ABSTRACT Collagen is a key protein in mammals that maintains structural integrity within tissues. A failure in fibrillar collagen reorganization can induce cancer or fibrosis formation, such as in spinal cord injury (SCI), where the healing process after the initial trauma leads to the formation of scar tissue, which includes fibrosis. As there is no current treatment targeting the fibrotic process directly, a better understanding of collagen properties can thus help to apprehend malignant states. Characterization of collagen fibers has been widely explored on second‐harmonic generation (SHG) images, due to the label‐free nature of the SHG imaging technique. It has been performed with various fibers extraction methods such as curvelet transform (CT) implemented in the open‐source software CurveAlign. However, when it comes to investigating undergrowth collagen fibers (collagen fibers that are still under reorganization) as observed in SCI, the CT method becomes complex to tune for nonadvanced users in order to properly segment the fibers. To improve collagen detection in the case of undergrowth fibers, we propose a methodology based on the fingerprint enhancement (FP‐E) algorithm that requires fewer user input parameters and is less time‐consuming. Our method was extensively tested on SHG data from injured spinal cord samples. We obtained metrics that depicted changes in collagen organization over time, particularly a significant increase in fiber density, demonstrating the FP‐E algorithm was properly adapted to address the evolution of collagen properties after SCI. Besides the simpler tuning of the method compared to commonly used software, the combination with further characterization of the extracted fibers could lead to consider fibrillar collagen as a biomarker in diseases where fibers are under development. The FP‐E algorithm is provided in the article.

Protein nanoparticles have been proven to be highly effective stabilizers of water-in-water emulsions obtained from a number of different types of aqueous two-phase systems (ATPS). The emulsion stabilizing efficiency of such particles is attributed to their affinity to the water/water interface of relevant ATPS, and emulsion formulations with long-term stability were reported in the recent years. In this study we investigated the macroscopic dynamics of the early-stage time evolution of dextran-in-polyethylene glycol emulsions obtained from a single ATPS and containing β-lactoglobulin microgel particles of various diameters (ca. 40-190 nm). The results revealed the existence of a threshold in microgel size above which the water-in-water emulsion is stabilized, and that the process of segregative phase separation is determined by the interplay of droplets coalescence and sedimentation. Efficient droplet coalescence inhibition was found for microgel particles larger than 60 nm. Based on previous literature results, we discuss our coalescence-driven phase separation data in the context of the formation of durable particle layers on the emulsion droplets and the resulting droplet-droplet interactions.

Controlling and manipulating radiative heat transfer remains a pivotal challenge in both scientific inquiry and technological advancement, traditionally tackled through the precise geometric design of metastructures. However, geometrical optimization cannot break the inherent shackles of local modes within individual meta-atoms, which hinders sustained progress in radiative heat transfer. Here, we propose a comprehensive strategy based on interatomic displacement to achieve superior heat transfer performance while obviating the need for increasingly complex structural designs. This meta-atomic displacement strategy enables a shift from quasi-isolated localized resonances to extended nonlocal resonant modes induced by strong interactions among neighboring meta-atoms, resulting in a radiative heat conductance that surpasses other previously reported geometrical structures. Furthermore, this meta-atomic displacement strategy can be seamlessly applied to various metastructures, offering significant implications for advancing thermal science and next-generation energy devices.

