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Biology subjects

Ali, S. R.

Publications and source records attributed to Ali, S. R..

2 recordsLinked to original sources

Towards Reliable Tracking of Natural Killer Cells Using Commercial Iron Oxide Nanoparticles and Magnetic Particle Imaging

Non-invasive tracking of natural killer (NK) cells remains a major challenge in cancer immunotherapy, limiting our understanding of their in vivo migration and persistence. Magnetic particle imaging (MPI) offers a quantitative, real-time method for visualizing labeled cells, yet optimal labeling protocols for NK cells have not been established. Here, we evaluate commercially available iron oxide nanoparticles (IONPs) for MPI labeling of both NK92MI cells and primary human NK cells. Labeled cells retained viability and cytotoxicity, including activity against three-dimensional tumor spheroids, and were detectable by MPI. To further examine imaging performance in a biologically relevant context, we employed mouse phantoms that recapitulate organ-specific signal distributions, enabling evaluation of quantification and liver spillover effects. We identify key tradeoffs between particle colloidal stability and per-cell iron content: VivoTrax and VivoTrax Plus provided higher MPI signal but required post-labeling purification, reducing cell recovery, whereas Synomag-D and Perimag were more stable and preserved cell yield despite lower signal intensity per cell. These results provide a framework for selecting nanoparticles that balance detection sensitivity, cell viability, and workflow practicality, advancing non-invasive NK cell tracking.

bioengineering↗

Deep Learning Identifies Cardiomyocyte Nuclei in Murine Tissue with High Precision

Proper identification and annotation of cells in mammalian tissues is of paramount importance to biological research. Various approaches are currently used to identify and label cell types of interest in complex tissues. In this report, we generated an artificial intelligence (AI) deep learning model that uses image segmentation to predict cardiomyocyte nuclei in mouse heart sections without a specific cardiomyocyte nuclear label. This tool can annotate cardiomyocytes highly sensitively and specifically (AUC 0.94) using only cardiomyocyte structural protein immunostaining and a global nuclear stain. We speculate that our method is generalizable to other tissues to annotate specific cell types and organelles in a label-free way.

cell biology↗