A perspective of advances in optical methods for biological sample characterization

1 Introduction

The past 5 years have marked an inflection point in optical bioscience. We are witnessing not merely incremental improvements in existing technologies, but a fundamental reimagining of how photonics can interrogate living systems (Schermelleh et al., 2019; Sahl et al., 2017). As editors of this Research Topic, we observe that many of the most transformative advances emerge at the intersection of three forces: breakthrough photonic technologies, computational intelligence, and an increasingly sophisticated understanding of biological complexity.

This perspective outlines our editorial vision for five critical domains that represent important directions in the current landscape of optical biological characterization and, more importantly, chart plausible trajectories for the next decade. Scope and intent: Rather than comprehensively reviewing the field—which would require more extensive treatment—we offer our informed opinion on selected advances within these technologies and where they may need to evolve to address challenges in life sciences. We focus on representative examples from recent literature (primarily 2020–2025) that illustrate key trends, while acknowledging that many important contributions necessarily fall outside our scope. Our goal is to provide context for the Research Topic contributions and to stimulate discussion about integration opportunities across domains, rather than to deliver definitive state-of-the-art conclusions.

The five domains we address are: (1) breakthroughs in bioimaging technologies pushing the frontiers of sensitivity and on the spatial and temporal resolution; (2) next-generation biosensors for detection of specific biomolecules; (3) optical tweezers for measuring nanoscale forces within biological samples; (4) advanced particle tracking techniques to map velocity fields and dynamic processes; and (5) artificial intelligence-driven approaches for enhanced data analysis and interpretation. Each section highlights selected conceptual and technological developments, current limitations, and emerging opportunities. A concluding section synthesizes cross-cutting themes and proposes integration strategies that may define the next-generation of optical biological characterization platforms.

2 Breakthroughs in bioimaging technologies

2.1 Super-resolution and expansion microscopy

The quest to visualize biological structures beyond the diffraction limit has driven remarkable innovation. Super-resolution microscopy (SRM) techniques—including stimulated emission depletion (STED), structured illumination microscopy (SIM), and single-molecule localization microscopy (SMLM)—now routinely achieve sub-50 nm resolution in living cells (Sigal et al., 2018). Advances in aggregation-induced emission (AIE) probes have improved photostability for long-term imaging (Mei et al., 2015). Expansion microscopy (ExM) offers complementary nanoscale performance by physically enlarging specimens before observation on conventional microscopes (Wassie et al., 2019). Integration with adaptive optics and computational reconstruction has extended SRM into thicker tissues and whole organisms (Ji et al., 2017). Lattice light-sheet microscopy combined with adaptive optics now enables low-phototoxic volumetric imaging of subcellular dynamics in developing embryos (Chen et al., 2014).

2.2 Quantitative phase and vibrational imaging

Phase-based approaches have expanded significantly, with quantitative phase imaging (QPI) enabling non-invasive measurements of cellular mass, morphology, and dynamics (Park et al., 2018). Improvements in holographic and interferometric methods now support millisecond-scale temporal resolution for real-time cell monitoring (Kim et al., 2015). Vibrational-based techniques, including Raman, Coherent Anti-Stokes Raman Scattering CARS and Stimulated Raman Scattering SRS, add chemical specificity by probing molecular vibrations, supporting visualization of lipids, proteins, and metabolites (Cheng and Xie, 2015). Hyperspectral SRS further accelerates chemical mapping for diagnostic applications.

2.3 Light-sheet fluorescence and nonlinear microscopy

Light-sheet fluorescence microscopy (LSFM) provides rapid, gentle volumetric imaging by illuminating samples with a thin, orthogonal light sheet. Innovations such as lattice illumination, adaptive optics, and multi-view imaging have improved resolution, depth, and speed (Olarte et al., 2018). Nonlinear microscopy, including two- and three-photon excitation (TPEF and ThPEF) or second- and third-harmonic generation (SHG, and THG), enables deep-tissue imaging with intrinsic optical sectioning and reduced photodamage. Advances in ultrafast lasers, pulse shaping, and adaptive optics continue to enhance sensitivity and penetration, while label-free nonlinear contrast mechanisms support growing diagnostic applications (Castro-Olvera et al., 2024).

