npj Imaging

The lifetime of fluorescent molecules provides an orthogonal readout to fluorescence intensity, opening experimental possibilities of measuring changes in local molecular environments, mechanical tension, and metabolism, among other factors. These changes are best studied live and in vivo; however, limitations of slow imaging speeds, high phototoxicity, and increased data size and complexity have significantly impeded progress on this front. Here, we present a complete and transferable pipeline consisting of a light sheet FLIM microscope and an accompanying machine learning model for data processing that renders long-term and/or high-speed volumetric FLIM (vFLIM) tractable in living systems. We benchmark this pipeline across several biological use cases, model systems, lifetime ranges, and spatiotemporal scales, showcasing a suite of possibilities that our workflow enables. This comprehensive pipeline from imaging to analysis is a crucial step forward towards disseminating the power of live vFLIM to the broader bioimaging community.

Cell morphology reflects cell health and can distinguish cell-cycle stage, growth arrest, and distinct pathways of cell death. Live, label-free quantitative phase imaging (QPI) captures these features non-invasively and with high temporal resolution, yet many image-based classifiers rely on single frames and cannot separate states whose differences emerge only over time. How much temporal information is needed, and which architecture best exploits it, remain open questions. We assembled 1,874 QPI timelapse sequences spanning six cell states (interphase, mitosis, cell cycle arrest, apoptosis, ferroptosis, and necroptosis) and compared two-dimensional convolutional neural networks (CNNs) with a three-dimensional (3D) spatiotemporal CNN across increasing frame counts. Accuracy improved as frames were added, with the largest gain between one and three frames. The 2D models saturated beyond three frames, whereas the 3D architecture kept improving, reaching 96.5% accuracy and a 3.5% error rate at eleven frames. The temporal information needed tracked the timescale of each process: mitosis was resolved from a single frame, while ferroptosis benefited most from extended sequences. Overall, these results show that dynamic information, rather than static morphology alone, drives accurate cell-state classification, and that 3D architectures are needed to fully exploit it for label-free dynamic phenotyping.

High-speed three-dimensional imaging in scattering tissues remains challenging because volumetric microscopy generally requires scanning, whereas snapshot light-field approaches divide limited detector pixels among multiple views. This constraint is particularly severe in the second near-infrared window (NIR-II), where commonly used InGaAs cameras typically have relatively small sensor formats and high detector noise. Here we introduce NIR-II squeezed light-field microscopy (NIR-II SLIM), which optically rotates and compresses multiple perspective views before detection, allowing efficient use of camera pixels while retaining complementary spatial information for three-dimensional reconstruction. NIR-II SLIM acquires volumes at up to 600 volumes s-1 with a reconstructed lateral sampling grid of 512 x 512 pixels. We use the method for label-free four-dimensional imaging of cardiac dynamics in pigmented late-larval zebrafish, resolving chamber deformation and millisecond-scale atrioventricular-valve motion, and for NIR-II fluorescence imaging of vascular and lymphatic transport in mice. NIR-II SLIM provides a detector-efficient approach for high-speed volumetric imaging of rapid biological dynamics in scattering tissues.

While light-sheet microscopy is emerging as a robust method for volumetric imaging with improved axial resolution, its capability regarding two-dimensional, surface-level mapping is often hindered by limitations in data redundancy and reconstruction efficiency stemming from volumetric registration methods. We demonstrate that a multiview imaging approach in an axially-swept, dithered light-sheet microscope paired with computational image reconstruction of view projections is able to address these trade-offs to enable large-scale mapping of surface structural features, leveraging the advantages of multiview light-sheet in scalable field of view, working distance, and near isotropic resolution across the entire imaging depth. To aid in the acquisition and analysis of two-dimensional surface structures, we present a tailored surface mapping workflow and a Fiji plugin for computational reconstruction, promoting robust and comprehensive visualization of surface features of uncleared volumetric samples. Our strategy, termed projection reconstruction for imaging surface morphology (PRISM), integrates axially swept dithered light-sheet microscopy and post-processing software for multiview imaging. The imaging hardware enables near-isotropic resolution across its entire field of view, while the software implementation leverages rigid and affine transformations to align two-dimensional projections of multiview samples. It is designed to work with the BigStitcher pipeline, leveraging its robust algorithm to provide support for two-dimensional image alignment and stitching. We demonstrate the capability of PRISM in studies of lymphatic network mapping in the epicardial layer of intact mouse hearts, as well as surface profiles of FaDu spheroids labeled with antibody-nanodiamond conjugates. This method allows us to quantify cardiac lymphatic branch numbers, diameters, and lengths of a Prox1-tdTomato mouse cardiac model, as well as cluster number and diameters of epidermal growth factor receptor within a FaDu spheroid labeled with a nanodiamond-antibody conjugate, with a significant reduction of post-processing data size. PRISM leverages multiview image projections to promote studies of cardiac lymphatics in mouse models and surface receptor distributions within spheroid models, enabling efficient surface mapping of large, intact, and uncleared biological samples across a variety of scales.

