Flow Cytometry Gating Strategist

Recommend optimal flow cytometry gating strategies for specific cell types and fluorophores

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

bash

# 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

Parameter	Type	Default	Required	Description
--cell-types	string	-	Yes	Comma-separated list of cell types (e.g., "CD4+ T cells,CD8+ T cells")
--fluorophores	string	-	Yes	Comma-separated list of fluorophores (e.g., "FITC,PE,APC")
--instrument	string	-	No	Flow cytometer model (e.g., "BD FACSCanto II")
--purpose	string	analysis	No	Purpose (analysis, cell sorting, screening)
--output, -o	string	stdout	No	Output file path for JSON results

Output Format

json

{
  "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

Fluorophore	Excitation Wavelength	Emission Wavelength	Detection Channel
FITC	488nm	525nm	BL1
PE	488nm	575nm	YL1/BL2
PerCP	488nm	675nm	RL1
PerCP-Cy5.5	488nm	695nm	RL1
PE-Cy7	488nm	785nm	RL2
APC	640nm	660nm	RL1
APC-Cy7	640nm	785nm	RL2
BV421	405nm	421nm	VL1
BV510	405nm	510nm	VL2
BV605	405nm	605nm	VL3
BV650	405nm	650nm	VL4
BV785	405nm	785nm	VL6
DAPI	355nm	461nm	UV
PI	488nm	617nm	YL2

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

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access	External API calls	High
File System Access	Read/write data	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Data handled securely	Medium

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