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 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 Validation of Reference Genes for Normalization of Relative qRT-PCR Studies in Papillary Thyroid Carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Adeleh Razavi ◽  
Mandana Afsharpad ◽  
Mohammad Hossein Modarressi ◽  
Maryam Zarkesh ◽  
Parichehreh Yaghmaei ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Internal Control ◽  
Reference Genes ◽  
Thyroid Tissue ◽  
Optimal Number ◽  
Thyroid Tumors ◽  
Papillary Thyroid ◽  
Tissue Samples ◽  
Qrt Pcr

Abstract Quantitative reverse transcription polymerase chain reaction (qRT-PCR) in thyroid tumors require accurate data normalization, however, there are no sufficient studies addressing the suitable reference genes for gene expression analysis in malignant and normal thyroid tissue specimens. The purpose of this study was to identify valid internal control genes for normalization of relative qRT-PCR studies in human papillary thyroid carcinoma tissue samples. The expression characteristics of 12 candidate reference genes (GAPDH, ACTB, HPRT1, TBP, B2M, PPIA, 18SrRNA, HMBS, GUSB, PGK1, RPLP0, and PGM1) were assessed by qRT-PCR in 45 thyroid tissue samples (15 papillary thyroid carcinoma, 15 paired normal tissues and 15 multinodular goiters). These twelve candidate reference genes were selected by a systematic literature search. GeNorm, NormFinder, and BestKeeper statistical algorithms were applied to determine the most stable reference genes. The three algorithms were in agreement in identifying GUSB and HPRT1 as the most stably expressed genes in all thyroid tumors investigated. According to the NormFinder software, the pair of genes including ‘GUSB and HPRT1’ or ‘GUSB and HMBS’ or ‘GUSB and PGM1’ were the best combinations for selection of pair reference genes. The optimal number of genes required for reliable normalization of qPCR data in thyroid tissues would be three according to calculations made by GeNorm algorithm. These results suggest that GUSB and HPRT1 are promising reference genes for normalization of relative qRT-PCR studies in papillary thyroid carcinoma.

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.

Identification of Differentially Expressed Genes Associated with Papillary Thyroid Carcinoma

2020 ◽  
Vol 23 (6) ◽  
pp. 546-553
Author(s):  
Hongyuan Cui ◽  
Mingwei Zhu ◽  
Junhua Zhang ◽  
Wenqin Li ◽  
Lihui Zou ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Cellular Proliferation ◽  
Mrna Level ◽  
Thyroid Tissue ◽  
Differentially Expressed ◽  
Thyroid Tumors ◽  
Papillary Thyroid

Objective: Next-generation sequencing (NGS) was performed to identify genes that were differentially expressed between normal thyroid tissue and papillary thyroid carcinoma (PTC). Materials & Methods: Six candidate genes were selected and further confirmed with quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry in samples from 24 fresh thyroid tumors and adjacent normal tissues. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to investigate signal transduction pathways of the differentially expressed genes. Results: In total, 1690 genes were differentially expressed between samples from patients with PTC and the adjacent normal tissue. Among these, SFRP4, ZNF90, and DCN were the top three upregulated genes, whereas KIRREL3, TRIM36, and GABBR2 were downregulated with the smallest p values. Several pathways were associated with the differentially expressed genes and involved in cellular proliferation, cell migration, and endocrine system tumor progression, which may contribute to the pathogenesis of PTC. Upregulation of SFRP4, ZNF90, and DCN at the mRNA level was further validated with RT-PCR, and DCN expression was further confirmed with immunostaining of PTC samples. Conclusion: These results provide new insights into the molecular mechanisms of PTC. Identification of differentially expressed genes should not only improve the tumor signature for thyroid tumors as a diagnostic biomarker but also reveal potential targets for thyroid tumor treatment.

scholarly journals Genetic Determinants for Prediction of Outcome of Patients with Papillary Thyroid Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2048
Author(s):  
Antónia Afonso Póvoa ◽  
Elisabete Teixeira ◽  
Maria Rosa Bella-Cueto ◽  
Rui Batista ◽  
Ana Pestana ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Patient Outcomes ◽  
Genetic Alterations ◽  
Papillary Thyroid ◽  
Tissue Samples ◽  
Primary Tumors ◽  
Persistent Disease ◽  
Increased Risk ◽  
Structural Disease

Papillary thyroid carcinoma (PTC) usually presents an excellent prognosis, but some patients present with aggressive metastatic disease. BRAF, RAS, and TERT promoter (TERTp) genes are altered in PTC, and their impact on patient outcomes remains controversial. We aimed to determine the role of genetic alterations in PTC patient outcomes (recurrent/persistent disease, structural disease, and disease-specific mortality (DSM)). The series included 241 PTC patients submitted to surgery, between 2002–2015, in a single hospital. DNA was extracted from tissue samples of 287 lesions (primary tumors and metastases). Molecular alterations were detected by Sanger sequencing. Primary tumors presented 143 BRAF, 16 TERTp, and 13 RAS mutations. Isolated TERTpmut showed increased risk of structural disease (HR = 7.0, p < 0.001) and DSM (HR = 10.1, p = 0.001). Combined genotypes, BRAFwt/TERTpmut (HR = 6.8, p = 0.003), BRAFmut/TERTpmut (HR = 3.2, p = 0.056) and BRAFmut/TERTpwt (HR = 2.2, p = 0.023) showed increased risk of recurrent/persistent disease. Patients with tumors BRAFwt/TERTpmut (HR = 24.2, p < 0.001) and BRAFmut/TERTpmut (HR = 11.5, p = 0.002) showed increased risk of structural disease. DSM was significantly increased in patients with TERTpmut regardless of BRAF status (BRAFmut/TERTpmut, log-rank p < 0.001; BRAFwt/TERTpmut, log-rank p < 0.001). Our results indicate that molecular markers may have a role in predicting PTC patients’ outcome. BRAFmut/TERTpwt tumors were prone to associate with local aggressiveness (recurrent/persistent disease), whereas TERTpmut tumors were predisposed to recurrent structural disease and DSM.

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.

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