In materials science, the integrity of scanning electron microscopy (SEM) images is paramount for quality control and validation of research outcomes. However, the introduction of sophisticated generative artificial intelligence, particularly Generative Adversarial Networks (GANs), has introduced a novel vulnerability: the potential for highly realistic, artificially synthesized SEM images to be used fraudulently in scientific literature. To address this challenge, we present a deep learning-based framework capable of distinguishing between authentic SEM images and those synthesized by Generative Adversarial Networks (GANs). Using FastGAN and StyleGAN2-ADA, two state-of-the-art GAN models, we generated synthetic SEM datasets to complement real imaging data. We fine-tuned a pre-trained Contrastive Language-Image Pre-training (CLIP) Vision Transformer (ViT-L-14) for binary classification. By unfreezing the final transformer blocks and appending a custom classification head, the model effectively captures the subtle, high-level artifacts inherent in GAN-generated upsampling. This work highlights the potential of deep learning to safeguard scientific imaging workflows and provides an important step toward detecting and mitigating image forgeries in materials science publications.

Targeted radiopharmaceutical development for ovarian cancer (OC) has been limited by the lack of molecular targets that combine broad tumor expression with minimal normal-tissue distribution. TRA-1-60 (TRA) is a cancer-associated glycoepitope carried by podocalyxin. Here, we evaluated TRA as a target for OC and developed a TRA-directed immunoPET imaging platform. Immunohistochemical analysis demonstrated significantly higher TRA expression in ovarian tumors than in normal adjacent ovarian tissue, with expression maintained across epithelial OC histotypes and disease stages. An engineered anti-TRA single-chain variable fragment-Fc (scFv-Fc) demonstrated robust penetration of three-dimensional tumor spheroids and selective accumulation in intraperitoneal tumors in an immunocompetent syngeneic OC model. Radiolabeling with zirconium-89 generated [Zr]Zr-DFO-anti-TRA scFv-Fc with >98% radiochemical yield. Serial PET/CT imaging demonstrated progressive and sustained radiotracer accumulation at tumor sites through 96 hours, accompanied by declining liver-associated activity and low uptake in most normal tissues. Together, these findings identify TRA as a broadly expressed and accessible tumor-associated glycoepitope and establish TRA-targeted immunoPET as a promising strategy for noninvasive detection of OC. The selective and sustained tumor localization of this platform further provides a foundation for development of TRA-directed radiopharmaceutical therapy, supporting a potential theranostic approach for OC.

Formalin-fixed prostate tissue samples were imaged by propagation-based synchrotron phase contrast micro computed tomography ({micro}CT) with a 3D spatial resolution of ca. 3 {micro}m. Post-{micro}CT, samples were prepared for histology with sections close to coplanar with the transverse {micro}CT image planes. Haematoxylin and eosin stained sections were examined by an expert prostate histopathologist and compared qualitatively with corresponding {micro}CT-visible microstructure features. There is potential for {micro}CT to provide complimentary information to conventional histology and light microscopy without the need for preparation of stained thin sections. For the imaging conditions and spatial resolution of our study, {micro}CT may provide tissue architectural features similar to those used in Gleason grading, albeit without clear subcellular microstructure detail. At the spatial resolution of our study {micro}CT may provide novel 3D microstructure information for validation of diffusion weighted magnetic resonance imaging (MRI) methods. As an example, we demonstrate a qualitative correlation between {micro}CT-derived stromal fibre orientation and preferential water diffusion direction measured by diffusion tensor MRI microscopy of the same sample.

