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In today’s spatial multi-omics landscape, data is generated by various detection technologies, instruments, and panels for spatial proteomics and spatial transcriptomics. Each device comes with its own data formats and requirements. However, researchers and clinical scientists who seek biological insights from spatial biology data shouldn’t have to battle file formats. Instead, generating actionable insights and achieving project goals should be the main focus.

That’s why we at Katana Labs built 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 as 𝘵𝘳𝘶𝘭𝘺 vendor-agnostic cloud-native spatial biology platform.

𝐖𝐢𝐭𝐡 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞, 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐮𝐩𝐥𝐨𝐚𝐝, 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐞 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐳𝐞 𝐰𝐡𝐨𝐥𝐞-𝐬𝐥𝐢𝐝𝐞 𝐢𝐦𝐚𝐠𝐞𝐬 𝐟𝐫𝐨𝐦 𝐚𝐥𝐥 𝐦𝐚𝐣𝐨𝐫 𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐛𝐢𝐨𝐥𝐨𝐠𝐲 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 — 𝐞𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬𝐥𝐲 𝐚𝐧𝐝 𝐢𝐧 𝐨𝐧𝐞 𝐮𝐧𝐢𝐟𝐢𝐞𝐝 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭.

Supported technologies include:
Akoya Biosciences, Inc. | Bruker Spatial Biology | Standard BioTools | NanoString Technologies, Inc. | Miltenyi Biotec | Lunaphore (a Bio-Techne brand) | RareCyte, Inc. | Lumito AB | Vizgen | 10x Genomics … and more to follow.

And once your data is in Katana Edge, every analysis performed with 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐞𝐬 𝐨𝐩𝐞𝐧, 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐞𝐝 𝐟𝐨𝐫𝐦𝐚𝐭𝐬:
✔️ 𝐂𝐞𝐥𝐥 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 📁 Parquet, 📁 CSV, 📁 Excel
✔️ 𝐂𝐞𝐥𝐥 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: 📁 Mask (tif), 📁 GeoJSON
✔️ 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐅𝐨𝐫𝐦𝐚𝐭𝐬: 📁 AnnData (h5ad), 📁 SpatialData (zarr), Flow Cytometry (fcs)

… ready for downstream tools like QuPath, Scanpy, Squidpy, FlowJo, Giotto, or Seurat.

𝐖𝐡𝐨𝐥𝐞-𝐬𝐥𝐢𝐝𝐞 𝐜𝐞𝐥𝐥 𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧, 𝐬𝐢𝐧𝐠𝐥𝐞-𝐜𝐞𝐥𝐥 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐞𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧, 𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬, 𝐚𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 all exported in common formats your team already knows and can integrate anywhere.

This means:
✔️ Singularity: benefit from one image analysis platform for your spatial omics data
✔️ Flexibility: get results no matter the instrument or downstream analysis tools
✔️ Interoperability: collaborate easily across labs, CROs, biotech & pharma teams
✔️ Reliability: standardize using a seamless, automated process from image to data

Let’s get in touch if you would like to learn more about how Katana Edge can support your image-to-data workflow.

𝘛𝘩𝘦 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘯𝘢𝘮𝘦𝘥 𝘪𝘯 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘢𝘳𝘦 𝘵𝘳𝘢𝘥𝘦𝘮𝘢𝘳𝘬𝘴 𝘰𝘧 𝘵𝘩𝘦𝘪𝘳 𝘳𝘦𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦 𝘰𝘸𝘯𝘦𝘳𝘴. 𝘒𝘢𝘵𝘢𝘯𝘢 𝘓𝘢𝘣𝘴 𝘪𝘴 𝘯𝘰𝘵 𝘢𝘧𝘧𝘪𝘭𝘪𝘢𝘵𝘦𝘥 𝘸𝘪𝘵𝘩, 𝘦𝘯𝘥𝘰𝘳𝘴𝘦𝘥 𝘣𝘺, 𝘰𝘳 𝘴𝘱𝘰𝘯𝘴𝘰𝘳𝘦𝘥 𝘣𝘺 𝘵𝘩𝘦𝘴𝘦 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴.

Join the Katana Labs co-founder and 𝐂𝐡𝐢𝐞𝐟 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐎𝐟𝐟𝐢𝐜𝐞𝐫 Walter de Back and 𝐂𝐡𝐢𝐞𝐟 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐎𝐟𝐟𝐢𝐜𝐞𝐫 Norman Gerstner in the Start-Up Zone of NextGen Omics UK 2025.

