High-throughput single-cell transcriptome and gene mutation integration analysis method

Abstract

The invention discloses a high-throughput single-cell transcriptome and gene mutation integration analysis method. The high-throughput single-cell transcriptome and gene mutation integration analysismethod comprises the following steps that (1) a high-throughput single-cell encoding chip is provided; (2) single-cell surface protein parting analysis is conducted; (3) single-cell transcriptome mutation analysis is conducted; (4) a database for single-cell surface protein parting and mutation integration analysis is established; and (5) a high-throughput single-cell transcriptome and gene mutation integration analysis model is established. According to the high-throughput single-cell transcriptome and gene mutation integration analysis method, by designing the single-cell encoding chip withtriple encoding technologies of microporous spatial coordinates, cell nucleic acid labels and molecular nucleic acid labels and by combining the modes of single-cell surface protein parting, single-cell transcriptome mutation analysis and gene sequencing, gene mutation information, transcriptome information and protein expression information of single cells can correspond one by one, the completedatabase for high-throughput single-cell transcriptome and gene mutation integration analysis is formed, and the multi-omics integration analysis model is obtained.

Description

technical field [0001] The invention relates to the field of biological detection, in particular to a high-throughput single-cell transcriptome and gene mutation integration analysis method. Background technique [0002] Tumor is one of the major diseases that seriously affect human health. There are great differences in tumor cells from genotype to phenotype (high heterogeneity of tumors), and this high heterogeneity is related to the degree of malignancy and drug resistance of tumors. Sex, recurrence and metastasis are closely related, which is one of the root causes of difficult early diagnosis of tumors, complicated clinical diagnosis and treatment, drug-resistant recurrence and poor prognosis. A comprehensive analysis of tumor heterogeneity is the key to precise tumor treatment. [0003] The development of high-throughput sequencing technology brings hope for the analysis of heterogeneous tumor populations. At present, conventional high-throughput sequencing at variou...

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Problems solved by technology

However, the three-generation sequencing technology used in this study to detect mutations has great limitations. The mutation detection rate is limited by the specific mutation site. The highest single mutation detection rate is only 23%, and the average number of cells that can detect mutations does not exceed 5%, the author finally used the random forest machine learning algorithm to predict the tumor population, which could not realize the direct identification of the tumor cell population, and did not correspond well to the heterogeneity of the genome and transcriptome

Moreover, the technique is cumbersome to operate, requires a high sample volume, and costs a lot, making it unsuitable for general promotion.

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