npj Imaging最新文献

Chemical modification of monoclonal antibodies (mAbs) and their fragments gives rise to imaging probes and targeted therapies. Depending on the isotope used, radiolabeled mAbs enable positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging and can also be applied as cytotoxic therapies. Fluorescent mAb conjugates are used for a range of preclinical applications with clinical utility for intraoperative visualization of tumors. Antibody-drug conjugates (ADCs) enhance the therapeutic efficacy of mAbs and are the topic of extensive clinical development. In all these cases, chemical modifications can significantly affect mAb tumor targeting and clearance. Whole-body imaging techniques provide crucial insights into the in vivo consequences of these changes by directly tracking antibody conjugate distribution and clearance. This review examines in vivo imaging studies that compare "parental" and "modified" mAbs imaged under identical conditions to assess the effects of the cargo itself (e.g. fluorophore, chelator, drug), as well as the chemical conjugation methods. Additionally, we also describe studies that evaluate alternative strategies, including pretargeting, Fc modifications and pre- or co-dosing strategies that seek to tune the biodistribution of a given conjugate. Overall, we highlight the critical role of imaging in characterizing the in vivo performance of mAb conjugates, underscoring how these insights can inform both therapeutic efficacy and toxicity, and enable clinical translation.

Deuterium metabolic imaging (DMI) is a new imaging approach that provides unique, complementary information to anatomical MRI of brain tumors. Preclinical DMI studies have demonstrated excellent image contrast following intravenous infusion of deuterated choline (²H₉-Cho) at a severalfold higher dose than recommended for humans. We investigated DMI performance in rat glioblastoma models after oral administration of a ²H₉-Cho dose recommended for humans. DMI, following the three daily oral low doses, resulted in ²H₉-Cho concentrations in the tumor and tumor-to-normal-brain image contrast comparable to a single, high intravenous dose. Further, ²H and 2D ¹H-¹⁴N HSQC NMR on excised tumor tissue revealed that oral administration led to increased contributions from Cho-derived molecules that were products of tumor metabolism compared to intravenous infusion of ²H₉-Cho. These results can advance clinical translation of Cho-DMI as a noninvasive imaging tool for brain tumor characterization by demonstrating the feasibility of an oral intake approach using a clinical dose.

Cell analysis technologies play a critical role in biomedical research, enabling precise evaluation of essential parameters such as cell viability, density, and confluency. In this article, we introduce Quantella, a smartphone-based platform designed to perform comprehensive cell analysis encompassing these key metrics. Addressing limitations of conventional systems, such as high cost, hardware complexity, and limited adaptability, Quantella integrates low-cost optics, a rinsable flow cell, bluetooth-enabled hardware control, and a cloud-connected mobile application. Its adaptive image-processing pipeline employs multi-exposure fusion, thresholding, and morphological filtering for accurate, morphology-independent segmentation without requiring deep learning or user-defined parameters. System validation studies across diverse cell types showed deviations under 5% from flow cytometry. With the capacity to analyze over 10,000 cells per test, Quantella delivers high-throughput, reproducible results. Its accessible, scalable design makes it a promising tool for biomedical research, diagnostics, and education, particularly in resource-limited settings.

Image-based profiling is rapidly transforming drug discovery, offering unprecedented insights into cellular responses. However, experimental variability hinders accurate identification of mechanisms of action (MoA) and compound targets. Existing methods commonly fail to generalize to novel compounds, limiting their utility in exploring uncharted chemical space. To address this, we present a confounder-aware foundation model integrating a causal mechanism within a latent diffusion model, enabling the generation of balanced synthetic datasets for robust biological effect estimation. Trained on over 13 million Cell Painting images and 107 thousand compounds, our model learns robust cellular phenotype representations, mitigating confounder impact. We achieve state-of-the-art MoA and target prediction for both seen (0.66 and 0.65 ROC-AUC) and unseen compounds (0.65 and 0.73 ROC-AUC), significantly surpassing real and batch-corrected data. This innovative framework advances drug discovery by delivering robust biological effect estimations for novel compounds, potentially accelerating hit expansion. Our model establishes a scalable and adaptable foundation for cell imaging, holding the potential to become a cornerstone in data-driven drug discovery.

Ferroptosis emerged as a cell death modality against cancer, but there are currently no available biomarkers for imaging ferroptosis-based therapies. To address this, we evaluated phosphatidylserine exposure and perforation of lipid membranes during ferroptosis to explore potential targeting opportunities. We demonstrated that nano-sized gaps at late stage ferroptosis can serve as entry points for dyes that can bind to intracellular structures. These changes were accompanied with cellular signaling components similar to platelet activation, with phosphatidylserine exposure on the cell surface as a potential target for imaging programed cell death, including ferroptosis. We employed a novel tumor-seeking dye CJ215 that can also label apoptotic cells and showed that CJ215 accumulates in ferroptotic cells both in vitro and in vivo by binding to phosphatidylserine, which can be prevented with ferroptosis inhibition. Since phosphatidylserine exposure also occurs during apoptosis, CJ215 can monitor both apoptosis and ferroptosis-based therapies.

Three-dimensional (3D) image reconstructions are often rendered as two-dimensional images, using maximum intensity projections (MIPs). However, MIP's rendering fidelity depends on the alignment of the individual slices along the projection direction. Also, the presence of noise and artifacts affects the contrast and the projected image elements. We introduce enhanced MIP (eMIP), a methodology that aligns the boundaries (e.g., skin boundary) of adjacent slices of the 3D volume onto the same coordinate system assumed by MIP (e.g., same depth) and applies robust contrast adjustment to normalize the intensities of the projected slices. We benchmark eMIP on 1725 clinical scans of human skin, using raster-scan optoacoustic mesoscopy (RSOM) that were assessed by 8 experts. Our results show that eMIP facilitates interpretability compared to conventional MIP and increases consistently the perceived image quality. The improved diagnostic ability of eMIP has the potential to replace MIP in RSOM and similar modalities.

Uncovering structural information of biological systems at the nanoscale is vital for understanding their dynamics and function. Nanoscale imaging techniques that obtain structural information down to the single-molecule level under physiologically relevant conditions and without affecting the fragile structure of biomaterials are limited. Thus, the realization of such techniques is highly attractive, especially within the biological sciences. Nanopipette-based imaging using scanning ion conductance microscopy (SICM) fulfills these requirements, but resolution limitations and artefact formation hinder obtaining accurate structural information on the scale comparable to the pipette tip. Here, we present a novel technique, super-resolution SICM (SR-SICM), based on image deconvolution using simulated pipette point-spread functions. The technique is demonstrated on different types of nanostructures, where it surpasses the lateral resolution limit of SICM and mitigates imaging artefacts considerably. SR-SICM is applicable to any SICM dataset through user-friendly downloadable software promoting the possibility of single-molecule studies on a routine basis.