npj Imaging

Coherent Raman spectroscopy enables label-free biochemical fingerprinting of live cells with subcellular resolution. We previously developed a machine learning framework capable of classifying glioma FFPE tissues using Raman spectral signatures. To accelerate live cell acquisition, we previously developed RADAR (Raman Spectral Analysis Using Deep Learning for Artifact Removal), a method that increases imaging speed by an order of magnitude while preserving spectral integrity. By integrating high-speed Raman imaging with supervised machine learning, we aimed to define unique biochemical fingerprints specific to cell type. We hypothesized that intrinsic biochemical composition alone is sufficient to distinguish cellular identity and tumor subtype. To test this, we generated metabolic maps of diverse brain-derived cell types--including astrocytoma, oligodendroglioma, and glioblastoma cells--using coherent Raman spectroscopy at single-cell resolution. Patient-derived brain tumor cell lines representing genetically heterogeneous backgrounds were analyzed. Samples were stratified by IDH1 mutation status (IDH1-mutant and IDH1-wild-type) and histologically classified as oligodendroglioma or astrocytoma. Raman spectral data were acquired from 286 live single cells across the two principal molecular classes, with further subdivision into two histologic subtypes within the IDH1-mutant group. Classification was performed using an XGBoost model with shallow tree depth (1-3), a 20% held-out test set, and grouped, stratified 5-fold cross-validation to control for sample-level bias. The machine learning framework distinguished IDH1-mutant from IDH1-wild-type cells with a ROC-AUC of 0.78 and further discriminated IDH1-mutant astrocytoma from oligodendroglioma cells with a ROC-AUC of 0.81. Feature importance analysis demonstrated that separation between IDH1-mutant and IDH1-wild-type cells was driven primarily by Raman peaks associated with protein amide bands, total NADH, unsaturated fatty acids, and heme-related vibrational modes. Within the IDH1-mutant class, discrimination between oligodendroglioma and astrocytoma was driven by lipid-rich vesicle signatures, protein/polyamide amide bands, and lipid-associated spectral features. Together, these findings support the feasibility of label-free, machine learning-assisted Raman profiling to resolve clinically relevant glioma subtypes at single-cell resolution. This scalable analytical framework provides a translational platform for investigating metabolic heterogeneity, therapeutic response, co-culture systems, and patient-derived organoid models.

AbstractThe infiltrative, non-enhancing margin of IDH wildtype high grade glioma (IDHwt HGG) harbors distinct molecular programs that drive invasion and therapeutic resistance, yet remains largely unevaluable by conventional tissue sampling approaches and by conventional imaging. Here we show that this invasive architecture is encoded within multiparametric MRI (mpMRI) feature relationships and can be decoded using a graph-based framework trained on multiregional image-localized biopsies. Across 134 spatially matched biopsy-imaging pairs from 35 patients with primary IDHwt HGG (29 glioblastomas (GBM) and 6 non-glioblastoma HGGs), unsupervised graph community detection identifies two imaging-defined clusters that localize to invasive tumor regions without molecular supervision. Transcriptomic profiling associates these clusters with neuronal (NEU) and glycolytic-plurimetabolic (GPM) molecular programs. Building on this framework, a graph convolutional network (GCN) accurately predicts NEU and GPM transcriptional states in independent training and validation cohorts and significantly outperforms conventional convolutional neural networks. Applied to whole-tumor mpMRI volumes, the trained GCN generates spatially resolved probability maps that quantify the distribution and relative burden of NEU and GPM programs across both MRI contrast-enhancing and non-enhancing invasive regions. These imaging-derived molecular maps stratify patients by overall survival. Increased GPM burden is associated with poorer survival, consistent with the aggressive behavior associated with mesenchymal-like transcriptional programs in IDHwt HGG. In contrast, increased NEU burden is associated with improved survival, identifying a previously unrecognized imaging-derived prognostic biomarker that was not detected by biopsy-based molecular classification alone. Together, these findings establish a graph-based imaging framework for spatially resolved molecular classification of invasive IDHwt HGG and demonstrate that whole-tumor molecular state architecture carries prognostic information beyond conventional tissue sampling.

