Blog:Novogene Single-cell and Spatial Multi-omics Case Study -Hematological Oncology Research and Application
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The team of Xiaofan Zhu and Tao Cheng from the Blood Disease Hospital of the Chinese Academy of Medical Sciences (Institute of Hematology, Chinese Academy of Medical Sciences) cooperated with the team of Jin Gu from Tsinghua University and conducted research on minimal residual disease of pediatric B-cell acute lymphoblastic leukemia. On February 10th, 2022, they jointly published an article titled "Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis" in the journal Nature Cell Biology (IF=28.824). The primary emphasis of this article is pediatric B-cell acute lymphoblastic leukemia (B-ALL). B-cell acute lymphoblastic leukemia (B-ALL) is the most common type of malignant cancer of the blood system in children. This disease is characterized by abnormal accumulation of blasts in the bone marrow and blood. These blasts infiltrate the spleen, lymph nodes, thymus, liver and central nervous system. They take over the hematopoietic system, and ultimately jeopardize the production of other necessary blood cell types. The study conducted in the article employed single-cell sequencing to construct a single-cell atlas of pediatric B-ALL ,and discovered the molecular basis of drug resistance in difficult-to-treat relapsed pediatric B-ALL. Novogene Corporation played a critical role in this study and participated in the single-cell capture, library preparation, sequencing, and analysis of this article.

Introduction

Although hematological malignancies have stronger heterogeneity as compared to solid tumors, the research progress on the biological mechanisms and microenvironment of their pathogenesis is relatively lagging behind. The reason for this is the complexity of hematological malignancies, which makes it difficult for the researchers to ascertain their underlying mechanisms. The emergence of single-cell sequencing technology provides an excellent platform for hematological malignancy-related research. Articles published using this technology have exploded in growth, covering various aspects such as the heterogeneity of hematological tumor cells, clonal evolution, epigenomic variation, the composition of the tumor microenvironment, and the interaction between cells. This technology will help bridge the gap and lead to more rapid advancements in research. Leukemia is one of the most common malignant tumors of the blood system in children , with B-ALL cells accounting for up to 85% of cases. Relevant studies have shown that the peak incidence age is 2 to 5 years old. Although childhood acute lymphoblastic leukemia (ALL) has become a disease that can be cured in over 80% of cases, 10% of cases still relapse. This relapse is the primary cause of pediatric cancer deaths. Minimal residual disease (MRD) is a widely used concept in hematological and solid malignancies because it plays an important role in treatment and prognosis. The objective of MRD study of B-ALL is to observe the initial response of the treatment and to assess the burden of the chemotherapy and detect relapse early. In fact, MRD is the most significant prognostic indicator for pediatric B-ALL and its use has been widespread to guide treatment intensity and prove bone marrow transplantation. Despite existing multiple MRD detection techniques, the molecular characteristics and maintenance mechanisms of MRD remain unknown. Capturing low-abundance residual cancer cells is the main technical challenge that hinders further exploration of the molecular basis of drug resistance.

Materials

The researchers conducted the study by using the bone marrow samples from 8 pediatric B-ALL patients in the experi-mental group, and sorted CD19+ cells using flow cytometry for single-cell 5' transcriptome and single-cell BCR sequenc-ing. The bone marrow samples from 2 healthy children were also used as the control group, and CD19+ cells and CD19- CD34+ cells were sorted for single-cell 3' transcriptome sequencing. The 10x Genomics platform successfully captured a total of 16,543 CD19- CD34+ cells and 20,392 CD19+ cells.

Sequencing Pipeline

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The researchers used a large number of bone marrow cell sorting samples, and therefore were able to generate a single-cell atlas of pediat-ric B-ALL employing single-cell sequencing analysis technology provided by Novogene. The distinct features of leukemic cells were studied, allowing researchers to discover the molecular basis of drug resistance in difficult-to-treat pediatric relapsed B-ALL.This single-cell analysis of minimal resid-ual disease provides a pathway for the identification o f effective treat-ment opportunities for B-ALL.

Results

The researchers initially conducted quality control on the sample data, subsequently filtering out low-quality cells. Afterwards, the profound project experience of the Novogene bioinformatics team was utilized in defining bone marrow sample cells, which was combined with their independently developed and highly accurate cell definition tool Novoalgor, to achieve precise cell identification for bone marrow samples. To carry out integrated analysis at different sample analysis levels, specific entry samples were selected. A reference single-cell atlas of B-cell development in children was constructed using bone marrow single nuclei from healthy pediatric donors. BCR clone diversity was used as the basis for the preliminary identification of leukemia cells and normal B cells. A machine learning method was then developed and used to classify and validate leukemia cells using characteristic tumor molecular markers of patients. The reliability of using BCR clone diversity to identify leukemia cells was confirmed. The constructed classifier was used to identify leukemia cells from four relapsed patients and determine the differentiation stage and cell cycle of the leukemia cells. Further study using differential gene pathway enrichment analysis revealed that the genes associated with hypoxia pathways were significantly up-regulated during the D19 treatment stage compared to the diagnostic and relapse stages. This revelation led to investigating the effect of hypoxia pathway inhibition on leukemia cells. Inhibiting the hypoxia pathway was found to sensitize leukemia cells to chemotherapy, thus reducing their chances of survival and providing a promising avenue for effective treatment of B-ALL.

Single-cell Bioinformatics Analysis Service

The Novogene single-cell bioinformatics analysis is divided into three major modules: matrix analysis, standard analysis, and advanced analysis (Figure 3). Novogene has been utilizing its extensive project experience to develop practical analysis modules to fully meet the demands and satisfy the needs of different types of customers. enter image description here Novogene has independently developed Novoalgor, the high-accuracy cell identification tool, with abundant applications in various fields of research. Novoalgor integrates the cell identification algorithm fast-celltype, cell marker database NovomarkerDB, and a cell identification result visualization tool. It has been applied to multiple projects, and is committed to providing customers with automated, accurate, and convenient data presentation. The Novogene single-cell bioinformatics analysis team has over 5 years of comprehensive experience and in-depth knowledge of omics analysis, with a collective sum of 16 software copyrights and patents (13 software copyrights and 3 patents). Single-cell spatial sequencing services have also been extended to dozens of countries worldwide and helped in many scientific breakthroughs, supporting scientists around the globe. 152 high-impact articles have been published by these scientists with a total impact factor of over 2500.

Reference 1 Zhang, Yingchi, et al. "Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis." Nature Cell Biology 24.2 (2022): 242-252. 2 Newman, Scott et al. “Genomes for Kids: The Scope of Pathogenic Mutations in Pediatric Cancer Revealed by Comprehensive DNA and RNA Sequencing.” Cancer Discovery vol. 11,12 (2021): 3008-3027. doi:10.1158/2159-8290.CD-20-1631

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