The research, led by Jitendra Nath Acharyya and Sandro Heuke together with Gina Benni, Anne Sentenac and Hervé Rigneault, exploits coherent Stokes Raman scattering, a four-wave mixing process in which pump and Stokes laser fields combine to produce light at a longer, red-shifted wavelength that carries the vibrational signature of the molecules in focus. The key insight is that nonlinear interactions obey a more flexible form of momentum conservation than ordinary linear scattering. In linear optics, redirecting light around a corner requires structure, such as a grating or a refractive-index discontinuity, to supply the missing spatial frequency. In nonlinear wave mixing, the wavevectors of the driving fields themselves combine to produce the output wavevector, so the medium never needs to provide the momentum. The result is that coherent photons can, in effect, be turned around inside a perfectly uniform material.

Whether this redirection is efficient depends critically on the generated wavelength. When the output is longer than the driving wavelengths, as is the case for Stokes Raman signals, backward phase matching becomes genuinely efficient under tight, high-numerical-aperture focusing. The team’s momentum-space simulations show that under high-NA focusing each beam is best described as a broad angular spectrum of plane-wave components, and the χ(3) nonlinear process can combine many of these components in ways that steer the emission into the backward hemisphere. The researchers identified three representative wavevector geometries, which they describe as counterpropagating, copropagating and orthogonal configurations of the interacting Stokes components, each with distinct trade-offs between the steepness of the angles required and the strength of the resulting signal.

The simulations, performed under a deliberately conservative constraint that equalizes the volume-integrated two-photon excitation load across different pupil geometries, pinpointed an especially favourable combination: an azimuthally polarized Stokes beam confined to the outer annulus of the objective pupil, paired with a circularly polarized pump filling the central region. Under this geometry, the backward-emitted signal reaches roughly a quarter of the maximum forward-emitted coherent Stokes power, and fully azimuthal excitation can push the total backward power towards 35 percent, albeit at the cost of an annular refocused mode that is less convenient for imaging. The circular-pump, azimuthal-Stokes combination was therefore selected as the best compromise between backward power and a compact, single-lobed refocused spot.

On the experimental side, the team illuminated samples with a 1,030-nanometre Stokes beam shaped into an azimuthal ring and a pump beam near 790 nanometres, reducing the effective repetition rate to 0.71 megahertz with a pulse-picking scheme to raise the energy per pulse without increasing average power. Detection relied on an InGaAs avalanche photodiode whose effective dark noise was cut by roughly an order of magnitude through boxcar gating. Calibrated against detector specifications, the researchers estimate a directly backscattered coherent Stokes photon flux of approximately seven billion photons per second emitted from the focal volume in sunflower oil, and switching from purely linear excitation to the azimuthal Stokes geometry boosted the signal by about half, matching the polarization dependence predicted by simulation.

A crucial control experiment addressed the obvious sceptical question: could the detected signal simply be forward-generated light that bounced off some surface back into the objective? When the team placed a mirror directly above the sample, the detected photon flux did not measurably increase, supporting a genuinely intrinsic backward origin. Residual forward-generated signal is further suppressed by the small effective collection area of the avalanche detector, roughly three Airy units, which behaves like a confocal pinhole. This stands in sharp contrast to conventional epi-detected coherent anti-Stokes Raman scattering, or CARS, where backward signals in realistic thick samples arise mainly from high spatial frequencies at refractive-index interfaces or from forward-generated light that is multiply scattered on its return path. Such back-reflected CARS cannot be refocused onto a detector plane, which precludes efficient fibre coupling, spectral analysis and accurate characterization of the imaging point spread function.

Because the backward Stokes signal is generated directly at the focus and remains coherent and refocusable, it supports true confocal, same-side collection. The simulations show that the backward field refocuses through the objective and tube lens into a compact, near-Gaussian spot, opening the door to fibre-based and spectrometer-based implementations. The imaging performance is respectable: edge measurements at polymethyl methacrylate interfaces in oil yielded an effective lateral resolution of about 330 nanometres and an axial resolution of roughly 0.92 micrometres, values the authors note should be read as edge-derived effective imaging resolutions that include generation, backward propagation, collection and confocal detection rather than as a direct measure of the nonlinear source alone.

The biological demonstrations are where the approach reveals its practical appeal. Imaging avocado flesh at the lipid resonance of 2,850 wavenumbers, the backward Stokes channel remained robust despite the refractive-index mismatch between lipid droplets and surrounding tissue, and it showed none of the fluorescence background that contaminated the epi-CARS channel even behind a narrowband filter, since detection at around 1,470 nanometres sits well clear of tissue autofluorescence. In human fatty liver tissue, a clinically significant specimen given that hepatic lipid accumulation marks an early and potentially reversible stage of metabolic liver disease, lipid droplets and surrounding tissue were clearly distinguished, with contrast differences relative to epi-CARS consistent with the distinct spatial transfer functions of the two signals. In sheep brain tissue, the method resolved bright ellipsoidal myelin sheaths, the lipid-rich insulating structures whose damage underlies disorders such as multiple sclerosis and Guillain–Barré syndrome, a capability the authors highlight as valuable for demyelination research.

The honest accounting is that backward Stokes emission still delivers fewer photons than forward CARS. The simulations attribute an effective wavelength-scaling exponent of about 2.8 to the extended, micrometre-scale coherent source, yielding a forward CARS-to-Stokes photon-flux ratio of roughly five in the carbon-hydrogen stretch region, and the conservative excitation constraint brings the overall reduction in backward flux to about a factor of twenty relative to forward CARS. Yet detector physics partially rescues the balance: photomultiplier tubes used for visible CARS reach quantum efficiencies near 20 percent, while InGaAs avalanche photodiodes at 1,500 nanometres routinely exceed 80 percent, so the experimentally detected flux differs by only about a factor of five. Measured signal-to-noise ratios of 49 for backward Stokes against 171 for forward CARS at one microsecond integration suggest the current implementation remains detector-noise limited, and the authors point to faster gating, heterodyne detection and nonlinear frequency conversion of the signal into the visible as realistic routes to improvement, alongside objective designs better matched to long wavelengths and steep angles.

The team is careful to position the technique not as a replacement for forward-detected CARS or stimulated Raman scattering when transmission access is available, but as a new contrast mechanism for the many situations where it is not: samples on opaque substrates, thick specimens, tissue biopsies and fibre- or spectrometer-based platforms, where it also removes the need for a separate transmission-side descanned detection path. By turning a nominally dark-field configuration into one that yields bright, efficiently collectable chemical contrast from structurally featureless media, the work converts a fundamental constraint of linear optics into an engineering opportunity, and suggests that same-side, label-free Raman imaging may soon be considerably easier to build than the direction of scattered light once implied.

Subject of Research: Intrinsic backward emission of coherent Stokes Raman photons from homogeneous media for epi-detected coherent Raman microscopy

Article Title: Intrinsic backward emission of coherent Raman photons from homogeneous media

Article References: Acharyya, J. N., Benni, G., Sentenac, A., Rigneault, H., & Heuke, S. (2026). Intrinsic backward emission of coherent Raman photons from homogeneous media. Nature Photonics. https://doi.org/10.1038/s41566-026-01995-z

Image Credits: AI Generated

DOI: 10.1038/s41566-026-01995-z

Keywords: coherent Raman scattering, nonlinear optics, four-wave mixing, Raman microscopy, backward emission, phase matching, biophotonics, CARS, label-free imaging, myelin imaging, InGaAs detectors, epi-detection

For most of the history of optics, designing a lens, a waveguide, or a laser cavity meant relying on analytical formulas and iterative refinement. That approach worked well enough for linear optical components, where light behaves predictably and design spaces remain manageable. But modern photonics has moved far beyond simple lenses and mirrors. Today’s researchers build metasurfaces that manipulate light at the nanoscale, nonlinear metamaterials whose properties change with intensity, and integrated photonic circuits that route signals across chips with extraordinary precision. Each of these platforms involves enormous design spaces and highly nonlinear interactions between light and complex materials, making purely analytical design methodologies increasingly impractical.

The editorial argues that machine learning offers a fundamentally new engineering strategy for this new era. Data-driven methods can explore vast parameter landscapes that would overwhelm human designers, identifying optimal configurations for advanced optical devices. Yet the authors also highlight a crucial limitation: purely data-driven approaches often struggle to capture the underlying physical principles that govern complex optical phenomena. A neural network trained only on examples may interpolate well within its training distribution but fail catastrophically when asked to extrapolate beyond it, and it may offer little insight into why a particular design works.

