---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.
license: MIT
metadata:
  author: AI Group
  version: "1.0.0"
  created: "2026-01-19"
compatibility:
  - system: Python 3.10+
allowed-tools:
  - run_shell_command
  - read_file
  - write_file

keywords:
  - deep-visual-proteomics-agent
  - automation
  - biomedical
measurable_outcome: execute task with >95% success rate.
---"

# 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

## Author

AI Group - Biomedical AI Platform