Spectroscopic single-molecule localization microscopy (sSMLM) enables multiplexed super-resolution imaging by simultaneously acquiring the spatial position and spectral information of individual fluorophores. Dual-wedge prism (DWP)-based implementations provide a compact, alignment-stable approach to spectral dispersion, but trade-offs between localization precision, spectral precision, and experimental complexity remain. We systematically compare five DWP-based sSMLM configurations, including two-dimensional (2D) and three-dimensional (3D) implementations using single DWP (DWP-sSMLM) and symmetrically-dispersed DWP (SDDWP-sSMLM). We evaluate lateral precision, spectral precision, and ease of use. SDDWP configurations acquire spectral images in both channels and utilize both for spatial localization, yielding the highest lateral and spectral precision. However, for applications that do not require axial information, 2D-DWP provides a simple, plug-and-play solution with robust performance. This work offers a guideline for selecting DWP configurations based on experimental needs.

Background: Accurate sampling of suspected peripheral lung cancers depends on access to the lesion and confirmation that the biopsy tool is in contact with target tissue. Current bronchoscopic navigation and imaging techniques can guide instruments to a target but do not provide real-time biological confirmation at the point of sampling. Fluorescence lifetime imaging microscopy (FLIM) provides molecular contrast by measuring fluorescence decay - how long photons continue to be emitted from fluorescent molecules. In the Precision Lung clinical study (ISRCTN15093468), the Prothea Imaging System (Generation 1) identified a candidate tumour-associated phenotype of spatially overlapped low fluorescence lifetime and low intensity (LLLI) from in-vivo imaging. We used this observation as the basis for a reverse-translational study to determine whether the LLLI phenotype is linked to cancer pathology; reproducible with the Imaging System (Generation 2); and distinguishable from normal lung tissue. Methods: Previously reported Precision Lung findings were used as the clinical starting observation and were not re-analysed. Validation was then performed using: (i) pathology linked benchtop FLIM of early-stage non-small-cell lung tissue microarrays encompassing malignant cell clusters of approximately 300 um2, matched to the EoT imaging scale; (ii) five sequential fresh lung-cancer resections imaged at tumour and comparator regions, including visibly blood-rich contact sites, using the (Generation 2) Imaging System; and (iii) systematic mapping of two ventilated non-cancer donor lungs, one from a smoker and one from a non-smoker, across all available lobes. The LLLI phenotype was defined as spatial co-localisation of low intensity and short lifetime. Results: Using a real time fibre based FLIM system, capable of deployment through a working channel of a bronchoscope, the LLLI tumour phenotype was optically identified in freshly resected tumour tissue. The same phenotype was identified in fixed tissue samples with known pathology, and with images taken in the Precision Lung clinical study. Whole human lung controls did not show evidence of the tumour phenotype. Conclusions: This evidence forms a reverse-translational chain that supports the concept of the Prothea Imaging System - as a platform that confirms that the tool is in contact with a region of cancer in the lesion, while preserving continuous access for biopsy or intervention.

Quantitative microscopy of microorganisms increasingly produces large, multidimensional datasets, yet their analysis often depends on fragmented workflows spanning file conversion, segmentation, quality control, fluorescence quantification, tracking, and visualization. Here, we present BactoMate, an open-source, cross-platform graphical user interface that integrates these steps into a unified workflow for microbial image analysis. BactoMate incorporates established segmentation methods and supports both single-file and batch processing. Its modules enable image preprocessing, cell segmentation, morphology-based quality control, fluorescence and foci quantification, single-cell tracking, lineage reconstruction, structured data export, and generation of quality-control and visualization outputs. We demonstrate the applicability of BactoMate across multichannel fluorescence imaging, bacterial swimming assays, microcolony lineage analysis, phage infection assay and a microfluidic time series. All user-configurable parameters are exposed through the interface, are recorded alongside structured outputs and can be loaded for reproducible image analyses across experiments to reduce introduction of bias. By reducing workflow handoffs while preserving parameter control and exportable results, BactoMate enables accessible, reproducible, and scalable quantitative analysis of microbial microscopy data.

Cross-modality image translation promises to provide multiple layers of biological information from a single image input, yet its practical application is stalled by a lack of interpretability and the inability to account for model imperfections. In silico labeling, the inference of organelle localization from label-free images, is a primary example where this black-box nature limits adoption. We present Mask Interpreter, a generalized method for semantic visual interpretability of image-to-image translation models. By uncovering organelle-specific "explanation signatures", Mask Interpreter validates that models rely on authentic biological structures rather than spurious artifacts. Beyond biological validation, it outperforms traditional explainable AI (xAI) approaches, identifies batch effects and localizes prediction errors when ground-truth fluorescence is unavailable. Semantic confidence modeling further provides fine-grained reliability assessment at single-cell resolution, enabling the automated exclusion of artifacts from downstream analyses. By bridging the gap between computational inference and meaningful biological features, Mask Interpreter transforms in silico labeling into a reliable tool for scientific discovery across diverse biomedical imaging modalities.

