Lymphomas encompass Hodgkin lymphoma and non-Hodgkin lymphoma. In this study, we have developed a static cytometry leveraging laser and microscope technology to capture 2D light scattering patterns of individual cells. Within this method, a single lymphoma cell is positioned in a liquid-based chip and vertically stimulated by a 532 nm green laser. The resulting light scattering pattern of the cell is observed and recorded by a COMS detector through a microscope optical system, covering a polar angle range of 75 to 105 degrees. By extracting and analyzing the characteristic values from these scattering patterns, we can achieve lymphoma cell identification. In this study, we successfully differentiated between HDLM-2 and Daudi cells using the SVM algorithm, achieving a classification accuracy of 88%. This outcome underscores the potential of our 2D light scattering static cytometry for lymphoma cell classification, offering a marker-free, cost-effective approach for early cancer screening at the single-cell level.
Efficient detection of cell clusters is of paramount importance in the production and packaging processes of single-cell suspensions, as it can significantly impact cell density uniformity, induce apoptosis, and compromise the accuracy of cell sorting outcomes. This study introduces a novel, high-throughput and label-free method for monitoring cell clusters, leveraging light scattering imaging and microfluidic technologies. Two-dimensional (2D) light scattering, as a vital label free analytical approach, proves effective in detecting and analyzing biological particles with intricate structures. Through optical microscopy coupled with a high-speed camera, this study examines the 2D light scattering patterns produced by cell clusters, facilitating the discrimination between single cells and clustered cells. This label-free methodology offers distinct advantages over traditional labeling techniques, as it preserves cellular integrity without invasive disruptions. Furthermore, innovative microfluidic chip design enables continuous real-time monitoring of cell clusters, empowering rapid and high-throughput detection. Experimental validation involved monitoring silicon oxide (SiO2) microsphere suspensions, demonstrating the method's capacity for high-throughput and high-sensitivity cell cluster monitoring. This research presents a promising tool for efficiently handling and monitoring the production of single-cell suspensions, with potential applications in various fields of cell biology and biotechnology.
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