This is where physics-informed and physics-inspired machine learning frameworks come in. By integrating physical constraints and prior knowledge directly into the learning process, these hybrid approaches yield models that are more accurate, more robust, more interpretable, and more computationally efficient than their purely data-driven counterparts. The editorial emphasizes that such methods are not merely accelerating device design; they are also improving our fundamental understanding of light propagation and nonlinear optical dynamics, and enabling the discovery of novel materials with targeted functionalities.

One of the collected studies, by Yang and colleagues, demonstrates the power of data-driven frameworks for capturing fundamental wave dynamics. The researchers applied Sparse Identification of Nonlinear Dynamics, known as SINDy, to numerically capture the evolution of moment quantities associated with the nonlinear Schrödinger equation, which governs a wide range of nonlinear wave phenomena. By combining sparse regression with optimization techniques, the method effectively reproduces complex nonlinear dynamics, rediscovers known analytical closures, and provides reduced-order descriptions even for systems lacking exact analytical formulations. This kind of capability is essential for developing all-optical computing and signal-processing technologies that rely on understanding nonlinear wave behavior.

Physics-inspired neural architectures also play an increasingly important role in inverse problems arising in optical sensing and spectroscopy. Witteveen and colleagues tackled the challenge of retrieving optical properties from diffuse scattering media, a computationally demanding task when based solely on traditional diffusion theory. Their physics-inspired neural network extracts physiological parameters from diffuse reflectance spectroscopy and hyperspectral imaging data. Trained on datasets generated by diffusion theory, the model achieves accurate retrieval of quantities such as blood volume fraction and scattering amplitudes while dramatically reducing computational cost relative to conventional inversion methods. The approach holds particular promise for medical and biomedical imaging, where rapid and accurate parameter extraction can directly impact diagnostic workflows.

Machine learning is likewise proving valuable in parameter optimization and experimental design. Aledda and colleagues investigated several machine learning strategies for wavelength selection in tunable mid-infrared laser systems, where protein and peptide characterization requires identifying optimal wavelength ranges across broad spectral domains. The researchers compared sparse modeling, filter-based approaches, and compression algorithms, and introduced a new method called w-CovSel. Their work demonstrates how data-driven optimization can improve the performance and efficiency of laser-based analytical instruments tailored to specific biochemical applications such as peptide analysis, showing that intelligent wavelength selection can make sophisticated spectroscopy more accessible and more effective.

The editorial also highlights a growing shift toward photonics for machine learning, where optical hardware itself is used to accelerate computational tasks. While much current research applies machine learning to photonic design, an emerging direction seeks to embed machine learning algorithms directly within optical systems to achieve ultrafast and energy-efficient information processing. Mahmoud and colleagues contribute to this direction by proposing an Optical Particle Swarm Optimization algorithm that employs an all-optical computational update mechanism. By exploiting the natural properties of coherent optical dynamics, including nonlinear light-matter interactions and complex-domain computation, their approach demonstrates that optical computing can significantly enhance search-space exploration and improve dyslexia detection performance using eye-tracking data.

Taken together, the studies indicate that photonics is entering a new era characterized by the synergistic integration of physics-informed artificial intelligence, nonlinear optical science, and optical computing hardware. The field is evolving beyond computationally assisted optimization toward increasingly autonomous and intelligent photonic design paradigms, where machine learning algorithms operate under explicit physical constraints and interact directly with optical hardware platforms. This represents a departure from the conventional workflow in which a human designer formulates a problem, a computer explores it, and a physicist interprets the results; instead, the entire loop is becoming more tightly integrated and more intelligent.

Looking forward, substantial challenges remain. Progress will require access to large, high-quality datasets, while experimental data acquisition, particularly in sensitive fields such as biophotonics, often remains costly and difficult. Transitioning from software-based machine learning models to scalable physical optical neural networks presents major challenges associated with scalability, robustness, and signal-to-noise limitations. Nevertheless, the contributions collected in this Research Topic provide a strong foundation for addressing these obstacles and point toward a future in which machine learning and photonics become deeply integrated components of a unified technological framework for scientific discovery and advanced device engineering.

Subject of Research: Machine-learning-assisted photonic design spanning fundamental physics and advanced devices

Article Title: Editorial: Machine-learning-assisted photonic design: from fundamental physics to advanced devices

Article References: Barmparis, G. D., Tsironis, G. P., & Psaltis, D. (2026). Editorial: Machine-learning-assisted photonic design: from fundamental physics to advanced devices. Frontiers in Photonics, 7, Article 1884316. https://doi.org/10.3389/fphot.2026.1884316

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1884316

Keywords: photonics, machine learning, physics-informed neural networks, metasurfaces, nonlinear optics, optical computing, inverse design, biophotonics, Editorial, Machine-learning-assisted, photonic, design

Most SHG research to date has relied on forward-detected signals acquired from ultra-thin tissue sections. Because the forward signal is coherent, the resulting images provide a clear and detailed representation of collagen in the sample under study. However, the requirement for the sensor to sit behind the sample generally limits forward-SHG to ex vivo experiments on thin sections. Backward-detected SHG, by contrast, uses the portion of the signal scattered back from the tissue and therefore holds real potential for translation to in vivo imaging contexts. The problem is that the origin of the backward signal is not fully understood. It represents complex optical interactions, and interpretation of backward images remains challenging, which has led to repeated misinterpretations of the collagen structure of biological tissues.

A team of researchers from University College London and the University of Arizona has now addressed this puzzle in a study published in Frontiers in Photonics. Using a custom optical chamber that allows bidirectional SHG imaging of the cornea under controlled pressure, they assessed the spatial relationship between features in backward images and the underlying structural features of collagen visible in paired forward images. Their central finding is striking: collagen waviness, known as crimp, is a key structural feature responsible for periodic bands of fluctuating signal intensity in backward SHG images. Structures that numerous previous studies had identified as collagen bundles, and even used to compute predominant orientations, are in fact oriented perpendicularly to the actual lamellar direction and originate from signal contributions from different regions of wavy lamellae.

The cornea was selected as the imaging target for several reasons. It is a transparent, collagenous tissue within which the collagen has a specific depth-dependent, highly ordered arrangement that has been characterized previously using histology, scanning electron microscopy, transmission electron microscopy, and X-ray diffraction techniques. The transparent nature of the tissue enables full-depth imaging in both directions of intact tissue, ensuring the structure closely represents the in vivo state, while the complex collagen arrangement and access to previously acquired structural information facilitate convenient validation and a means to assess the origin of imaging artefacts. Ten fresh porcine eyes were obtained from a local abattoir, and corneas were imaged within seventy-two hours postmortem in an intact, non-processed state to prevent structural modifications induced by sectioning or fixation agents.

Imaging was performed with a Zeiss LSM980 on an upright Examiner.Z1 microscope, with laser excitation provided by a Spectra-Physics Insight DS+ laser tuned to 820 nanometers, linearly polarized in the x-axis of the image plane. Initial imaging was performed with the cornea remaining within the eye globe at normal physiological intraocular pressure, using reflection-mode SHG at two magnifications. The cornea was then dissected from the globe with a three-millimeter rim of sclera and re-imaged to acquire simultaneous forward- and backward-detected SHG signals for direct comparison. The custom-designed chamber allowed the cornea to be pressurized with phosphate buffered saline while a manometer provided real-time pressure monitoring, enabling the team to investigate the effects of pressure variation on collagen bundles and the subsequent effect on the generated images. To the best of the authors’ knowledge, this is the first experimental setup described that enables forward imaging of the cornea in its unaltered, pressurized state, providing layer-by-layer detail and the ability to capture structural changes under different loads.

The results revealed a consistent spatial relationship between the two signals. Backward-detected images of the posterior corneal stroma showed highly organized, vertically aligned bands of high-intensity signal with periodic spacing, structures previously interpreted as collagen bundles, while areas of low signal were believed to be regions absent of collagen, corresponding to voids or interlamellar spacing. Higher magnification resolved speckled, elongated patterns oriented perpendicularly, rather than aligned, to the structures previously identified as bundles. When periodic regions of the backward images were overlaid with the corresponding regions of the forward images, the aligned bands in the backward image corresponded to regions of the wavy bundles observed in the forward images. The dark regions did not correspond to voids but to absent or weak backward signal due to the specific collagen position along the wavy lamellae. The forward-detected bundles consistently ran perpendicular to the backward-detected high-signal bands throughout the entire imaging plane.