On 13th and 14th November we demo our 𝐜𝐥𝐨𝐮𝐝-𝐧𝐚𝐭𝐢𝐯𝐞, 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 designed for life scientists and biologists. Katana Edge provides easy access to advanced data analytics and effortless collaboration in spatial omics.

📍 𝐕𝐢𝐬𝐢𝐭 𝐮𝐬 𝐚𝐭 𝐨𝐮𝐫 𝐛𝐨𝐨𝐭𝐡 in the Start-Up Zone to experience the Katana Edge platform live, explore its user experience and discuss how it can improve your R&D team’s productivity in unlocking spatial biology for advancing precision medicine.

Spatial multi-omics is transforming how we understand disease biology. By mapping proteins, RNA, and other molecular features directly within the intact tissue context, it provides an unprecedented view of cellular interactions unlocking new paths for 𝐛𝐢𝐨𝐦𝐚𝐫𝐤𝐞𝐫 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲, 𝐝𝐫𝐮𝐠 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭, 𝐚𝐧𝐝 𝐩𝐚𝐭𝐢𝐞𝐧𝐭 𝐬𝐭𝐫𝐚𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 in precision medicine.

We invite everybody to attend our short talk on November 13th, introducing the Katana Edge platform and how it helps R&D teams to accelerate spatial biology data analytics while significantly reducing associated project costs and timelines. No installation costs. No IT barriers. Just seamless, scalable science.

🧬 Spatial transcriptomics maps gene expression. Spatial proteomics maps proteins. Each modality provides valuable insights into how disease biology unfolds in affected tissues. But biology doesn’t happen in isolated layers. The same tissue section embeds morphology, transcripts, proteins, and functional changes simultaneously. Spatial multi-omics is the new platform technology that makes the various layers of biological organization accessible for coherent analysis.

🎯 This increasingly matters in multiple research fields and application areas, from immuno-oncology to biopharma R&D. The tumor microenvironment is defined by local alterations across several levels of biological organization – from tissue structure to molecular signaling. Spatial multi-omics connects these levels into one coherent image, something previously impossible by single-modality approaches. It identifies molecular structures for targeted therapies in direct relation to local tissue vulnerabilities that can be exploited for personalized treatments. This enables discovery and validation of novel composite biomarkers that better predict treatment response and reveals druggable pathways previously missed when analyzing molecular layers in isolation.

🛑 Spatial multi-omics generates three cost- and resource-intensive challenges for people and organizations using this platform technology. Data volumes are enormous – terabytes per experiment. Single-cell resolution requires precise segmentation across multiple imaging modalities. Complex data analysis must integrate morphology with diverse molecular layers.

✔️ Overcoming these challenges demands robust computational infrastructure for data storage and processing. It necessitates software tools designed specifically for multi-modal spatial analysis. It needs bioinformatics expertise to navigate the analytical complexity. These requirements create significant accessibility barriers and budget strains for many R&D teams.

✨ 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 from Katana Labs makes spatial multi-omics broadly accessible, by overcoming the above mentioned hurdles. Our AI-powered cloud platform provides the computational infrastructure R&D teams need. It handles terabyte-scale datasets automatically. AI-driven segmentation achieves single-cell resolution across modalities. Integrated workflows simplify multi-modal analysis – no coding required.

🆕 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 natively supports NanoString CosMx SMI and 10X Genomics Xenium platforms, as well as spatial proteomics platforms from established vendors. We enable R&D teams to analyze spatial multi-omics data in one unified environment – no extra bioinformatics team required. This empowers life scientists across biopharma, CROs, and academia to finally unlock the full potential of their spatial multi-omics data.

Lumito AB (publ) (“Lumito” or the “Company”) today announces that the Company and Katana Labs GmbH (“Katana Labs”) have signed a Letter of Intent (“LOI) formalising a strategic partnership to jointly commercialise an end-to-end, ultra-sensitive immunohistochemistry (IHC) workflow that integrates Lumito’s labelling and scanning technology with Katana Labs’ AI-powered image analysis platform. This is a result of the first phase of the collaboration, which was successful, after which the companies now strengthen the commercial collaboration.

“We are excited that we are taking concrete steps towards offering a plug-and-play IHC workflow solution that includes Lumito’s SCIZYS system and Katana Labs’ cloud-native software for AI-powered image analysis and quantification. This will enable our customers to address challenging research questions with a powerful and integrated solution,” says Sanna Wallenborg, CEO of Lumito.