2.4 Emerging challenges and opportunities

Despite substantial progress, phototoxicity remains a central limitation for long-term live-cell imaging, especially in high-intensity super-resolution modalities (Laissue et al., 2017). Techniques such as LSFM help mitigate these constraints but may introduce challenges related to optical aberrations and instrument complexity. Standardized imaging protocols and quantitative metrics are increasingly necessary for reproducibility across laboratories (Boehm et al., 2021). Looking ahead, multimodal platforms integrating fluorescence, SRM, quantitative phase, Raman, LSFM, and nonlinear signals, together with brighter fluorophores, improved adaptive optics, novel lasers and machine-learning-guided acquisition—promise more comprehensive and robust biological characterization.

3 Next-generation optical biosensors

3.1 Plasmonic and photonic biosensors

Optical biosensors have evolved from laboratory curiosities to clinical diagnostic tools, driven by advances in nanofabrication and surface chemistry. Surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) sensors exploit the sensitivity of plasmonic nanostructures to local refractive index changes, enabling label-free detection of biomolecular binding events (Willets et al., 2007). Recent innovations in nanostructured substrates and metamaterials have pushed detection limits toward single-molecule sensitivity (Jackman et al., 2017).

Photonic crystal sensors and whispering gallery mode resonators offer alternative transduction mechanisms with high quality factors and compact footprints (Su, 2017). These platforms have demonstrated femtomolar detection limits for disease biomarkers and are increasingly integrated into microfluidic systems for point-of-care diagnostics.

3.2 Fiber-optic and paper-based sensors

Fiber-optic biosensors combine the sensitivity of optical detection with the flexibility and remote sensing capabilities of optical fibers (Loyez et al., 2019). Functionalized fiber tips and tapered fibers enable minimally invasive in vivo measurements, with applications ranging from intracellular pH sensing to real-time monitoring of metabolites in tissue.

Paper-based biosensors represent a paradigm shift toward low-cost, disposable diagnostics for resource-limited settings (Morales-Narváez and Dincer, 2020). By integrating colorimetric or fluorescent detection with microfluidic paper devices, these sensors achieve rapid, equipment-free analysis of complex samples. Recent work has demonstrated multiplexed detection of infectious disease markers with sensitivity approaching laboratory-based assays.

3.3 Future directions

The next-generation of biosensors will likely emphasize multiplexing, miniaturization, and integration with digital health platforms. Wearable and implantable sensors for continuous monitoring of biomarkers represent a particularly promising Frontier (Kim et al., 2019). Key challenges include improving selectivity in complex biological matrices, extending sensor lifetime and stability, and developing robust calibration methods for quantitative measurements. The integration of nanomaterials with novel optical properties and the application of machine learning for signal processing will be essential for realizing these goals.

4 Optical tweezers for nanoscale force measurements

4.1 Principles and recent advances

Optical tweezers use focused laser beams to trap and manipulate microscopic objects, enabling precise force measurements at the piconewton scale (Ashkin et al., 1986). This technique has revolutionized single-molecule biophysics, allowing direct observation of molecular motors, DNA-protein interactions, and protein folding dynamics. Recent advances in instrumentation—including high-speed detection, active feedback control, and multi-trap configurations—have expanded the range of accessible biological phenomena (Neuman and Nagy, 2008).

Holographic optical tweezers (HOT) enable simultaneous manipulation of multiple particles in three dimensions, facilitating studies of collective cellular behaviors and complex mechanical interactions (Grier, 2003). The integration of optical tweezers with fluorescence microscopy provides simultaneous mechanical and biochemical information, revealing correlations between force and molecular conformation.

4.2 Applications in cell mechanics and microrheology

Beyond single-molecule studies, optical tweezers have become essential tools for probing cellular mechanics and the viscoelastic properties of biological fluids (Tassieri, 2016). Active microrheology using optically trapped probe particles reveals the frequency-dependent mechanical response of cytoplasm, extracellular matrix, and mucus. These measurements provide insights into cellular organization, mechanotransduction, and disease-related changes in tissue mechanics.

