scholarly journals Single-cell transcriptomics predicts relapse in MLL-rearranged acute lymphoblastic leukemia in infants

2020 ◽  
Author(s):  
Tito Candelli ◽  
Pauline Schneider ◽  
Patricia Garrido Castro ◽  
Luke A. Jones ◽  
Rob Pieters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Risk Stratification ◽  
Single Cell ◽  
Rna Sequencing ◽  
Lymphoblastic Leukemia ◽  
Leukemic Cells ◽  
List Type ◽  
Relapse Risk ◽  
Single Cell Rna Sequencing

AbstractInfants with MLL-rearranged acute lymphoblastic leukemia (ALL) undergo intense therapy to counter a highly aggressive leukemia with survival rates of only 30-40%. The majority of patients initially show therapy response, but in two-thirds of cases the leukemia returns, typically during treatment. Accurate relapse prediction would enable treatment strategies that take relapse risk into account, with potential benefits for all patients. Through analysis of diagnostic bone marrow biopsies, we show that single-cell RNA sequencing can predict future relapse occurrence. By analysing gene modules derived from an independent study of the gene expression response to the key drug prednisone, individual leukemic cells are predicted to be either resistant or sensitive to treatment. Quantification of the proportion of cells classified by single-cell transcriptomics as resistant or sensitive, accurately predicts the occurrence of future relapse in individual patients. Strikingly, the single-cell based classification is even consistent with the order of relapse timing. These results lay the foundation for risk-based treatment of MLL-rearranged infant ALL, through single-cell classification. This work also sheds light on the subpopulation of cells from which leukemic relapse arises. Leukemic cells associated with high relapse risk are characterized by a smaller size and a quiescent gene expression program. These cells have significantly fewer transcripts, thereby also demonstrating why single-cell analyses may outperform bulk mRNA studies for risk stratification. This study indicates that single-cell RNA sequencing will be a valuable tool for risk stratification of MLL-rearranged infant ALL, and shows how clinically relevant information can be derived from single-cell genomics.Key PointsSingle-cell RNA sequencing accurately predicts relapse in MLL-rearranged infant ALLIdentification of cells from which MLL-rearranged infant ALL relapses occur

scholarly journals Single Cell Transcriptomics Predicts Relapse in Infants with Acute Lymphoblastic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3951-3951
Author(s):  
Tito Candelli ◽  
Pauline Schneider ◽  
Patricia Garrido Castro ◽  
Luke A Jones ◽  
Rob Pieters ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Single Cell ◽  
Single Cells ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Pattern ◽  
Disease Classification ◽  
Leukemic Cells ◽  
Response To Treatment ◽  
Bone Marrow Biopsies ◽  
Relapse Risk

Care of infants (<1 year of age) diagnosed with MLL (KMT2A)-rearranged acute lymphoblastic leukemia (MLLr-iALL) suffers from two major drawbacks. First, a poor survival rate due to a high rates of early relapse and chemo-resistance. Additionally, the approximately 30-50% of patients that do survive, suffer from life-long, debilitating side-effects of current treatment. While almost all MLLr-iALL patients show an initial promising response to treatment, two-third of the patients relapse, typically within the first year from diagnosis and while still on treatment. Accurate relapse prediction would allow implementation of treatment strategies that take relapse risk into account, with great potential benefit for all patients. Here, we show that Single-cell RNA sequencing (scRNA-seq) can be valuable for risk stratification and that the abundance of chemo-resistant cells within the diagnosis sample might be a powerful indicator of the likelihood of relapse. We have used scRNA-seq to analyze the response to treatment of leukemic cells in bone marrow biopsies of seven MLLr-iALL patients, expressing either the oncogenic MLL-AF4 or MLL-ENL fusion gene, at the time of initial diagnosis. Three of these patients successfully underwent treatment and remained disease-free during 7 years of follow-up, while in the remaining four cases the disease returned within a year from diagnosis. All samples were subjected to scRNA-seq by FACS index sorting with the aim of identifying differences between early relapsers and long-term survivors. Quantification of the proportion of cells classified by single cell transcriptomics, categorized as either chemo-resistant or chemo-sensitive, accurately predicts the occurrence of future relapse in individual patients. Strikingly, the single cell-based classification is even consistent with the order of relapse timing. Additionally, leukemic cells associated with high relapse risk are typified by a small phenotype, which coincides with an apparent quiescent gene expression pattern. This study clearly and, to the best of our knowledge, for the first time shows how disease classification and treatment management can directly benefit from single cell genomics. It demonstrates how classification based on a pivotal functional characteristic of single cells can be performed, despite individual patient variation. Our results shed light on the subpopulation from which leukemic relapse originates, and opens up opportunities for strong, risk-based strategies for future MLLr-iALL treatment regimens. Disclosures Pieters: medac: Consultancy; jazz farmaceuticals: Consultancy.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

scholarly journals Single-cell RNA sequencing of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205883 ◽  
Author(s):  
Joseph C. Mays ◽  
Michael C. Kelly ◽  
Steven L. Coon ◽  
Lynne Holtzclaw ◽  
Martin F. Rath ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pineal Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Mammalian Pineal Gland ◽  
Daily Patterns

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

Abstract A10: Using single-cell RNA sequencing to assess the impact of pancreatic oncogenic Kras on macrophage gene expression in vitro

2019 ◽  
Author(s):  
Katelyn Donahue ◽  
Yaqing Zhang ◽  
Veerin Sirihorachai ◽  
Stephanie The ◽  
Arvind Rao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Rna Sequencing ◽  
The Impact

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

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