scholarly journals Overcoming Genetic Drop-outs in Variants-based Lineage Tracing from Single-cell RNA Sequencing Data

2020 ◽  
Author(s):  
Tianshi Lu ◽  
Seongoh Park ◽  
James Zhu ◽  
Xiaowei Zhan ◽  
Xinlei Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Process ◽  
Variant Calling ◽  
Lineage Tracing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Bayesian Hierarchical ◽  
Single Cell Rna Sequencing ◽  
Drop Outs

ABSTRACTLineage tracing provides key insights into the fates of individual cells in complex tissues. Recent works on lineage reconstruction based on the single-cell expression data are suitable for short time frames while tracing lineage based on more stable genetic markers is needed for studies that span time scales over months or years. However, variant calling from the single-cell RNA sequencing (scRNA-Seq) data suffers from “genetic drop-outs”, including low coverage and allelic bias, which presents significant obstacles for lineage reconstruction. Prior studies focused only on mitochondrial (chrM) variants and need to be expanded to the whole genome to capture more variants with clearer physiological meaning. However, non-chrM variants suffer even more severe drop-outs than chrM variants, although drop-outs affect all variants. We developed strategies to overcome genetic drop-outs in scRNA-Seq-derived whole genomic variants for accurate lineage tracing, and we developed SClineger, a Bayesian Hierarchical model, to implement our approach. Our validation analyses on a series of sequencing protocols demonstrated the necessity of correction for genetic drop-outs and consideration of variants in the whole genome, and also showed the improvement that our approach provided. We showed that genetic-based lineage tracing is applicable for single-cell studies of both tumors and non-tumor tissues using our approach, and can reveal novel biological insights not afforded by expressional analyses. Interestingly, we showed that cells of various lineages grew under the spatial constraints of their respective organs during the developmental process. Overall, our work provides a powerful tool that can be applied to the large amounts of already existing scRNA-Seq data to construct the lineage histories of cells and derive new knowledge.

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.

Abstract 305: Single Cell RNA Sequencing of Adult Cardiac c-kit+ Cells in a Murine Lineage Tracing Model

2015 ◽  
Vol 117 (suppl_1) ◽  
Author(s):  
Bryan D Maliken ◽  
Onur Kanisicak ◽  
Jeffery D Molkentin
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Rna Sequencing ◽  
Cardiac Injury ◽  
Mesenchymal Cells ◽  
Lineage Tracing ◽  
Tyrosine Kinase Receptor ◽  
Early Differentiation ◽  
Gfp Reporter ◽  
Single Cell Rna Sequencing

A subset of adult cardiac resident cells defined by the stem cell factor tyrosine kinase receptor termed c-kit, are believed to have myogenic potential and are now being delivered via intracoronary infusion to presumably promote cardiac regeneration and improve ventricular function after ischemic cardiac injury. However, recent studies have shown that despite these benefits, c-kit+ progenitor cells in the adult murine heart are more inclined to take on an endothelial rather than cardiomyocyte lineage. To better define the factors involved in early differentiation of these resident cardiac progenitor cells and to distinguish distinct cell subpopulations, we performed single cell RNA sequencing on c-kit+ cells from Kit-Cre lineage traced GFP reporter mice versus total mesenchymal cells from the heart that were CD31- and CD45-. Cells were isolated by cardiac digestion and FACS was performed, positively sorting for the c-kit+ lineage while negatively sorting for CD31 and CD45 to eliminate endothelial and leukocyte progenitor contamination, respectively. Following this isolation, cells were examined to determine GFP reporter status and then submitted for single cell RNA sequencing using the Fluidigm A1 system. Clustering of 654 genes from this data identified 6 distinct subpopulations indicating various stages of early differentiation among CD31- and CD45-negative cardiac interstitial cells. Furthermore, comparison of GFP+ c-kit cells to the total non-GFP mesenchymal cells identified 75 differentially expressed transcripts. These unique gene signatures may help parse the genes that underlie cellular plasticity in the heart and define the best molecular lineages for transdifferentiation into cardiac myocytes.

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

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