Lensless digital holographic microscopy has long approached this problem from a radically different direction. Instead of magnifying the sample through an objective lens, the specimen is placed directly above a bare image sensor. A compact light source illuminates it, and the sensor records an interference pattern rather than an ordinary photograph. Numerical propagation then reconstructs two complementary maps: amplitude, which describes how strongly the specimen attenuates light, and phase, which describes how much it delays the wave. Because no objective narrows the scene, the entire sensor surface becomes the field of view. In the new study, that amounted to roughly 116 square millimeters captured in a single exposure, without raster scanning or image stitching.

Yet this elegant simplicity has always concealed a fundamental weakness. The in-line holography scheme pioneered by Dennis Gabor assumes that a strong, unscattered reference wave interferes with a much weaker object wave. That assumption holds beautifully for isolated cells and very thin, transparent specimens. Once multiple scattering becomes strong, the reference wave is depleted, holographic fringes drown in diffuse background, and reliable reconstruction breaks down. For decades, the method has therefore remained confined to the weak-scattering side of microscopy, limited to visible wavelengths and thin samples.

A team at the Quantitative Computational Imaging Lab at Warsaw University of Technology, led by Professor Maciej Trusiak, asked whether that boundary was as fixed as it appeared. Their reasoning rested on a well-known physical fact: near-infrared light is scattered less strongly by many tissue structures than visible light, so more useful information survives propagation through a turbid sample. In a conventional microscope, exploiting that advantage would require infrared-optimized detectors, lenses, and coatings. In a lensless system, there are no imaging lenses to replace, and even the faint residual response of an ordinary silicon sensor near 1100 nanometers might be enough for holographic reconstruction. The idea was disarmingly simple: change the wavelength, preserve the architecture, and test whether image information lost to visible light could be recovered through the near-infrared wave.

The researchers tested this concept across six illumination wavelengths spanning 480 to 1100 nanometers, all on the same core imaging platform. Tissue-mimicking milk layers provided controlled and repeatable levels of scattering, while calibrated resolution targets measured the finest features that remained recoverable under each condition. The findings were published online in the journal Opto-Electronic Advances on September 20, 2026, in a paper led by first author Dr. Emilia Wdowiak.

The result was a decisive spectral shift in performance. With visible-light illumination, the system became fully unresolved once the scattering layer reached thicknesses between 180 and 360 micrometers, depending on the exact wavelength. At 1100 nanometers, resolvable features survived through approximately 1.4 millimeters of the tissue-like medium, nearly four times the thickness achievable at 632 nanometers. Perhaps most strikingly, the standard visible-range CMOS sensor delivered robust reconstructions even though its quantum efficiency at 1100 nanometers was just 0.19 percent. The experiment demonstrated that residual sensor sensitivity, usually dismissed as negligible, can become genuinely useful when paired with a holographic method that operates on a low photon budget.

The study also overturned one of the field’s most deeply held practical instincts. Lensless systems normally position the sample as close to the sensor as possible to maximize resolution, since diffraction-based reconstruction improves with proximity. Under strong scattering, however, the team found that increasing the sample-sensor distance from about 3 to 12 millimeters actually improved lateral resolution from roughly 11 to 5.5 micrometers. The proposed explanation is that the useful interference-carrying light remains more spatially concentrated than the broadly diffused background; with additional propagation distance, the noise spreads faster than the signal, making the holographic fringes easier to detect. The effect still requires deeper mechanistic study, but it immediately reframes sample-sensor distance as a parameter to optimize rather than simply minimize.

Most importantly, the controlled experiments translated directly to biology. At 1100 nanometers, the system produced ultra-wide-field, label-free amplitude and phase images of uncleared mouse liver sections up to 60 micrometers thick and brain sections up to 250 micrometers thick, revealing internal organization that visible illumination largely obscured. In the liver sections, near-infrared illumination exposed internal trabecular patterns and vascular pathways that remained invisible at 532 nanometers, even though the visible light preserved the overall tissue outline. Rather than claiming non-invasive imaging deep inside a living organ, the advance establishes a new operating regime for compact in vitro holography: rapid, wide-area recovery of intrinsic tissue structure in thick, strongly scattering sections, without stains, clearing agents, or sectioning to vanishing thinness.

