Federal grant · project grant (b)
Pan-xlsm: an Ai-powered Microscopy Platform for Imaging and Analysis of Highly Expanded Tissue Samples - Project Summary Addressing the Complexities of Biological Function and Disorders at the Molecular Level Requires a Spatial Biology Technology That Can Map the 3D Distribution of Molecular Markers at the Nanoscale Within Tissues. While Spatial Proteomics Technologies Have Revolutionized in Situ Molecular Profiling of Tissues, Limitations in Resolution, Epitope Accessibility, Sample Degradation, and 3D Imaging Capability Hinder Their Widespread Impact. to Address These Issues, We Developed Pan-expansion Microscopy (pan-exm), Achieving 3D Multiplexed Protein Imaging at ~20 NM Resolution in 3D Ultrastructural Context. Despite Its Promising Adoption, Pan-exm Is Still a Manual, Labor-extensive, and Data-intensive Process, Underscoring the Need for Automation. This Is the Focus of Our Project. Termed Pan-xlsm, We Propose to Develop an Automated Fluidics Light Sheet Microscope and a Robust Software Platform for Ai-driven Image Segmentation and Analysis. Panluminate, Inc. Is a Leading Company in the Spatial Biology Field. Our Proposed Project Aims to Develop Pan-xlsm and Validate It for the Characterization of Mitochondria Morphology Disruption in Kidney, Liver, and Brain Tissues. Specifically, We Propose to (1) Develop a Commercial Prototype of an Automated Microfluidics Axially-swept Light-sheet Microscope; (2) Develop Ai-driven Image Analysis Software for Sub-organelle Segmentation; and (3) Characterize Mitochondria Ultrastructure Disruption in Kidney, Liver, and Brain Tissues as Test Bed Applications. the Developments Proposed in This Project Will Significantly Enhance Panluminate's Existing Commercial Preparation Service. Pan-xlsm Will Offer Our Customers a Cutting-edge, Automated 3D Imaging and Segmentation Analysis Module That Surpasses Current Technologies by Far. Additionally, This Project Will Pave the Way for the Future Commercialization of a Standalone Platform Product.
Committed
$2.5 Million
Paid out
$774.6K
31%
Committed, not yet paid
$1.7M
69%
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