Lumito and Katana Labs will develop and jointly commercialise an end-to-end, ultra-sensitive immunohistochemistry (IHC) workflow that integrates Lumito’s up-converting nanoparticle (UCNP)-based imaging technology with Katana Labs’ advanced AI-powered image analysis platform. The strategic collaboration aims to deliver a fully integrated ultra-sensitive IHC platform for enhanced tissue analysis and reliable biomarker detection, addressing critical unmet research needs in IHC e.g. the detection and quantification of low/ultralow expressed biomarkers such as HER2. Users can implement the end-to-end workflow in-house or access it as scanning-as-a-service.

“This partnership brings together two highly complementary technologies that, together, redefine what’s possible in reliable and robust biomarker detection. By uniting Lumito’s physical detection sensitivity with Katana Labs’ computational intelligence, we are setting a new benchmark for precision oncology and translational research,” says Dr. Norman Gerstner, Chief Business Officer at Katana Labs.

The LOI precedes a non-exclusive agreement under which both parties will continue their independent commercial activities. At the same time, they will collaborate to further validate and promote their combined end-to-end workflow through co-marketing and joint commercialisation efforts. By presenting each other’s solutions in customer meetings and shared presentations, both companies aim to expand market exposure and provide CRO and biopharma R&D customers with an integrated, all-in-one workflow solution, from labeling kit to scanner and cloud-based image analysis.

For further information, please contact:

Sanna Wallenborg, CEO Lumito
E-mail: sw@lumito.se
Ph: +4670-870 01 68

About Katana Labs
Katana Labs is a software engineering and artificial intelligence (AI) company that builds AI-powered and cloud-native software to decode biology in the context of complex diseases. Katana Labs develops best-in-class deep learning models for the automated analysis of large image datasets. These models are designed to meet the specific needs of various applications in advanced histopathology and tissue analysis, including accurate cell segmentation, biomarker quantification, and multidimensional data extraction. Made readily available through Katana Labs’ cloud platform, R&D teams can easily explore, analyse, and collaborate on whole slide images and tissue microarrays directly via a browser-based interface. By automating time-consuming processing, segmentation, and bioinformatics tasks, Katana Labs’ AI platform enables researchers and clinical scientists to enhance workflow efficiency and productivity.

About Lumito
Lumito specialises in medical technology and translational research in digital tissue imaging. Lumito offers a groundbreaking, highly sensitive imaging technique to locate and measure protein biomarkers in tissue samples using up-converting nanoparticles (UCNPs) through its patented research platform. The technology combines image data with precise biomarker detection, enabling images with higher contrast where irrelevant background information is filtered out. The technique can enhance the analysis of tissue samples by increasing objectivity, thereby contributing to research for more quantifiable diagnoses and optimised treatments. Lumito primarily focuses on drug development and digital pathology and is a spin-off from a research group at Lund University’s Department of Atomic Physics and Laser Center. www.lumito.se/en/

The share is traded on NGM Nordic SME under the name LUMITO, and Mentor is Mangold Fondkommission.

𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐛𝐢𝐨𝐥𝐨𝐠𝐲 is a rapidly evolving field, propelled by interdisciplinary and international collaboration among experts in life sciences, translational research, and biopharma R&D.

Unlocking the wealth of data hidden within these stunning images requires true interdisciplinarity. Pathologists, immunologists, microscopists, and image and data analysts must work together to gain deep insights. However, collaborating on the same image datasets across different sites and organizational boundaries remains a significant challenge.

That is why we built 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 at Katana Labs. Real breakthroughs in clinical research, biomarker development, and biopharma R&D happen when cross-disciplinary experts 𝐰𝐨𝐫𝐤 𝐚𝐧𝐝 𝐭𝐡𝐢𝐧𝐤 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫, ideally in real-time. With 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞, R&D teams can seamlessly and securely collaborate on spatial omics image datasets within their organization but also with their external collaboration partners or clients. Whether running a global CRO project or coordinating a biotech R&D pipeline, 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 brings all stakeholders together in a single intuitive, AI-powered cloud-platform.

Because 𝐊𝐚𝐭𝐚𝐧𝐚 𝐄𝐝𝐠𝐞 is 𝐜𝐥𝐨𝐮𝐝-𝐧𝐚𝐭𝐢𝐯𝐞 and 𝐛𝐫𝐨𝐰𝐬𝐞𝐫-𝐛𝐚𝐬𝐞𝐝, there is:
✅ 𝐍𝐨 𝐢𝐧𝐬𝐭𝐚𝐥𝐥𝐚𝐭𝐢𝐨𝐧, 𝐧𝐨 𝐥𝐨𝐜𝐚𝐥 𝐬𝐞𝐭𝐮𝐩, 𝐚𝐧𝐝 𝐧𝐨 𝐈𝐓 𝐨𝐯𝐞𝐫𝐡𝐞𝐚𝐝: just open your browser and start working.
✅ 𝐄𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: easily and instantly share whole-slide images, ROIs, annotations and AI-based analysis results with colleagues or clients around the globe using deep links.

