaxation reflected by a prolonged isovolumic relaxation time, along with reduced GLS, all preceding changes in left ventricular ejection fraction or detectable IFM abnormalities. Notably, in mice lacking Ephrin-B1, and therefore mature crest/SSM, DOX exposure triggered an unusually rapid onset of systolic dysfunction, highlighting the cardioprotective role of these surface mitochondrial populations. Conclusions: Crest/SSM at the CM surface are the earliest selective mitochondrial targets of DOX, and their loss precedes IFM remodeling. This spatial-temporal hierarchy reveals a compartment-specific functional distinction, with SSM supporting diastolic performance and IFM sustaining systolic contraction. Hence, preserving SSM emerges as a promising early target to prevent progression of anthracycline cardiotoxicity toward systolic failure. Clinically, our findings also support early diastolic monitoring as a sensitive approach for detecting anthracycline cardiotoxicity. ### Competing Interest Statement The authors have declared no competing interest.

Abstract Nanobiomechanical data have an interest in biomedical research, but the capability of deep learning (DL) based on convolutional neural networks (CNN) has not been explored to classify such data. We propose to use these strategies to treat nanobiomechanical data acquired by atomic force microscopy (AFM) on Candida albicans living cells, an opportunistic pathogenic micro-organism of medical interest. Data, acquired by force spectroscopy, allowed us to generate force vs. distance curves (FD curves) which its profile is linked to nanobiomechanical properties of C. albicans. DL was applied to classify FD curves, considered as images, into 3 groups: adhesive nanodomains, non-adhesive domains or in between domains. We achieved a real multiclass classification with a validation accuracy, macro-average of F1, and the weighted average of 92%, without the need to perform the usual dropout or weight regularisation methods. Transfer learning with a pre-trained (PT) VGG16 architecture with and without fine tuning (FT) permitted us to verify that our model is less computationally complex and better fitted. The generalisation was done by classifying on other C. albicans cells with more that 99% of confidence, to finally predict 16,384 FD curves in less than 90 seconds. This model could be employed by a non-machine learning specialist as the trained model can be downloaded to predict the adhesiveness, within seconds, on C. albicans cells characterized by AFM.

The extracellular-matrix (ECM) is a complex interconnected three-dimensional network that provides structural support for the cells and tissues and defines organ architecture as key for their healthy functioning. However, the intimate mechanisms by which ECM acquire their three-dimensional architecture are still largely unknown. In this paper, we study this question by means of a simple three-dimensional individual based model of interacting fibres able to spontaneously crosslink or unlink to each other and align at the crosslinks. We show that such systems are able to spontaneously generate different types of architectures. We provide a thorough analysis of the emerging structures by an exhaustive parametric analysis and the use of appropriate visualization tools and quantifiers in three dimensions. The most striking result is that the emergence of ordered structures can be fully explained by a single emerging variable: the number of links per fibre in the network. If validated on real tissues, this simple variable could become an important putative target to control and predict the structuring of biological tissues, to suggest possible new therapeutic strategies to restore tissue functions after disruption, and to help in the development of collagen-based scaffolds for tissue engineering. Moreover, the model reveals that the emergence of architecture is a spatially homogeneous process following a unique evolutionary path, and highlights the essential role of dynamical crosslinking in tissue structuring.

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).

The Extra-Cellular-Matrix (ECM) is a complex interconnected 3D network that provides structural support for the cells and tissues and defines organ architecture key for their healthy functioning. However, the intimate mechanisms by which ECM acquire their 3D architecture are still largely unknown. In this paper, we address this question by means of a 3D individual based model of interacting fibers able to spontaneously crosslink or unlink to each other and align at the crosslinks. We show that such systems are able to spontaneously generate different types of architectures. We provide a thorough analysis of the emerging structures by an exhaustive parametric analysis and the use of appropriate visualization tools and quantifiers in 3D. The most striking result is that the emergence of ordered structures can be fully explained by a single emerging variable : the proportion of crosslinks in the network. This simple variable becomes an important putative target to control and predict the structuring of biological tissues, to suggest possible new therapeutic strategies to restore tissue functions after disruption, and to help in the development of collagen-based scaffolds for tissue engineering. Moreover, the model reveals that the emergence of architecture is a spatially homogeneous process following a unique evolutionary path, and highlights the essential role of dynamical crosslinking in tissue structuring.

