Pumpkinseed is Unlocking Biology’s Most Valuable Hidden Data Layer
- Karan Bhatia
- May 6
- 2 min read
Pumpkinseed, the biology mining company, led by Dr. Jennifer Dionne, Dr. Jack Hu, and Nhat Vu, has announced a $20 million Series A funding round to scale its protein sequencing platform, mining biological functional data at a resolution and scale that simply hasn't existed before. The Series A round was co-led by NfX and Future Ventures, with participation from Base4 Capital, ADVentures, and Stanford, among others.
The funding will accelerate Pumpkinseed’s deSIPHR platform to full-length protein sequencing, expand key partnerships, and advance its AI models.
Proteins underpin all biology, yet many remain undetected due to catalog-dependent platforms. As AI drives novel protein design, this gap becomes critical. Pumpkinseed’s nanophotonic chip enables rapid, single-molecule sequencing of any protein, known or unknown, without reference catalogs, delivering high-fidelity proteomic data at scale.
Pumpkinseed has secured over $12 million in near-term revenue through active contracts with Genentech, DARPA, and BARDA, spanning immunology, precision medicine, and biothreat detection. The team is expanding in Palo Alto as the platform scales for large-scale data generation.
Pumpkinseed was founded on the conviction that biology’s primary bottleneck lies in actionable information density per cell per dollar, according to co-founder Jen Dionne. The deSIPHR platform is designed to overcome this constraint by directly reading protein sequence and structure from molecular vibrations.
Pumpkinseed is described as defining a category-creating moment in biology, according to Omri Amirav-Drory, General Partner at NfX, with the potential to close the measurement gap between real-world biological samples and the digital input required for AI models.
Steve Jurvetson and Maryanna Saenko of Future Ventures highlight a similar inflection point, noting that once the proteome becomes fully legible at scale, the impact will extend across medicine, biology, and AI in ways still being uncovered.