Fluorescence microscopy is central to the study of bacterial cell biology, multicellular behaviours, and host-pathogen interactions. Bright, robust and photostable labelling is required for bacterial identification, sorting and quantitative analysis, driving continuous development of state-of-the-art labelling tools. Here, we developed a multicolor fluorescent cell labelling toolkit for Gram-negative bacteria carrying the attTn7 site, using the opportunistic human pathogen Pseudomonas aeruginosa as a model. Cell labelling is achieved by constitutive chromosomal expression of genes encoding a choice of four novel fluorescent proteins, mNeonGreen, mJuniper, mLychee and mScarlet-I3, codon-optimised for P. aeruginosa. These reporters provide bright, stable fluorescence with minimal photobleaching and excellent spectral separation during long-term imaging of single cells, macrocolonies and biofilms. Chromosomal expression of mNeonGreen yielded brighter and more homogeneous labelling than expression of the same construct from a plasmid. Importantly, dual-color labelling of macrocolonies uncovered previously unrecognised phenomena of collective motility when two isogenic swarming populations interact. Finally, we demonstrate the applicability of our constructs in biologically relevant host-pathogen contexts by imaging both live and fixed P. aeruginosa-infected human airway epithelial cells. This versatile cell labelling platform enables reliable bacterial identification, segmentation, tracking, and quantitative fluorescence imaging across spatial and temporal scales, and is readily adaptable to most other Gram-negative bacteria as the attTn7 integration site is well conserved.

The limited efficacy of immune checkpoint inhibitor (ICI) therapy in triple-negative breast cancer (TNBC) highlights the need for combination strategies that enhance antitumor responses. Sorafenib, a multikinase inhibitor with anti-angiogenic and immunomodulatory activity, represents a rational partner for ICI-based combination therapy. However, therapeutic responses to such combinations are biologically complex and cannot be fully characterized by any single biomarker or imaging modality. Here, we evaluated ICI therapy combined with sorafenib in the aggressive and ICI-refractory orthotopic 4T1 TNBC model. Therapeutic responses were assessed using a unique longitudinal multimodal imaging framework integrating [Zr]Zr-DFO-anti-CD8 minibody and [{superscript 1}F]FDG PET, as well as perfluorocarbon (PFC)-based {superscript 1}F MRI and hyperpolarized {superscript 1}3C MRS, together with ex vivo analyses. Only the ICI-sorafenib combination suppressed tumor growth, whereas both monotherapies showed limited antitumor activity. Multimodal imaging, together with complementary ex vivo analyses, uncovered coordinated tumor microenvironment (TME) remodeling, including vascular normalization, elevated CD8 cell presence with modest enrichment in the tumor center, delayed increase in phagocyte-associated {superscript 1}F MRI signal coupled with reduced CD206 cell infiltration, and sustained metabolic activity. These findings support ICI-sorafenib combination therapy as a promising therapeutic strategy for TNBC. Therapeutic efficacy reflected coordinated vascular, immune, and metabolic remodeling. This multimodal imaging framework enables non-invasive longitudinal monitoring of these complementary TME changes, providing a comprehensive strategy for treatment assessment in immunotherapy-based combination therapies. One Sentence SummaryLongitudinal multimodal imaging identified a multidimensional TME response signature of effective ICI-sorafenib therapy in TNBC.

