New tool unlocks immunofluorescence data for drug discovery


A new spatial biology platform using deep learning could establish new levels of accuracy, speed, and generalisability in multiplex immunofluorescence (mIF) analysis.

While mIF is a powerful technology for cancer research, current tools for mIF image analysis are time-consuming, manual, and not robust enough to generalise across different platforms, markers and indications.

The new platform brings 40% improvement in accuracy compared to other solutions, while also reducing mIF analysis time-to-results from months to weeks.

Dr Ken Bloom, Head of Pathology at Nucleai, said: “Our deep learning approach will enable the standardisation of mIF-based analysis and help establish multiplex as common practice throughout drug R&D.”

Nucleai’s AI spatial models, which are optimised for multiplex assays, derive new insights from tissue biopsies, including novel drug targets, mechanisms of action, and potential biomarkers to advance the field of precision medicine. The tumour microenvironment is a highly complex ecosystem, and spatial biology can be used to unlock the important relationships and interactions.

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