Integrating tissue-level organisation with sub-cellular resolution and molecular information often requires combining multiple microscopy modalities and scales. However, aligning images acquired with different modalities, settings, or instruments remains challenging. Here, we introduce NucleiSky, a microscopy image registration framework that utilises the spatial arrangement of nuclei or other landmarks as an intrinsic biological fingerprint. NucleiSky represents images as constellations of centroids and aligns them using geometric algorithms and spatial consensus scoring. In benchmark datasets, NucleiSky could localise query regions within larger reference images using as few as five nuclei. We show that NucleiSky can locate high-magnification fields of view within low-magnification overview scans, map these alignments to additional channels, support live brightfield-to-fixed registration using synthetic nuclear labels, and guide microscope retargeting. We further show that the same constellation-matching principle can be extended to 3D localisation and to non-nuclear landmarks. These findings establish local landmark geometry as an intrinsic spatial fingerprint that enables localisation and registration across imaging scales, modalities and microscopy platforms. NucleiSky is available as an open-source Python package and as notebook-based applications.

Due to its ability to establish a pool of undetectable and latently infected cells that can initiate viral production through random reactivation, a cure to human immunodeficiency virus (HIV) infections has remained elusive. Many approaches have been proposed, including the "shock and kill" method where latency reversing agents (LRAs) are administered to reactivate latently infected cells out of latency and remove them through immune targeting and clearance, and the "block and lock" method where latency promoting agents (LPAs) are administered to inhibit reactivation and potentially induce a "deep latency" state where infected cells can no longer reactivate. Previous large scale drug screen studies have demonstrated a correlation between a compound's capability to modulate the fluctuations (or "noise") in HIV gene expression and its potential to modulate HIV latency. However, measurements of gene expression noise are labor- and cost-intensive. To circumvent these drawbacks, we trained a variational autoencoder (VAE) on a previously published large scale time-lapse fluorescence microscopy dataset, and performed an in silico screening of ~175,000 compounds for HIV latency modulators. Out of the top 113 predicted modulators that were experimentally tested, 16 latency reversing agent (LRA) synergizers and 2 latency promoting agents (LPAs) were confirmed, yielding an overall experimental hit rate of 15.9%. Our work demonstrates that in silico drug screening modalities, guided by existing large-scale experimental datasets, can yield high experimental hit rates, reducing costs incurred from labor-intensive wet lab-focused methodologies.

Chemotherapy resistance remains a critical barrier in treating osteosarcoma. Hypoxia-activated prodrugs (HAPs) target oxygen-deprived tumor regions that evade conventional chemotherapy. Here, we applied integrated spatial multimodal mass spectrometry imaging of metabolites (DESI-MSI, MALDI-MSI), targeted proteomics (IMC), and metallomics (LA-ICP-MSI) to naive and newly developed doxorubicin-resistant osteosarcoma spheroids treated with a novel HAP tirapazamine analogue, TPZ-A-X, and doxorubicin. Combination treatment selectively downregulated GLUT1 and suppressed pro-survival pAkt in doxorubicin-resistant spheroids whilst inducing comparable DNA damage ({gamma}H2AX) across both phenotypes. Metabolomics imaging identified ferroptosis pathway suppression in doxorubicin resistance, which combination treatment reversed, whilst simultaneously depleting glycolytic fuels. Integrative protein-metabolite correlation analysis uncovered functional couplings between glucose transport and CoA-dependent metabolism and spatially revealed anabolic signaling at spheroid peripheries. Combination treatment induced endogenous copper, zinc and magnesium depletion, independent of ATP/ADP collapse reflecting an adaptive survival remodeling of the metalloproteome. HAP/chemotherapy combinations exploit metabolic vulnerabilities via coordinated disruption of ferroptosis suppression, glycolytic dependence, and survival pathways underlying apoptotic resistance. These findings demonstrate a framework for informing mechanistic reasoning and combination strategy design in chemotherapy-resistant tumors.

We developed a convolutional neural network-based machine learning technique to simultaneously analyze the morphology and motility of spirochetal bacteria swimming with continuous cellular deformation. Matching probabilities between experimental images and learned models realizes quantification of cell morphology and association with motility. This method can be applied to diverse transformable cells, offering critical biophysical insights into microbial dynamics.

