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Tumor microenvironment studies are hindered by spatial data complexity and constraints.

1. Large data volumes
   Whole slide multiplex immunofluorescence (mIF) scanning of tissue samples produces an enormous amount of data.
2. Complex, multi-step workflows
   Reliably processing large data sets often requires manual navigation of complex, multi-program worflows.
3. Limitations in throughput
   Traditional workflows lack the capacity to handle the increasing sizes and intricacies of pathology datasets.
4. Manual intervention
   Time-consuming manual data manipulation amplifies the risk for errors and prolongs the path to insights.

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Experience next-generation “samples to insights”

Unrivaled AI-powered spatial image analysis

Rapidly process hundreds of images in parallel

Cloud-based design optimizes performance and increases throughput by 5-100x

Effortless high-confidence insights

Deep learning models seamlessly adapt to biomarker, artifact, and tissue variability, delivering robust AI-powered cell classification

Easily customize your spatial analysis

Focus on sub-populations of interest by quickly defining custom phenotypes and regions

Collaborative portal

Easy-to-use web portal allows flexible access to data for downstream analysis

Unprecedented spatial insights

Determine tissue segmentation, cellular spatial relationships, biomarker localization within the tissue, and more.

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Meet STARVUE™

Our automated STARVUE™ (Spatial Tissue Analytics & Reporting for VUE panels) platform combines the latest innovations in artificial intelligence (AI) and image data science to seamlessly analyze images from 8- or 12-plex InSituPlex® assays at scale to generate high-confidence spatial insights into the intra- and inter-cellular dynamics of the tissue microenvironment.

Start your path to deeper spatial insights

Discover the STARVUE™ platform, a comprehensive end-to-end solution

mIF and H&E

powered by InSituPlex^®

Upload your samples stained with our InSituPlex^® technology OR Submit your samples and our experts will stain for you with optional same-section H&E.

InSituPlex^® assays use a proprietary isometric, single-molecule signal amplification chemistry that directly correlates detected signal intensity to the number of target biomarkers.

Image quality reviewed by our experts

Image co-registration

with UltiStacker.AI™

We configure and run image co-registration.

UltiStacker.AI™ performs high throughput image co-registration of whole slide InSituPlex^® 8- or 12-plex biomarker assay images.

Image analysis

with UltiAnalyzer.AI™

AI-driven image analysis with flexible phenotyping

UltiAnalyzer.AI™ utilizes deep-learning models to analyze biomarkers within composite whole slide mIF images at scale. Cell-level quantitative data is generated based on cell morphology and detected signal intensities.

Analyzed slides reviewed with Omero Plus

Image analysis results

Deep characterizations are reported in portal

Direct links to third-party data management and analytics platforms enable deep characterization.

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Integrated, Full Lifecycle Spatial Analysis

Gain powerful insights through an integrated ecosystem that streamlines your entire workflow from image management to in-depth analysis, all within a unified platform. STARVUE™ leverages AWS cloud infrastructure to power your spatial biology analysis. Concentriq allows you to document, manage, and visualize your whole slide stained images. Once analyzed, seamlessly transition to Omero Plus to explore the AI-identified cells, markers, and phenotypes within your slides.

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Ready to Get Started?

Partner with us for services or join our early access program

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Expert Spotlight

Relevant for: Biopharma, CROs

For which indications is my drug candidate most efficacious?

After discovering an interesting drug candidate, finding the most promising indications to focus on for the next stage of drug development is often the next step. Once a predictive biomarker signature has been identified, this panel of markers can be applied across many indications.

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Relevant for: Biopharma, CROs

How does the tumor micro-environment change after treatment with a new drug?

After the identification and initial work to determine a potential new drug candidate, data from a small Phase 1 study with a low number of patients and multiple drug doses is available. Tumor samples were collected pre-treatment and post-treatment and from earlier studies the drug candidate’s expected mode of action is known.

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Relevant for: Biopharma, CROs

What is the most relevant assay for my study?

In the early exploratory phase of drug development, researchers need to identify the markers, phenotypes, spatial interactions which can best distinguish different patient groups, such as responders versus non-responders. There may be many ideas about which markers, phenotypes or spatial interactions might be predictive, but its important to narrow down this long list of possibilities to the most promising subset of markers.

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Ready to start generating powerful insights from your data?

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