Super-resolved live imaging of thick biological samples with 3D Random Illumination Microscopy (3D-RIM)

Mangeat, T.; Mazzella, L.; Rogez, B.; Giroussens, G.; Bernard, M.; Vargas, P.; Allain, M.; Labouesse, S.; Idier, J.; LeGoff, L.; Sentenac, A.

doi:10.1101/2025.08.06.668879

bioRxiv · 10.1101/2025.08.06.668879

Super-resolved live imaging of thick biological samples with 3D Random Illumination Microscopy (3D-RIM)

Mangeat, T.·Mazzella, L.·Rogez, B.·Giroussens, G.·Bernard, M.·Vargas, P.·Allain, M.·Labouesse, S.·Idier, J.·LeGoff, L.·Sentenac, A.

View original source ↗Export metadata ↓

Abstract

Super-resolved volume imaging of thick, live specimens is greatly hampered by sample induced aberrations and out-of-focus fluorescence. In this work, using a combination of speckled illuminations, remote focusing, three-dimensional photon reassignement and variance processing, we obtained volume images with super-resolution (110 nm transverse, 270 nm axial) on thin samples and maintained a high contrast and high resolution level throughout tens of microns of highly aberrant tissues and up to hundreds of microns in collagen scaffolds.

Explore related subjects

bioengineering Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mangeat, T., Mazzella, L., Rogez, B., Giroussens, G., Bernard, M., Vargas, P., Allain, M., Labouesse, S., Idier, J., LeGoff, L., Sentenac, A.. 2025-08-08. Super-resolved live imaging of thick biological samples with 3D Random Illumination Microscopy (3D-RIM). https://doi.org/10.1101/2025.08.06.668879

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Dynamic Compression Platform for Live Imaging of Scaffold-Transmitted Cellular Mechanoresponses

Mechanical characterization of biomaterial scaffolds is essential to evaluate their capacity to meet the functional demands of target tissues in tissue engineering and regenerative medicine applications. Scaffolds designed to interface with living tissues must support the transmission of mechanical cues to resident cells and stimulate mechanosignaling pathways that are essential to their function. In joints, bone and cartilage cells act as primary mechanosensors, converting mechanical stimuli into biochemical signals that regulate tissue homeostasis and remodelling. Therefore, evaluating cellular mechanoresponses to scaffold-transmitted compression in vitro can inform the development of functional tissue-engineered constructs. For example, poly({epsilon}-caprolactone) (PCL) scaffolds are highly relevant for bone and cartilage tissue engineering due to their biocompatibility, stable mechanical properties and slow degradation. Here, we applied a custom-built device to study compression-induced mechanosignaling in MC3T3-E1 pre-osteoblast cells. The device is composed of a polydimethylsiloxane (PDMS) pillar, a force-sensing load cell, and a piezoelectric linear track. A protocol is described in which MC3T3-E1 cells are repeatedly compressed, while in parallel live tracking of force measurements and live imaging of intracellular calcium dynamics in MC3T3-E1 cells are recorded. PCL scaffolds fabricated by melt electrowriting (MEW) were subsequently integrated into the platform. Scaffold-transmitted compression triggered dynamic increases in cytosolic calcium; in MC3T3-E1 cells located directly under the PCL microfibers, but also in cells located in the interfiber spaces. This device and workflow facilitate in vitro investigations of real-time cellular mechanoresponses to dynamic compression applied with biomaterial scaffolds, and provides a testing platform for evaluating the mechanotransductive properties of scaffolds intended for tissue engineering applications.

bioengineering↗

bioRxiv2026

Ultrasound Tracking Reveals Progressive Regional Strain Differences in Human Achilles Tendons During Fatigue Loading

Ultrasound is commonly used to assess structural changes in symptomatic Achilles tendons, but quantitative biomechanical metrics for progressive tendon deterioration remain limited. The goal of this study was to develop and validate an automated ultrasound tracking algorithm for regional tendon deformation and evaluate strain progression in survived and ruptured tendons during fatigue loading. We hypothesized that maximum strain, average strain, and strain heterogeneity would exhibit different trajectories between groups. Ten cadaveric Achilles tendons underwent cyclic loading with stress tests every 500 cycles until rupture or 150,000 cycles. Ultrasound images acquired during stress tests were analyzed using an automated tracking algorithm to generate spatially resolved regional strain fields. Ultrasound-derived bulk strain was highly correlated with actuator-derived strain in survived (R^2 = 0.968 +/- 0.017) and ruptured tendons (R^2 = 0.972 +/- 0.014). Maximum and average longitudinal strains progressively diverged between groups across fatigue life (Group x FatigueLife: p = 0.003 and p < 0.0001, respectively). During the first 10,000 cycles, average strain decreased in survived tendons ({beta} = -0.0268%, p = 0.0215) but not ruptured tendons ({beta} = 0.0147%, p = 0.1197), with a significant Group x Cycle interaction (p = 0.0061). This study demonstrates that the algorithm quantified Achilles tendon deformation with high fidelity and enabled spatially resolved strain assessment throughout fatigue loading. Maximum and average strain followed different trajectories between groups, whereas strain heterogeneity did not. Early differences in tendon biomechanics suggest that regional strain behavior may change before pronounced differences in absolute magnitude develop.

bioengineering↗

bioRxiv2026

Brain organoid computing for robotic decision-making

Biomimicry has inspired the evolution of robotics toward greater autonomy, adaptability, and symbiosis with humans and dynamic environments. However, current robotic systems still face major challenges in recapitulating the high-efficiency decision-making capabilities of the human brain under complex and dynamic conditions. Here, we present Brainobot, a biohybrid robotic system that establishes a brain organoid controller as a high-level robotic decision-making layer for closed-loop embodiment. By leveraging brain organoid reservoir computing, Brainobot interacts with dynamic environments by receiving and processing sensory inputs and generating motor actions. As a proof-of-concept demonstration, Brainobot is implemented in a humanoid robotic system to perform real-world tasks, including object grasping and laser chasing. Interestingly, Brainobot exhibits unique features, including cross-task adaptivity, high computing efficiency, and low energy consumption. Thus, our approach may provide insights for advancing robotic embodiment and understanding biological decision-making.

bioengineering↗