To demonstrate this mechanism dynamically, the cornea was mounted in the transparent chamber and imaged throughout its depth, first at low pressure of approximately five millimeters of mercury and then at supraphysiological levels of approximately sixty millimeters of mercury, pressures relevant to disease states such as glaucoma and hypotony. It had been theorized, but not yet proven, that collagen waviness is a structural mechanism governing the corneal response to increasing intraocular pressure. At low pressure, the collagen bundles appeared wavy and crimped, with periodic features clearly evident in the backward images. When the pressure was raised, the forward image revealed straightened bundles, while the backward image showed a more uniform forward-to-backward signal and loss of the periodic bands previously observed. In some imaging planes where the collagen did not uncrimp completely, periodic patterns remained in the backward images in regions directly corresponding to the crimped areas in the forward images. A complete layer-by-layer recording across a thickness of 985 micrometers showed the collagen becoming noticeably uncrimped after the pressure increase, with clear differences in the backward signal between crimped and uncrimped states.

The findings also clarify the theoretical frameworks proposed to explain SHG directionality. Theoretical models based on fibril size suggest that fibrils with diameters on the order of the SHG wavelength generate predominantly forward-directed signals, whereas smaller fibrils produce nearly equal forward and backward signals. However, this theory cannot account for the difficulty in correlating backward and forward signals in the cornea, which has a consistent fibril diameter of approximately thirty-one nanometers in pigs across the depth. A more comprehensive quasi-phase matching model treats the fibril no longer as an isolated SHG generator but considers the collective packing of fibrils within a domain, which influences phase-matching conditions and the continuity of coherent second harmonic generation. The team extended this framework by introducing a tilt parameter, defined as the out-of-plane angle between the collagen bundles and the axial propagation direction. This inclination, inherent to the crimp pattern, induces a change in the effective domain size, affecting phase-matching conditions and thus the forward-to-backward signal distribution, creating a periodic alternation between different forward-to-backward ratios that matches the sides of the crimped patterns in the forward images.

The implications extend well beyond correcting image interpretation. A previous study using backward SHG imaging to evaluate corneal response to intraocular pressure changes had observed a narrowing of dark low-signal bands and attributed it to changes in interlamellar spacing. The new study clarifies that changes to these periodic intensity levels occur due to straightening of the crimped lamellae under increased pressures, meaning a key biomechanical mechanism governing the corneal response to pressure was previously overlooked. Because these structural changes can occur within very short timescales, during each heartbeat when the cornea experiences pulsatile intraocular pressure variations, this dynamic behavior must be considered when analyzing backward signals, particularly if an in vivo diagnostic device or AI-based model is to be developed to distinguish between normal and diseased states. The authors note that a key limitation of the work is the absence of quantitative analysis of the correlation between the two signals, since many confounding variables at both the fibrillar and bundle level are expected to affect both signals, but this remains a future aim of the continued work.

With respect to clinical translation, the potential is vast. Diseases such as keratoconus and ectasia are associated with structural and biomechanical changes but are currently not fully understood and are diagnosed at a late stage due to the lack of diagnostic tools capable of clinical evaluation of structure and biomechanics. In vivo SHG imaging of the cornea has already been demonstrated to be achievable, and with continued advancements such as those presented in this study, widespread clinical translation may become possible. In the shorter term, SHG can be employed ex vivo to understand the structural and biomechanical changes that occur during common ocular surgeries, including refractive procedures, to optimize their application and safety profile. Although further work remains in understanding the correlation between forward and backward signals in full, this research represents an important step forward in the interpretation of backward SHG and the association between specific structural features and their representation in the image.

Subject of Research: Biophotonics

Article Title: Correlation between forward and backward generated SHG images of biological tissue relating to collagen structure and biomechanics

Article References: Boutkhil, M., Moulding, D., Sawyer, T. W., & Wilson, A. (2026). Correlation between forward and backward generated SHG images of biological tissue relating to collagen structure and biomechanics. Frontiers in Photonics, 7, Article 1771907. https://doi.org/10.3389/fphot.2026.1771907

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1771907

Keywords: SHG microscopy, collagen, cornea, biomechanics, collagen crimp, nonlinear optics, biophotonics, Correlation, between, forward, backward, generated

The mystery centers on a single molecular substitution. In RNA, one of the four main building blocks is uracil, a pyrimidine base that pairs with adenine. When life transitioned to DNA, uracil was replaced by thymine, a methylated version of the same molecule. Chemically, the two bases are nearly identical, differing only by a single methyl group, so the substitution has long appeared redundant. What makes the change genuinely puzzling is that thymine is more susceptible to ultraviolet damage than uracil, at least by conventional measures. On the young Earth, before photosynthetic organisms built up a protective ozone layer, the surface was bathed in intense solar UV radiation. Choosing the more fragile base would seem to be an evolutionary blunder, and generations of scientists have asked why nature made such an unconventional choice.

To resolve the paradox, the Texas A&M team subjected thymine and uracil to controlled ultraviolet conditions designed to mimic the radiation environment of primordial Earth. Rather than relying on standard biochemical assays, they turned to biophotonics, an interdisciplinary field that applies light-based spectroscopic methods to biological molecules. Using absorption, fluorescence and Raman spectroscopy, the researchers tracked in real time how UV energy was absorbed, redistributed and dissipated within each base. This approach allowed them to observe not just whether the molecules were damaged, but precisely what form the damage took and how the excited molecules behaved in the instants after absorbing a photon.

The measurements revealed a striking difference between the two bases. Thymine, the team found, tends to channel UV damage into a more reversible form. Instead of allowing absorbed ultraviolet energy to break chemical bonds in ways that permanently corrupt the molecule, thymine funnels the excitation into pathways from which the molecule can recover. In other words, the base absorbs the harmful energy and then releases it in a way that leaves the genetic information intact. This behavior suggests that early genetic molecules may have survived intense sunlight not by avoiding damage altogether, but by controlling it and making it easier to self-repair.

Keyvan Khosh Abady, one of the research assistants on the project, described the finding in vivid terms. According to the team, thymine may have acted as a molecular sunscreen, absorbing harmful UV energy and keeping the damage confined to the genetic bases themselves rather than allowing it to spread to more vulnerable parts of the molecule. This interpretation reframes the UV vulnerability of thymine as a feature rather than a flaw. A base that soaks up radiation and converts it into a benign, recoverable form could protect the surrounding sugar-phosphate backbone and the delicate machinery of the early cell, effectively sacrificing itself in a controlled manner to shield the rest of the genetic system.

The implications of this microscopic discovery extend far beyond the question of how life began. Understanding how nucleobases dissipate ultraviolet energy informs a wide range of modern research areas, from radiation biology to astrobiology, where scientists assess whether genetic molecules could survive on planets with thin or absent ozone layers. The Texas A&M work provides quantitative spectral data on thymine and uracil under UV exposure, giving researchers a rigorous physical basis for modeling how the first genomes coped with radiation and for evaluating whether similar chemistry could operate on other worlds.

The team’s measurements also establish a new reference framework with immediate practical applications. The distinct spectral signatures they recorded for thymine and uracil could support faster and more sensitive pathogen detection, since the bases of microbial DNA and RNA produce characteristic optical responses that can be exploited for identification. The same signatures could be used to monitor whether UV disinfection systems have effectively inactivated microorganisms, a question of particular importance in water-treatment facilities where ultraviolet lamps are relied upon to render pathogens harmless. Khosh Abady noted that the framework could eventually underpin both detection and verification technologies, allowing operators to confirm in real time that disinfection doses have done their job.

For Rentzepis, whose career has spanned pioneering work in ultrafast spectroscopy and picosecond photography, the study represents a natural extension of light-based engineering methods into the origins of life. By treating the nucleobases as optical systems whose response to ultraviolet light can be measured and characterized with precision, the team has demonstrated how tools developed for photonics and electrical engineering can answer questions that conventional molecular biology approaches have struggled to settle. The interdisciplinary strategy highlights a growing trend in which engineers contribute decisive insights to fundamental problems in chemistry and evolutionary science.

The Texas A&M group is not stopping with the UV-shielding question. In future work, the researchers plan to move to an earlier stage of the origin-of-life process by investigating the forces that brought thymine, uracil and other components of early genetic systems together in the first place. A central goal is to determine whether life’s chemical building blocks formed and assembled entirely on Earth or whether some were delivered to the planet from elsewhere by meteorites and later incorporated into early genetic systems. Khosh Abady expressed gratitude to Texas A&M University and the research sponsors for supporting the work and enabling the team to explore such fundamental questions about life’s origins.

