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

1. AI Image Segmentation: Deep learning models segment cells and identify phenotypes from brightfield, H&E, or immunofluorescence images.

2. Phenotype Classification: CNN/transformer classifiers identify cell types, disease states, and morphological abnormalities.

3. LMD Coordinate Generation: Automated generation of laser microdissection coordinates for cells of interest.

4. MS Data Integration: Processes MaxQuant/DIA-NN output to link protein abundances to spatial coordinates.

5. Spatial Proteome Mapping: Creates spatially-resolved proteome maps linking morphology to molecular profiles.

6. 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

Analysis Outputs

1. Spatial Protein Maps: Protein abundance overlaid on tissue coordinates
2. Phenotype-Proteome Links: Proteins enriched in specific cell types
3. Pathway Activation: Spatial patterns of pathway activity
4. Differential Analysis: Comparison between regions/phenotypes
5. 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

1. Tumor Heterogeneity: Map proteome across tumor microenvironment regions
2. Single-Cell Resolution: Proteome profiles of rare cell populations
3. Disease Mechanisms: Link morphological changes to molecular drivers
4. 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