🌟 𝐓𝐞𝐬𝐭𝐢𝐦𝐨𝐧𝐢𝐚𝐥 𝐛𝐲 𝐨𝐧𝐞 𝐨𝐟 𝐨𝐮𝐫 𝐮𝐬𝐞𝐫𝐬:
Dr. Anke Brix (R&D Manager at Bruker Spatial Biology)

Working with large digital histopathology and multiplexed image data is becoming standard practice in biopharma R&D and clinical research. These images contain millions of cells labeled for numerous proteins and/or transcripts at once. This data is rich of deep insights waiting to be uncovered and translated into actionable biomarkers, targets and drug candidates.

But there are bottlenecks: Most R&D teams still rely on complicated and outdated workflows, struggling to decode the information from large image datasets. Software has to be installed locally or configured and maintained on on-premise servers. IT teams spend a lot of time setting up infrastructure for compute, network and storage while scientists are waiting and loosing time. Moreover, collaboration across CROs, pharma, and biotech partners is slowed down by outdated local systems and incompatible tools.

The economic implications are detrimental:
⏳ Projects are delayed by weeks or even months before advanced analysis provides conclusive results
💸 High IT costs for infrastructure, licensing, and maintenance stress R&D budgets
🔄 Fragmented workflows create friction instead of collaboration

➡️ The combined effects result in time-consuming, costly and frustrating R&D iterations, which hinders the efficient development of novel targeted therapies.

At Katana Labs, we do not accept that status quo. That’s why we built Edge: an AI-powered, cloud-native platform that brings spatial multiomics analysis directly into your browser.

🚀 With Edge, your workflow improves significantly:
✔️ Immediate access to image visualization, exploration and data analysis through standard web browsers

✔️ No installations, no server setups, no upfront invests, no hidden IT costs

✔️ Share images and results with colleagues or customers anywhere in the world

🔬 For our partners in CROs, biotech, and pharma, this means:

• Faster biomarker discovery → spend time on science, not on IT bottlenecks
• Accelerated drug development → results delivered sooner, decisions made faster
• Reduced costs → scale analysis flexibly without scaling your IT budget

💡 The future of spatial multiomics isn’t locked in local PCs or server rooms. It’s cloud-native. And it runs in your browser instantly and independently of your local infrastructure.

👉 Let’s connect if you want to save time, cut costs, and accelerate your path to breakthrough discoveries.

In clinical practice, the highest level of precision is essentiell. The rapidly expanding knowledge of biological processes and relevant biomarkers demands ever-increasing expertise from medical professionals. Artificial intelligence (AI) can provide critical support by automating time-consuming routine tasks, especially in complex, image-based cancer diagnostics, thereby enabling faster, more accurate and more precise results. This marks a significant advancement for personalized precision medicine. 

For AI to become a truly reliable tool in medical workflows, it must deliver not only correct results but also transparent, traceable and quantifiable levels of confidence. It is not sufficient for an algorithm to simply perform an analysis and output a result. Clinicians must also understand how trustworthy that result is. Key questions must be answerable: How reliable is this output? Which algorithmic parameters influenced it, and how were they weighted? Only when such information is available in a transparent and interpretable manner can AI be responsibly integrated into everyday clinical practice. 

This is precisely where trustPAIKON comes in. The project focuses on developing methods for quantitative assessment and visualization of uncertainty in AI-assisted image analysis. In collaboration with the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Katana Labs is pioneering novel techniques for uncertainty estimation in deep learning models, directly integrated into the PAIKON platform. 

Katana Labs’ PAIKON platform supports pathologists in the digital analysis of histopathological tissue samples. trustPAIKON enhances this platform with robust uncertainty indicators for image analysis, enabling precise and transparent instance segmentation of tumor structures and cells, accompanied by clear information about the confidence level of each individual AI-assisted decision. 

Beyond the medical domain, trustPAIKON also aims to develop generalizable methodologies and modular, reusable software components for AI models. These components can be applied across other safety-critical and highly regulated fields where the reliability and interpretability of AI decisions are essential. 

With trustPAIKON, we aim to establish a foundation for explainable, robust, and responsibly deployable AI in medical diagnostics. 

Project duration: October 2024 until September 2026 
Project leads: Dr. Peter Steinbach (HZDR) and Dr. Walter de Back (Katana Labs) 

This project is supported by the SAB and funds from the ESF European Social Fund.