Department of Automation, Tsinghua University, Beijing 100084, China.
Biosensors (Basel). 2023 Aug 15;13(8):821. doi: 10.3390/bios13080821.
Microfluidic droplets accommodating a single cell as independent microreactors are frequently demanded for single-cell analysis of phenotype and genotype. However, challenges exist in identifying and reducing the covalence probability (following Poisson's distribution) of more than two cells encapsulated in one droplet. It is of great significance to monitor and control the quantity of encapsulated content inside each droplet. We demonstrated a microfluidic system embedded with a weakly supervised cell counting network (WSCNet) to generate microfluidic droplets, evaluate their quality, and further recognize the locations of encapsulated cells. Here, we systematically verified our approach using encapsulated droplets from three different microfluidic structures. Quantitative experimental results showed that our approach can not only distinguish droplet encapsulations (F1 score > 0.88) but also locate each cell without any supervised location information (accuracy > 89%). The probability of a "single cell in one droplet" encapsulation is systematically verified under different parameters, which shows good agreement with the distribution of the passive method (Residual Sum of Squares, RSS < 0.5). This study offers a comprehensive platform for the quantitative assessment of encapsulated microfluidic droplets.
作为独立的微反应器，容纳单个细胞的微流控液滴经常被要求用于单细胞表型和基因型分析。然而，在识别和降低一个液滴中封装的两个以上细胞的共价概率（遵循泊松分布）方面存在挑战。监测和控制每个液滴内封装内容的数量非常重要。我们展示了一个嵌入弱监督细胞计数网络 (WSCNet) 的微流控系统，用于生成微流控液滴、评估其质量，并进一步识别封装细胞的位置。在这里，我们使用来自三种不同微流控结构的封装液滴系统地验证了我们的方法。定量实验结果表明，我们的方法不仅可以区分液滴封装（F1 分数>0.88），还可以在没有任何监督位置信息的情况下定位每个细胞（准确率>0.89%）。在不同参数下系统地验证了“一个液滴一个细胞”封装的概率，这与被动方法的分布（残差平方和，RSS<0.5）吻合较好。这项研究为封装微流控液滴的定量评估提供了一个全面的平台。
