Nucleai Launches Deep Learning Model to Automate mIF Normalization in Spatial Proteomics

Nucleai, a Grove Ventures portfolio company, has announced the launch of a first-of-its-kind deep learning model designed to automate multiplex immunofluorescence (mIF) normalization. This breakthrough significantly accelerates biomarker discovery in spatial biology, with a particular focus on advancing antibody-drug conjugates (ADCs), bispecifics, and immunotherapies.

How Do You Solve One of Spatial Biology’s Toughest Bottlenecks?

Spatial proteomics offers powerful insights into tissue microenvironments and biomarker interactions, but mIF—one of the leading technologies in the field—has been hindered by complexity and variability across samples. What is more, Nucleai’s new deep learning model addresses this challenge by delivering automated, scalable, and reproducible mIF normalization, enabling high-throughput analysis across large patient cohorts.

The platform empowers biopharma and translational research teams to derive more consistent and precise spatial insights, ultimately reducing the time and cost required for drug development and biomarker validation.

Enabling Next-Generation Precision in Oncology R&D

This launch represents a major step forward for spatial biology in drug discovery and development. By automating a previously manual and error-prone step, Nucleai’s solution enhances the ability of R&D teams to detect spatial patterns that correlate with therapeutic response, resistance, and patient stratification.

To learn more about the model and its applications in precision medicine, read the full press release.

At Grove Ventures, we are proud to support pioneering platforms like Nucleai that are transforming precision oncology at scale.