Label-free enrichment of human blast cells from whole blood for leukemia monitoring

Fu, Yatian; Zou, Shangjie; Khoo, Bee Luan

doi:10.1016/j.xpro.2022.101584

Cited by 1 publication

(3 citation statements)

References 6 publications

(12 reference statements)

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“…Bypass valves maintained constant pressure drops between the inlet and outlet. Our device was fabricated using photolithography and soft lithography techniques following established protocols 18 , 19 .…”

Section: Resultsmentioning

confidence: 99%

“…We further evaluated the potential of cell diameter gating to distinguish cancer cells from white blood cells in liquid blood biopsy samples. MCF7 and MDA-MB-231 cells were used to represent cancer cells, while white blood cells were separated from whole human blood by red blood cell lysis following procedures stated in previous literature 18 . We used the ATMQcD framework to determine an appropriate threshold for cell diameter.…”

Section: Resultsmentioning

confidence: 99%

“…During the simulation, polydimethylsiloxane (PDMS) was applied as the material for the channel walls. The microchannel layer was developed using standard photolithography and soft lithography with a negative photoresist (Cat#SU-8 2025, MicroChem, United States) and PDMS (Cat#01673921, Dow, United States), following the instructions as indicated and as previously reported 18 , 19 . When fabricating the master mold, the photoresist was spin-coated on a silicon wafer with a spinner (Cat# KW-4A, SETCAS Electronics, China) by ramping to 500 rpm for 5–10 s with a 100 rpm/s acceleration and then ramping to 2500 rpm with a 300 rpm/s acceleration for 30 s, resulting in a 30 μm-thick photoresist layer.…”

Section: Methodsmentioning

confidence: 99%

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A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning

Hua,

Zou,

et al. 2023

Microsyst Nanoeng

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Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%