Broadband CARS imaging is used to evaluate changes underlying the lesion of a spinal cord injury in a murine model. After injury the axonal regeneration in spinal cord is partly inhibited by the glial scar, acting as a physical and chemical barrier. Recently we have reported that the SHG signal exhibited by fibrillar collagen enabled to specifically monitor it as a biomarker of a spinal cord lesion1. However, the injury and the scarring process involves also other elements as the myelin sheets, microglia, astrocytes and extracellular matrix components. We assess the additional structural and metabolic modifications of the injured tissue via BCARS imaging of excised murine spinal cord tissue at different time points (72h, 1, 2, 6 weeks) after injury. BCARS is based on the detection of the fingerprint region of molecules (500-1500 cm-1) and could probe multiple Raman transitions simultaneously, to allow a “chemical” imaging of biological tissues with improved molecular contrast2. When compared to spontaneous Raman microscopy, BCARS microscopy provided 10-100x faster image acquisition for quantitative and qualitative assessment of pharmaceuticals at much higher spatio-chemical resolution and with spectra of much higher signal-to noise ratio.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects both upper and lower motor neurons. As motor neurons degenerate, they lose their connection to muscle cells, leading to the onset of fasciculations and muscle atrophy. Eventually, ALS results in paralysis, with symptoms progressively worsening over time. The disease typically leads to death within 3–5 years after diagnosis. Currently, there is no definitive cure. Understanding the morphological and mechanical modifications in skeletal muscle fibers of an ALS mouse model (SOD1-G93A) could be crucial for uncovering the molecular mechanisms underlying the disease.In this study, second harmonic generation (SHG) multiphoton microscopy and atomic force microscopy (AFM) were used to evaluate sarcomere periodicity, stiffness, and orientation in gastrocnemius muscle fibers from symptomatic SOD1-G93A mice (90 days), comparing them to wild-type (WT) controls. Our results reveal a significant reduction in sarcomere periodicity in SOD1-G93A mice compared to WT controls. Furthermore, AFM data indicate the presence of two populations of muscle fibers: slower Type-I and faster Type-II fibers. Notably, SOD1-G93A tissue exhibits a dramatic softening of fast fibers, while not statistical difference was detected for slow fibers. This result could indicate that ALS, at this stage of the disease, selectively affects skeletal muscle fibers specialized for fast contraction.These alterations in mechanical properties and morphology reflect structural and molecular changes in the sarcomeres, suggesting a change in inner structure of the main structural proteins.

Broadband CARS imaging is one of the Coherent Raman imaging (CRI) modalities with sub-cellular spatial resolution and providing an intrinsic fingerprint of chemical composition1. For biological samples, most of the characteristic Raman peaks are found in the ‘fingerprint’ spectral region, (~500 cm−1 to 1,800 cm−1 ), with the remaining found in the higher-energy CH-/OH-stretch region (~2,700 cm−1 to 3,300 cm−1). The various CRI techniques exhibit a large range in both performance and experimental setup complexity. For example, narrowband CARS microscopes are commercially available and could acquire image pixels very quickly but only probes narrow regions of the Raman spectrum. On the other hand, B-CARS acquire the full spectrum at the cost of longer pixel dwell time. In this talk, we will present the principles of the B-CARS microscopy we’ve developed in our laboratory and some examples of biological applications.

Collagen is essential for maintaining tissue integrity, playing a key role in both fibrotic scar formation and the potential for regenerative healing in spinal cord injuries (SCI). When tissue is damaged, collagen starts to form within the affected area, leading to structural adjustments that impact the healing process. While previous studies have focused on tracking individual fibers using segmentation method, they often fail to capture global 3D spatial heterogeneity in collagen organization. In this study, we propose a 3D reconstruction approach combined with 3D variance analysis based on Second Harmonic Generation (SHG) imaging to investigate collagen distribution within the SCI lesion. This methodology enables a more comprehensive assessment of fibrosis evolution by analyzing spatial pattern and alignment of collagen fibers across different regions of the lesion, particularly along the rostral-caudal axis. Understanding whether collagen follows a preferential organization could provide crucial insights fibrosis mechanisms and its potential impact on regenerative process. Our results indicate that a non-uniform collagen patterns along the lesion axis, with significant differences in fiber anisotropy depending on the region, suggesting region-specific remodeling mechanisms. These findings suggest that collagen remodeling follows spatial dynamics that may influence fibrosis severity. By integrating 3D spatial analysis with advanced imaging techniques, this study provides a deeper understanding of collagen organization in SCI, which could help in identifying biomarkers for fibrosis progression and therapeutic interventions.