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潘托亚物种的快速分类潘托亚物种的快速分类通过拉曼流细胞计

Daoshun Zhang¹, Shuhua Tian², Bing Feng²

¹State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

Analytical chemistry

|March 6, 2026

概括

一个新的平台结合了二电泳激活的拉曼分类和深度学习,用于快速的微生物识别. 这种方法可以在多样样样本中准确地分类Pantoea等细菌,克服了传统技术的局限性.

科学领域:

微生物学和光谱学
计算生物学和机器学习

背景情况:

准确的微生物分类学识别对于理解生态系统功能至关重要.
像纯培养技术这样的传统方法是缓慢的,昂贵的,分辨率有限的.
需要高通量,高分辨率的微生物识别方法.

研究的目的:

为快速和准确的微生物分类开发一个综合平台.
建立一种深度学习模型,用于物种和菌株级别的识别.
将平台应用于对分类学上具有挑战性的属 *Pantoea*.

主要方法:

集成平台结合了正极电泳激活拉曼激活细胞分类 (pDEP-RACS) 和深度ResNet (ResNet-18) 的组合.
构建一个参考Ramanome数据库,包含来自12种*Pantoea*的18万个单细胞Raman光谱 (SCRS).
使用SCRS数据开发和验证ResNet-18分类模型.

主要成果:

该分类模型实现了高精度 (96.9%的平均精度,97.3%的回忆) 的 *Pantoea* 隔离物.
该平台显示出高可重复性,特别是在缺乏营养的样本中 (87.9%的准确性).
在合成群体中精确的物种识别 (≤3.21%绝对丰度误差) 和与大米种子微生物组中的16SrRNA测序一致 (34.8%vs45%).

结论:

通过pDEP-RACS和ResNet平台,可以快速对Pantoea*进行物种和菌株级别的分类.
这种综合方法显著提高了微生物识别吞吐量 (>7,200 SCRS/小时).
该平台有效分析培养微生物和复杂的环境样本,如微生物组.

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