Background: Three-dimensional tumor spheroids are an established radiobiology model, but scalable, reproducible readouts of dose-dependent radiation response are lacking. We evaluated whether optical coherence tomography (OCT) radiomics can quantify dose-associated response in spheroids, and how it compares with conventional brightfield morphology. Methods: This in vitro, cross-sectional study used SAS oral squamous cell carcinoma spheroids seeded at two densities (5000 and 10000 cells), irradiated at 0 to 12 Gy, and imaged on days 1 to 11 post-irradiation. Each OCT acquisition yielded co-registered structural-intensity and speckle-variance volumes. Radiomic features (shape, first-order, texture) were extracted with Radiomics.jl, filtered for repeatability, correlation-pruned, and ensemble-ranked. Dose correlation was assessed by repeated 5-fold cross-validation across five regressors, comparing brightfield-only (BF), OCT-only, and combined OCT+BF feature sets with paired Wilcoxon tests. Results: OCT-only models consistently outperformed the BF baseline (median R2 0.77 to 0.85 versus 0.61 to 0.69; p<0.001 for all regressors). Adding brightfield to OCT gave no consistent benefit, reaching significance only for Random Forest (p=0.026, power 0.62). A compact shared feature subset combined brightfield area dynamics with OCT texture, shape, and speckle-variance descriptors, all showing low repeat-scan variability relative to cohort variability. Conclusions: OCT radiomics provides a sensitive, reproducible, label-free high-throughput readout of spheroid radiation dose response that outperforms the current brightfield-based approach, without requiring concurrent brightfield acquisition.

Clinical treatment planning of near-infrared (NIR) brain stimulation requires patient-specific light dosimetry to optimize fluence delivery to cortical targets. The gold-standard Monte Carlo (MC) photon transport forward solver is accurate but computationally expensive and non-differentiable for personalized inverse design across subjects. Here, we present a foundation-model (FM)-encoded, differentiable implicit-neural surrogate for the MC solver. A pretrained 3D MRI/CT foundation model, VISTA3D, is domain-adapted to head phantoms with known optical properties to encode the subject anatomy. Next, an implicit neural representation is used to predict light fluence at arbitrary continuous coordinates. This formulation enables off-grid queries and gradients with respect to illumination parameters. Trained with a physics-informed, decade-stratified loss, the surrogate attains R2 {approx} 0.90 on held-out subjects. Ablation results show that the FM benefit is contingent on domain adaptation. Benchmarked against standard learned surrogates, our model is the most accurate in the high-dose region and best on dose-fidelity metrics ({gamma}-index, treated-volume DICE). Finally, gradient-based optimization through the surrogate recovers MC-consistent illumination configurations 50-240 x faster.

Viscoelastic characterization of tissue has significant diagnostic value in oncology, as tumor progression alters both elasticity and viscosity in ways that neither property alone can fully capture. Existing acoustic radiation force (ARF)-based methods such as Viscoelastic Response (VisR) ultrasound estimate relative elasticity and viscosity through per-A-line nonlinear model fitting, which is computationally intensive and requires auxiliary simulations to correct elasticity-dependent bias. This work presents VESTA (Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features), a two-stage data-driven pipeline that predicts elasticity ratio (ER) and viscosity ratio (VR) directly from seven normalized ARFI displacement features at the A-line level, without model fitting or compensation. Stage~1 is an MLP classifier that detects inclusion boundaries from normalized peak displacement and negative peak velocity ratios; Stage~2 is a dilated Conv1D regression model that estimates ER and VR along the full axial sequence using the predicted mask alongside displacement features. The pipeline was trained on 500 simulated inclusion scenarios spanning three geometries, five focal depths, two F-numbers, and a broad range of material contrasts. In silico, mean predicted ER and VR were within 12\% of ground truth across all geometries, with performance best when ER and VR were moderate or decoupled. Experimental validation on a chicken breast phantom demonstrated plausible generalization to real tissue heterogeneity. Applied to an in vivo murine 4T1 breast cancer model, the pipeline tracked treatment-related attenuation of mechanical contrast in paclitaxel-treated tumors relative to controls over a 36-day imaging period, supporting its relevance for tumor monitoring.

