scholarly journals Single-cell RNA sequencing of intramedullary canal tissue to improve methods for studying fracture repair biology

BioTechniques ◽  
2021 ◽  
Author(s):  
James M Dominguez ◽  
Sharon M Moe ◽  
Neal X Chen ◽  
Todd O McKinley ◽  
Krista M Brown ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Prospective Studies ◽  
Fracture Repair ◽  
Bone Microenvironment ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Intramedullary Canal ◽  
Single Cell Rna Sequencing ◽  
Fracture Nonunion

The ability to study the bone microenvironment of failed fracture healing may lead to biomarkers for fracture nonunion. Herein the authors describe a technique for isolating individual cells suitable for single-cell RNA sequencing analyses from intramedullary canal tissue collected by reaming during surgery. The purpose was to detail challenges and solutions inherent to the collection and processing of intramedullary canal tissue samples. The authors then examined single-cell RNA sequencing data from fresh and reanimated samples to demonstrate the feasibility of this approach for prospective studies.

scholarly journals A new bioinformatics tool to recover missing gene expression in single-cell RNA sequencing data

2020 ◽  
Author(s):  
Jingyi Jessica Li
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Rapid Evolution ◽  
Cell Types ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Technological Advances ◽  
Single Cell Rna Sequencing ◽  
Or Genes

Abstract Single-cell RNA sequencing (scRNA-seq) is a burgeoning field where experimental techniques and computational methods have been under rapid evolution in the past six years. These technological advances have allowed biomedical researchers to identify new cell types, delineate cell sub-populations, and infer cell differentiation trajectories in various tissue samples. Among the important features extractable from scRNA-seq data, the predominant ones are individual genes’ expression levels in single cells. Most analyses require a preprocessing step that converts a scRNA-seq dataset into a count matrix, where rows correspond to cells (or genes), columns correspond to genes (or cells), and entries are counts, i.e. a count is the number of sequenced reads or uniquely mapped identifiers (UMIs) mapped to a gene in a cell. Single-cell count matrices are highly sparse; for example, a typical matrix constructed from a droplet-based dataset may have >90% of counts as zeros.

scholarly journals Single-cell RNA sequencing demonstrates the intratumoral heterogeneityof papillary thyroid carcinoma

2021 ◽  
Author(s):  
Zhengshi Wang ◽  
Youlutuziayi Rixiati ◽  
Wenli Jiang ◽  
Caiguo Huang ◽  
Binghua Jiao ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Therapy ◽  
Thyroid Tissue ◽  
Papillary Thyroid ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Single Cell Rna Sequencing

AbstractPapillary thyroid carcinoma(PTC) is the most common thyroid malignancy, to investigate the intratumoral heterogeneity of PTC, we analyzed single-cell RNA-sequencing data and identified 10 major cell types from primary papillary thyroid carcinoma, lymph metastatic, or paired normal thyroid tissue samples. In this study, we verified that the increase in the proportion of CD4+Tregs may be key factor responsible for the immunosuppressive property of PTC. Inhibitory checkpoints, such as TIGIT and CD96 may be better targets for immune therapy in lymph metastatic papillary thyroid carcinoma. Our results will further the understanding of the heterogeneity among papillary thyroid carcinoma and provide an essential resource for drug discovery in the future.

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.

scholarly journals Correction to: The effect of methanol fixation on single-cell RNA sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinlei Wang ◽  
Lei Yu ◽  
Angela Ruohao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data

2017 ◽  
Vol 45 (19) ◽  
pp. 10978-10988 ◽  
Author(s):  
Cheng Jia ◽  
Yu Hu ◽  
Derek Kelly ◽  
Junhyong Kim ◽  
Mingyao Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Technical Noise ◽  
Single Cell Rna Sequencing
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