DropGenie Lands $5M Seed+ to Scale Gene-Editing in Scarce Patient Cells

DropGenie has closed a $5 million Seed+ round to commercialize an automated gene-editing platform that runs experiments in primary patient-derived cells while using up to 100 times fewer cells than conventional workflows. For drug-discovery labs that routinely run out of precious patient samples, that's the whole pitch in one number.

The financing came from BDC Thrive Venture Fund and Skeleton Key, with participation from Merck Global Health Innovation Fund, CQDM, Real Ventures and Anges Quebec. The company, which operates out of Montreal and Boston, did not disclose its valuation, individual investor checks or revenue targets.

What does the platform actually change for researchers?

Primary human cells — taken directly from patients — are the gold standard for understanding real biological responses, but they're scarce. That scarcity caps how many experiments a lab can run. DropGenie's microfluidic technology miniaturizes and automates gene-editing workflows, cutting both the cells and the reagent volumes each experiment consumes.

The result, according to the company, is high-efficiency gene editing in cells that labs previously couldn't afford to study at scale. The platform targets applications in functional genomics, cell therapy research and AI-assisted drug development. DropGenie says its technology is already being integrated into research workflows at major pharmaceutical, biotechnology and research organizations — though it declined to name any of them.

"Primary human cells hold the answers drug developers need, but they've been too scarce to study at scale," said Alison Hirukawa, Co-founder and Chief Executive Officer of DropGenie. "We make those experiments practical, and BDC Thrive Fund helps us bring that to more teams, faster."

Three ways to buy in

The new capital funds a three-track commercial model. Customers can:

• Run DropGenie protocols inside their own laboratories
• Contract the company to conduct studies as a service
• Establish strategic partnerships to generate standardized biological datasets

The third track is the strategic bet. DropGenie is building standardized functional datasets from primary patient cells with pharmaceutical and biotechnology partners — data that supports research into gene function, disease mechanisms and therapeutic targets, and that could feed and evaluate AI-driven drug discovery tools. Those data partnerships sit alongside hardware and services as a separate commercial line.

A new institutional backer at the board table

BDC Thrive's participation brings a new institutional investor — and a long-term Canadian partner at the board level. The $350 million fund, operated by the Business Development Bank of Canada, invests in women-led Canadian technology companies from pre-seed through Series B.

"DropGenie is building critical infrastructure for the future of drug discovery by making high-quality, human-relevant biological data more accessible and scalable," said Mona Minhas, Managing Partner of BDC Thrive Venture Fund.

"We see an opportunity to build a globally important company from Canada, and Alison has the scientific depth, ambition and vision to lead it. We're proud to back her and the DropGenie team as they scale globally."

Skeleton Key framed its check around the AI angle. "We look for science companies that are accelerating discovery with innovative technology and DropGenie is doing just that by generating reliable experimentation data to enable the adaptation of AI-assisted innovation in the future," said Ann Lai, Managing Partner of Skeleton Key. "We're excited to have BDC Thrive Fund join the Board and show support for a homegrown deep-tech company."

The timeline that matters

n DropGenie is working with partners across several primary-cell research applications now, with a broader commercial rollout planned for early 2027. The undisclosed valuation and unnamed pharma customers make this a story to verify at scale — but if the 100x cell-reduction claim holds in partner labs, the bottleneck shifts from sample supply to dataset demand, and that's a market AI drug developers are already paying to fill.