MGI Tech has launched its VisiOmics multiplex immunofluorescence platform commercially in Europe, adding automated tissue staining, imaging and AI-based analysis to its pathology portfolio. The system is aimed at research applications spanning spatial proteomics, tumor biology and digital pathology.
MGI Tech is expanding its presence in European digital pathology with the commercial launch of VisiOmics (PMIF-20RS), a fully automated multiplex immunofluorescence (mIF) staining and imaging system designed to combine high-plex tissue analysis with AI-assisted image interpretation.
The launch took place at the 38th European Congress of Pathology (ECP 2026) in Stockholm, where the European Society of Pathology brought together more than 5,000 participants from pathology, clinical research, molecular biology, bioinformatics and related fields. The congress specifically highlighted digital pathology, AI integration and emerging technologies as areas shaping the discipline.
MGI is positioning VisiOmics within a broader “From Tissue to Answer” strategy that links tissue-based protein analysis with molecular pathology workflows.
The core challenge VisiOmics addresses is the growing complexity of analyzing tissue at cellular and spatial levels. Traditional pathology can identify morphology and individual biomarkers, but multiplex immunofluorescence allows researchers to examine multiple protein markers within the same tissue section while retaining information about where those markers occur.
According to MGI, VisiOmics automates the staining and imaging process and can detect more than 20 markers in a run, with panel expansion beyond 100 proteins through cyclic staining. MGI’s product documentation says the PMIF-20RS uses a repeated stain-imaging-elution process to analyze multiple proteins while preserving their spatial distribution.
The system is paired with FluoXpert Vision, MGI’s AI-enabled image-analysis software. The company says the software supports cell segmentation, phenotyping and spatial analysis, with an AI assistant intended to make downstream interpretation and visualization easier.
That software layer is becoming increasingly important as spatial biology generates larger and more complex image datasets. Automated imaging can increase the amount of data available to researchers, but the resulting images still require computational methods to identify cells, classify phenotypes and understand spatial relationships.
MGI is also working with the Fraunhofer Institute for Integrated Circuits IIS to demonstrate compatibility between VisiOmics-generated mIF images and Fraunhofer’s MIKAIA digital pathology software. A Fraunhofer application example used a 20-plex protein panel plus a nuclear marker and applied cell segmentation, phenotyping and spatial-neighborhood analysis to a tonsil dataset.
The collaboration points to an important issue for AI-powered pathology: interoperability. Laboratories may not want to depend on a single vendor for staining, imaging and downstream analytics. Supporting external analysis environments can allow researchers to combine automated image acquisition with software better suited to particular research questions.
MGI is extending the same automation strategy to higher-throughput tissue imaging with Path-P96 WF. The platform is designed to automate multiplex immunofluorescence workflows using standard pathology slides, with capacity for up to 32 slides per batch and more than 100 protein markers through cyclic staining, according to the company.
The potential applications include drug discovery, target validation, spatial phenotyping and the creation of image datasets for AI-based pathology research. That last use case is particularly significant because machine-learning systems require large, consistently characterized datasets for development and validation.
The company is simultaneously expanding AI into molecular pathology. PrepLPS, described as the PrepALL Library Prep Edition, is a liquid-handling system for next-generation sequencing library preparation that incorporates machine vision and automated workflows. MGI also demonstrated AIO, an integrated sample-to-result platform combining library preparation, sequencing and reporting.
MGI says its AI strategy extends across these systems through its Genoria AI subsidiary. The portfolio includes AI image analysis for VisiOmics, natural-language workflow orchestration for Path-P96 WF, machine-vision monitoring for PrepALL and intelligent library-pooling strategies for AIO.
The approach reflects a broader movement in life sciences toward AI-enabled laboratory automation, where machine learning is increasingly embedded in instruments and workflows rather than deployed solely as a separate analytics application.
IDC expects AI investment to continue accelerating across healthcare and life sciences. Its 2026 research estimates that AI spending across EMEA will reach $319 billion in 2026, growing 19.2% year over year. IDC also reports that 61.3% of organizations globally remain in the two least mature stages of its AI maturity framework, underscoring the gap between AI interest and operational execution.
For pathology laboratories, that execution challenge is particularly relevant. AI systems need reliable image acquisition, standardized workflows, appropriate data, interoperability and human oversight before they can produce useful research outcomes.
MGI’s strategy therefore goes beyond adding an AI model to a microscope or imaging system. Its portfolio connects automated sample processing, multiplex imaging, sequencing and computational analysis into a wider digital pathology workflow.
The VisiOmics launch also comes with an important limitation: PMIF-20RS is currently designated for research use only, rather than clinical diagnostic use.
That distinction matters as AI-assisted pathology moves toward clinical environments, where validation, regulatory requirements and reproducibility become critical. For now, MGI’s European expansion is primarily about giving researchers more automated ways to generate and interpret high-dimensional tissue data.
The longer-term opportunity is to make spatial biology more scalable. If automated staining, imaging and AI analysis can reduce the manual effort required to generate usable datasets, researchers could examine tissue microenvironments at greater scale and integrate spatial protein information with genomic and molecular measurements.
That convergence could make AI infrastructure an increasingly important component of modern pathology research—not simply for analyzing results, but for generating the structured biological data on which future AI models depend.
Market Landscape
Digital pathology is moving toward increasingly automated workflows combining whole-slide imaging, multiplex tissue analysis, spatial biology and AI. The competitive landscape includes pathology-imaging companies, life-sciences instrumentation vendors, sequencing providers and specialist AI software developers.
The strategic shift is from isolated AI image analysis toward integrated workflows. Vendors increasingly need to address the entire data pipeline: sample preparation, imaging, data management, AI analysis and downstream interpretation.
MGI is taking a multi-omics approach by connecting spatial protein analysis with sequencing and laboratory automation. Its collaboration with Fraunhofer IIS also highlights the importance of interoperability as laboratories build heterogeneous digital pathology environments.
Top Insights
- MGI has commercially launched VisiOmics in Europe, bringing automated multiplex immunofluorescence staining, imaging and AI-assisted analysis to research laboratories.
- VisiOmics can analyze multiple protein markers in one tissue section and expand beyond 100 markers through cyclic staining, according to MGI.
- Fraunhofer IIS is demonstrating VisiOmics compatibility with MIKAIA for segmentation, phenotyping and spatial-neighborhood analysis.
- MGI is extending AI across tissue imaging, sequencing preparation and reporting rather than treating machine learning as a standalone analytics layer.
- The PMIF-20RS remains a research-use-only system, making validation and regulatory development important considerations for future clinical applications.
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