Identification of Myeloma-Initiating Cells By Single-Cell RNA Sequencing
Authors: Gong Lixin , Sun Hao , Lv Junqiang , Wei Xiaojing , Liu Lanting , Yu Zhen , He Yi , An Gang , Zou Dehui , Qiu Lugui , Hao Mu 
Published: November 2021

Abstract
Background: The outcomes of patients with multiple myeloma (MM) have improved due to treatment advances. However, some patients still experience rapid progression, multiple drug resistance or recurrent relapse. Tumor-initiating cells, also known as cancer stem cells (CSC) in some cancer types, have been speculated to induce recurrence of the disease. The aim of this study is to infer the identity of myeloma-initiating cells (MICs) utilizing single-cell transcriptome analysis and explore the unique biological characteristics relating with high-risk and drug-resistance.
Method: We applied single cell RNA sequencing to fresh bone marrow mononuclear cell samples collecting from 7 healthy donors and 12 newly diagnosed MM patients utilizing 10x Chromium platform.
Results: Firstly, we segregated the patients by tumor cell infiltration at single cell resolution and found that Myc pathway was significantly enriched in patients with high tumor burden (HTB). Next, we performed clustering analysis to tumor cells and identified 13 tumor subpopulations in total. Surprisingly, the distribution pattern of tumor subpopulations presented similarity among HTB patients, whereas we did not find the uniform subpopulation composition among the patients with low (LTB) or medium tumor burden (MTB). Via the tumor subpopulation analysis, we clarified the divergence in biological characteristics of these 13 malignant subpopulations. We identified plasmablasts as displaying high expression of B-cell gene signatures (CD19, CD27, MS4A1 and CD79B) and relatively low expression of plasma cell gene signatures (SDC1 and BCMA). Additionally, we also noted that they showed high level of CD24, which has been validated to be the marker gene for MICs. We next examined proliferative capability and utilized the 70 high-risk gene model and 56 drug resistance-related gene model to further distinguish subpopulations with the most malignant gene expression features. Notably, we found that plasmablasts possessed characteristics of high proliferation, drug-resistance and high-risk gene profiling, indicating their role as the root of myeloma, namely MIC subpopulation. Gene enrichment analysis also implicated that Wnt pathway, Notch pathway, stem cell differentiation pathway and Hedgehog pathway were enriched in MIC subpopulation which were associated with the proliferation, migration and drug resistance of MM. Differentially expressed gene (DEG) analysis showed that common driver genes in myeloma, such as CCND2, ITGB7 and CD74, were upregulated in MIC subpopulation comparing with other subpopulations.
Conclusion: Our work presents an integral profiling for tumor cells in myeloma at single cell resolution. We uncovered divergence in the distribution of malignant subclusters across patients and distinct heterogeneity in gene expression across malignant subclusters as well. Plasmablasts expressing high level of CD24, CD27 and dim CD138 presented as the MICs with characteristics of higher proliferation, drug-resistance and high-risk gene profiling.

Disclosures
No relevant conflicts of interest to declare.