If the early findings hold up under continued scrutiny, the thymine paradox may come to be regarded as a textbook example of how evolution optimizes for survival under extreme conditions rather than for chemical elegance. A single methyl group, once dismissed as an inexplicable substitution, now appears to have been a deliberate molecular strategy that helped the first genomes weather the harshest sunlight our planet has ever known. The research, published in Proceedings of the National Academy of Sciences under DOI 10.1073/pnas.2615278123, offers a vivid reminder that the story of life on Earth was written in the language of light as much as in the language of chemistry, and that engineers armed with lasers and spectrometers are now fluent readers of that ancient text.

Subject of Research: Biophotonics study of why thymine replaced uracil in DNA under primordial UV radiation

Article Title: Electrical engineering research unlocks DNA mystery

Article References: Electrical engineering research unlocks DNA mystery. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: Texas A&M University, thymine, uracil, DNA, biophotonics, UV radiation, molecular evolution, PNAS, origins of life, Rentzepis, Electrical, engineering

At the conceptual heart of the collection lies a review by Alvarez and colleagues surveying biological processes where quantum phenomena may play a functional role. The authors synthesize representative cases including photon capture in vision, the highly efficient energy transfer that underpins photosynthesis, and vertebrate magnetoreception with particular emphasis on the radical pair mechanism. By clarifying the types of biological systems in which quantum dynamics may become functionally relevant, the review establishes a conceptual foundation for the diverse studies that follow, helping readers distinguish between processes where quantum effects are plausibly exploited by evolution and those where they remain incidental.

Building on that foundation, Parisio and coauthors contribute a pedagogical introduction to the quantum-mechanical concepts most relevant to biology, using the avian compass as a guiding example. The article presents the radical pair mechanism didactically, showing how spin dynamics and quantum coherence can influence biochemical reactions in living organisms. The avian magnetoreception case is especially compelling: certain birds appear to navigate using Earth’s magnetic field through light-sensitive cryptochrome proteins whose radical pair intermediates may retain spin coherence long enough for magnetic-field effects to modulate reaction outcomes. By bridging fundamental quantum theory with this biologically motivated model system, the contribution situates magnetoreception within the broader landscape of quantum effects in living matter.

The origin of life emerges as one of the most important themes in the collection. Quilici and colleagues offer a mini-review on how computational quantum methods can inform prebiotic chemistry, addressing scenarios ranging from thermal reactions on early Earth to photochemical processes under astrophysical conditions. Computational simulations of electronic structure and reaction pathways allow researchers to test how life could emerge from inorganic chemistry without needing to recreate every experimental condition in the laboratory. Quantum chemical calculations of reaction energetics, transition states, and photochemical excitation provide a mechanistic window onto the molecular-scale events that may have bridged nonliving and living chemistry billions of years ago.

Neuroscience enters the quantum biology conversation through a theoretical study of ion channels. Neuronal ion channels are complex, coupled, many-body quantum systems in which electronic coherence has recently been speculated to persist for relatively long timescales despite the decohering effects of their environments, possibly assisted by noise. Da Silva Ramos and colleagues model an ion channel as a linear quantum chain using a tight-binding Hamiltonian with Lindblad source-drain dynamics, examining how correlated on-site energies act as a tunable switch between trapping and conduction. The work suggests that correlated disorder, rather than being purely detrimental, could modulate transport efficiency in biological ion channels. If quantum coherence in ion transport proves functionally relevant, the implications for neuroscience and medicine could be profound, opening new avenues for understanding neuronal signaling and designing interventions at the molecular scale.

Light-matter interactions in human tissue feature prominently in two contributions on retinal and skin photophysics. Brash and Gaillard examine the molecular mechanisms of chemiexcitation and review emerging evidence that melanin in the human retina may undergo chemiexcitation as part of a protective response against age-related macular degeneration. Chemiexcitation refers to the generation of electronically excited states through chemical reactions rather than photon absorption, and the review highlights how such excited states can influence biological function. Herrera and colleagues, meanwhile, investigate how red-light irradiation influences cellular responses, with particular attention to nitric oxide homeostasis and its implications for phototherapeutic interventions. Their work examines how photon energy interacts with biological chromophores to modulate cellular signaling and metabolic pathways, contributing to a growing body of research on photobiomodulation in living systems.

At the subtle end of the biophysical spectrum, Gallep analyzes patterns of ultraweak photon emission in seedlings that appear to follow periodicities resembling local gravimetric tidal cycles across several geographic locations. Auto-luminescence in seedlings, if genuinely coupled to gravimetric tides, would situate biophoton dynamics within a chronobiological framework and suggest that environmental physical fluctuations may leave measurable signatures on biological photon emission rhythms. The study exemplifies the field’s willingness to explore subtle physical influences on biological systems, a hallmark of quantum biology that demands rigorous statistical treatment across multiple sites and conditions.

Systems-level and translational perspectives round out the collection. Casotti and colleagues present an integrative view of cancer research combining systems biology, evolutionary thinking, and multidisciplinary synergy, emphasizing the reduction of disciplinary silos, open data sharing, and patient-centered feedback in therapeutic development. Costa and colleagues report an electrochemical aptasensor for detecting dengue NS1 protein, built on a self-assembled monolayer of DNA aptamers and thiol molecules and characterized through electrochemical capacitance spectroscopy, with optimization via aptamer-to-thiol ratio tuning. The device illustrates how molecular recognition integrated with nanoscale electrochemical sensing can translate bio-inspired physical principles into practical diagnostics. Davinack outlines how quantum computing and quantum machine learning, through algorithms for classification, clustering, and optimization, could support population-genetic inference and molecular ecology, while Martins and colleagues review the biological mechanisms and clinical evidence for photobiomodulation therapy in neuropathic pain, discussing multimodal strategies, personalized dosimetry, and wearable and transcranial technologies.

Together, the contributions reflect the diverse theoretical, computational, experimental, and translational frameworks now converging on quantum phenomena in biological systems. The editorial expresses hope that the Research Topic provides a useful overview of current research activity and supports continued dialogue and collaboration across disciplines. As quantum technologies mature and biological instrumentation grows ever more sensitive, the boundary between quantum physics and biology is likely to blur further, promising discoveries that range from the molecular origins of life to next-generation medical therapies.

Subject of Research: Quantum biology

Article Title: Editorial: Quantum biology

Article References: Pires de Sousa, M. V. (2026). Editorial: Quantum biology. Frontiers in Photonics, 7, Article 1826400. https://doi.org/10.3389/fphot.2026.1826400

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1826400

Keywords: quantum biology, biophotonics, magnetoreception, radical pair mechanism, prebiotic chemistry, ion channels, photobiomodulation, biosensors, quantum computing, retinal photochemistry, Editorial, Quantum

The physical principle underlying diffusive optics is both a strength and a challenge. When near-infrared photons enter biological tissue, they undergo multiple scattering events off structures such as cell membranes, organelles, and collagen fibers. Rather than traveling in straight lines, the photons propagate diffusively, following random-walk trajectories through the medium. This behavior enables quantitative measurements of tissue absorption and scattering coefficients, which are related to physiological composition, structure, and function. Absorption features reveal chromophore concentrations such as oxygenated hemoglobin, deoxygenated hemoglobin, water, and lipids, while scattering reflects structural organization. Over the past decades, techniques including diffuse optical tomography, spatial frequency-domain imaging, time-resolved spectroscopy, and diffuse correlation spectroscopy have advanced rapidly, supporting applications in brain monitoring, breast imaging, wound assessment, and metabolic studies.

Despite these advances, important challenges remain. Light scattering complicates image reconstruction and leads to ill-posed inverse problems, meaning that small errors in measurement can produce large errors in reconstructed images. Spatial resolution and penetration depth also remain limited in highly scattering tissues. The Research Topic was organized to highlight methodological developments that improve quantitative accuracy, depth sensitivity, and clinical applicability, with contributions in structured illumination, time-resolved and frequency-domain techniques, and optical spectroscopy methods.