Background: Trophoblast cell surface antigen 2 (TROP2, encoded by TACSTD2) is a transmembrane glycoprotein overexpressed in multiple aggressive epithelial carcinomas. While antibody drug conjugates targeting TROP2 have achieved regulatory approvals, acquired payload resistance and systemic off-target toxicities limit sustained remissions. Radionuclide Drug Conjugates (RDCs) represent a potent alternative modality capable of delivering cytotoxic ionizing radiation directly to target cells. However, selecting the optimal therapeutic radioisotope between long-range beta emitters (177Lu) and short-range, high linear energy transfer (LET) alpha emitters (225Ac) under heterogeneous TROP2 spatial distributions remains an unaddressed clinical challenge. Methods: We developed an automated computational pathology and spatial microdosimetry pipeline to resolve microscopic TROP2 expression gradients and simulate absorbed radiation dose distributions from digitized whole-tissue immunohistochemistry (IHC) sections (N = 14). Optical density matrices were de-convoluted in Hematoxylin-Eosin-DAB (HED) color space to isolate the DAB chromogen. Continuous 2D spatial density distributions and topological surface profiles were reconstructed. Physical radiation energy deposition was modeled using radial dose point kernels for 177Lu (mean range ~670 m, LET 0.2 keV/m) and 225Ac (mean range ~65 m, LET 100 keV/m, 4 alpha particles per decay cascade). Therapeutic Index (TI, ratio of mean target to non-target absorbed dose), target coverage, and spatial specificity were quantified across all specimens. Results: Quantitative image deconvolution revealed that TROP2 expression across the cohort was characteristically focal and clustered, with a mean positive area fraction of 1.55 +/- 2.22% (range: 0.08% to 6.85%) and mean DAB signal intensity of 0.256 +/- 0.043. In all 14 evaluated specimens (100%), 225Ac-labeled RDCs demonstrated superior tumor-to-stroma dose localization compared to 177Lu-labeled RDCs. The cohort-wide mean Therapeutic Index was significantly higher for 225Ac (1.26 +/- 0.14) than for 177Lu (1.01 +/- 0.02, p < 0.0001, paired two-tailed t-test). Because the path length of 177Lu beta particles exceeded target cell nest dimensions by up to 30-fold, 177Lu suffered from severe off-target crossfire spillover into antigen-negative stroma. In contrast, 225Ac confined high-LET ionization tracks strictly within the micro-geographic boundaries of TROP2-expressing clusters. Conclusions: In tumors displaying focal or sparse TROP2 micro-architecture, Targeted Alpha Therapy with 225Ac-RDCs offers a superior biophysical profile over beta-emitting 177Lu-RDCs, maximizing cluster cell kill while sparing adjacent normal tissue stroma. This computational microdosimetry framework provides a practical tool to guide rational isotope pairing in RDC drug design.

The sea anemone Aiptasia is a model system for understanding cnidarian loss of symbiotic algae under heat stress (bleaching). While Aiptasia polyps have been widely used to study this process, accurate symbiosis phenotyping grapples with discordant length scales: fine spatial resolution (~100 um) is needed across a whole organism (~5 mm). To address this, we consider small (~100 um), optically transparent Aiptasia larvae as a bleaching model suitable for whole-organism phenotyping by fluorescence microscopy with larvae classified as symbiotic when algae are localized within gastrodermal cells. To expedite phenotyping, we introduce a machine-learning (ML) image-analysis pipeline (SYMPHONY) designed for single-larva resolution analysis of intact larvae. SYMPHONY efficiently identifies the cellular location of internalized algae (accuracy: 79%, precision: 82%, recall: 79%, F1 score: 79%; training dataset composed of 1611 total objects). Additionally, SYMPHONY reports statistically significant larval bleaching under heat stress and corroborates manual phenotyping results, while significantly reducing operator labor from hours to minutes. The combination of the Aiptasia larvae model and the SYMPHONY pipeline aims to accelerate our understanding of symbiosis breakdown.

Ultrathin endoscopy is highly attractive for real-time tissue imaging in narrow and hard-to-reach regions of the body. A single multimode fibre (MMF) is an attractive probe because of its small diameter, flexibility, and diffraction-limited spatial resolution enabled by the large number of transverse modes guided within a single core. Because the distal fibre tip is inaccessible during endoscopy, reflection-mode imaging, in which the same fibre delivers illumination and collects backscattered light, is more practical than transmission-mode imaging. However, image recovery from the resulting speckle pattern is challenging because light undergoes double-pass propagation through the MMF, with mode coupling and dispersion; the backscattered signal is weak, and the camera records intensity only, without phase information. Here, we propose a single-shot reflection-mode MMF imaging framework that combines a reflected real-valued intensity transmission matrix (reflected-RVITM) with an image restoration network. The reflected-RVITM is calibrated using intensity-only measurements, without interferometry or phase retrieval, and provides a physics-guided initial reconstruction from a single backscattered speckle frame. A restoration network then refines this initial reconstruction instead of inverting the raw speckle. Four restoration backbones are evaluated: HPM-Attention-UNet, GAM, MambaIRv2, and CICPNet. On matched datasets, hybrid models outperformed corresponding networks trained to map raw speckle directly to images. For example, HPM-Attention-UNet on MNIST improved mean PCC from 0.572 to 0.944 (+65.1%). Under domain shift, with training only on Fashion-MNIST and tested on unseen CIFAR scenes, hybrid models achieved mean PCC of 0.61-0.65, compared with 0.36-0.50 for direct learning. This framework is further demonstrated using physical objects at the distal fibre tip. These results demonstrate that a reflected-RVITM physics prior combined with a restoration network enables single-shot image recovery after intensity-only calibration, offering a phase-retrieval-free and generalisable route towards minimally invasive reflection-mode MMF endoscopy.

