---
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**:
```bash
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
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