In their contribution, Livecchi and colleagues demonstrate the use of LED-based spatial frequency-domain imaging in the shortwave infrared spectral region for non-invasive assessment of tissue hydration. Spatial frequency-domain imaging is a powerful yet sometimes underrepresented technique for directly quantifying tissue optical properties. By projecting sinusoidal illumination patterns at multiple spatial frequencies and phase shifts, the method separates absorption and reduced scattering coefficients that are otherwise convolved in conventional diffuse reflectance measurements. The authors employ the technique in the shortwave infrared range, where absorption features of water and lipids are more pronounced and increasingly accessible due to recent advances in camera technology. Through experiments using tissue phantoms, ex vivo porcine skin undergoing desiccation, and in vivo human measurements during exercise, the study demonstrates that reduced scattering is highly sensitive to hydration changes. Such measurements may enable non-invasive monitoring of physiological processes including wound healing, edema formation, and skin hydration dynamics associated with exercise or thermoregulation.

In another contribution, Maffeis and colleagues investigate non-invasive lung monitoring using time-domain diffuse optical spectroscopy. This technique launches ultrashort light pulses into tissue and records photon arrival times after propagation. These time-of-flight measurements provide depth-sensitive information about tissue absorption and scattering properties, a key strength that enables probing of deeper structures than many other diffuse optical methods. Using Monte Carlo simulations and in vivo measurements, the authors study photon propagation through the chest wall to identify optimal probing conditions, an essential step toward a future goal of determining lung composition in terms of tissue constituents. Their results show that wavelength selection and measurement geometry influence sensitivity to lung expansion during breathing. Current lung imaging methods often rely on ionizing radiation or invasive procedures, so optical approaches may provide safer and more accessible monitoring. The work highlights both the promise and the challenges of applying diffuse optics to deep organs and may support future non-invasive monitoring of lung function and pulmonary physiology.

In another study, Parham and colleagues investigate diffuse reflectance spectroscopy for estimating retinal tissue oxygen saturation. Diffuse reflectance spectroscopy measures tissue absorption spectra related to hemoglobin oxygenation, but scattering variations, strong melanin absorption in the retinal pigment epithelium, and tissue geometry complicate interpretation of these spectra. To address this challenge, the authors apply principal component analysis to extract relevant spectral features. Principal component analysis identifies patterns in complex datasets and isolates spectral components associated with oxygenation. The method is evaluated using simulated datasets and retinal tissue phantoms, and results show that the approach can estimate oxygen saturation despite overlapping optical effects. Improved retinal oxygen measurements could support earlier detection and monitoring of diseases affecting ocular and systemic health, highlighting how data-driven analysis can improve quantitative parameter extraction in diffuse optical measurements.

In a further study, Herzog and Sick investigate fluorescence imaging of the anterior segment of the eye. The technique uses ultraviolet excitation to induce fluorescence from endogenous biomolecules in ocular tissues. The authors implement a two-color imaging system to distinguish spectral signals from different molecular sources, enabling detection of biochemical changes that may not appear in conventional reflectance-based imaging methods. Such molecular sensitivity may improve early detection of infections and other ocular diseases affecting the cornea. Experiments using ex vivo porcine eyes demonstrate visualization of fluorescence signals from tissue structures and bacterial contamination. Although not strictly a diffuse optical technique, the work illustrates how optical spectroscopy and imaging can complement diffusive optical methods, providing molecular contrast that may improve diagnostic capabilities in biomedical optics.

Together, the contributions highlight advances in instrumentation, modeling, and data analysis for diffusive optical measurements. The presented studies demonstrate applications including tissue hydration monitoring, lung assessment, retinal oxygenation measurement, and fluorescence-based ocular diagnostics. Future progress will depend on advances in optical instrumentation, computational modeling, and clinical translation. These developments may enable diffusive optical technologies to become routine tools for medical diagnostics and physiological monitoring, ultimately helping to improve patient outcomes, accessibility, and broader improvements in human health and wellbeing.

Subject of Research: Biophotonics

Article Title: Editorial: Diffusive optics for medical imaging

Article References: Urban, B., & Morgado, A. M. (2026). Editorial: Diffusive optics for medical imaging. Frontiers in Photonics, 7, Article 1842429. https://doi.org/10.3389/fphot.2026.1842429

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1842429

Keywords: diffusive optics, medical imaging, biophotonics, near-infrared spectroscopy, spatial frequency-domain imaging, time-domain diffuse optical spectroscopy, Frontiers in Photonics, Editorial, Diffusive, optics, medical, imaging

In bioimaging, the quest to see beyond the diffraction limit has produced a mature toolkit. Super-resolution microscopy techniques, including stimulated emission depletion, structured illumination microscopy, and single-molecule localization microscopy, now routinely achieve sub-50 nanometer resolution in living cells. Expansion microscopy offers a complementary route by physically enlarging specimens before observation on conventional microscopes, delivering nanoscale performance without exotic optics. Meanwhile, aggregation-induced emission probes have improved photostability for long-term imaging sessions, and the integration of adaptive optics with computational reconstruction has pushed super-resolution methods into thicker tissues and whole organisms. Lattice light-sheet microscopy combined with adaptive optics now enables low-phototoxic volumetric imaging of subcellular dynamics in developing embryos, a capability that was largely theoretical a decade ago.

Phase-based and vibrational imaging have expanded the optical repertoire beyond fluorescence. Quantitative phase imaging enables non-invasive measurements of cellular mass, morphology, and dynamics, with improvements in holographic and interferometric methods supporting millisecond-scale temporal resolution for real-time cell monitoring. Vibrational techniques, including Raman spectroscopy, coherent anti-Stokes Raman scattering, and stimulated Raman scattering, add chemical specificity by probing molecular vibrations, allowing researchers to visualize lipids, proteins, and metabolites without labels. Hyperspectral stimulated Raman scattering further accelerates chemical mapping for diagnostic applications. Light-sheet fluorescence microscopy, which illuminates samples with a thin orthogonal sheet of light, provides rapid and gentle volumetric imaging, while nonlinear approaches such as two- and three-photon excitation and second- and third-harmonic generation enable deep-tissue imaging with intrinsic optical sectioning and reduced photodamage.

Despite this progress, the authors identify phototoxicity as a central limitation for long-term live-cell imaging, particularly in high-intensity super-resolution modalities. Light-sheet methods help mitigate these constraints but introduce their own challenges related to optical aberrations and instrument complexity. The Perspective also stresses that standardized imaging protocols and quantitative metrics are increasingly necessary for reproducibility across laboratories, a concern echoed by community initiatives such as QUAREP-LiMi, which seeks to advance quality assessment and reproducibility in light microscopy. Looking ahead, the authors anticipate multimodal platforms that integrate fluorescence, super-resolution, quantitative phase, Raman, light-sheet, and nonlinear signals, together with brighter fluorophores, improved adaptive optics, novel lasers, and machine-learning-guided acquisition.

Optical biosensors have evolved from laboratory curiosities into clinical diagnostic tools, driven by advances in nanofabrication and surface chemistry. Surface plasmon resonance and localized surface plasmon resonance sensors exploit the sensitivity of plasmonic nanostructures to local refractive index changes, enabling label-free detection of biomolecular binding events. Recent innovations in nanostructured substrates and metamaterials have pushed detection limits toward single-molecule sensitivity. Photonic crystal sensors and whispering gallery mode resonators offer alternative transduction mechanisms with high quality factors and compact footprints, and these platforms have demonstrated femtomolar detection limits for disease biomarkers while becoming increasingly integrated into microfluidic systems for point-of-care diagnostics.

At the opposite end of the cost spectrum, paper-based biosensors represent what the authors call a paradigm shift toward low-cost, disposable diagnostics for resource-limited settings. By integrating colorimetric or fluorescent detection with microfluidic paper devices, these sensors achieve rapid, equipment-free analysis of complex samples, and recent work has demonstrated multiplexed detection of infectious disease markers with sensitivity approaching laboratory-based assays. Fiber-optic biosensors combine optical detection sensitivity with remote sensing capability, and functionalized fiber tips enable minimally invasive in vivo measurements ranging from intracellular pH sensing to real-time metabolite monitoring in tissue. The next generation of biosensors will likely emphasize multiplexing, miniaturization, and integration with digital health platforms, with wearable and implantable sensors for continuous biomarker monitoring seen as a particularly promising frontier. Key challenges include improving selectivity in complex biological matrices, extending sensor lifetime and stability, and developing robust calibration methods.

