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Levin, C.

Publications and source records attributed to Levin, C..

2 recordsLinked to original sources

Rhizo-PET: A Dedicated PET System for 4D Imaging of Carbon Dynamics in the Rhizosphere

Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant-soil systems. The system achieved a global energy resolution of 11.93 {+/-} 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants (N= 3) over 180 min following 11CO2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments (p <0.001). Hierarchical variability analysis showed that within-plant spatial organization (CVTTP = 0.03) is significantly more stable than inter-plant variation (CVTTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.

plant biology↗

A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue section

In the ever-changing world of digital pathology, being able to extract a maximum amount of information from a patient tissue sample is of paramount importance for better diagnosis, disease characterization, and therapeutic strategies. Recent technologies such as multiplex immunofluorescence imaging and spatial transcriptomic now enable a deep analysis of protein and gene expression while retaining the spatial context of the tissue. Here, we describe an innovative approach combining a 34-protein Phenocycler panel and transcriptome analysis using Visium on a single head and neck squamous cell carcinoma section. While protein analysis reveals the complexity of the immune phenotypes involved in the disease, transcriptome analysis reveals the intricate cellular states of cancer cells that coexist within the patients tumor. Finally, integrating both omics modalities, we uncover unique comparison of gene and protein expression of spatially resolved cellular subspaces.

cancer biology↗