Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Blood. 2023 Jul 27;142(4):313-324. doi: 10.1182/blood.2022017145.
In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.
在很短的时间内,单细胞平台已经成为许多研究领域的标准,包括多发性骨髓瘤(MM)。事实上,MM 中大量的细胞异质性使得单细胞平台特别有吸引力,因为批量评估可能会错过有关细胞亚群和细胞间相互作用的有价值信息。单细胞平台的成本降低和可及性增加,加上同一细胞获得多组学数据的突破以及用于分析数据的创新计算程序,使得单细胞研究能够深入了解 MM 的发病机制;然而,仍有许多工作要做。在这篇综述中,我们首先将重点介绍单细胞分析的类型以及设计单细胞分析实验的注意事项。然后,我们将讨论从单细胞分析中了解到的骨髓瘤克隆进化、转录重编程和耐药性,以及在疾病前期和晚期 MM 微环境的情况。