The work was a genuinely interdisciplinary effort. Optical engineering from Warsaw University of Technology was combined with biological and clinical expertise from the Laboratory of Neurobiology at the Nencki Institute of Experimental Biology of the Polish Academy of Sciences, the Department of Immunology at the Medical University of Warsaw, the Department of Pathology at the Maria Sklodowska-Curie National Research Institute of Oncology, and the Centre of Postgraduate Medical Education. According to Dr. Wdowiak, this collaboration enabled the team to move beyond resolution targets and test the method on biologically meaningful tissue architecture, a step that separates a laboratory demonstration from a potentially practical imaging tool.

Looking ahead, Professor Trusiak notes that future work can combine distance-aware acquisition with scattering-aware or physics-informed reconstruction algorithms and more sensitive detectors, pointing toward faster tissue surveying, computational imaging through complex media, and compact instruments for laboratories where conventional microscopy platforms are difficult to scale. The QCI Lab, which develops optical methods in which computation becomes part of the microscope, has previously contributed lensless techniques for suppressing holographic twin images, reconstructing data recorded with very limited photon budgets, and creating gigavoxel-scale three-dimensional maps of biological specimens. The research was supported by Poland’s National Centre for Research and Development through the LIDER XIV programme, with equipment co-funded by the Warsaw University of Technology Excellence Initiative: Research University programme. The authors declare no competing financial interests. If the approach continues to mature, the humble silicon sensor sitting in countless laboratories may soon see far deeper into scattering tissue than anyone assumed its visible-range design would ever allow.

Subject of Research: Near-infrared lensless holographic microscopy for label-free imaging through multiple scattering in thick biological tissue

Article Title: Breakthrough near-infrared holography pushes lensless microscopy beyond its traditional visible-range scattering limit

Article References: Breakthrough near-infrared holography pushes lensless microscopy beyond its traditional visible-range scattering limit. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: lensless microscopy, digital holography, near-infrared imaging, light scattering, CMOS sensor, quantitative phase imaging, label-free imaging, mouse liver sections, mouse brain sections, computational imaging, tissue imaging, Opto-Electronic Advances

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

At the core of this pioneering technique is holographic cytometry, an offshoot of quantitative phase imaging that captures subtle optical path length differences within living cells. By precisely measuring phase shifts caused by cellular components, this technique constructs three-dimensional refractive index tomograms, effectively creating high-resolution, label-free images of intracellular structures. Trusiak’s study leverages this ability to “roll into the genome,” visualizing how specific mutations manifest as physical fingerprints in subcellular morphology without perturbing native cellular functions.

One of the most striking innovations within this work is the seamless integration of holographic data with genomic sequencing information. Traditionally, linking genetic data to phenotypic cellular changes has relied on indirect markers or labor-intensive staining protocols that obscure delicate intracellular features. Here, label-free holographic cytometry bridges this gap, allowing researchers to witness how mutations alter the mechanical properties, density distribution, and spatial organization of organelles in real time, and without any chemical interference. This fusion not only accelerates phenotypic characterization but also opens pathways for early diagnostics at a cellular level.

Delving deeper, the study reveals that mutations often induce subtle but measurable changes in the refractive landscape of the cell. Variations in chromatin compaction, mitochondrial swelling, or cytoskeletal rearrangements all result in detectable shifts using this optical modality. These changes correlate closely with specific mutational profiles captured through advanced genomic sequencing, offering a holistic picture of how genotype translates into biophysical phenotype. This insight could radically transform personalized medicine by identifying structural biomarkers tied to pathogenic mutations.

Moreover, the technique surpasses conventional cytometry by providing dynamic, label-free monitoring capabilities. Cells can be observed longitudinally, capturing the progressive impact of mutations as they unfold during cell cycles or in response to environmental stressors. Unlike fluorescence-based methods, which can cause phototoxicity or alter cell behavior, this optical technique preserves cell viability and function, affording more physiologically relevant data. This feature is particularly critical for studying slow-developing diseases such as cancer or neurodegeneration where early structural shifts precede overt symptoms.

