Cell density detection based on a microfluidic chip with two electrode pairs

Simultaneous Multipass Resistive-Pulse Sensing and Fluorescence Imaging of Liposomes

Simultaneous multipass resistive-pulse sensing and fluorescence imaging have been used to correlate the size and fluorescence intensity of individual E. coli lipid liposomes composed of E. coli polar lipid extracts labeled with membrane-bound 3,3-dioctadecyloxacarbocyanine (DiO) fluorescent molecules. Here, a nanopipet serves as a waveguide to direct excitation light to the resistive-pulse sensing zone at the end of the nanopipet tip. Individual DiO-labeled liposomes (>50 nm radius) were multipassed back and forth through the orifices of glass nanopipets' 110-150 nm radius via potential switching to obtain subnanometer sizing precision, while recording the fluorescence intensity of the membrane-bound DiO molecules. Fluorescence was measured as a function of liposome radius and found to be approximately proportional to the total membrane surface area. The observed relationship between liposome size and fluorescence intensity suggests that multivesicle liposomes emit greater fluorescence compared to unilamellar liposomes, consistent with all lipid membranes of the multivesicle liposomes containing DiO. Fluorescent and nonfluorescent liposomes are readily distinguished from each other in the same solution using simultaneous multipass resistive-pulse sensing and fluorescence imaging. A fluorescence "dead zone" of ∼1 μm thickness just outside of the nanopipet orifice was observed during resistive-pulse sensing, resulting in "on/off" fluorescent behavior during liposome multipassing.

Fully integrated point-of-care blood cell count using multi-frame morphology analysis

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by "stationary" images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using "in-flow" imaging of 3 μL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame "in-flow" imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

A Study on the Uniform Distribution and Counting Method of Raw Cow's Milk Somatic Cells

The somatic cell count (SCC) in raw milk is an important basis for determining whether a cow is suffering from mastitis. To address the problem of an uneven distribution of somatic cells due to cell-adherent sedimentation, among other reasons, during milk sampling, which in turn results in unrepresentative somatic cell counting, a method is proposed for obtaining a uniform distribution of somatic cells and improving the counting accuracy based on a nine-cell grid microfluidic chip. Firstly, a simulation was performed to verify the uniformity of the somatic cell distribution within the chip observation cavities. Secondly, a nine-cell grid microfluidic chip was prepared and a negative-pressure injection system integrating staining and stirring was developed to ensure that the somatic cells were uniformly distributed and free from air contamination during the injection process. As well as the structure of the chip, a microscopic imaging system was developed, and the nine chip observation cavities were photographed. Finally, the somatic cells were counted and the uniformity of the somatic cell distribution was verified using image processing. The experimental results show that the standard deviation coefficient of the SCC in each group of nine images was less than 1.61%. The automatic counting accuracy of the system was between 97.07% and 99.47%. This research method lays the foundation for the detection and prevention of mastitis in cows.