Fluorescent reporters are powerful tools to reveal intercellular heterogeneity among proliferating cells. However, there are few tools to analyze differences among quiescent (G0) cells, though such differences are relevant for development, tissue maintenance, and cancer cell behavior. Quiescence heterogeneity, also known as quiescence depth, typically correlates with time after cell cycle arrest, yet directly measuring cell age is not feasible for all cell types or most tissues. Here, we describe ELDR-Glo, a genetically-encoded fluorescent biosensor that estimates relative cell age, i.e., time since the last cell cycle. The biosensor integrates replication-coupled degradation in S phase with a slow-maturing mCherry and a normalization module. We demonstrate that ELDR-Glo signal correlates with true cell age by both live-cell imaging and in fixed cells. ELDR-Glo distinguishes early and late G0 cells and functions as a relative quiescence depth reporter in situ. The biosensor is compatible with multiplexed immunofluorescence and flow cytometry. ELDR-Glo provides a unique and scalable tool to investigate cell proliferation control.

Background: Gadolinium-based contrast agents (GBCAs) are routinely used in magnetic resonance imaging (MRI). Although macrocyclic GBCAs were initially considered biologically inert, it is now known that a fraction of patients retains gadolinium (Gd) for prolonged periods in tissues such as blood, bone, and brain. Because the first cellular interactions of GBCAs occur in the bloodstream, this study aimed to elucidate the uptake mechanism but also the intracellular persistence and release dynamics of gadoterate meglumine, one of the most widely used macrocyclic agents, in white blood cells (WBCs). Methodology and principal findings: WBCs and K562 cells were incubated with gadoterate meglumine under different conditions to investigate its cellular entry mechanisms. Uptake of the contrast agent was quantified by measuring intracellular Gd using single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS). Time and concentration-dependent incubation of K562 cells revealed saturable uptake kinetics consistent with a Michaelis-Menten model which is independent of the phase of the cell cycle. Gadoterate meglumine uptake in both WBCs and K562 cells was shown to be an active process, as uptake was strongly reduced or abolished at low temperature (16C and 4C) and in the presence of metabolic inhibitors (sodium azide and 2-deoxyglucose). Co-incubation with multiple endocytosis inhibitors (Dyngo 4a, Dynole 2-24 and chlorpromazine) did not significantly decrease intracellular Gd levels in K562 cells and caused only a slight reduction in WBCs, indicating that endocytosis is not the main entry pathway for gadoterate meglumine in these cells. Furthermore, we assessed the retention time of the Gd inside the cells, showing that only after 24 hours post incubation 80% percent of the intracellular Gd was released through an active process. Finally, we demonstrate that one of the mechanisms of Gd release from WBCs involves extracellular vesicles, which may substantially increase its potential for downstream accumulation in different tissues, including immunoprivileged tissues like brain. Significance: The observed time-dependent accumulation, temperature and energy dependence of gadoterate meglumine uptake demonstrate that active cellular mechanisms are primarily responsible for GBCA internalization. Furthermore, our results indicate that macropinocytosis, phagocytosis, and clathrin-mediated endocytosis are not the primary routes of gadoterate meglumine entry. Hereby, we also describe that Gd externalization is an active process involving extracellular vesicles which may influence the Gd distribution in different tissues and its consequent long-term retention. Further studies are required to explore strategies to block this process in order to mitigate potential long-term gadolinium retention.

Medical imaging pipelines routinely copy single-channel grayscale data into three identical RGB channels before classification, usually without justification. This study tests whether that step affects model predictions. Four coordinated experiments on bit-identical RGB inputs sorted eleven classical machine learning models into three groups: five that were invariant to the copy, two that were nearly invariant, and four whose predictions changed. On the Kaggle Alzheimer MRI Dataset (6,400 images, four classes, five seeds), five models (AdaBoost, HistGradientBoosting, KNN, SVM_Polynomial, and SVM_RBF) produced identical predictions in both conditions for every seed, where KNN is k-nearest neighbors and SVM a support vector machine, with polynomial and radial basis function (RBF) kernels. Two models (GaussianNB and SVM_Linear) differed by at most one of 1,280 samples, a dataset-dependent gap rather than exact invariance. The remaining four (DecisionTree, ExtraTrees, RandomForest, and LogisticRegression) differed substantively. A regularization sweep on Logistic Regression traced its gap to a single cause. As L2 regularization weakened, the color-minus-grayscale macro F1 gap shrank steadily, from +12.07 percentage points at C=0.001 to near zero at C=100 (paired Wilcoxon p=0.0020 under strong regularization), showing the effect scales with feature count rather than image content. A replication on the OASIS dataset, matched in size and class balance, reproduced every grouping, and the Logistic Regression gap reappeared in the same direction at smaller magnitude (+5.30 points macro F1). Two deep networks, ResNet18 and DenseNet121, gave identical predictions across all twenty paired conditions. Channel triplication left most models unchanged while multiplying classical training time 2.3 to 4.0 times without benefit.

