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
CODEBLOCK0
Parameters
| Parameter | Type | Default | Required | Description |
|---|
| INLINECODE0 | string | - | Yes | Comma-separated list of cell types (e.g., "CD4+ T cells,CD8+ T cells") |
| INLINECODE1 |
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
CODEBLOCK1
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
- Control Setup: Must use FMO (fluorescence minus one) and isotype controls
- Dead Cell Exclusion: Strongly recommend using viability dyes
- 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
- Edge Case: Invalid input → Graceful error handling
- 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
技能：流式细胞术设门策略师
为给定的细胞类型和荧光染料推荐最优的流式细胞术设门策略。
基本信息
- - ID：103
- 名称：流式细胞术设门策略师
- 目的：流式细胞术数据分析和设门策略推荐
使用方法
命令行
bash
推荐格式：逗号分隔的细胞类型和荧光染料
python scripts/main.py CD4+ T细胞,CD8+ T细胞 FITC,PE,APC
或分别指定参数
python scripts/main.py --cell-types CD4+ T细胞,CD8+ T细胞 --fluorophores FITC,PE,APC
支持更多选项
python scripts/main.py \
--cell-types B细胞 \
--fluorophores FITC,PE,PerCP-Cy5.5,APC \
--instrument BD FACSCanto II \
--purpose 细胞分选
参数
| 参数 | 类型 | 默认值 | 必填 | 描述 |
|---|
| --cell-types | 字符串 | - | 是 | 逗号分隔的细胞类型列表（例如：CD4+ T细胞,CD8+ T细胞） |
| --fluorophores |
字符串 | - | 是 | 逗号分隔的荧光染料列表（例如：FITC,PE,APC） |
| --instrument | 字符串 | - | 否 | 流式细胞仪型号（例如：BD FACSCanto II） |
| --purpose | 字符串 | analysis | 否 | 目的（分析、细胞分选、筛选） |
| --output, -o | 字符串 | stdout | 否 | JSON结果输出文件路径 |
输出格式
json
{
recommended_strategy: {
name: 顺序设门策略,
description: 基于FSC-A/SSC-A设门，随后进行荧光强度分析,
steps: [
{
step: 1,
gate: FSC-A vs SSC-A,
purpose: 识别目标细胞群体，排除碎片和死细胞,
recommendation: 在淋巴细胞区域设置椭圆形门
}
]
},
fluorophore_recommendations: [
{
fluorophore: FITC,
channel: BL1,
detector: 530/30,
considerations: [可能与GFP发生光谱重叠]
}
],
panel_optimization: {
suggestions: [建议将弱表达抗原与亮荧光染料配对],
avoid_combinations: [FITC和GFP同时使用]
},
compensation_notes: [FITC和PE需要仔细进行补偿],
quality_control: [建议设置FMO对照, 使用活力染料排除死细胞]
}
支持的细胞类型
- - T细胞：CD4+ T细胞、CD8+ T细胞、Treg细胞、Th1、Th2、Th17、γδ T细胞
- B细胞：B细胞、浆细胞、记忆B细胞、初始B细胞
- 髓系细胞：单核细胞、巨噬细胞、树突状细胞、中性粒细胞、嗜酸性粒细胞
- 干细胞：HSC、MSC、iPSC
- 肿瘤细胞：肿瘤细胞、肿瘤干细胞
- 其他：NK细胞、NKT细胞、血小板、红细胞
支持的荧光染料
| 荧光染料 | 激发波长 | 发射波长 | 检测通道 |
|---|
| 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 |
设门策略类型
1. 顺序设门
适用场景：简单的免疫表型分析
- - FSC-A/SSC-A → 排除碎片/死细胞 → 荧光强度分析
2. 布尔设门
适用场景：复杂细胞亚群分析
- - 使用逻辑运算符（AND、OR、NOT）定义细胞群体
3. 降维设门
适用场景：高维数据（>15色）
4. 无监督聚类
适用场景：发现未知细胞群体
注意事项
- 1. 光谱重叠补偿：多色方案必须进行补偿计算
- 对照设置：必须使用FMO（荧光减一）和同型对照
- 死细胞排除：强烈建议使用活力染料
- 仪器校准：实验前进行QC和标准微球检测
依赖项
- - Python 3.8+
- 无外部依赖（纯Python标准库）
版本
v1.0.0 - 初始版本，支持基本设门策略推荐
风险评估
| 风险指标 | 评估 | 等级 |
|---|
| 代码执行 | 带工具的Python脚本 | 高 |
| 网络访问 |
外部API调用 | 高 |
| 文件系统访问 | 读写数据 | 中 |
| 指令篡改 | 标准提示词指南 | 低 |
| 数据泄露 | 数据安全处理 | 中 |
安全检查清单
- - [ ] 无硬编码凭据或API密钥
- [ ] 无未经授权的文件系统访问（../）
- [ ] 输出不暴露敏感信息
- [ ] 已实施提示注入保护
- [ ] API请求仅使用HTTPS
- [ ] 输入已根据允许模式进行验证
- [ ] 已实施API超时和重试机制
- [ ] 输出目录限制在工作空间内
- [ ] 脚本在沙盒环境中执行
- [ ] 错误消息已清理（不暴露内部路径）
- [ ] 依赖项已审计
- [ ] 不暴露内部服务架构
前提条件
无需额外的Python包。
评估标准
成功指标
- - [ ] 成功执行主要功能
- [ ] 输出符合质量标准
- [ ] 优雅处理边缘情况
- [ ] 性能可接受
测试用例
- 1. 基本功能：标准输入 → 预期输出
- 边缘情况：无效输入 → 优雅的错误处理
- 性能：大数据集 → 可接受的处理时间
生命周期状态
- - 当前阶段：草案
- 下次审核日期：2026-03-06
- 已知问题：无
- 计划改进：
- 性能优化
- 额外功能支持
