IMMU-30. IDENTIFICATION OF COORDINATELY REGULATED IMMUNO-MODULATORY GENES USING SINGLE-CELL RNA SEQUENCING OF DECITABINE TREATED GLIOMA CELLS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi99-vi99
Author(s):  
Thomas Lai ◽  
Janet Treger ◽  
Jingyou Rao ◽  
Robert Prins ◽  
Richard Everson
Keyword(s):  
New York ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Bisulfite Sequencing ◽  
Malignant Gliomas ◽  
Glioma Cells ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Cell Data

Abstract INTRODUCTION The immunotherapeutic targeting of New York-esophageal squamous cell carcinoma (NY-ESO-1) and other cancer/testis antigens (CTA) is an appealing strategy for the treatment of malignant gliomas because CTA are not expressed in most normal adult tissues and their expression can be induced in tumors for targeting by T-cells. Basally, NY-ESO-1 is often poorly expressed in glioblastoma (GBM), presumably through promoter methylation. Our previous data has shown that the hypomethylating agent decitabine (DAC) is a strong sensitizer of GBM to NY-ESO-1 specific adoptive T-cell induced cytotoxicity. However, we hypothesized that DAC alters expression of other immuno-modulatory genes in addition to NY-ESO-1 that may also increase T-cell mediated killing in GBM. The extent of regulation of other immuno-modulatory genes to DAC demethylation has not yet been thoroughly investigated. METHODS We performed single-cell RNA sequencing on DAC and non-DAC treated glioma cells. We confirmed DAC treatment induced NY-ESO-1 expression with quantitative real-time PCR and demethylation using bisulfite sequencing. We analyzed our single-cell RNA sequencing data with Seurat and our customized R-based pipelines to identify coordinate differential expression of immuno-modulatory genes and their tumor subpopulations. RESULTS Using our single-cell data, we identified tumor subpopulations with coordinate differentially expressed immuno-modulatory genes including cancer testes antigens, antigen presentation proteins, and apoptosis regulators. Amongst these candidate genes, we validated their expression with qPCR and promoter demethylation with bisulfite sequencing and TA bisulfite cloning. CONCLUSION Exposure of glioma cells to DAC results in promoter demethylation of NY-ESO-1, with increased expression of CTA and other immunomodulatory genes. DAC treatment may therefore sensitize GBM to the immunotherapeutic targeting of these antigens and reveals a feasible strategy for clinical translation.

scholarly journals ECOA-2. Immuno-sensitization of Glioblastoma to NY-ESO-1 Targeting via Promoter Demethylation

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii1-ii1
Author(s):  
Thomas Lai ◽  
Janet Treger ◽  
Jingyou Rao ◽  
Tie Li ◽  
Albert Lai ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Promoter Methylation ◽  
Bisulfite Sequencing ◽  
Malignant Gliomas ◽  
Cancer Testis Antigens ◽  
Single Cell Rna Sequencing ◽  
Cancer Testis

Abstract Introduction The immunotherapeutic targeting of New York-esophageal squamous cell carcinoma (NY-ESO-1) and other cancer/testis antigens (CTA) is an appealing strategy for the treatment of malignant gliomas because CTA are not expressed in most normal adult tissues and their expression can be induced in tumors for targeting by T-cells. Basally, NY-ESO-1 is often poorly expressed in glioblastoma (GBM), presumably through promoter methylation. Mechanisms governing the expression of CTA have been explored in other cancers; however, neither the prevalence of NY-ESO-1 downregulation in GBM patient tumors nor the presumed mechanism of downregulation by promoter methylation in GBM has been formally established. Methods We characterized baseline CpG methylation of NY-ESO-1 in 30 bulk patient GBM samples, 10 patient-derived gliomaspheres, and three established tumor cell lines using bisulfite sequencing. We induced NY-ESO-1 expression in vitro in U251 human GBM cells using the hypomethylating agent decitabine (DAC). We investigated the epigenetic response of DAC-treated U251 with bisulfite sequencing and NY-ESO-1 expression with quantitative real-time PCR. Lastly, we performed single-cell RNA sequencing on DAC-treated GBM U251 to evaluate tumor subpopulations that upregulate NY-ESO-1 and other co-expressed CTA after DAC treatment. Results Baseline NY-ESO-1 expression is associated with promoter methylation in the majority of GBM. Treatment of cells with 1 µM DAC every day for 4 days explicitly demethylated the promoter region of NY-ESO-1 and resulted in a 1000-fold increase in mRNA expression. DAC treatment upregulates NY-ESO-1 coordinately with other cancer/testis antigens CTAG2 and MAGEA4 as demonstrated by single-cell RNA sequencing. Conclusion Exposure of U251 to DAC results in promoter demethylation in NY-ESO-1 and increased expression of CTA. DAC treatment may therefore render GBM susceptible to targeting of these antigens by T-cells, revealing a feasible strategy of NY-ESO-1 and co-expressed CTA promoter demethylation to sensitize GBM to immunotherapy.