Optical tweezers, which use focused laser beams to trap and manipulate microscopic objects, have revolutionized single-molecule biophysics by enabling precise force measurements at the piconewton scale. Researchers can now directly observe molecular motors, DNA-protein interactions, and protein folding dynamics. Recent instrumentation advances, including high-speed detection, active feedback control, and multi-trap configurations, have expanded the range of accessible biological phenomena. Holographic optical tweezers enable simultaneous manipulation of multiple particles in three dimensions, facilitating studies of collective cellular behaviors, and integration with fluorescence microscopy provides simultaneous mechanical and biochemical information. Beyond single molecules, optical tweezers have become essential for probing cellular mechanics and the viscoelastic properties of biological fluids: active microrheology using optically trapped probe particles reveals the frequency-dependent mechanical response of cytoplasm, extracellular matrix, and mucus, while measurements of forces during cell division, migration, and adhesion have connected mechanical cues to cellular decision-making and revealed mechanosensitive signaling pathways.

Particle tracking techniques round out the experimental toolkit. Single-particle tracking has become indispensable for studying molecular dynamics in living cells, revealing heterogeneous diffusion behaviors, transient binding events, and spatial organization that ensemble measurements obscure. Recent advances in localization algorithms and high-speed imaging have pushed temporal resolution to microseconds and spatial precision to nanometers, and sophisticated analysis methods now extract quantitative information about diffusion modes, confinement, and molecular interactions from trajectories. Particle image velocimetry and particle tracking velocimetry map velocity fields in biological fluids and tissues, with applications from cardiovascular flow analysis to ciliary beating, and optical coherence tomography-based particle tracking extends these capabilities to optically scattering samples for non-invasive flow measurements in tissues. The authors expect future methods to emphasize label-free detection, increased throughput, machine-learning-assisted analysis, and super-resolution tracking capable of resolving nanoscale dynamics in crowded cellular environments.

Artificial intelligence has transformed the analysis side of the field. Convolutional neural networks now routinely perform cell segmentation, classification, and tracking with accuracy exceeding human experts, and 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. Generative models have enabled virtual staining, predicting fluorescence images from label-free brightfield or phase contrast data. Yet the authors caution that many AI models require large annotated datasets that are expensive to generate and may not generalize across experimental conditions or laboratories, making standardization of training data, validation protocols, and performance metrics essential for clinical translation. They highlight physics-informed machine learning, which incorporates physical constraints and domain knowledge into neural network architectures, as a promising route toward more reliable and interpretable analysis, alongside causal inference frameworks and explainable AI techniques that reveal which image features drive model predictions.

The Perspective closes with a unifying vision. The greatest opportunities, the authors argue, lie at the intersections of the five domains: multimodal platforms that combine complementary optical techniques, the transition from qualitative observation to quantitative measurement through calibration and reference materials, and the democratization of advanced optics through open-source hardware, smartphone-based imaging, and cloud-based analysis. They also flag ethical considerations, noting that data privacy, algorithmic bias, transparency, and accountability must be addressed proactively as AI becomes embedded in biological research and clinical diagnostics. Among the grand challenges for the next decade, they list 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. Realizing these ambitions, the authors conclude, will require sustained interdisciplinary collaboration among physicists, engineers, biologists, and computer scientists, with attention to reproducibility, standardization, and accessibility so that advanced optical technologies benefit the broadest possible community.

Subject of Research: Advances in optical methods for biological sample characterization

Article Title: A perspective of advances in optical methods for biological sample characterization

Article References: Avila, R., Morales-Narváez, E., & Loza-Alvarez, P. (2026). A perspective of advances in optical methods for biological sample characterization. Frontiers in Photonics, 7, Article 1773615. https://doi.org/10.3389/fphot.2026.1773615

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1773615

Keywords: biophotonics, super-resolution microscopy, optical biosensors, optical tweezers, particle tracking, artificial intelligence, quantitative phase imaging, light-sheet microscopy, machine learning, biosensing, quantitative imaging, interdisciplinary research

The technique, called Spectro-Temporal Laser Imaging by Diffractive Excitation, or SLIDE, sidesteps mechanical scanning along the fast axis entirely. It exploits a Fourier domain mode-locked (FDML) laser, a swept-source laser whose narrow-linewidth output sweeps rapidly through its spectrum at megahertz rates. When this sweep is directed onto a diffraction grating, each wavelength in the sweep maps to a different angle, and therefore to a different position on the sample. The result is an inertia-free scan in which the laser’s wavelength sweep effectively paints one line of an image per sweep, at line rates in the megahertz regime. A galvanometric mirror handles only the slow axis, allowing kilohertz frame rates with pixel rates that can reach gigapixels per second.

That extraordinary speed, however, carries a hidden cost. In the original SLIDE configuration, the pixel dwell time is roughly one nanosecond, because each picosecond pulse from the laser addresses exactly one pixel. Most common fluorescent dyes emit over several nanoseconds, meaning the glow from one pixel is still decaying when the laser has already moved on to its neighbors. The consequence is bleed-through: photons from an excited spot contaminate adjacent pixels, blurring the image and corrupting any attempt to extract fluorescence lifetime information, the basis of fluorescence lifetime imaging microscopy (FLIM). For signals that respond essentially instantaneously, such as second harmonic generation (SHG) from collagen or urea crystals, the nanosecond dwell time is no problem at all, but for fluorescence it is fundamentally too short.

The Lübeck team’s solution, published in Frontiers in Photonics, is elegant in its simplicity: rather than forcing the sample to fit the microscope’s timing, they let the microscope’s timing fit the sample. Their approach, interleaved sampling, keeps the laser’s sweep rate constant but splits the illumination of each image line across multiple successive sweeps. For an interleaving factor of two, for example, the pulse repetition rate is halved and the pulse train is shifted in phase by half a pulse interval after every sweep. Two consecutive line scans are then binned together, reconstructing a single line with the same 512-pixel density as the original, but with twice the temporal spacing between excitation pulses.

The key enabling technology is a reprogrammable arbitrary waveform generator (AWG) card that drives the electro-optic modulator (EOM) which carves the FDML output into 35-picosecond pulses. In previous SLIDE systems, a phase-locked loop generated a fixed clock for a constant pulse rate of 820 megahertz, locked to the 1.6 megahertz FDML sweep. By replacing this fixed clock with an AWG pattern, the team gained pulse-on-demand operation: the pulse pattern can be changed on the fly, and the effective pixel dwell time becomes a programmable parameter. The AWG card communicates over PCIe at up to 2.8 gigabytes per second, allowing rapid reprogramming, and the system can update its imaging parameters with a latency of only 5 to 10 milliseconds.

The flexibility cuts both ways. At an interleaving factor of eight, the pixel dwell time stretches to nearly 10 nanoseconds, comfortably longer than typical fluorescence decays, while the pulse rate drops to 102.5 megahertz. Because the two-photon excitation efficiency depends quadratically on peak power, the lower pulse rate also concentrates the same average laser power into fewer, more intense pulses, boosting the fluorescence signal. The researchers report that the mean signal level increased fivefold under the interleaved pulse pattern, and that no jitter or inter-sweep phase mismatches were observed across all interleaving factors. At the opposite extreme, an interleaving factor of 0.5 pushes the pulse rate to 1.64 gigahertz, ideal for quasi-instantaneous SHG signals, though this regime demanded a substantially higher average power of 717 milliwatts on the sample.

The experimental validation spanned several sample types. Imaging micrometer-sized fluorescent beads showed that spatial resolution is fully retained under interleaved sampling, with no timing artefacts from jitter or sweep inhomogeneities. With 500-nanometer beads, the team quantified the bleed-through problem directly: under non-interleaved operation, the fluorescence decay exceeded the pixel dwell time and the bead appeared elongated along the scan direction, whereas eightfold interleaving suppressed the effect and restored a circular image. The improvement in localization precision was striking, with the standard deviation of a frame-wise two-dimensional Gaussian fit shrinking from 990 nanometers to 186 nanometers when moving from non-interleaved to eightfold-interleaved imaging.

On biological tissue, the technique demonstrated genuine lifetime-based contrast. Histological sections stained with SYTO82 for cell nuclei and Rhodamine 6G for non-specific labeling were imaged with single-pulse FLIM, and the fluorescence lifetimes, color-coded between 0.8 and 2.5 nanoseconds in an HSV representation, clearly distinguished nuclei from surrounding tissue using a single detector. Unaveraged images were acquired at 400 hertz, while tenfold frame averaging yielded higher-fidelity fits at 40 hertz, still fast enough for 25-millisecond temporal resolution. The authors note that a quantitative evaluation of single-pulse FLIM under interleaved SLIDE, including lifetime standards and fit-quality assessment, is ongoing work to be reported separately.

