bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.07.27.665782

Ultrabright NIR-II Nanoprobes for Ex Vivo Bioimaging: Protein Nanoengineering Meets Molecular Engineering

Abstract

Near-infrared (NIR) fluorescence imaging is a powerful, non-invasive tool for cancer diagnosis, enabling real-time, high-resolution visualization of biological systems. While most probes target the first NIR window (NIR-I, 750-950 nm), recent advances focus on the second window (NIR-II, 1000-1700 nm), which offers deeper tissue penetration and reduced interferences from scattering and autofluorescence. However, many current NIR-II nanoprobes show suboptimal brightness and limited validations in more human-centric models. Here, we present an orthogonal strategy combining molecular engineering, by modulating the amount and position of thiophene moieties in semiconducting polymers (SPs), with protein nanoengineering to develop ultrabright NIR-II imaging probes optimized for ex vivo bioimaging in large animal models. The molecular tuning amplifies the NIR-II fluorescence brightness while screening endogenous proteins as encapsulating matrices to improve colloidal stability and enable active targeting. Molecular docking identified bovine serum albumin as the effective candidate, and the resulting protein-complexed nanoprobes were characterized for size, colloidal stability under physiological conditions, and optical performances. Imaging performances were evaluated using tumor-mimicking phantoms in porcine lungs, simulating cancer surgery, and injected at clinically relevant concentrations into ovine brains and porcine ovaries for microvascular visualization and tissue discrimination, respectively. In all scenarios, our protein-complexed nanoprobes outperformed the FDA-approved clinical dye indocyanine green in signal-to-background ratios. Initial in vitro assays confirmed their hemocompatibility, biocompatibility, and cellular uptake in ovarian adenocarcinoma cells. This integrated approach offers a promising platform for developing next-generation ultrabright NIR-II nanoprobes with improved brightness and stability, advancing the potential for image-guided surgery and future clinical translation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/665782v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8a20org.highwire.dtl.DTLVardef@1403bedorg.highwire.dtl.DTLVardef@1e474corg.highwire.dtl.DTLVardef@1daed69_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Vasquez, I., Harun, A., Posey, R., Reddy, R., Bickel, U., Tropp, J., Srivastava, I.. 2025-07-31. Ultrabright NIR-II Nanoprobes for Ex Vivo Bioimaging: Protein Nanoengineering Meets Molecular Engineering. https://doi.org/10.1101/2025.07.27.665782

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