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. Utilizing numerous amounts of different kinds of data from various sources can contribute to further understanding of acute myelogenous leukemia, playing a positive role in different aspects of research and applications to benefit patients, and the discoveries in different fields can even interact to provide one-way or mutual promotion as well. Among various data sources, single-cell sequencing is an emerging option that is powerful and impressive. image