The implications for cancer research are profound. Tumor evolution is driven by a complex interplay of genetic mutations and microenvironmental factors that alter cellular mechanics and architecture. By directly visualizing these biomechanical shifts through label-free holography, researchers can obtain a more nuanced understanding of tumor heterogeneity, metastatic potential, and therapeutic responsiveness. The capacity to correlate mutation-induced structural changes with drug resistance emergence could pave the way for adaptive treatment strategies tailored in near real-time.

Beyond oncology, this methodology offers exciting prospects in regenerative medicine and developmental biology. Stem cell differentiation and tissue morphogenesis are processes heavily influenced by genetic instructions and resultant physical cues within cells. The ability to track how mutations or epigenetic modifications reorganize intracellular structure without intrusive labeling techniques provides an invaluable tool for deciphering developmental programs and engineering tissue scaffolds that mimic natural conditions.

Technically, the approach utilizes a finely tuned optical setup to record holograms from multiple illumination angles. Advanced reconstruction algorithms then generate volumetric refractive index maps, enabling resolution of sub-organelle features such as nuclei, nucleoli, mitochondria, and cytoskeletal filaments. These high-fidelity images, when integrated with molecular profiles, reveal the biophysical consequences of genetic mutations with remarkable clarity. The ease of sample preparation—essentially just live cells suspended in physiological medium—makes the system amenable to high-throughput and clinical workflows.

The study also incorporates powerful computational frameworks to analyze and visualize the copious phase data. Machine learning models trained on holographic morphometric parameters classify mutation states and predict cellular behavior. This analytical synergy transforms raw optical data into actionable biological insights, speeding up translational applications. By automating the recognition of phenotypic alterations associated with diverse mutations, the platform offers a scalable route to precision diagnostics.

A particularly captivating element of this work lies in its ability to portray cells as dynamic, living landscapes shaped continually by their genetic make-up. Unlike static snapshots from fixed samples, label-free holographic cytometry reveals cells as evolving entities where the genome literally molds architectural terrain from inside out. This conceptual shift—from viewing cells as mere carriers of DNA to active responders whose physical forms reflect genomic information—could inspire new paradigms in cell biology research.

Additionally, the non-invasive nature of this imaging method fosters deeper exploration into mutation propagation within cell populations over time. Tracking how mutated cells physically interact, cluster, or diverge from healthy counterparts could illuminate early steps in disease progression or tissue dysfunction. These insights are vital for developing intervention strategies that target the physical as well as the molecular signatures of pathology.

Apart from its scientific potential, this technology is accessible and cost-effective relative to many fluorescence-based platforms. Its reliance on relatively simple optics and computation makes it feasible for widespread adoption in research and clinical laboratories. This democratization could accelerate discoveries by providing more researchers with tools to link genomics with biophysical cellular phenotypes.

Trusiak’s work thus encapsulates a visionary leap in bioimaging—melding the intricacies of genetics with the tangible textures of cellular physiology through cutting-edge photonics. As the technique matures, it promises to not only unravel the mysteries of mutation-induced cellular remodeling but also inspire new diagnostic and therapeutic innovations across biomedical fields. In an era where precision medicine demands nuanced understanding of genotype-phenotype relationships, label-free holographic cytometry shines as a beacon illuminating the path forward.

As the research community embraces these methods, the potential for integrating holographic cytometry with other emerging modalities, such as single-cell transcriptomics or CRISPR gene editing, holds even greater promise. Such multimodal platforms could decode the layered complexity of living systems with unprecedented depth. For now, this label-free optical approach stands out as a transformative window into how mutations indelibly shape the living cell from within.

In conclusion, the fusion of label-free holographic cytometry with genomic analysis heralds a new frontier in cellular biophysics. By capturing the often-invisible structural echoes of mutations, this methodology offers a powerful tool for unraveling the physical underpinnings of disease and development. As this technology gains traction, it could redefine how researchers visualize, quantify, and ultimately harness the intimate link between genome and cellular structure, pushing the boundaries of biomedical science into an exciting new era.

Subject of Research: Linking genetic mutations to changes in cellular structure using label-free holographic cytometry.

Article Title: Rolling into the genome: linking mutations to cellular structure through label-free holographic cytometry.

Article References:

Trusiak, M. Rolling into the genome: linking mutations to cellular structure through label-free holographic cytometry.
Light Sci Appl 14, 368 (2025). https://doi.org/10.1038/s41377-025-02053-z

Image Credits: AI Generated