Senseera’s platform supports the early identification and management of liver conditions. Credit: Jo Panuwat D / Shutterstock.

Senseera has closed a seed funding round, securing $7.1m to further develop its GEM BIOMARKERS liquid biopsy platform.

Lightspeed Venture Partners led the financing round, with contributions from the Lowy Medical Research Institute and I-Next Capital.

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The investment is set to propel the platform’s development, which leverages cfChIP-seq technology.

The platform analyses gene expression and cell states specific to individual cells from a blood sample, offering in-depth molecular insights for disease diagnosis and monitoring.

This method provides biopsy-level ‘accuracy’ while offering the convenience of a non-invasive blood test.

Current diagnostic methods frequently depend on invasive, high-risk biopsies or non-specific tests, leading to delayed diagnoses and compromised patient care.

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Senseera noted that its platform supports the early identification and management of liver conditions such as liver transplant rejection, metabolic dysfunction-associated steatotic liver disease (MASLD) / metabolic dysfunction-associated steatohepatitis (MASH), and hepatocellular carcinoma.

Senseera CEO Dr Ronen Sadeh said: “This funding marks a major milestone in Senseera’s journey to transform diagnostics for liver diseases and beyond, and to accelerate drug development.

“With the support from Lightspeed and our investors, we will advance clinical trials, strengthen collaborations with global pharma leaders, and expand our pipeline to improve patient outcomes.”

The funding will be instrumental in accelerating the commercialisation of the company’s transplant rejection test, advancing clinical trials, and fostering partnerships within the biopharmaceutical industry.

GEM BIOMARKERS’ potential extends beyond liver diseases, offering advantages for therapy development in oncology and immunology by monitoring solid tissue and immune cell states.

This provides clinicians and researchers with a better understanding of the underlying mechanisms to support personalised treatments.