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deep-visual-proteomics-agent

Maintainer FreedomIntelligence · Last updated April 1, 2026

Deep visual proteomics: spatial proteomic analysis from laser-capture microdissection MS data.

OpenClawNanoClawAnalysisReproductiondeep-visual-proteomics-agent🧠 bioos extended suiteoncology & precision medicine agentsdeep

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/deep-visual-proteomics-agent

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • 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 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.

Source Doc

Excerpt From SKILL.md

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.

Example Usage

User: "Identify tumor vs. stroma cells in this H&E image and generate proteome profiles for each population."

Agent Action:

Key Components

ComponentTool/MethodDescription
SegmentationCellpose, StarDistInstance segmentation of cells
ClassificationCustom CNN/ViTPhenotype assignment
LMD InterfaceLeica LMD7, PALMCoordinate export formats
MS ProcessingMaxQuant, DIA-NNProtein quantification
IntegrationCustom PythonSpatial mapping

Use cases

  • 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.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • Pathology_AI - For histopathology analysis
  • Proteomics_MS - For standard proteomics workflows
  • Spatial_Transcriptomics - For complementary spatial RNA

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