ZEISS @ SLAS 2026

Spatial biology is increasingly central to understanding mechanism of action, target engagement, and cellular interactions within complex biological systems. ZEISS spatial biology workflows support multiplexed imaging and quantitative analysis while maintaining consistency and reproducibility across experiments. Designed for translational and high-content environments, these workflows integrate automation and analysis to help teams move from rich spatial data to actionable insight—without sacrificing biological context.

Not every project—or organization—requires building advanced imaging infrastructure in-house. ZEISS Imaging & Analysis as a Service provides flexible access to expert workflows, advanced microscopy systems, and high-performance analysis to support discovery and development efforts.

This service-based approach allows teams to scale imaging capabilities as needed while maintaining scientific rigor, reproducibility, and efficiency.

ZEISS is participating in SLAS PartnershipOne to engage with early-stage companies developing next-generation spatial biology and enabling technologies. Through this program, ZEISS aims to identify and connect with emerging platforms aligned with scalable, quantitative imaging workflows, contributing to our broader Spatial Biology Startup Program.

This effort reflects ZEISS’s commitment to supporting innovation beyond individual products—helping shape the infrastructure behind the next wave of translational biology.

ZEISS will present a poster at SLAS 2026 highlighting imaging and analysis workflows designed to support new approach methodologies. The poster focuses on how advanced microscopy and scalable image analysis enable quantitative insight from large, multidimensional datasets generated by complex, human-relevant models.

Through collaborations with InSphero and Emulate, ZEISS will demonstrate both organoid and organ-on-a-chip workflows, illustrating how complementary model systems can be supported within a unified imaging and analysis framework.

This work highlights practical approaches for translating innovative model systems into reproducible, decision-ready data for translational and screening applications.