Single cell dispensing techniques mainly include limiting dilution, fluorescent-activated cell sorting (FACS) and microfluidic approaches. Limiting dilution process is complicated by statistical analysis of clonally derived cell lines. Flow cytometry and conventional microfluidic chip methods utilize excitation fluorescence signals for detection, potentially causing a non-negligible effect on cell activity. In this paper, we implement a nearly non-destructive single-cell dispensing method based on object detection algorithm. To realize single cell detection, we have built automated image acquisition system and then employed PP-YOLO neural network model as detection framework. Through architecture comparison and parameter optimization, we select ResNet-18vd as backbone for feature extraction. We train and evaluate the flow cell detection model on train and test set consisting of 4076 and 453 annotated images respectively. Experiments show that the model inference an image of 320 × 320 pixels at least 0.9 ms with the precision of 98.6% on a NVidia A100 GPU, achieving a good balance of detection speed and accuracy.

Research on cell detection method for microfluidic single cell dispensing.

Using microfluidic chips to produce micro droplets wrapped with microorganisms is of great significance and value in life science research and analysis. One of the most obvious advantages is that it has a volume limiting effect on microorganisms. For example, tiny droplets of picoliter size can only allow single cells to enter, so that single cell separation in cell populations can be achieved. In this work, we designed a microfluidic impact printing system to encapsulate cells in Pico-liter level droplet (PL) through the inverse piezoelectric effect of piezoelectric materials. This method can isolate single cells in a label-free and high-throughput manner. The structure of the microfluidic chip designed in this paper can realize liquid feeding through capillary force without additional pumps and valves. The microfluidic chip and the driving module are not in direct contact, which avoids damage to cells activity. Experiments show that the system designed in this paper can stably generate 30PL, 12PL and 10PL droplet volumes in water, ink, and alcohol media respectively. The stable generation of picoliter-sized droplets provides an important basis for the subsequent research and development of single cell analysis.

Design and Research on Microfluidic Impact Printing for Picoliter-sized Droplet

In droplet-on-demand flow cell sorter where a single cell is encapsulated in a single droplet as designed, complex physical interactions govern the droplet characteristics, such as position, size, and shape. These droplet characteristics, in turn, determine the functional performance of expected cell sorting. Hence, to ensure repeatable and reliable cell sorting, it is necessary to monitor and control the droplet characteristics. The existing methods based on high-speed photography only obtain droplet features through simple image processing, which limits their robustness. And the existing droplet quality control strategies only focus on the overall instrument system, lacking direct and intuitive monitoring methods for the droplet itself. To overcome this challenge, the objective of this work is to build a visual monitoring method that can effectively evaluate the stability of droplet generation before flow cytometry sorting experiments. Specifically, we introduced a deep semantic segmentation model to segment each droplet image, then extracted 5 features that characterize droplet stability and calculate equivalent feature, and drew a quality control curve based on the statistical values of equivalent feature. Finally, based on the quality control curve, we determine whether the quality of droplet generation was controlled. The experiment showed that the droplet segmentation model achieved a segmentation accuracy of 99.32% and a prediction speed of 123.35ms per image on CPU. A demonstration application of the quality control approach was provided through case studies. Thus, this work achieves a practically visual monitoring approach to droplet stability generation for flow cell sorter in a simple and convenient way, with direct monitoring of droplet generation quality before flow cytometry experiment.

A Visual Monitoring Approach to Droplet Stability Generation for Flow Cell Sorter

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Research on cell detection method for microfluidic single cell dispensing.

Design and Research on Microfluidic Impact Printing for Picoliter-sized Droplet

A Visual Monitoring Approach to Droplet Stability Generation for Flow Cell Sorter