Spectral bleed-through remains a persistent practical problem in multichannel fluorescence microscopy. Signal from one fluorophore can be recorded in the detection channel of another, thereby biasing intensity measurements, inflating apparent colocalization, and complicating the interpretation of dynamic microscopy data. Although many correction strategies exist, routine workflows often remain fragmented across ad hoc scripts, manually tuned graphical procedures, or method-specific blind-unmixing implementations with limited provenance. Here we present spectral-unmixing, an open-source Python package for reproducible linear spectral unmixing in multidimensional microscopy stacks. The package unifies directed two-channel correction with multiple alpha-estimation strategies, optional bidirectional two-channel correction through explicit inversion of a 2 x 2 mixing model, and PICASSO-family blind unmixing for multichannel data. Microscopy inputs are normalized at the API boundary to canonical TZCY X stacks, allowing the same unmixing code to be applied across file formats without manual axis handling. Machine-readable sidecar reports preserve the effective processing configuration and estimated coefficients for every output, so that workflows can be audited and reproduced. Synthetic and real-data-derived benchmarks show that the implemented workflows accurately estimate and correct bleed-through when their model assumptions are satisfied. In fixed-alpha two-channel simulations, the mean-ratio and linear-fit estimators recovered {approx} 0.283 for a ground-truth value of 0.28 and reduced target-channel normalized root mean squared error from approximately 0.029 to 0.003. In time-varying simulations, per-time-point estimation tracked coefficient drift substantially better than reference-time-point estimation. Bidirectional inversion recovered reciprocally mixed channels accurately when coefficients were known or well estimated. PICASSO-family benchmarks further showed a practical trade-off between reducing residual inter-channel dependence and preserving fluorophore identity, with MATLAB-style workflows behaving more conservatively and source-sink formulations providing stronger dependence suppression when meaningful directional priors are available. Together, these elements make spectral-unmixing a practical, transparent, and extensible platform for reproducible spectral unmixing of fluorescence microscopy data in neuroscience and other quantitative bioimage-analysis settings.

High-grade serous carcinoma (HGSOC) is the deadliest subtype of ovarian cancer, characterized by high metastatic rates. HGSOC is typically diagnosed at late stages, and treatment options are limited, resulting in a 60% recurrence rate. HGSOC cells exhibit metabolic plasticity, dynamically shifting between glycolysis and oxidative phosphorylation (OXPHOS) to meet energy demands for tumor progression. To evaluate therapeutic strategies that target metabolic vulnerabilities, we developed a microphysiological system (MPS) that recapitulates the heterogenous cell states and bioenergetic distribution of HGSOC solid tumors. Our platform utilized HGSOC spheroids embedded in a collagen hydrogel that mimics the extracellular matrix to capture tumor progression in the ovary. We used atovaquone (ATO), an FDA-approved OXPHOS inhibitor, to prototype the capabilities of our platform to investigate metabolic plasticity in HGSOC. Treatment with ATO decreased viability and invasion of HGSOC spheroids. Crucially, ATO exhibited no cytotoxicity toward biomimetic blood vessels, preserving their integrity and permeability. Metabolic imaging revealed that ATO induces an oxidative state in the outer region of the spheroids. At the invasive front, ATO disrupted mitochondrial organization, forcing collective cell migration and eventually inducing breakdown of mitochondrial networks. Furthermore, ATO decreased YAP/TAZ pathway activity in the outer region of the spheroid, providing a potential mechanism for hindered cell invasion. Collectively, our data demonstrates that a low-potency OXPHOS inhibitor like ATO can effectively target metabolic plasticity to suppress HGSOC spheroid progression. Overall, this platform successfully recapitulated metabolic heterogeneity and provided a workflow for safely testing other drugs that target cancer metabolism.

