Skill: Flow Cytometry Gating Strategist

Recommend optimal flow cytometry gating strategies for given cell types and fluorophores.

Basic Information

• ID: 103
• Name: Flow Cytometry Gating Strategist
• Purpose: Flow cytometry data analysis and gating strategy recommendations

Usage

Command Line

# Recommended format: comma-separated cell types and fluorophores
python scripts/main.py "CD4+ T cells,CD8+ T cells" "FITC,PE,APC"

# Or specify parameters separately
python scripts/main.py --cell-types "CD4+ T cells,CD8+ T cells" --fluorophores "FITC,PE,APC"

# Support more options
python scripts/main.py \
  --cell-types "B cells" \
  --fluorophores "FITC,PE,PerCP-Cy5.5,APC" \
  --instrument "BD FACSCanto II" \
  --purpose "cell sorting"

Parameters

--cell-types

--fluorophores

--instrument

--purpose

--output

-o

Output Format

{
  "recommended_strategy": {
    "name": "Sequential Gating Strategy",
    "description": "Gating based on FSC-A/SSC-A, followed by fluorescence intensity analysis",
    "steps": [
      {
        "step": 1,
        "gate": "FSC-A vs SSC-A",
        "purpose": "Identify target cell population, exclude debris and dead cells",
        "recommendation": "Set oval gate in lymphocyte region"
      }
    ]
  },
  "fluorophore_recommendations": [
    {
      "fluorophore": "FITC",
      "channel": "BL1",
      "detector": "530/30",
      "considerations": ["May spillover with GFP"]
    }
  ],
  "panel_optimization": {
    "suggestions": ["Recommend pairing weakly expressed antigens with bright fluorophores"],
    "avoid_combinations": ["FITC and GFP used simultaneously"]
  },
  "compensation_notes": ["FITC and PE require careful compensation"],
  "quality_control": ["Recommend setting FMO controls", "Use viability dyes to exclude dead cells"]
}

Supported Cell Types

• T cells: CD4+ T cells, CD8+ T cells, Treg cells, Th1, Th2, Th17, γδ T cells
• B cells: B cells, Plasma cells, Memory B cells, Naive B cells
• Myeloid cells: Monocytes, Macrophages, Dendritic cells, Neutrophils, Eosinophils
• Stem cells: HSC, MSC, iPSC
• Tumor cells: Tumor cells, Cancer stem cells
• Others: NK cells, NKT cells, Platelets, Erythrocytes

Supported Fluorophores

Gating Strategy Types

1. Sequential Gating

Applicable scenario: Simple immunophenotyping analysis

• FSC-A/SSC-A → Exclude debris/dead cells → Fluorescence intensity analysis

2. Boolean Gating

Applicable scenario: Complex cell subset analysis

• Use logical operators (AND, OR, NOT) to define cell populations

3. Dimensionality Reduction Gating

Applicable scenario: High-dimensional data (>15 colors)

• t-SNE/UMAP visualization-assisted gating

4. Unsupervised Clustering

Applicable scenario: Discovery of unknown cell populations

• FlowSOM, PhenoGraph and other algorithms

Notes

1. Spectral Overlap Compensation: Multi-color panels must undergo compensation calculation
2. Control Setup: Must use FMO (fluorescence minus one) and isotype controls
3. Dead Cell Exclusion: Strongly recommend using viability dyes
4. Instrument Calibration: Perform QC and standard bead detection before experiments

Dependencies

• Python 3.8+
• No external dependencies (pure Python standard library)

Version

v1.0.0 - Initial version, supports basic gating strategy recommendations

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support