Electron microscopy (EM) is essential for resolving cellular ultrastructure, yet quantitative analysis remains limited by labor-intensive segmentation and the scarcity of generalizable models. Here we present QuantEM, an open-source platform for segmentation and analysis of EM data across imaging modalities, tissues, and species. We assembled the largest curated collection of intracellular EM datasets to date, comprising over 15,000 two-dimensional images and 1,700 three-dimensional acquisitions from more than 600 datasets, including nearly 4,000 newly released acquisitions. Using this resource, we trained an EM-specific vision transformer foundation model and systematically optimized adaptation strategies for organelle segmentation. QuantEM provides pretrained models for mitochondria, endoplasmic reticulum, nuclei, and lipid droplets, integrated with interactive proofreading and downstream quantitative analyses through standalone and napari interfaces. Across diverse naive datasets, QuantEM consistently matches or exceeds existing models on zero-shot segmentation while requiring less data for finetuning. We further demonstrate its utility by revealing previously unrecognized subcellular compartmentation of hepatic glucokinase using immuno-electron microscopy.

Cardiac blood flow is a regulator of several important developmental and remodelling processes in the heart, including through fluid shear forces sensed by the endothelial cells lining the heart. However, optically mapping these flow fields in the complex 3D geometry of the heart is challenging even in transparent animal models such as the zebrafish. One of the main challenges is the difficulty in measuring the out-of-plane (axial) velocity component, preventing accurate mapping of the complete 3-component-3-dimension (3C-3D) blood flow velocity field; image-based techniques such as microscopic particle image velocimetry ({micro}PIV) traditionally only provide the in-plane flow components. Here we present a computational approach to achieve full time-varying 3C-3D blood flow vector mapping using a standard selective plane illumination microscope (SPIM), based on robust cardiac phase assignment, precise measurement-driven registration of sequentially acquired z-stacks, and PIV data fusion from multiple sample orientations. Our approach holds the key to understanding the complex dynamic flow fields within the developing heart, and their role in shaping cardiac development.

Preparing electron-transparent cryo-lamellae is inherently a serial, low-throughput process. During sample handling, milling, and transfer, cryo-fixed cells and their supporting films are subjected to mechanical forces as well as thermal stresses caused by temperature fluctuations. After milling, these extremely thin lamellae remain vulnerable to both mechanical and thermal stress, often leading to cracking or complete disintegration. Consequently, the loss of valuable lamellae is frequently an unavoidable aspect of working with such fragile specimens. In this work, we reconsider the conventional lamella geometry, which is typically a flat, thin cross-sectional slab. During milling, lamellae often become unintentionally bent, complicating the final polishing step required to achieve uniform thinning across their width. To address this limitation, we propose deliberately fabricating lamellae in a pre-bent configuration, i.e. specifically, adopting an arch-shaped profile instead of the traditional flat geometry. The arch shape is intrinsically more mechanically stable than a flat structure, thereby reducing lamella loss due to mechanical failure. Moreover, pre-bent milling patterns facilitate uniform thinning of bent lamellae, which is difficult to achieve using conventional flat milling approaches. In addition to the arch geometry, we investigate corrugated lamellae, characterised by a sinusoidal variation around the plane of a conventional flat lamella. Similarly to the arch shape, the corrugated design offers enhanced mechanical stability compared to traditional flat lamellae. We fabricated a series of test lamellae incorporating both arches and corrugations. High-resolution cryo-TEM imaging was performed to evaluate these structures, demonstrating that non-flat geometries do not compromise cryo-electron tomography performance. Furthermore, finite element method (FEM) simulations were conducted to provide insight into stress distributions within bent and corrugated lamellae.

SignificanceRare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells. AimWe developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-naive mice. ApproachPBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure. ResultsOf the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by >99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918-0.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91-10.99; two-sided p=0.071; prespecified one-sided p=0.036). ConclusionsSpecimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.