Discrete single-cell microRNA analysis for phenotyping the heterogeneity of acute myeloid leukemia. (December 2022)

• Record Type: Journal Article
• Authors: Zhao, Xi; Wang, Zixun; Ji, Xianglin; Bu, Shuyu; Fang, Peilin; Wang, Yuan; Wang, Mingxue; Yang, Yang; Zhang, Wenjun; Leung, Anskar Y.H.; Shi, Peng
• Is Part Of: Biomaterials. Volume 291(2022)
• Publication Date: 2022-12
• Subjects: Cellular heterogeneity -- Acute myeloid leukemia -- AML subtype -- Cancer prognosis -- miRNA profiling -- Nanomedicine; Biomedical materials -- Periodicals; Biocompatible Materials -- Periodicals; Biomatériaux -- Périodiques; 610.28
• DOI: 10.1016/j.biomaterials.2022.121869
• Languages: English
• ISSNs: 0142-9612

Abstract

Abstract: Acute myeloid leukemia (AML) is a highly heterogenous cancer in hematopoiesis, and its subtype specification is greatly important in the clinical practice for AML diagnosis and prognosis. Increasing evidence has shown the association between microRNA (miRNA) phenotype and AML therapeutic outcomes, emphasizing the need for novel techniques for convenient, sensitive, and efficient miRNA profiling in clinical practices. Here, we describe a nanoneedle-based discrete single-cell microRNA profiling technique for multiplexed phenotyping of AML heterogeneity without the requirement of sequencing or polymerase chain reaction (PCR). In virtue of a biochip-based and non-destructive nature of the assay, the expression of nine miRNAs in large number of living AML cells can be simultaneously analyzed with discrete single-cell level information, thus providing a proof-of-concept demonstration of an AML subtype classifier based on the multidimensional miRNA data. We showed successful analysis of subtype-specific cellular composition with over 90% accuracy and identified drug-responsive leukemia subpopulations among a mixed suspension of cells modeling different AML subtypes. The adoption of machine learning algorithms for processing the large-scale nanoneedle-based miRNA data shows the potential for powerful prediction capability in clinical applications to assist therapeutic decisions. We believe that this platform provides an efficient and cost-effective solution to move forward the translational prognostic usage of miRNAs in AML treatment and can be readily and advantageously applied in analyzing rare patient-derived clinical samples.