SeekGene and EonArk have entered a strategic collaboration, deeply integrating SeekGene's full-spectrum single-cell data capabilities with EonArk's AI virtual cell platform. Anchored on cross-species, cross-organ single-cell multi-omics modeling, the partnership will explore new pathways for discovering therapeutic targets for aging-related disease.

Under the collaboration, SeekGene will draw on its multi-omics technology matrix to provide EonArk with high-quality single-cell data spanning the epigenome, transcriptome, proteome, and spatial dimensions. This data foundation rests on two pillars: SeekGene's soon-to-launch upgraded SeekOne™ DD 3' scRNA-seq, which underpins the data foundation end to end, and the SeekOne™ DD Single Cell Multiome Methylation + RNA Kit — launched at FOG London in January 2026 — which serves as the core innovation driving the partnership forward. A steadier foundation paired with a sharper edge of innovation, together making every layer of data more complete.

EonArk, in turn, will apply its strengths in AI virtual cell modeling and cross-species target prediction to build regulatory networks for aging-related genes and a virtual knockout platform. Combining wet-lab and dry-lab approaches, the two teams aim to connect the full path from foundational data to drug targets, accelerating the application of AI virtual cells in drug development for aging-related disease.

Single-cell data, layer by layer

Single-cell research keeps deepening its questions. The transcriptome answers “what,” capturing a cell's molecular identity. Spatial biology answers “where,” reconstructing a cell's true position within tissue. The epigenome answers “why,” revealing the regulatory forces behind gene expression.

High-quality single-cell data functions as the “digital genes” of AI virtual cells: higher sensitivity and deeper gene detection mean more complete transcriptomic input — the prerequisite for virtual cell models to accurately simulate aging-related regulatory networks and deliver reliable target predictions. When high-quality, high-dimensional single-cell data meets artificial intelligence, biological systems once considered too complex to model — aging among them — start to become readable, predictable, and ultimately, addressable. This is where the collaboration begins.

About EonArk

EonArk is a rising force in AI-driven longevity drug discovery. Built on core technologies including single-cell data augmentation, graph neural network (GNN)-based gene regulatory network (GRN) construction, and hierarchical gene architecture, the company has developed a model iteration and target validation platform spanning C. elegans, mice (including CRISPR knockout/activation aged-mouse models), and cynomolgus monkeys. On this foundation, EonArk is building a verifiable Longevity Foundation Model for life sciences.

The company has also developed a proprietary cross-species transfer learning model that aligns multi-omics data across humans, dogs, cats, and mice. Using domain-adaptive neural networks to filter out signals with excessive cross-species variance, the model precisely retains candidate targets that are evolutionarily conserved, strongly associated with aging, and hold strong drug development potential — positioning EonArk as an industry pioneer in unifying human and companion-animal drug development pathways.

About SeekGene

SeekGene is a life science technology company building single-cell and spatial multi-omics tools to decode the biology behind health and disease. SeekGene pioneered single-cell epigenomic and methylation multi-omics research, developing the technology to co-detect DNA methylation alongside RNA, protein, and other modalities from the same single cell — opening a dimension of biology that reveals not just what a cell is, but why it behaves the way it does. Built on the SeekOne™ DD platform, this integrated set of instruments and reagent kits gives academic and biopharmaceutical researchers a more complete view of cellular biology at single-cell resolution. SeekGene's technology has supported research published in more than 300 papers and is used by more than 1,000 research institutions and biopharmaceutical companies worldwide.