Two-Dimensional Multi-parameter Cytometry Platform for Single-Cell Analysis.
Chengxin WuXue MenMeijun LiuYujia WeiXing WeiYong-Liang YuZhang-Run XuMing-Li ChenJian-Hua WangPublished in: Analytical chemistry (2023)
A 2D flow cytometry platform, known as CytoLM Plus, was developed for multi-parameter single-cell analysis. Single particles or cells after hydrodynamic alignment in a microfluidic unit undergo first-dimension fluorescence and side scattering dual-channel optical detection. They were thereafter immediately directed to ICP-MS by connecting the microfluidic unit with a high-efficiency nebulizer to facilitate the second-dimension ICP-MS detection. Flow cytometry measurements of fluorescent microspheres evaluated the performance of CytoLM Plus for optical detection. 6434 fluorescence bursts were observed with a valid signal proportion as high as 99.7%. After signal unification and gating analysis, 6067 sets of single-particle signals were obtained with 6.6 and 6.2% deviations for fluorescence burst area and height, respectively. This is fairly comparable with that achieved by a commercial flow cytometer. Afterward, CytoLM Plus was evaluated by 2D flow cytometry measurement of Ag + -incubated and AO-stained MCF-7 cells. A program for 2D single-cell signal unification was developed based on the algorithm of screening in lag time window. In the present case, a lag time window of -4.2 ± 0.09 s was determined by cross-correlation analysis and two-parameter optimization, which efficiently unified the concurrent single-cell signals from fluorescence, side scattering, and ICP-MS. A total of 495 sets of concurrent 2D signals were screened out, and the statistical analysis of these single-cell signals ensured 2D multi-parameter single-cell analysis and data elucidation.
Keyphrases
- single cell
- flow cytometry
- rna seq
- high throughput
- mass spectrometry
- multiple sclerosis
- induced apoptosis
- machine learning
- label free
- high resolution
- body mass index
- physical activity
- oxidative stress
- circulating tumor cells
- high efficiency
- high frequency
- high speed
- deep learning
- radiation therapy
- sensitive detection
- artificial intelligence
- rectal cancer
- neural network