Forensic microtrace investigation relies on time- and labour-intensive microscopic analyses. To aid forensic experts in their investigations, an image recognition model for microtrace localisation and classification is needed. In this work, we use deep learning to automate trace recognition in images captured with automated microscopy. We localise and classify fibres, hairs, skin, glass and sand in microscopy scans through pixel-wise classification of tape-lift samples. As deep learning requires extensive amounts of annotated training data, we additionally investigate various pretraining strategies to minimise the required annotation workload. We compare ImageNet pretraining, pretraining with self-supervised learning and a sequential application of these approaches. We find that pretrained models are able to reduce the required annotated data twofold compared to models trained from scratch while retaining the prediction accuracy. While our ImageNet-pretrained models outperform our self-supervised-pretrained models, we achieve the highest accuracy by combining the two approaches, resulting in a factor 4 reduction of manual annotated microtraces or a 65 % improvement in recognition and localisation accuracy (mean intersection over union increases from 0.34 to 0.56 due to pretraining) when training on only 2.2 dm² of annotated tape lift scans. The developed models offer a solid fundament for automated analysis of forensic microtrace scans.

Correction to: Communications Biologyhttps://doi.org/10.1038/s42003-026-09984-2, published online 16 April 2026 In the version of this article initially published, there were errors in the Fig. 1g x-axis labels, where the labels currently reading “CD56” and “CD68” from left-to-right were originally interchanged. The figure is now amended in the HTML and PDF versions of the article.

Smart microscopy is transforming biological imaging by integrating real-time analysis with adaptive acquisition to enhance imaging efficiency. Whereas many emerging implementations are event-driven and focus on on-demand data acquisition to reduce phototoxicity, we here present ‘outcome-driven’ microscopy, a framework combining smart microscopy with optogenetics to control cell biological processes and achieve predefined outcomes. We validate this approach using light-based control of cell migration and nucleocytoplasmic transport, demonstrating robust spatiotemporal control of cellular behaviour in single cells and in cell populations.

Accurate phenotyping of cells in the tumor microenvironment is essential for understanding cancer biology but typically requires precise cell segmentation, limiting scalability. Here, we introduce Contrastive Learning Enabled Accurate Registration of Immune and Tumor cells (CLEAR-IT), a self-supervised framework that learns cell-level features from multiplexed images using only cell locations. CLEAR-IT encoders achieve strong linear evaluation performance, improve substantially with hyperparameter optimization, and maintain high accuracy across imaging modalities and with up to 90% fewer labels. When substituted for handcrafted features in a state-of-the-art classifier, CLEAR-IT features yield higher performance, and their combination enables comparable accuracy with less than half of the labeled data otherwise required. The learned representations also support prognostic modeling: using annotations from a single patient, CLEAR-IT-based phenotyping identifies survival-associated tissue features that generalize across two cohorts and modalities. CLEAR-IT provides a segmentation-light, label-efficient approach for scalable cell phenotyping and enhances existing workflows in digital pathology and tumor microenvironment analysis.

Image quality in single-molecule localization microscopy depends largely on the accuracy and precision of the localizations. While under ideal imaging conditions, the theoretically obtainable precision and accuracy are achieved; in practice, this changes if (field-dependent) aberrations are present. Currently, there is no simple way to measure and incorporate these aberrations into the point-spread function (PSF) fitting; therefore, the aberrations are often taken as constant or neglected altogether. Here we introduce a model-based approach to estimate the field-dependent aberration directly from single-molecule data without a calibration step. This is made possible by using nodal aberration theory to incorporate the field dependency of aberrations into our fully vectorial PSF model. This results in a limited set of aberration fit parameters that can be extracted from the raw frames without a bead calibration measurement, also in retrospect. The software implementation is computationally efficient, enabling the fitting of a full 2D or 3D dataset within a few minutes. We demonstrate our method on 2D and 3D localization data of microtubuli, nuclear pore complexes, and nuclear lamina over fields of view of up to 180 µm and compare it with Gaussian fitting, spline-based fitting, and a deep-learning-based approach.

Super-resolution microscopy (SRM) has undeniable potential for scientific discovery, yet still presents many challenges that hinder its widespread adoption, including technical trade-offs between resolution, speed and photodamage, as well as limitations in imaging live samples and larger, more complex biological structures. Furthermore, SRM often requires specialized expertise and complex instrumentation, which can deter biologists from fully embracing the technology. In this Perspective, a follow-up to our recent Q&A article, we aim to demystify these challenges by addressing common questions and misconceptions surrounding SRM. Experts offer practical insights into how biologists can maximize the benefits of SRM while navigating issues such as photobleaching, image artifacts and the limitations of existing techniques. We also highlight recent developments in SRM that continue to push the boundaries of resolution. Our goal is to equip researchers with the crucial knowledge they need to harness the full potential of SRM.