Recent work has demonstrated the use of optical tweezers for measuring forces during cell division, migration, and adhesion, connecting mechanical cues to cellular decision-making (Bambardekar et al., 2015). The ability to apply controlled forces while simultaneously imaging cellular responses has revealed mechanosensitive signaling pathways and their roles in development and disease.

4.3 Emerging opportunities

Future developments will likely focus on increasing throughput, extending measurements to more complex environments, and integrating optical tweezers with other characterization modalities. Miniaturized and chip-based optical trapping systems may enable parallelized force measurements for high-throughput screening applications (Padgett and Bowman, 2011). The combination of optical tweezers with super-resolution microscopy and advanced spectroscopic techniques will provide unprecedented insights into the mechanochemical coupling underlying biological function.

5 Advanced particle tracking techniques

5.1 Single-particle tracking and diffusion analysis

Single-particle tracking (SPT) has become indispensable for studying molecular dynamics in living cells. By following individual fluorescently labeled molecules over time, SPT reveals heterogeneous diffusion behaviors, transient binding events, and spatial organization that are obscured in ensemble measurements (Manzo and Garcia-Parajo, 2015). Recent advances in localization algorithms and high-speed imaging have pushed temporal resolution to microseconds and spatial precision to nanometers.

Sophisticated analysis methods now extract quantitative information about diffusion modes, confinement, and molecular interactions from SPT trajectories (Metzler et al., 2014). These approaches have revealed the complex, non-Brownian nature of intracellular transport and the role of membrane organization in cellular signaling.

5.2 Particle image velocimetry and flow characterization

Particle image velocimetry (PIV) and particle tracking velocimetry (PTV) provide complementary approaches for mapping velocity fields in biological fluids and tissues (Adrian, 2005). These techniques have applications ranging from cardiovascular flow analysis to characterization of ciliary beating and intracellular transport. Recent developments in three-dimensional and time-resolved implementations have enabled volumetric flow measurements with high spatiotemporal resolution (Schanz et al., 2016).

Optical coherence tomography (OCT)-based particle tracking extends these capabilities to optically scattering samples, enabling non-invasive flow measurements in tissues (Lee et al., 2012). The integration of microfluidic devices with advanced particle tracking has facilitated in vitro studies of cellular responses to controlled flow conditions.

5.3 Future perspectives

The next-generation of particle tracking methods will likely emphasize label-free detection, increased throughput, and integration with machine learning for automated analysis. Super-resolution particle tracking promises to resolve nanoscale dynamics in crowded cellular environments (Balzarotti et al., 2017). The development of standardized analysis pipelines and open-source software tools will be critical for ensuring reproducibility and facilitating adoption across disciplines.

6 Artificial intelligence-driven data analysis and interpretation

6.1 Deep learning for image analysis

Artificial intelligence (AI), particularly deep learning, has transformed the analysis of optical microscopy data. Convolutional neural networks (CNNs) now routinely perform tasks such as cell segmentation, classification, and tracking with accuracy exceeding human experts (Moen et al., 2019). Recent architectures enable end-to-end learning from raw images to biological insights, bypassing traditional feature engineering.

Notable applications include AI-enhanced super-resolution microscopy, where neural networks predict high-resolution images from diffraction-limited inputs, dramatically reducing acquisition time and phototoxicity (Ouyang et al., 2018). Generative models have enabled virtual staining, predicting fluorescence images from label-free brightfield or phase contrast data (Christiansen et al., 2018).

6.2 Physics-informed and interpretable AI

While deep learning has achieved remarkable performance, concerns about interpretability and generalization have motivated the development of physics-informed machine learning approaches (Karniadakis et al., 2021). These methods incorporate physical constraints and domain knowledge into neural network architectures, improving robustness and reducing data requirements. For microscopy applications, physics-informed models that respect optical principles and biological constraints show promise for more reliable and interpretable analysis.

