IMU Biosciences

IMU Biosciences is a biotechnology company founded in 2021 by Dr. Adam Laing, Dr. Tom Hayday, and Mario Cantero, developing a platform to map the human immune system. The company's technology is based on a decade of research from King's College London and the Francis Crick Institute. The COVID-19 pandemic was a catalyst for the team's research to better understand the human immune response to viruses.

The company's core product is the CytAtlas™ platform, which combines systems-level immune profiling with a proprietary AI and machine learning platform. This technology analyzes over 200 million data points from a small blood sample to quantify 2,500 discrete cell subsets, creating a detailed atlas of the immune system. The platform aims to provide a deeper understanding of the immune system's role in health and disease, with applications in immuno-oncology, cell therapy, autoimmune disorders, and transplantation. By partnering with biotech firms, pharmaceutical companies, and academic institutions, IMU Biosciences provides clinically actionable insights for diagnostics, drug development, and predicting patient response to treatments. This approach allows for the matching of patients with the most effective therapies and real-time immune monitoring throughout treatment.

In January 2024, IMU Biosciences secured £11.5 million in a Series A funding round led by Molten Ventures, with participation from LifeX Ventures. This capital is being used to advance the CytAtlas™ platform, foster new strategic partnerships, and expand its operations into the Boston/Cambridge, MA area in the US. The company is a key participant in the MANIFEST consortium, a major UK initiative investigating patient responses to cancer immunotherapies, where it leads large-scale immune phenotyping projects.

Keywords: immune profiling, precision medicine, AI in biotech, immunology, CytAtlas, systems biology, drug discovery, diagnostics, immuno-oncology, cell therapy, autoimmune disorders, biomarker discovery, translational research, machine learning, liquid biopsy, patient stratification, treatment response, multi-omics, immunophenotyping, UK biotech