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Abstract Blood clotting is the body’s natural reaction in wound healing and is also the cause of many pathologies. Fibrin – the main protein in the clotting process provides clots’ mechanical strength by forming a scaffold of complex fibrin fibers. Fibrin fibers exhibit high extensibility and primarily elastic properties under static loading, which differ from in vivo dynamic forces. In many biological materials, the mechanical response changes under repeated loading/unloading (cyclic loading). Using lateral force microscopy, we show fibrin fibers possess viscoelastic behavior and experience irreversible damage under cyclic loading. Cross-linking results in a more rigid structure with permanent damage occurring mostly at larger strains, which is corroborated by computational modeling of fibrin extension using a hyperelastic model. Molecular spectroscopy analysis with broadband coherent anti-Stokes Raman scattering spectroscopy in addition to molecular dynamic simulations allow identification of the source of damage, the unfolding pattern, and inter and intramolecular changes in fibrin. The results show partial recovery of protein’s secondary and tertiary structures under load, providing deeper understanding of fibrin’s unique behavior in wound healing or pathologies like stroke and embolism. 

The extracellular matrix (ECM) is a dynamic and complex microenvironment that modulates cell behavior and cell fate. Changes in ECM composition and architecture have been correlated with development, differentiation, and disease progression in various pathologies, including breast cancer [1]. Studies have shown that aligned fibers drive a pro-metastatic microenvironment, promoting the transformation of mammary epithelial cells into invasive ductal carcinomaviathe epithelial-to-mesenchymal transition (EMT) [2]. The impact of ECM orientation on breast cancer metabolism, however, is largely unknown. Here, we employ two non-invasive imaging techniques, fluorescence-lifetime imaging microscopy (FLIM) and intensity-based multiphoton microscopy, to assess the metabolic states of cancer cells cultured on ECM-mimicking nanofibers in a random and aligned orientation. By tracking the changes in the intrinsic fluorescence of nicotinamide adenine dinucleotide and flavin adenine dinucleotide, as well as expression levels of metastatic markers, we reveal how ECM fiber orientation alters cancer metabolism and EMT progression. Our study indicates that aligned cellular microenvironments play a key role in promoting metastatic phenotypes of breast cancer as evidenced by a more glycolytic metabolic signature on nanofiber scaffolds of aligned orientation compared to scaffolds of random orientation. This finding is particularly relevant for subsets of breast cancer marked by high levels of collagen remodeling (e.g. pregnancy associated breast cancer), and may serve as a platform for predicting clinical outcomes within these subsets [3–6]. 

Thromboembolism – that is, clot formation and the subsequent fragmentation of clot – is a leading cause of death worldwide. Clots’ mechanical properties are critical determinants of both the embolization process and the pathophysiological consequences thereof. Thus, understanding and quantifying the mechanical properties of clots is important to our ability to treat and prevent thromboembolic disease. However, assessing these properties from in vivo clots is experimentally challenging. Therefore, we and others have turned to studying in vitro clot mimics instead. Unfortunately, there are significant discrepancies in the reported properties of these clot mimics, which have been hypothesized to arise from differences in experimental techniques and blood sources. The goal of our current work is therefore to compare the mechanical behavior of clots made from the two most common sources, human and bovine blood, using the same experimental techniques. To this end, we tested clots under pure shear with and without initial cracks, under cyclic loading, and under stress relaxation. Based on these data, we computed and compared stiffness, strength, work-to-rupture, fracture toughness, relaxation time constants, and prestrain. While clots from both sources behaved qualitatively similarly, they differed quantitatively in almost every metric. We also correlated each mechanical metric to measures of blood composition. Thereby, we traced this inter-species variability in clot mechanics back to significant differences in hematocrit, but not platelet count. Thus, our work suggests that the results of past studies that have used bovine blood to make in vitro mimics – without adjusting blood composition – should be interpreted carefully. Future studies about the mechanical properties of blood clots should focus on human blood alone. 

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load–displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load–displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.