The recently developed One-Step Poly(amidoamine) (OS-PAMAM) dendrimers stand out for their characteristics as the high drug-load capacity and cell-delivery improvement of therapeutic agents. The OS-PAMAM dendrimers have proven to be useful in the biomedical field as nanocarrier or nanosystems for therapy. In the present research it was encouraging to determine their physicochemical characteristics which were compared with commercial PAMAM generation-6 (G6). The spectroscopic measurement of amides, nanoparticle size (10-30 nm), polydispersity index and zeta potential measurements correlates with the commercial product. The OS PAMAM has cavities detected by AFM, and the force analysis showed the same adhesion force and different elasticity than PAMAM-G6. The molecular weight (MW) was 10 times lower than the commercial one for both techniques employed, resembling the MW of a PAMAM generation-3 (G3). OS-PAMAM/PAMAM-G6 MS-MS mirror plots demonstrate chemical equivalency amongst herein analyzed dendrimers, with the advantage that OS-PAMAM is produced with a faster and low-cost synthetic protocol, which allows them to be applied for both research and industry in the biomedical field.

The method presented in this paper aims to automate Bio-AFM experiments and the recording of force curves. Using this method, it is possible to record forces curves on 1000 cells in 4 hours automatically. To maintain a 4 hour analysis time, the number of force curves per cell is reduced to 9 or 16. The method combines a Jython based program and a strategy for assembling cells on defined patterns. The program, implemented on a commercial Bio-AFM, can center the tip on the first cell of the array and then move, automatically, from cell to cell while recording force curves on each cell. Using this methodology, it is possible to access the biophysical parameters of the cells such as their rigidity, their adhesive properties, etc. With the automation and the large number of cells analyzed, one can access the behavior of the cell population. This is a breakthrough in the Bio-AFM field where data have, so far, been recorded on only a few tens of cells.

In this work, we studied the impact of magnetic nanoparticles (MNPs) interactions with HeLa cells when they are exposed to high frequency alternating magnetic field (AMF). Specifically, we measured the nanobiomechanical properties of cell interfaces by using atomic force microscopy (AFM). Magnetite (Fe3O4) MNPs were synthesized by coprecipitation and encapsulated with silica (SiO2): Fe3O4@SiO2 and functionalized with amino groups (-NH2): Fe3O4@SiO2-NH2, by sonochemical processing. HeLa cells were incubated with or without MNPs, and then exposed to AMF at 37 degrees C. A biomechanical analysis was then performed through AFM, providing the Young's modulus and stiffness of the cells. The statistical analysis (p < 0.001) showed that AMF application or MNPs interaction modified the biomechanical behavior of the cell interfaces. Interestingly, the most significant difference was found for HeLa cells incubated with Fe3O4@SiO2-NH2 and exposed to AMF, showing that the local heat of these MNPs modified their elasticity and stiffness.

This work describes a novel strategy for surface functionalization, the aim of which is to significantly increase the lifetime of an electrochemical sensor dedicated to Hg(II) trace determination. In order to tailor stable mixed organic/inorganic interfaces, gold nanoparticles were electrodeposited onto a glassy carbon electrode previously functionalized by a thick 4-thiophenol diazonium film, which affords a good anchoring to the nanoparticles. AFM and FEG-SEM were used to characterize the film thickness and the nanoparticles average size and density, respectively. By using square wave anodic stripping voltammetry, the sensor exhibited a linear response between 1 and 10 nM Hg(II) and a normalized sensitivity 0.03 mu A nM(-1) min(-1). Compared to previous works, the storage lifetime of the interface was at least three times longer, being more than three weeks.

The biological effects and cellular activations triggered by monosodium urate (MSU) and calcium pyrophosphate dihydrate (monoclinic: m-CPPD) crystals might be modulated by protein coating on the crystal surface. This study is aimed at: (i) Identifying proteins adsorbed on m-CPPD crystals, and the underlying mechanisms of protein adsorption, and (ii) to understand how protein coating did modulate the inflammatory properties of m-CPPD crystals. The effects of protein coating were assessed in vitro using primary macrophages and THP1 monocytes. Physico-chemical studies on the adsorption of bovine serum albumin (BSA) upon m-CPPD crystals were performed. Adsorption of serum proteins, and BSA on MSU, as well as upon m-CPPD crystals, inhibited their capacity to induce interleukin-1-β secretions, along with a decreased ATP secretion, and a disturbance of mitochondrial membrane depolarization, suggesting an alteration of NLRP3 inflammasome activation. Proteomic analysis identified numerous m-CPPD-associated proteins including hemoglobin, complement, albumin, apolipoproteins and coagulation factors. BSA adsorption on m-CPPD crystals followed a Langmuir-Freundlich isotherm, suggesting that it could modulate m-CPPD crystal-induced cell responses through crystal/cell-membrane interaction. BSA is adsorbed on m-CPPD crystals with weak interactions, confirmed by the preliminary AFM study, but strong interactions of BSA molecules with each other occurred favoring crystal agglomeration, which might contribute to a decrease in the inflammatory properties of m-CPPD crystals. These findings give new insights into the pathogenesis of crystal-related rheumatic diseases and subsequently may open the way for new therapeutic approaches.