Goals and approaches for each processing step for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chunyu Wang ◽  
Lingling Zhao ◽  
Quan Zou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Level ◽  
Sequencing Data ◽  
Analysis Tools ◽  
Processing Step ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Data

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.

scholarly journals Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

2021 ◽  
Author(s):  
Siyuan Zheng ◽  
Noureen Nighat ◽  
Zhenqing Ye ◽  
Yidong Chen ◽  
Xiaojing Wang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Enrichment Analysis ◽  
Bulk Sample ◽  
Gene Set Enrichment Analysis ◽  
Sequencing Data ◽  
Gene Set Enrichment ◽  
Single Cell Rna Sequencing ◽  
Supervised Methods ◽  
Cell Data

Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark four such supervised methods, including single sample gene set enrichment analysis (ssGSEA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

scholarly journals IMMU-27. SINGLE CELL RNA-SEQUENCING IDENTIFIES NOVEL BONE MARROW DERIVED MYELOID CELLS IN GLIOBLASTOMA ASSOCIATED WITH TUMOR AGGRESSION

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii110-ii110
Author(s):  
Christina Jackson ◽  
Christopher Cherry ◽  
Sadhana Bom ◽  
Hao Zhang ◽  
John Choi ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Tumor Cells ◽  
Rna Sequencing ◽  
Metabolic Pathways ◽  
Myeloid Cells ◽  
Tumor Grade ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Two Populations

Abstract BACKGROUND Glioma associated myeloid cells (GAMs) can be induced to adopt an immunosuppressive phenotype that can lead to inhibition of anti-tumor responses in glioblastoma (GBM). Understanding the composition and phenotypes of GAMs is essential to modulating the myeloid compartment as a therapeutic adjunct to improve anti-tumor immune response. METHODS We performed single-cell RNA-sequencing (sc-RNAseq) of 435,400 myeloid and tumor cells to identify transcriptomic and phenotypic differences in GAMs across glioma grades. We further correlated the heterogeneity of the GAM landscape with tumor cell transcriptomics to investigate interactions between GAMs and tumor cells. RESULTS sc-RNAseq revealed a diverse landscape of myeloid-lineage cells in gliomas with an increase in preponderance of bone marrow derived myeloid cells (BMDMs) with increasing tumor grade. We identified two populations of BMDMs unique to GBMs; Mac-1and Mac-2. Mac-1 demonstrates upregulation of immature myeloid gene signature and altered metabolic pathways. Mac-2 is characterized by expression of scavenger receptor MARCO. Pseudotime and RNA velocity analysis revealed the ability of Mac-1 to transition and differentiate to Mac-2 and other GAM subtypes. We further found that the presence of these two populations of BMDMs are associated with the presence of tumor cells with stem cell and mesenchymal features. Bulk RNA-sequencing data demonstrates that gene signatures of these populations are associated with worse survival in GBM. CONCLUSION We used sc-RNAseq to identify a novel population of immature BMDMs that is associated with higher glioma grades. This population exhibited altered metabolic pathways and stem-like potentials to differentiate into other GAM populations including GAMs with upregulation of immunosuppressive pathways. Our results elucidate unique interactions between BMDMs and GBM tumor cells that potentially drives GBM progression and the more aggressive mesenchymal subtype. Our discovery of these novel BMDMs have implications in new therapeutic targets in improving the efficacy of immune-based therapies in GBM.

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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