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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Jun 25, 2025

Characterization of Aquatic Biofilms with Flow Cytometry

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Published on: June 6, 2018

区分微塑料与水性悬浮中的自然颗粒,使用流动细胞计与机器学习.

Xinjie Wang^1,2,3, Yang Li², Alexandra Kroll⁴

¹Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland.

Environmental science & technology

|May 28, 2024

概括

这项研究引入了一种使用机器学习检测天然水中的微塑料 (MPs) 的快速,无污染流动细胞计量方法. 该技术在复杂的环境样本中准确量化了MP,有助于污染监测.

关键词:

藻类是一种藻类.微塑料是微塑料中的一种.快速选快速选快速选快速选快速选沉积物的沉积物半自动化的方法.

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科学领域:

环境科学环境科学
分析化学分析化学
生物技术是生物技术.

背景情况:

微塑料 (MP) 是普遍存在的环境污染物,通常与水生生态系统中的自然颗粒混合.
准确和快速的检测方法对于了解MP分布和影响至关重要.
区分MP与天然合物,如藻类和沉积物,具有分析挑战.

研究的目的:

开发一种快速,无污染的方法来识别和量化天然水中的微塑料.
为了利用流量细胞计和机器学习来分析MP散射和光特性.
评估该方法在含有自然颗粒的复杂环境矩阵中的有效性.

主要方法:

建立了微塑料散射和光特性数据库,使用无污染流细胞计.
用无监督 (viSNE) 和监督 (随机森林) 机器学习算法分析高维数据.
测试了该方法的模型MP在悬浮与光合作用微生物,生物膜,矿物质和沉积物.

主要成果:

在微生物光和高有机碳沉积物中实现了微塑料的精确量化 (>93%的准确性).
证明了该方法作为环境样本中微塑料快速选工具的适用性.
通过将MP添加到淡水样本中来验证工作流程,证实其实际实用性.

结论:

开发的工作流提供了一种节省时间和易于应用的方法来评估微塑料的存在.
流量细胞计数据的机器学习分析能够在复杂的自然水中进行强大的微塑料检测.
这种方法大大加快了微塑料监测和环境评估的分析工作流程.