The integration of causal inference frameworks with machine learning may enable more robust identification of biological mechanisms from observational data (Schölkopf et al., 2021). Attention mechanisms and explainable AI techniques are beginning to provide insights into which image features drive model predictions, facilitating biological interpretation.

6.3 Challenges and future directions

Despite rapid progress, several challenges remain. Many AI models require large annotated datasets that are expensive to generate and may not generalize across experimental conditions or laboratories (Varoquaux and Cheplygina, 2022). Standardization of training data, validation protocols, and performance metrics is essential for clinical translation. The development of foundation models—large-scale models pre-trained on diverse datasets—may address some of these limitations by enabling transfer learning to new tasks with minimal additional data.

Looking forward, we anticipate increasing emphasis on real-time, on-the-fly AI that adapts imaging parameters during acquisition to optimize information content while minimizing photodamage (Qiao et al., 2023). The integration of AI with automated microscopy platforms will enable autonomous experimentation, where systems iteratively design and execute experiments to test biological hypotheses.

7 Cross-cutting themes and integration opportunities

7.1 Multimodal integration

A recurring theme across all five domains is the power of multimodal integration. Combining complementary optical techniques—such as fluorescence, phase imaging, Raman spectroscopy, and optical manipulation—provides richer information than any single modality alone. Integrated platforms that seamlessly combine these capabilities with intelligent control systems will enable more comprehensive characterization of biological samples.

7.2 Quantification and standardization

The transition from qualitative observation to quantitative measurement is essential for reproducible science and clinical translation. This requires careful attention to calibration, standardization of protocols, and development of reference materials (Aaron et al., 2018). Community-driven initiatives for quality assessment and reproducibility in light microscopy are establishing best practices and guidelines.

7.3 Accessibility and democratization

Reducing the cost and complexity of advanced optical technologies will broaden their impact. Open-source hardware designs, smartphone-based imaging systems, and cloud-based analysis platforms are making sophisticated capabilities accessible to researchers in resource-limited settings (Diederich et al., 2019). This democratization of technology has the potential to accelerate discovery and enable global participation in biological research.

7.4 Ethical considerations and responsible innovation

As AI becomes increasingly integrated into biological research and clinical diagnostics, attention to ethical considerations is paramount. Issues of data privacy, algorithmic bias, transparency, and accountability must be addressed proactively (Topol, 2019). The development of interpretable models and rigorous validation frameworks will be essential for responsible deployment of AI-driven optical technologies in healthcare.

8 Conclusion and outlook

The convergence of advanced photonics, nanotechnology, and artificial intelligence is ushering in a new era of biological characterization. The five domains discussed in this Perspective—bioimaging, biosensors, optical tweezers, particle tracking, and AI-driven analysis—represent complementary approaches to understanding life at multiple scales. While each field has achieved remarkable progress independently, we believe the greatest opportunities lie at their intersections.

Table 1 summarizes key characteristics, recent advances, and future directions for each domain, highlighting common themes of multimodality, quantification, and intelligent automation. As we look toward the next decade, several grand challenges emerge: achieving molecular-resolution imaging in living organisms over extended timescales; developing multiplexed, wearable biosensors for continuous health monitoring; creating autonomous microscopy systems that design and execute experiments; and building interpretable AI models that not only analyze data but generate testable biological hypotheses.

TABLE 1. Summary of key domains in optical biological characterization.

Realizing these ambitions will require sustained interdisciplinary collaboration among physicists, engineers, biologists, and computer scientists. It will also demand attention to reproducibility, standardization, and accessibility to ensure that advanced optical technologies benefit the broadest possible community. The contributions to this Research Topic represent important steps toward these goals, and we hope this Perspective stimulates further dialogue and innovation at the frontiers of optical biological characterization.

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Footnotes

Edited by: Wenfeng Xia, King’s College London, United Kingdom

Reviewed by: Giuseppe Sancataldo, University of Palermo, Italy

Feng He, King’s College London, United Kingdom