Weave

Weave® is an end-to-end spatial multi-omics analysis platform designed for life sciences organizations that need to unify, analyze, and govern complex biological data at enterprise scale. The platform brings together spatial transcriptomics, proteomics, metabolomics, mass spectrometry imaging (MSI), single-cell data, histology, clinical metadata, and annotations into a single governed workspace — preserving biological context across all modalities so scientists can focus on understanding mechanisms, relationships, and tissue architecture rather than reconciling formats or pipelines.

Weave is built for cross-functional teams — including biology, pathology, and computational scientists — working across programs, cohorts, sites, and research partners. It combines spatial analysis with the flexibility to integrate custom computational pipelines, interactively explore and share results, train AI models on structured data, and deploy outputs back into wet-lab or computational workflows.

Spatial Context Analysis Capabilities

• Identify, search, and annotate cellular neighborhoods and their functional behavior
• Study cell–cell interactions within relevant spatial neighborhoods
• Map ligand–receptor and pathway activity within true tissue context
• Detect treatment-driven phenotypic shifts earlier in the research process
• Connect morphological features with molecular changes
• Link biology to clinical metadata to generate stronger hypotheses

Core Platform Workflow

1. Manage & Integrate: Bring spatial and molecular data into a coherent reference framework, ingesting inputs from any vendor for end-to-end processing.
2. Analyze: Run built-in QC, segmentation, and neighborhood analysis, or integrate custom pipelines and models through Weave's SDKs.
3. Visualize & Collaborate: Interactively explore tissue structure, molecular signals, and cellular neighborhoods, and share findings across teams, programs, and global sites.
4. Model & Reuse: Train and deploy AI/ML models on governed datasets, feeding predictions and annotations back into Weave for traceable reuse and closed feedback loops.

Structured Data and Spatial Data Products

• Consistent schemas, metadata standards, ontologies, and provenance tracking make data structured, comparable, reusable, and model-ready
• Reproducible analyses across sites, cohorts, and assay types
• Governed collaboration with CROs, partners, and internal teams
• Smooth integration with internal and external pipelines including LIMS, catalogs, and digital pathology tools
• Reliable AI/ML model development on consistent, domain-validated inputs
• Closed-loop iteration between wet-lab and computational teams, reducing rework and turnaround time

Weave Insights: Spatiomolecular Knowledge Base

• Standardized datasets feed directly into Weave Insights, a growing spatiomolecular knowledge base built from every experiment
• Contextualize new data points with prior in-house or public knowledge
• Validate findings without reprocessing old datasets
• Assess mechanistic novelty across tissues, cohorts, or programs
• Build a durable knowledge resource that compounds in value over time as new cohorts, modalities, or research partners are added

Platform Architecture

• Unified Data Inputs: Ingest spatial transcriptomics, proteomics, metabolomics, MSI, histology, and single-cell data from any vendor
• Core Processing Engine: Integrate multimodal spatial data while preserving spatial context; run QC, segmentation, and multicellular environment analytics to turn raw readouts into reusable biological insight
• Enterprise Data Management: Govern data with access controls, lineage tracking, versioning, and integrations into catalogs, LIMS, and internal cloud environments
• Access & Interfaces: Use the Weave web interface for visual analysis or connect through the SDK for custom pipelines and AI model development

Key Use Cases

• End-to-end spatial multi-omics integration and analysis: Align histology with spatial molecular profiles using registration, patented multi-omics fusion, flexible cell annotation, QC, and reproducible workflows
• Scalable workflow implementation: Fit seamlessly into existing digital pathology, computational pipelines, and IT ecosystems without retooling
• AI/ML workflow support: Leverage structured, domain-validated spatial datasets for LLM-driven analysis, agentic RAG, and foundation model training, underpinned by a FAIR-aligned data foundation that maps insights into a reusable, enterprise-wide knowledge base

Weave® is designed to scale from pilot studies to enterprise programs — handling tens to hundreds of samples — while maintaining lineage-tracked, AI-model-ready datasets. Its integrations with LIMS, data catalogs, and internal cloud environments make it suitable for organizations seeking a governed, reproducible, and collaborative foundation for spatial biology research.