Three-dimensional spheroids are widely used as cancer models to study tumor cell proliferation and to evaluate new anticancer drugs. Growth-induced stress (i.e., stress that persists in tumors after external loads removal) influences tumor growth and resistance to treatment. However, it is not clear whether spheroids recapitulate the tumor physical properties. Here, we demonstrated experimentally and with the support of mathematical models that, like tumors, spheroids accumulate growth-induced stress. Moreover, we found that this stress is lower in spheroids made of 5,000 cancer cells and grown for 2 days than in spheroids made of 500 cancer cells and grown for 6 days. These two culture conditions associated with different growth-induced stress levels also had different effects on the spheroid shape (using light sheet microscopy) and surface topography and stiffness (using scanning electron microscopy and atomic force microscopy). Finally, the response to irinotecan was different in the two spheroid types. Taken together, our findings bring new insights into the relationship between the spheroid physical properties and their resistance to antitumor treatment that should be taken into account by the experimenters when assessing new therapeutic agents using in vitro 3D models or when comparing studies from different laboratories.

1. ENCB-Instituto Politécnico Nacional (IPN), Av. Wilfrido Massieu, Unidad Adolfo López Mateos, 07738, Mexico City, Mexico 2. ITAV-CNRS, Université de Toulouse, CNRS, Toulouse, France 3. CIC-Instituto Politécnico Nacional (IPN), Av. Juan de Dios Bátiz S/N, Nueva Industrial Vallejo, 07738, Mexico City, Mexico 4. LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France Ø. Equal contribution *: nanobiomex@hotmail.com, edague@laas.fr

This paper reports a methodology which includes an algorithm able to move an AFM tip onto a single cell and through several cells combined with a smart strategy of cell immobilization.

Stimuli-responsive hydrogels are essential for the future development of synthetic materials that could exchange information with living tissues. In this Article, we present the synthesis of biocompatible hydrogels with an unprecedented range of photocontrolled rigidity. The hydrogels are based on dual physical and chemical crosslinking. Chemical crosslinks are the result of thiol maleimide Michael addition; physical crosslinks are based on host-guest interactions between azobenzene and b-cyclodextrin moieties. The final properties of the materials are tuned by a design-of-experiment approach. This strategy ena-bles us to obtain a hydrogel with mechanical properties close to routinely used agarose gel while maintaining a low UV-visible absorption. The Young's modulus is monitored in real time during AFM nanoindentation experiments under irradiation. Upon UV and visible irradiation cycles, the hydrogel exhibits a range of reversible evolution greater than 30%, which is also associated with cycles of swelling/shrinking. A biocompatible hydrogel with predictable and phototunable stiffness and a high variation in mechanical properties has thus been obtained for the first time.

Micropatterning and manipulation of mammalian and bacterial cells are important in biomedical studies to perform in vitro assays and to evaluate biochemical processes accurately, establishing the basis for implementing biomedical microelectromechanical systems (bioMEMS), point-of-care (POC) devices, or organs-on-chips (OOC), which impact on neurological, oncological, dermatologic, or tissue engineering issues as part of personalized medicine. Cell patterning represents a crucial step in fundamental and applied biological studies in vitro, hence today there are a myriad of materials and techniques that allow one to immobilize and manipulate cells, imitating the 3D in vivo milieu. This review focuses on current physical cell patterning, plus chemical and a combination of them both that utilizes different materials and cutting-edge micro-nanofabrication methodologies.

PeakForce Quantitative Nanomechanical Mapping (PeakForce QNM) multiparametric AFM mode was adapted to qualitative and quantitative study of the lateral membrane of cardiomyocytes (CMs), extending this powerful mode to the study of soft cells. On living CM, PeakForce QNM depicted the crests and hollows periodic alternation of cell surface architecture previously described using AFM Force Volume (FV) mode. PeakForce QNM analysis provided better resolution in terms of pixel number compared to FV mode and reduced acquisition time, thus limiting the consequences of spontaneous living adult CM dedifferentiation once isolated from the cardiac tissue. PeakForce QNM mode on fixed CMs clearly visualized subsarcolemmal mitochondria (SSM) and their loss following formamide treatment, concomitant with the interfibrillar mitochondria climbing up and forming heaps at the cell surface. Interestingly, formamide-promoted SSM loss allowed visualization of the sarcomeric apparatus ultrastructure below the plasma membrane. High PeakForce QNM resolution led to better contrasted mechanical maps than FV mode and provided correlation between adhesion, dissipation, mechanical and topographical maps. Modified hydrophobic AFM tip enhanced contrast on adhesion and dissipation maps and suggested that CM surface crests and hollows exhibit distinct chemical properties. Finally, two-dimensional Fast Fourier Transform to objectively quantify AFM maps allowed characterization of periodicity of both sarcomeric Z-line and M-band. Overall, this study validated PeakForce QNM as a valuable and innovative mode for the exploration of living and fixed CMs. In the future, it could be applied to depict cell membrane architectural, mechanical and chemical defects as well as sarcomeric abnormalities associated with cardiac diseases.