The approach also extends naturally into three dimensions. Using a piezo-driven objective scanner oscillating at 12 hertz, the team imaged living, highly motile Euglena gracilis microalgae, whose chloroplast autofluorescence requires no staining. Operating at a fourfold interleaving factor set the pixel dwell time to about 5 nanoseconds, enough to suppress bleed-through, while a 781-hertz frame rate distributed over 32 axial planes delivered a 24-hertz volume rate across a 210 by 150 by 64 cubic micrometer field of view. The cells showed no photodamage at the applied laser powers, although the authors caution that the thermal load in the fastest, highest-power regime would need separate investigation. Because volume rate equals frame rate divided by the number of planes, the interleaving factor propagates linearly into the third dimension, making it a programmable trade-off knob between bleed-through suppression and volumetric speed.

Looking forward, the researchers envision smart SLIDE systems in which the microscope adapts itself to the specimen in real time. Because each waveform contains the control signals for a full image scan, the pulse repetition rate can in principle be defined per pixel, allowing individual regions of interest to be addressed at individually optimized interleaving settings. Combined with real-time signal analysis, such a microscope could automatically choose pulse timing and excitation settings based on measured fluorescence lifetime or signal strength, balancing imaging speed, signal-to-noise ratio and laser dose for each region of the sample. For neuroscientists chasing kilohertz-scale voltage dynamics, or pathologists seeking rapid lifetime contrast in stained tissue, the message is clear: the fastest point-scanning multiphoton microscope has just learned to slow itself down precisely when, and only when, the biology requires it.

Subject of Research: Adaptive interleaved sampling in SLIDE two-photon microscopy for tunable pixel dwell times

Article Title: Interleaving SLIDE microscopy

Article References: Stock, C., Berg, G., Thielking, M., Jurkevičius, J., & Karpf, S. (2026). Interleaving SLIDE microscopy. Frontiers in Photonics, 7, Article 1883605. https://doi.org/10.3389/fphot.2026.1883605

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1883605

Keywords: two-photon microscopy, SLIDE, fluorescence lifetime imaging, interleaved sampling, FDML laser, second harmonic generation, electro-optic modulator, high-speed imaging, biophotonics, volumetric imaging, adaptive microscopy, FLIM

The study, published in Frontiers in Photonics, focused on hyper-Rayleigh scattering, or HRS, a technique in which molecules in solution scatter light at precisely double the frequency of the incoming laser. Since Clays and Persoons introduced the method in 1991, it has become a workhorse for measuring the first hyperpolarizability of organic chromophores and for probing symmetry, molecular orientation, micelle formation and supramolecular assembly. But HRS is a second-order process, meaning the scattered intensity scales with the square of the laser intensity. Any effect that quietly reduces the intensity at the focus therefore exacts a punishing, quadratic toll on the signal.

Two culprits dominate. The first is thermal lensing: even weakly absorbing samples heat up under an intense beam, and because the refractive index of most liquids changes with temperature, the heated region behaves like an extra lens that shifts and smears the focus. The second is Kerr lensing, a purely nonlinear effect in which the refractive index itself depends on the instantaneous light intensity, causing the beam to self-focus when peak power climbs high enough. Both effects steal intensity from the intended focal point and can slide the scattering volume out of alignment with the detection optics. Above a certain threshold, the measured signal stops following the expected quadratic dependence on laser power, and the data become unreliable.

The research team, led by Ahmet Dok, Thibaut Legat, Yovan de Coene and Stijn Van Cleuvenbergen, worked in a relatively unexplored middle regime of laser parameters. Traditional HRS experiments use either Q-switched nanosecond lasers firing a few pulses per second with millijoule energies, or mode-locked oscillators running at roughly 80 megahertz with nanojoule pulses. The Leuven setup instead employed an amplified femtosecond laser emitting 220-femtosecond pulses at 1030 nanometres, tunable between 100 and 500 kilohertz, with microjoule pulse energies. This regime promises a balance between per-pulse and per-second nonlinear response while leaving microseconds between pulses for heat to dissipate, but the team found that neither thermal nor Kerr artifacts vanish automatically.

As test systems they chose pure water, the archetypal biological solvent, and a 10-millimolar solution of para-nitroaniline, a classic chromophore with a strong second-order response, in methanol. The key diagnostic was what they call the quadratic range: the window of input power over which the HRS signal still tracks the expected quadratic scaling, detected algorithmically as the point where deviations exceed 7.5 percent. Under their starting conditions, with a 50-millimetre focusing lens at 500 kilohertz, water remained quadratic only up to about 112 milliwatts, while the pNA solution broke down at a mere 18 milliwatts. Intriguingly, methanol solutions of pNA and of the fluorescent dye Rhodamine 6G showed nearly identical limits, revealing that the solvent, not the solute, sets the boundary.

By varying the repetition rate, the researchers could disentangle the two mechanisms. For water, lowering the rate from 500 to 200 and then 100 kilohertz expanded the quadratic range, and plotting the data against peak power revealed that breakdown always occurred at the same fluence of roughly 14 to 16 millijoules per square centimetre, the signature of Kerr lensing, which depends on peak power. But at 500 kilohertz the limit instead tracked average power, indicating a shift to thermal dominance. The methanolic pNA solution behaved differently: its limit stayed pinned at 10 to 15 milliwatts of average power regardless of repetition rate, the fingerprint of thermal lensing throughout. The distinction matters because it dictates the remedy: thermal problems call for less average power or better heat dissipation, while Kerr problems call for lower peak intensity.

The first obvious fix, simply defocusing with a longer-focal-length lens, produced a surprise. Theory predicts that if the collection optics capture the whole focal volume, the detected HRS signal should be roughly independent of focal length at fixed average power, because the drop in intensity is offset by the larger scattering volume. Instead, weaker focusing shrank the quadratic range. The explanation lies in the fixed collection geometry: lensing gradually shifts the effective focus along the optical axis, and with long focal lengths the enlarged scattering volume fills the camera’s field of view, so even small shifts push part of the signal out of the imaged region. Infrared imaging of the focus confirmed the drift, with the pNA solution showing a pronounced shift as power increased.

The winning strategy combined two geometrical tricks. Cylindrical lenses, which focus the beam only along one axis while leaving the other dimension at its original 3.6-millimetre width, slash the power density and dramatically enlarge the focal volume. In water at 100 kilohertz the quadratic range jumped from 38 milliwatts to 470 milliwatts, and in pNA from around 15 milliwatts to roughly 250 milliwatts. Shrinking the cuvette path length from 10 millimetres to 1 millimetre, with a Rayleigh length far exceeding the cell thickness, then attacked both lensing mechanisms at once, since each scales with path length. With this final configuration, water stayed quadratic up to 4148 milliwatts at 500 kilohertz, close to the laser’s full 4500-milliwatt output, while pNA remained quadratic to about 2600 milliwatts at all repetition rates.

The performance gains were striking. Relative to the starting configuration, water measurements improved roughly 3.5-fold in usable signal, while the thermally limited pNA system enjoyed a roughly 63-fold boost, nearly two orders of magnitude. Both systems reached sub-millisecond time resolution, and crucially, ultraviolet-visible spectra recorded before and after each experiment confirmed that the pNA had not photodegraded. For a technique whose weak signals have traditionally forced exposures of seconds or longer, that combination of speed and sample integrity is transformative for tracking fast processes such as nucleation, aggregation, interfacial adsorption and the shifting hydrogen-bonding networks of water.

The implications extend well beyond solution chemistry. The authors emphasize that the same design rules, governing spot size, repetition rate and interaction length, apply to vibrational sum-frequency scattering, hyper-Raman scattering and emerging wide-field nonlinear microscopy schemes, where cumulative heating and focus stability set hard limits on imaging speed and sample viability. Biomedical applications loom large: real-time volumetric imaging of tissue morphology, label-free monitoring of cellular metabolism, and dynamic studies in living organisms all depend on extracting maximal signal at minimal photodamage. By defining a stable power window with predictable scaling, this systematic taming of thermal and Kerr lensing offers photonics labs a practical recipe for pushing nonlinear optical techniques into faster, gentler and more sensitive territory.

