A method for effectively removing mononuclear cell interference in automatic analysis of lymphocyte subsets

Abstract

A method for effectively removing mononuclear cell interference in the automatic analysis of lymphocyte subsets, through CD3, SSC‑A, CD45 and a density-based clustering algorithm, plus a kernel density estimation algorithm to determine the strength of the fluorescence intensity of CD3, identify For T lymphocytes, determine the two-dimensional distribution area of ​​lymphocytes on CD45 and SSC‑A and the corresponding position parameters, and combine with the kernel density estimation algorithm to fit the density curves of the distribution of cells on CD45 and SS, respectively, to determine monocytes and lymphocytes The threshold value of the density curve distributed on SS and the quantile of CD45 distribution of T lymphocytes were used to estimate the distribution of lymphoid T cells on CD4 by using kernel density estimation to determine the threshold value of non-expression and weak expression of CD4; on the basis of determining the threshold value, The multi-dimensional data of SSC‑A, CD4, CD45, CD16 and CD19 were classified into cell groups, and non-T lymphocytes and monocytes were distinguished according to the thresholds of SSC‑A, CD45 and CD4. The invention can effectively remove noise interference.

Description

technical field [0001] The present invention relates to a method for removing monocyte interference. In particular, it relates to a method for effectively removing monocyte interference in the automatic analysis of lymphocyte subsets. Background technique [0002] In flow cytometry, the sample cells to be tested are transported through a liquid flow system to form a single-cell flow. In the laser irradiation area, the fluorescent dyes labeled on the cells are excited by the laser to generate fluorescent signals. In different experimental systems, depending on the fluorescein labeled by cells, different wavelengths of fluorescence are emitted under excitation at different wavelengths, and these fluorescent signals can reflect different biological characteristics of cells. The components of cells can be quantitatively measured by photometry, and various information about different components can be obtained for the same cell, which can be used as the basis for the identificat...

AI Technical Summary

Problems solved by technology

[0003] In the identification of lymphocyte subsets with 6-color antibody combination, the existing automatic and manual analysis of lymphocyte subsets cannot accurately separate monocytes and lymphocytes

[0004] In the absence of CD45-FIFC / CD14-PE as reverse gating for lymphocytes, the existing artificial cell subpopulation analysis is to gate lymph by SSC / CD45, due to the lack of monocyte-specific CD14 fluorescence Marking, in the lymphatic hilum, cannot identify the mixed monocytes; the instrument simulates manual analysis, which reduces subjective errors caused by personal preference or other factors, but does not perfect the isolation of monocytes

Images