Big data and single-cell sequencing in acute myeloid leukemia research

The advancement of diverse technologies has led to a substantial increase in valuable biomedical data, particularly in the field of acute myeloid leukemia (AML). Effective utilization of this wealth of data is crucial for attaining a comprehensive and in-depth understanding of AML, thereby facilitating optimal diagnosis, treatment, and prognosis. Among the various approaches to data acquisition, single-cell sequencing has emerged as an impressive tool. The developments of single-cell sequencing methods have empowered researchers to analyze the genome, transcriptome, proteome, and epigenome data at the single-cell level. It also offers a means to uncover fine information, providing unique prognostic insights and aiding in the identification of therapeutic targets. Furthermore, it enhances our understanding of AML heterogeneity, clonal evolution, and resistance mechanisms, ultimately leading to the development of better treatment strategies. In this review, we present an overview of AML as well as single-cell sequencing technologies, then explore their potential contributions to AML research in different aspects, and provide some information about resources and data processing.