Immunometabolism is a rapidly developing field that holds great promise for diagnostic and therapeutic benefits to human diseases. The field has emerged based on seminal findings from in vitro and ex vivo studies that established the fundamental role of metabolism in immune cell effector functions. Currently, the field is acknowledging the necessity of investigating cellular metabolism within the natural context of biological processes. Examining cells in their native microenvironment is essential not only to reveal cell-intrinsic mechanisms but also to understand how cross-talk between neighboring cells regulates metabolism at the tissue level in a local niche. This necessity is driving innovation and advancement in multiple imaging-based technologies to enable analysis of dynamic intracellular metabolism at the single-cell level, with spatial and temporal resolution. In this review, we tally the currently available imaging-based technologies and explore the emerging methods of Raman and autofluorescence lifetime imaging microscopy, which hold significant potential and offer broad applications in the field of immunometabolism.
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http://dx.doi.org/10.1097/IN9.0000000000000044 | DOI Listing |
PLoS Pathog
December 2024
Regional Centre for Biotechnology, Faridabad, India.
Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus causing fever, myalgia, and debilitating joint swelling and pain, which in many patients becomes chronic. The frequent epidemics of CHIKV across the world pose a significant public health burden necessitating the development of effective antiviral therapeutics. A cellular imaging-based high-content screening of natural compounds identified withaferin A (WFA), a steroidal lactone isolated from the plant Withania somnifera, as a potent antiviral against CHIKV.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Orthopedics, Kaohsiung Medical University Hospital, Kaohsiung 807378, Taiwan.
The hospital-at-home (HaH) model delivers hospital-level acute care, including diagnostics, monitoring, and treatments, in a patient's home. It is particularly effective for managing conditions such as pneumonia. Point-of-care ultrasonography (PoCUS) is a key diagnostic tool in the HaH model, and it often serves as a substitute for imaging-based diagnosis in the HaH setting.
View Article and Find Full Text PDFImaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P. R. China.
Background: This study aims to explore the value of habitat-based magnetic resonance imaging (MRI) radiomics for predicting the origin of brain metastasis (BM).
Purpose: To investigate whether habitat-based radiomics can identify the metastatic tumor type of BM and whether an imaging-based model that integrates the volume of peritumoral edema (VPE) can enhance predictive performance.
Methods: A primary cohort was developed with 384 patients from two centers, which comprises 734 BM lesions.
Sci Rep
December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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