Poor aqueous solubility remains a major obstacle to the translational development of targeted anticancer compounds. VS1, a first-in-class inhibitor of the cholesterol-transfer protein STARD3, has emerged as a promising chemosensitizing agent in colorectal cancer (CRC), but its clinical applicability is limited by its poor water solubility. Here, we combine structural biology, nanotechnology, and functional pharmacology to establish STARD3 inhibition as a delivery-enabled strategy to potentiate fluoropyrimidine therapy. To define the molecular basis of STARD3 inhibition, we solved the crystal structure of VS1 bound to the STARD3 ligand-binding domain at 2.1 [A] resolution, revealing direct occupation of the sterol-binding cavity. Molecular dynamics simulations confirmed a stable binding mode and identified the {Omega}1 loop as a dynamic gate regulating ligand binding and dissociation. To overcome the formulation barrier of VS1, we engineered carrier-free, albumin-coated nanocrystals through sonication-assisted nanocrystallization followed by surfactant exchange with human serum albumin. The resulting rod-shaped nanocrystals displayed nanometric size, narrow size distribution, sustained release, and improved aqueous dispersibility, increasing the apparent solubility of VS1 by more than 14-fold while preserving its molecular integrity and crystallinity. Biologically, VS1 selectively potentiated 5-fluorouracil (5-FU) in CRC cells, with synergistic effects restricted to 5-FU-sensitive models and associated with enhanced reactive oxygen species accumulation. Albumin-coated formulation retained the chemosensitizing activity of the free compound. In HCT-116 xenografts, combined treatment with albumin-coated VS1 nanocrystals and 5-FU significantly reduced tumor growth, prolonged tumor doubling time, and increased intratumoral necrosis without exacerbating systemic toxicity. Together, these findings establish that albumin-coated nanocrystals can overcome the delivery limitations of an insoluble STARD3 inhibitor and provide a formulation-enabled strategy to enhance fluoropyrimidine therapy in colorectal cancer.

The MS2 system has been widely adopted for live-cell single molecule imaging of mRNA, but is of limited applicability for RNAs present in the cell nucleus due to excess nuclear-localised fluorescent capsid protein. Here we describe Nuclear-BRITE, an improved methodology that reduces nuclear background signal, enabling fast live-cell imaging of a wide range of endogenously tagged nuclear RNAs at single-molecule resolution without perturbing their abundance, localisation or function.

Designing transcutaneous skeletal muscle oxygenation (SmO2) sensors requires jointly optimizing source--detector geometry and wavelength selection while guaranteeing performance across populations that vary in subcutaneous fat thickness and skin pigmentation. We present a multi-fidelity Bayesian optimization (MFBO) framework that couples Monte Carlo light-transport simulations at two photon-count fidelities to a distributionally robust design objective. An autoregressive Gaussian-process surrogate learns the correlation between inexpensive low-photon-count and accurate high-photon-count simulations, and a cost-aware acquisition function decides both where and at what fidelity to sample. Robustness across the population is enforced with Conditional Value-at-Risk (CVaR) and entropic-risk (ERM) objectives that target worst-case subjects rather than the population average. On a five-layer forearm tissue model with anthropometric variability we find (i) a fidelity regime that is favorable for MFBO where the low-fidelity surrogate is rank-informative (Spearman {rho} = 0.84) but biased, at 100x lower cost; (ii) MFBO attains 23% higher robust sensitivity than a strong high-fidelity single-fidelity baseline at equal budget (p = 0.035), and avoids the optimistic bias that causes low-fidelity-only optimization to collapse when its designs are validated at high fidelity; (iii) CVaR/ERM objectives improve worst-case tail performance by {approx}23% relative to a mean objective without sacrificing average sensitivity; and (iv) discovered designs improve robust tail sensitivity by roughly 3--6x over commercial and heuristic optode layouts, with the largest gains in the high-fat and high-melanin subpopulations. The methodology bridges stochastic light-transport physics with sample-efficient machine-learning optimization and generalizes to cerebral oximetry, photodynamic therapy planning, and wearable physiological monitors.

