scholarly journals Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Miao Su ◽  
Kuang-Yuan Qiao ◽  
Xiao-Li Xie ◽  
Xin-Ying Zhu ◽  
Fu-Lai Gao ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Intrahepatic Cholangiocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Functional Enrichment ◽  
Marker Genes ◽  
Tissue Cell ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.

scholarly journals Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

2019 ◽  
Vol 21 (5) ◽  
pp. 1581-1595 ◽  
Author(s):  
Xinlei Zhao ◽  
Shuang Wu ◽  
Nan Fang ◽  
Xiao Sun ◽  
Jue Fan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Reference Data ◽  
Predictive Accuracy ◽  
Cell Types ◽  
Superior Performance ◽  
Marker Genes ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

scholarly journals VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder

2017 ◽  
Author(s):  
Dongfang Wang ◽  
Jin Gu
Keyword(s):  
Dimension Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Original Data ◽  
Marker Genes ◽  
Single Cell Level ◽  
Sequencing Data ◽  
Cell Level ◽  
Variational Autoencoder ◽  
Single Cell Rna Sequencing

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities in single cell level. It is an important step for studying cell sub-populations and lineages based on scRNA-seq data by finding an effective low-dimensional representation and visualization of the original data. The scRNA-seq data are much noiser than traditional bulk RNA-Seq: in the single cell level, the transcriptional fluctuations are much larger than the average of a cell population and the low amount of RNA transcripts will increase the rate of technical dropout events. In this study, we proposed VASC (deep Variational Autoencoder for scRNA-seq data), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. It can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on twenty datasets, VASC shows superior performances in most cases and broader dataset compatibility compared with four state-of-the-art dimension reduction methods. Then, for a case study of pre-implantation embryos, VASC successfully re-establishes the cell dynamics and identifies several candidate marker genes associated with the early embryo development.

scholarly journals SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics

2021 ◽  
Author(s):  
Michael E Nelson ◽  
Simone G Riva ◽  
Ann Cvejic
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Marker Gene ◽  
Marker Genes ◽  
Sequencing Data ◽  
Computational Framework ◽  
Data Set ◽  
Spatially Resolved ◽  
Single Cell Rna Sequencing ◽  
The Given

Spatial transcriptomics is revolutionising the study of single-cell RNA and tissue-wide cell heterogeneity, but few robust methods connecting spatially resolved cells to so-called marker genes from single-cell RNA sequencing, which generate significant insight gleaned from spatial methods, exist. Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA sequencing data for spatial transcriptomics approaches. SMaSH extracts robust and biologically well-motivated marker genes, which characterise the given data-set better than existing and limited computational approaches for global marker gene calculation.

scholarly journals Automated identification of Cell Types in Single Cell RNA Sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Complex Data ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

scholarly journals Discovery of potential imaging and therapeutic targets for severe inflammation in COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyunjong Lee ◽  
Jeongbin Park ◽  
Hyung-Jun Im ◽  
Kwon Joong Na ◽  
Hongyoon Choi
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Lavage Fluid ◽  
Severe Illness ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Key Factor ◽  
Target Binding ◽  
Druggable Targets

AbstractThe Coronavirus disease 2019 (COVID-19) has been spreading worldwide with rapidly increased number of deaths. Hyperinflammation mediated by dysregulated monocyte/macrophage function is considered to be the key factor that triggers severe illness in COVID-19. However, no specific targeting molecule has been identified for detecting or treating hyperinflammation related to dysregulated macrophages in severe COVID-19. In this study, previously published single-cell RNA-sequencing data of bronchoalveolar lavage fluid cells from thirteen COVID-19 patients were analyzed with publicly available databases for surface and imageable targets. Immune cell composition according to the severity was estimated with the clustering of gene expression data. Expression levels of imaging target molecules for inflammation were evaluated in macrophage clusters from single-cell RNA-sequencing data. In addition, candidate targetable molecules enriched in severe COVID-19 associated with hyperinflammation were filtered. We found that expression of SLC2A3, which can be imaged by [18F]fluorodeoxyglucose, was higher in macrophages from severe COVID-19 patients. Furthermore, by integrating the surface target and drug-target binding databases with RNA-sequencing data of severe COVID-19, we identified candidate surface and druggable targets including CCR1 and FPR1 for drug delivery as well as molecular imaging. Our results provide a resource in the development of specific imaging and therapy for COVID-19-related hyperinflammation.

Single cell RNA sequencing of upper tract urothelial carcinoma to reveal significant heterogeneity of the tumor and immune microenvironment.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 484-484
Author(s):  
Peter Reisz ◽  
Andrew Tracey ◽  
Fengshen Kuo ◽  
Jasmine Thomas ◽  
Timothy Nguyen Clinton ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Marker Genes ◽  
Low Grade ◽  
Significant Heterogeneity ◽  
High Grade ◽  
Single Cell Rna Sequencing ◽  
Tissue Specimens

