--- name: deep-visual-proteomics-agent description: "---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:" --- --- 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 ## Author AI Group - Biomedical AI Platform --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Proteomics/Deep_Visual_Proteomics_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Proteomics/Deep_Visual_Proteomics_Agent/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences, Product Management Roles: Business Intelligence Analyst, Clinical Research, Data Scientist, Product Manager, Researcher Specialties: API Tools: Cellpose, Leica Lmd7, Maxquant, Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: d9aae77e35a9a58f551331bc444a504a7c4253f847f011cbee40233ede9df11d Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.