In single-molecule microscopy, a big question is how precisely we can estimate the location of a single molecule. Our research shows that by using iterative localisation microscopy and factoring in the prior information, we can boost precision and reduce the number of photons needed. Leveraging the Van Trees inequality aids in determining the optimal precision achievable. Our approach holds promise for wider application in discerning the optimal precision across diverse imaging scenarios, encompassing various illumination strategies, point spread functions and overarching control methodologies.

Optical emitters in hexagonal boron nitride (hBN) are promising probes for single-molecule sensing platforms. When engineered in nanoparticle form, they can be integrated as detectors in nanodevices, yet positional control at the nanoscale is lacking. Here we demonstrate the functionalization of DNA origami nanopores with optically active hBN nanoparticles (NPs) with nanometer precision. The NPs are active under three wavelengths of visible illumination and display both stable and blinking emission, enabling their accurate localization by using wide-field optical nanoscopy. Correlative opto-structural characterization reveals deterministic binding of bright, multicolor hBN NPs at the pore rim due to π-π stacking interactions at site-specific locations on the DNA origami. Our work provides a scalable, bottom-up approach toward deterministic assembly of solid-state emitters on arbitrary structural elements based on DNA origami. Such a nanoscale arrangement of optically active components can advance the development of single-molecule platforms, including optical nanopores and nanochannel sensors.

Super-resolution microscopy (SRM) is gaining popularity in biosciences; however, claims about optical resolution are contested and often misleading. In this Viewpoint, experts share their views on resolution and common trade-offs, such as labelling and post-processing, aiming to clarify them for biologists and facilitate deeper understanding and best use of SRM.

In the present study, the influence of topographic and mechanical cues on neuronal growth cones (NGCs) and network directionality in 3D-engineered cell culture models is explored. Two-photon polymerization (2PP) is employed to fabricate nanopillar arrays featuring tunable effective shear modulus. Large variations in mechanical properties are obtained by altering the aspect ratio of the nanostructures. The nanopillar arrays are seeded with different neuronal cell lines, including neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (iPSCs), I³Neurons, and primary hippocampal neurons. All cell types exhibit preferential orientations according to the nanopillar topology, as shown by neurites creating a high number of oriented orthogonal networks. Furthermore, the differentiation and maturation of NPCs are affected by the topographic and mechanical properties of the nanopillars, as shown by the expression of the mature neuronal marker Synapsin I. Lastly, NGCs are influenced by effective shear modulus in terms of spreading area, and stochastic optical reconstruction microscopy (STORM) is employed to assess the cytoskeleton organization at nanometric resolution. The developed approach, involving laser-assisted 3D microfabrication, neuro-mechanobiology, and super-resolution microscopy, paves the way for prospective comparative studies on the evolution of neuronal networks and NGCs in healthy and diseased (e.g., neurodegenerative) conditions.

Image scanning microscopy (ISM) achieves resolution beyond the diffraction limit by a factor of √2. However, prior ISM research predominantly employs scalar diffraction theory, neglecting critical physical effects such as polarization, aberrations, and Stokes shift. This paper presents a comprehensive vectorial ISM point spread function (PSF) model that accounts for these phenomena. By considering the effect of polarization in emission and excitation paths, as well as aberrations and Stokes shift, our model provides a more accurate representation of ISM. We analyze the differences between scalar and vectorial theories in ISM and investigate the impact of pinhole size and aberration strength on resolution. At a numerical aperture of 1.2, the full width half maximum (FWHM) discrepancy between scalar and vectorial ISM PSFs can reach 45 nm, representing a 30% deviation from the vectorial model. Additionally, we explore multiphoton excitation in ISM and observe increased FWHM for 2-photon and 3-photon excitation compared to 1-photon excitation. The FWHM of the 2-photon excitation ISM PSF increases by 20% and the FWHM of the 3-photon excitation ISM PSF increases by 28% compared to the 1-photon excitation ISM. In addition, we found that the optimal sweep factor for 2-photon ISM is 1.22, and the optimal sweep factor of 3-photon ISM is 1.12 instead of the 2 predicted by the one-photon scalar ISM theory. Our work improves the understanding of ISM and contributes to its advancement as a high-resolution imaging technique.

