Dutch-based
startup QT Sense has raised €4 million to advance Quantum Nuova, a
quantum-based platform that monitors cellular stress in living cells at
single-cell resolution.The funding includes a €3 million seed round led by
Cottonwood Technology Fund, alongside follow-on funding from QDNL Participationsand an angel investor.The total funding includes a €0.6 million ONCO-Q grant to fast-track oncology applications, along with €0.4 million from the Quantum Forward Challenge to support collaborative deployment and validation of Quantum Nuova in real research environments.
While
traditional biological methods typically analyse fixed tissue or non-living
cells, QT Sense’s Quantum Nuova platform measures biochemical activity in
living cells and tissues in real time, enabling new approaches in spatial
biology.
The
platform uses fluorescent nanodiamond quantum sensors to detect oxidative
stress, metabolic changes, and free radical activity—signals that are central
to disease processes but have been difficult to observe directly.
By
providing a live view of cellular behaviour at the individual cell level,
Quantum Nuova allows researchers to study how cells respond to drugs, adapt to
stress, and differentiate into distinct subpopulations, offering insights
beyond those available through genomics, proteomics, or conventional imaging.
The
technology has already been used to study the mechanisms of action of
FDA-approved drugs. With support from the ONCO-Q grant, it will now be applied
to colorectal cancer research, with the aim of mapping oxidative stress and
metabolic vulnerabilities in tumour models to support future diagnostic and
therapeutic development.
The new investment will support the transition of Quantum Nuova from a high-performing
prototype to a deployable discovery platform.
Planned developments include
improvements in hardware robustness, throughput, and integrated analytics to
support real-world use. Early-access systems will be deployed with strategic
partners to enable mechanism-of-action studies, functional heterogeneity
analysis, and rapid, label-free measurements across multiple samples.