484 Background: Upper tract urothelial carcinoma (UTUC) comprises 5-10% of urothelial malignancies but demonstrates unique clinical and molecular characteristics compared to urothelial carcinoma of the bladder. Prior investigations have used bulk profiling of tumor tissue to identify molecular subtypes, classifying the majority of UTUC as luminal and T-cell depleted. However, bulk sequencing does not allow for analysis of the significant heterogeneity known to be present in urothelial tumors. Single-cell RNA sequencing (scRNA-seq) allows examination of intra-tumoral heterogeneity, clonality, and the complex interactions of the immune tumor microenvironment (TME). We sought to apply this technology to better characterize UTUC and the TME. Methods: Single cell RNA sequencing (scRNA-seq) was performed on nine UTUC tissue specimens from six different patients collected fresh via ureteroscopic biopsy using an established institutional process and the 10X Genomics platform. Sequencing reads were normalized and analyzed using R/Seurat package. We assessed the composition of each tumor specimen with known marker genes for molecular subtypes (luminal, basal, squamous, EMT, and claudin-low). We then assessed the composition of immune cells in each specimen using known marker genes. We compared high- and low-grade specimens by subtype composition and immune cell infiltrates. Results: Lineage density analyses demonstrate the intra- and inter-tumoral heterogeneity of the nine endoscopic samples analyzed by molecular subtype composition. There is higher expression of luminal and claudin-low subtypes across all samples. The high-grade samples have higher expression of squamous markers. There is significant heterogeneity of immune cell infiltrates in seven specimens (two specimens were excluded due to low CD45+ cell counts). There is higher macrophage infiltration in high-grade samples, which was the only significant difference (Wilcoxon two-sided p-value = 0.05). Conclusions: This is the first known study using scRNA-seq expression analysis to characterize the notable heterogeneity of high and low-grade UTUC and the associated TME. Lineage density analysis demonstrates high luminal gene expression across samples, which has been demonstrated on prior bulk sequencing studies. The immune TME is also heterogeneous, with notable increased infiltration of macrophages in high-grade disease. There are unique limitations to performing and analyzing scRNA-seq of fresh UTUC tissue specimens, thus data should be interpreted cautiously. However, this study demonstrates the marked heterogeneity of UTUC tumors and frames our current approaches to bulk molecular subtyping of urothelial cancers and immune deconvolution. Further high-resolution studies are needed to characterize UTUC and inform bulk-sequencing efforts.

Integrative single cell RNA-sequencing descrambles a substantial divergence of adaptive immune cell identities and transcriptional programs in mouse and human atherosclerosis

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
H Horstmann ◽  
N Anto Michel ◽  
X S Sheng ◽  
S Hansen ◽  
A Lindau ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Immune Cell ◽  
Atherosclerotic Plaques ◽  
Cell Repertoire ◽  
Single Cell Rna Sequencing ◽  
Human Atherosclerosis ◽  
Human Immune

Abstract Aims The distinct function of immune cells in human atherosclerosis has been mostly defined by preclinical mouse studies. Contrastingly, the immune cell composition of human atherosclerotic plaques and their contribution to disease progression is only poorly understood. It remains uncertain whether genetic animal models allow for valuable translational approaches. Methods and results We performed single cell RNA-sequencing (scRNAseq) to define the immune cell landscape in human carotid atherosclerotic plaques. The human immune cell repertoire was dominated by T cells with a considerable inter-patient variability and an unexpected heterogeneity. We performed bioinformatical integration with 7 mouse data sets and discovered a total of 38 cellular identities, of which some were not conserved between species and exclusively found in mice or humans. Locations, frequencies, and transcriptional programs of immune cells in preclinical mouse models did not resemble the immune cell landscape in human atherosclerosis. In contrast to mice, human plaques were not myeloid- and B cell-dominated and instead contained several T cell phenotypes with hallmarks of T cell memory, dysregulation, exhaustion, and activation. Human immune cells were predominantly enriched for transcriptional programs of hypoxia, glucose, and autoimmunity. In a validation cohort of 43 patients activated immune cell subsets defined by multi-colour flow cytometry associated with cerebral ischemia and coronary artery disease. Conclusion Here, we uncover yet undefined immune cell types associating with clinical disease. This leukocyte atlas of human atherosclerosis builds the conceptual basis for subsequent identification of cellular targets for clinical immunomodulatory therapies and risk prediction. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): ERC Starting Grant

scholarly journals Clinical and molecular characteristics of COVID-19 patients with persistent SARS-CoV-2 infection

2020 ◽  
Author(s):  
Chaoyang Sun ◽  
Junpeng Fan ◽  
Jia Huang ◽  
Ensong Guo ◽  
Yu Fu ◽  
...  
Keyword(s):  
Nk Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Phenotype ◽  
Molecular Characteristics ◽  
Clinical Parameters ◽  
Sequencing Data ◽  
Viral Shedding ◽  
Single Cell Rna Sequencing

Abstract The clinical features, molecular characteristics, and immune responses of COVID-19 patients with persistent SARS-CoV-2 infection are not yet well described. In this study, we investigated the differences in clinical parameters, laboratory indexes, plasma cytokines, and peripheral blood mononuclear cell responses, which were assessed using single-cell RNA-sequencing in patients with non-critical COVID-19 with long durations (LDs) and short durations (SDs) of viral shedding. Our results revealed that clinical parameters and laboratory indexes, such as c-reactive protein (CRP) and D-dimer, were comparable between SDs and LDs. Most inflammatory cytokines/chemokines, such as IL-2, IL2R, TNFα/β, IL1β, and CCL5 were present at low levels in LDs. Our single-cell RNA-sequencing revealed a reconfiguration of the peripheral immune cell phenotype in LDs, including decreases in natural killer (NK) cells and CD14+ monocytes and an increase in regulatory T cells (Tregs). Furthermore, most cell subsets in LDs consistently exhibited reduced expression of ribosomal protein (RP) genes, indicating dysfunctions in cytokine/chemokine synthesis, folding, modification, and assembly. Accordingly, the negative correlation between the RP levels and viral shedding duration was validated in an independent cohort of bulk-RNA-sequencing data from 103 non-critical patients, which may help guide clinical management and resource allocation. Moreover, peripheral T and NK cells and memory B cells in LDs likely failed to activate, which contributed to the persistence of viral shedding.

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.

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