A rapid and robust method to fabricate transmission diffractive optical elements in the visible wavelengths is presented. By additive manufacturing of a polymeric photo-resin using 2-photon lithography followed by encasing of the structure in another resin with similar refractive index, the height of the structure can be made much larger, thus trading-off fabrication height for refractive index difference of the two materials. After adjusting for resin shrinkage, different diffractive optical element designs including an m = 1 vortex plate, and Laguerre-Gaussian beams with azimuthal and radial indices of (1,1), (1,2), and (2,1) were demonstrated. Experimental results show intensity patterns matching that of simulations, including size and features, although some aberration was observed, possibly due to fabrication tolerance errors or beam misalignment. This technique adds to the toolkit of micro-optics fabrication methods using additive manufacturing and 3D printing, and it would be beneficial for rapid prototyping and integration with miniaturised systems.

We propose to use the State Estimation by Sum-of-Norms Regularisation (STATESON-)algorithm for recovering the tip-sample interaction in high-speed tapping mode atomic force microscopy (AFM). This approach enables accurate sample height estimation for each independent cantilever oscillation period, provided that the tip-sample interaction dominates the noise. The entire course of the cantilever deflection signal is compared to a modelled counterpart in subsequent convex minimisations, such that the sparse tip-sample interaction can be recovered. Afterwards, the sample height is determined using the minimum smoothed cantilever deflection per cantilever oscillation period. Results from simulation experiments are in favour of the proposed approach as it consistently reveals sharp edges in sample height, as opposed to both the conventional and a closely related existing approach. However, the non-processed cantilever deflection provided most accurate sample height estimation. It is recommended to implement the STATESON-algorithm in the form of a filter to use it in feedback control of the scanner and cantilever excitation.

We demonstrate that a cavitation bubble initiated by a Nd:YAG laser pulse below breakdown threshold induces crystallization from supersaturated aqueous solutions with supersaturation and laser-energy-dependent nucleation kinetics. Combining high-speed video microscopy and simulations, we argue that a competition between the dissipation of absorbed laser energy as latent and sensible heat dictates the solvent evaporation rate and creates a momentary supersaturation peak at the vapor-liquid interface. The number and morphology of crystals correlate to the characteristics of the simulated supersaturation peak.

A low-cost glass-based microfluidic flow cell with a piezo actuator is built using off-the-shelf parts (total cost €9 per device) to apply acoustophoretic force on polystyrene micro-beads. The main challenge in the fabrication of these devices was to ensure their leak tightness, which we solved using double-sided tape and nail polish. Beads with 1.5 μm diameter flowing in a 100 μm deep channel were trapped at 7.5 MHz using a 23.7 peak-to-peak voltage (V_pp) sinusoidal input. The trap located at 50 ± 0.1 μm depth was measured to have a stiffness of approximately 0.6 pN/μm. With this simple device we can trap and control the axial position of micrometer scale objects, which allows for the manipulation of beads and cells. We intend to use the device for force spectroscopy on micro-bead tethered DNA. This can be combined with super-resolution imaging techniques to study mechanics and binding of protein structures along a DNA strand as a function of induced tension.

Single-molecule localization microscopy requires sparse activation of emitters to circumvent the diffraction limit. In densely labeled or thick samples, overlap of emitter images is inevitable. Single-molecule localization of these samples results in a biased parameter estimate with a wrong model of the number of emitters. On the other hand, multiple emitter fitting suffers from point spread function degeneracy, which increases model and parameter uncertainty. To better estimate the model, parameters and uncertainties, a three-dimensional Bayesian multiple emitter fitting algorithm was constructed using Reversible Jump Markov Chain Monte Carlo. It reconstructs the posterior density of both the model and the parameters, namely the three-dimensional position and photon intensity, of overlapping emitters. The ability of the algorithm to separate two emitters at varying distance was evaluated using an astigmatic point spread function. We found that for astigmatic imaging, the posterior distribution of the emitter positions is multimodal when emitters are within two times the in-focus standard deviation of the point spread function. This multimodality describes the ambiguity in position that astigmatism introduces in localization microscopy. Biplane imaging was also tested, proving capable of separating emitters up to 0.75 times the in-focus standard deviation of the point spread function while staying free of multimodality. The posteriors seen in astigmatic and biplane imaging demonstrate how the algorithm can identify point spread function degeneracy and evaluate imaging techniques for three-dimensional multiple-emitter fitting performance.