Glioblastoma (GBM) remains one of the most lethal primary brain tumors, in part because the blood-brain barrier (BBB), restricts delivery of most systemically administered chemotherapeutics. Although focused ultrasound (fUS) can transiently increase BBB permeability, therapeutic efficacy remains limited by reliance on systemic drug exposure and heterogeneous intratumoral distribution. Here, we report a pressure-gated ultrasound-triggered drug delivery strategy that enables localized intravascular release of chemotherapy at the site of sonication. Freebase doxorubicin and afatinib were encapsulated within ultrasound-sensitive mPEG-PDLLA/PFOB microdroplets and administered systemically to N-TVA::Ink4a/Arflox/lox;Ptenlox/lox mice bearing genetically engineered glioblastomas. Animals received repeated transcranial focused ultrasound over a 30-day treatment period. Ultrasound-triggered release of the dual-drug formulation significantly extended survival compared with untreated controls, with median survival increased by over two weeks - approximately a 30% improvement. Furthermore, this survival improvement was reflected in histological analysis, showing decreased tumor burden and severity. These improvements were not found in any control groups, demonstrating that spatially and temporally controlled intravascular drug release can substantially improve therapeutic efficacy in an aggressive immunocompetent glioblastoma model. These findings support pressure-gated ultrasound-triggered chemotherapy as a promising activation-based strategy for overcoming BBB-associated delivery limitations and improving outcomes in malignant brain tumors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/735435v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1767043org.highwire.dtl.DTLVardef@c46048org.highwire.dtl.DTLVardef@8d3b44org.highwire.dtl.DTLVardef@2df0b8_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPressure-gated focused ultrasound enables localized release of doxorubicin and afatinib in glioblastoma. C_LIO_LIUltrasound-triggered chemotherapy significantly extends survival in a genetically engineered immunocompetent GBM model. C_LIO_LILocal activation outperforms systemic administration of identical drug combinations. C_LIO_LIThis strategy shifts focused ultrasound therapy from general BBB opening to spatially controlled drug activation. C_LI

Whole-genome sequencing identifies focal DNA amplifications with base-pair resolution but cannot determine whether amplified sequences reside on extrachromosomal DNA (ecDNA, also known as double minutes) or within chromosomally integrated homogeneously staining regions (HSRs). DNA fluorescence in situ hybridization (DNA-FISH) metaphase spreads remain the gold standard for distinguishing these amplification states at single-cell resolution. Here, we present a detailed protocol for DNA-FISH metaphase spreads using human cancer cell lines, encompassing cell culture, metaphase arrest, hypotonic treatment, fixation, chromosome spreading, fluorescent probe hybridization, and fluorescence imaging. The protocol incorporates intermediate quality-control steps to verify successful chromosome dispersion and optimize metaphase spread quality, making the workflow accessible to laboratories without specialized cytogenetics expertise. Results demonstrate clear visualization of ecDNA and HSR amplification states using locus-specific probes and illustrate common technical artifacts that can affect interpretation. This protocol provides a robust and reproducible approach for studying the structural organization of oncogene amplification in cancer cells.

Objective: Preclinical small-animal irradiators such as the FLASH-SARRP can support the advancement of photon-FLASH toward the clinic. This study aimed at characterizing the FLASH-SARRP and established a robust quality assurance (QA) workflow to enable accurate and reproducible preclinical experiments. Approach: Custom 3D-printed spacers were designed to ensure reproducible X-ray tube alignment, sample positioning and mounting of the dosimetric tools. Beam characteristics were evaluated using a combined dosimetric approach. High spatially resolved dose distributions were obtained from Gafchromic films, whereas a plastic scintillating fiber was employed to monitor in real-time the temporal pulse structure and synchronization between the two X-ray tubes. Day-to-day variability of the delivery was evaluated over several sessions. Main results: The FLASH-SARRP achieved dose-rates of around 80 Gy/s when both tubes were used simultaneously and provided a homogeneous irradiation field suitable for small-animal studies. A desynchronization between the two tubes was observed with an average delay of 10 ms, resulting in temporal dose-rate heterogeneity. Additionally, a substantial inter-session variability (~11%) was found, whereas the intra-session variability was relatively low (~4%). Inter-session variability was reduced to 5%, approaching the intra-session variability, by adding Gafchromic films/scintillator-based quality assurance (QA) workflow into the irradiation routine. Significance: This work highlights the importance of temporal dosimetry for preclinical FLASH studies. Additionally, a practical QA framework is proposed integrating real-time monitoring with reference dosimetry. The proposed work enables adaptive dose delivery, thereby enhancing the reproducibility of the irradiations, which is crucial for reliable preclinical studies on the FLASH effect.