BioTender-max name: 'deep-visual-proteomics-agent' description: 'AI-driven integration of cellular imaging, laser microdissection, and ultra-sensitive mass spectrometry for spatially-resolved single-cell proteomics.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command
Deep Visual Proteomics Agent
The Deep Visual Proteomics Agent implements the Deep Visual Proteomics (DVP) workflow that combines AI-driven image analysis of cellular phenotypes with automated laser microdissection and ultra-high-sensitivity mass spectrometry. It links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context.
When to Use This Skill
- When studying spatially-resolved protein expression in tissue sections.
- To link single-cell morphological phenotypes to proteome profiles.
- For identifying cell-type specific protein signatures in heterogeneous tissues.
- When analyzing subcellular proteome compartmentalization.
- To discover spatially-restricted biomarkers in tumor microenvironments.
Core Capabilities
AI Image Segmentation: Deep learning models segment cells and identify phenotypes from brightfield, H&E, or immunofluorescence images.
Phenotype Classification: CNN/transformer classifiers identify cell types, disease states, and morphological abnormalities.
LMD Coordinate Generation: Automated generation of laser microdissection coordinates for cells of interest.
MS Data Integration: Processes MaxQuant/DIA-NN output to link protein abundances to spatial coordinates.
Spatial Proteome Mapping: Creates spatially-resolved proteome maps linking morphology to molecular profiles.
Biologically-Informed Analysis: Neural networks incorporating pathway knowledge for interpretable biomarker discovery.
DVP Workflow
Tissue Section
↓
[AI Image Analysis] → Cell Segmentation → Phenotype Classification
↓
[Region Selection] → LMD Coordinates → Automated Microdissection
↓
[Sample Processing] → Low-input LC-MS/MS → Proteome Quantification
↓
[Data Integration] → Spatial Proteome Map → Pathway Analysis
Example Usage
User: "Identify tumor vs. stroma cells in this H&E image and generate proteome profiles for each population."
Agent Action:
python3 Skills/Proteomics/Deep_Visual_Proteomics_Agent/dvp_analyzer.py \
--image tissue_section.tiff \
--segmentation cellpose \
--classifier tumor_stroma_cnn \
--generate_lmd true \
--ms_data maxquant_output/ \
--analysis differential \
--output dvp_results/
Key Components
| Component | Tool/Method | Description |
|---|---|---|
| Segmentation | Cellpose, StarDist | Instance segmentation of cells |
| Classification | Custom CNN/ViT | Phenotype assignment |
| LMD Interface | Leica LMD7, PALM | Coordinate export formats |
| MS Processing | MaxQuant, DIA-NN | Protein quantification |
| Integration | Custom Python | Spatial mapping |
Analysis Outputs
- Spatial Protein Maps: Protein abundance overlaid on tissue coordinates
- Phenotype-Proteome Links: Proteins enriched in specific cell types
- Pathway Activation: Spatial patterns of pathway activity
- Differential Analysis: Comparison between regions/phenotypes
- Biomarker Candidates: Spatially-restricted markers
Biologically-Informed Neural Networks (BINNs)
The agent implements BINNs that integrate:
- A priori knowledge of protein-pathway relationships
- Sparse neural network architecture mirroring biological networks
- Enhanced interpretability for clinical applications
- Validated in septic AKI, COVID-19, and ARDS cohorts
Input: Protein abundances
↓
Pathway Layer: Proteins → Pathways (sparse connections)
↓
Process Layer: Pathways → Biological processes
↓
Output: Phenotype classification + pathway importance scores
Prerequisites
- Python 3.10+
- PyTorch with vision models
- Cellpose/StarDist for segmentation
- MS data processing tools
- GPU recommended for image analysis
Related Skills
- Pathology_AI - For histopathology analysis
- Proteomics_MS - For standard proteomics workflows
- Spatial_Transcriptomics - For complementary spatial RNA
Applications
- Tumor Heterogeneity: Map proteome across tumor microenvironment regions
- Single-Cell Resolution: Proteome profiles of rare cell populations
- Disease Mechanisms: Link morphological changes to molecular drivers
- Drug Response: Spatial patterns of treatment response
Technical Specifications
Sensitivity: 100-500 cells per sample for robust quantification Throughput: 1,000-5,000 proteins per sample Resolution: Single-cell to ~10-cell resolution Formats: TIFF/SVS images, MaxQuant/DIA-NN output
Author
AI Group - Biomedical AI Platform