Subject of Research: Mitigation of thermal and Kerr lensing effects to optimize hyper-Rayleigh scattering performance with kHz femtosecond lasers

Article Title: Mitigating thermal and nonlinear lensing for optimized performance in nonlinear optical scattering

Article References: Dok, A. R., Legat, T., de Coene, Y., & Van Cleuvenbergen, S. (2026). Mitigating thermal and nonlinear lensing for optimized performance in nonlinear optical scattering. Frontiers in Photonics, 7, Article 1832085. https://doi.org/10.3389/fphot.2026.1832085

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1832085

Keywords: hyper-Rayleigh scattering, nonlinear optics, Kerr lensing, thermal lensing, femtosecond lasers, photonics, para-nitroaniline, second harmonic generation, biophotonics, laser repetition rate, cylindrical lenses, time-resolved spectroscopy

The collection was curated by Eden Morales-Narváez and Remy Avila of the Centro de Física Aplicada y Tecnología Avanzada at the Universidad Nacional Autónoma de México in Querétaro, together with Pablo Loza-Alvarez of ICFO – Institut de Ciències Fotòniques in Barcelona. When they opened the Research Topic, the editors deliberately set a wide scope, inviting work on bioimaging, biosensors, optical tweezers, particle tracking, and AI-assisted data analysis. In their own Perspective contribution, Avila and colleagues used those five domains as a scaffold for a broader argument: the next substantial gains in optical biological characterization are likely to come less from any single modality reaching a new performance ceiling, and more from combining modalities, from tighter integration with computation, and from a sharper distinction between what an optical signal actually shows and what it is generally assumed to mean. The three remaining contributions each speak to that argument from a different angle, and read together they form a kind of state-of-the-field report with unusually concrete examples.

The first of those examples tackles a problem that has limited the clinical translation of second harmonic generation microscopy for years. SHG is a nonlinear optical process in which certain highly ordered, non-centrosymmetric biological structures — collagen above all — convert two incoming photons into a single outgoing photon of exactly twice the frequency, effectively turning the extracellular matrix into its own microscopic light source. No dye is required, which makes SHG uniquely attractive for imaging living tissue. But there is a catch: in thick tissue, most of the signal propagates forward, in the direction of the illuminating beam, while the only geometry realistically available for in vivo imaging is backward detection, where the signal is collected on the same side the light enters. Backward-detected SHG has historically been harder to interpret than the cleaner forward signal obtained from thin, ex vivo sections, and that interpretive gap has slowed the technique’s move from the bench to the clinic.

To close that gap, Boutkhil and colleagues imaged intact porcine corneas inside a custom pressure-controlled chamber, allowing them to vary intraocular pressure while watching the backward SHG signal change in real time. Their results overturn a long-standing reading of corneal SHG images. The periodic high- and low-intensity bands that had been interpreted in backward images as discrete collagen bundles turn out to be an artifact of collagen crimp — the natural waviness of corneal lamellae. Raise the intraocular pressure and the fibers straighten; the bands fade in step. The banding pattern, in other words, is not a map of anatomical structures but a mechanical signature of how much tension the tissue is under. The authors are candid that a complete quantitative model linking the forward-to-backward signal ratio to tissue structure remains out of reach, but the structural mechanism they have identified should change how the next generation of corneal SHG images — and the diagnostic claims built on them — are read. For a technique whose most promising clinical application is detecting early corneal weakening, that correction matters.

A second axis of the same general problem, quite literally whether to label a sample at all, runs through the review by Cuevas-Galindo and Jiménez-Sánchez. Small-molecule fluorescent probes are among the most indispensable tools in modern biology, but the authors build a sustained case that the very properties that make a probe bright and selective — high-affinity binding, lipophilic partitioning, excited-state reactivity — also make it an active participant in the biology it is meant to report on. The review’s most concrete anchor is its discussion of calcium indicators. These genetically encoded or synthetic dyes work by binding calcium ions, which means they act as buffers inside the cell; their buffering capacity measurably reshapes the very signalling kinetics they are used to track. The measurement instrument, in effect, becomes part of the experiment, and the recorded dynamics can differ from the unperturbed ones in ways that are easy to overlook and hard to correct.

Importantly, the review does not call for abandoning labels. Instead, it positions label-free approaches — autofluorescence imaging of the metabolic coenzymes NAD(P)H and flavins, along with fluorescence lifetime imaging, which exploits the fact that a fluorophore’s decay time is often more informative than its intensity — as a benchmark against which any probe’s effect on underlying physiology should be checked. It also surveys a spectrum of middle-ground strategies designed to narrow the gap between measurement and perturbation. HaloTag and SNAP-tag constructs allow specific proteins to be covalently labelled with tunable dyes; bioorthogonal fluorogenic chemistry keeps probes dark until they react at their cellular target; ratiometric sensing builds an internal reference into the measurement itself. Each strategy represents an engineering compromise between brightness, specificity, and biological neutrality, and the review’s contribution is to make those trade-offs explicit rather than implicit.

The fourth contribution moves from imaging to sensing, and it may be the most immediately striking of the four. Guilcapi and colleagues describe a plasmonic metasurface — a carefully engineered nanostructured surface patterned with Y-shaped nanocavity arrays and tuned to 785-nanometer excitation — that supports surface-enhanced Raman spectroscopy sensitive enough to discriminate human from murine norovirus particles by their vibrational signature alone. SERS works by concentrating electromagnetic fields into nanoscale gaps and cavities, amplifying the intrinsically weak Raman scattering of molecules adsorbed at the surface by many orders of magnitude. Because every biological particle has a characteristic set of vibrational modes, the resulting spectrum functions as a molecular fingerprint. Owing to the structural similarities between the two virus species, this result carries implications well beyond a simple proof of concept: principal component analysis of the SERS spectra recovered two components accounting for over 98 percent of the total variance, enough to separate the species reliably with no antibodies, no PCR, and no labelling step of any kind.

That last point deserves emphasis. Conventional virus identification depends on immunological reagents that bind specific antigens, or on nucleic-acid amplification that requires sample preparation, reagents, and laboratory infrastructure. A label-free plasmonic platform that distinguishes closely related pathogens directly from their spectra could therefore serve as a rapid screening complement in settings where those resources are scarce or turnaround times are critical. Given how disruptive norovirus outbreaks remain for food safety and clinical care — the virus is a recurring cause of gastroenteritis outbreaks in hospitals, cruise ships, and food supply chains — a fast, reagent-free screening route is an appealing prospect. The authors also argue that the underlying nanofabrication approach should generalize to other closely related pathogens, suggesting a platform rather than a single-purpose device.

Taken together, the four contributions converge on a theme that recurs across biophotonics more broadly: an optical signal is rarely as simple to interpret as it first appears. Sometimes the geometry of signal collection changes what a band or a bundle actually represents, as the corneal SHG study demonstrates. Sometimes the probe used to generate contrast measurably perturbs the system it is meant to probe, as the fluorescent labelling review insists. And sometimes telling two nearly identical biological targets apart requires statistical tools as much as photons, as the norovirus biosensor shows. None of the four pieces resolves those tensions outright, but each narrows one of them — by correcting a long-standing source of misreading, by naming a bias that is easy to overlook, by showing that closely related samples can be distinguished without labels, and by setting out a framework against which the field’s broader progress can be measured.

For the wider community, the message is that the frontier of optical biological characterization is as much interpretive as it is instrumental. Better lasers, faster cameras, and smarter algorithms will continue to arrive, but the studies collected here suggest that the field’s most valuable currency in the coming years will be a rigorous understanding of what each photon-based signal genuinely encodes about the underlying biology — and a willingness to question the assumptions baked into decades-old imaging conventions. As the editors close their Research Topic with thanks to the contributing authors and reviewers, the collection stands as a useful reference point for anyone working at the interface of optics and biology, and a reminder that in biophotonics, seeing clearly and interpreting correctly are two separate, equally demanding achievements.

Subject of Research: Advances in optical methods for biological sample characterization, including corneal second harmonic generation imaging, fluorescent labelling trade-offs, and plasmonic SERS virus detection.

Article Title: Editorial: Advances in optical methods for biological sample characterization

Article References: Morales-Narváez, E., Avila, R., & Loza-Alvarez, P. (2026). Editorial: Advances in optical methods for biological sample characterization. Frontiers in Photonics, 7, Article 1917795. https://doi.org/10.3389/fphot.2026.1917795

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1917795

Keywords: biophotonics, second harmonic generation, corneal imaging, fluorescent probes, label-free detection, autofluorescence, fluorescence lifetime imaging, surface-enhanced Raman spectroscopy, plasmonic metasurface, norovirus, biosensors, bioimaging