scholarly journals scRepertoire: An R-based toolkit for single-cell immune receptor analysis

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 47
Author(s):  
Nicholas Borcherding ◽  
Nicholas L. Bormann
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Cell Receptor ◽  
R Package ◽  
Process Data ◽  
Immune Receptor ◽  
Single Cell Sequencing ◽  
Rna Quantification ◽  
Software Packages ◽  
Immune Profiling

Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.

scholarly journals scRepertoire: An R-based toolkit for single-cell immune receptor analysis

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 47 ◽  
Author(s):  
Nicholas Borcherding ◽  
Nicholas L. Bormann ◽  
Gloria Kraus
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Cell Receptor ◽  
R Package ◽  
Process Data ◽  
Immune Receptor ◽  
Rna Quantification ◽  
Software Packages ◽  
Cell Expression ◽  
Immune Profiling

Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with a number of popular R packages for single-cell expression, such as Seurat. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.

scholarly journals Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

2021 ◽  
Author(s):  
Jiami Han ◽  
Raphael Kuhn ◽  
Chrysa Papadopoulou ◽  
Andreas Agrafiotis ◽  
Victor Kreiner ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Single Cell ◽  
Learning Strategies ◽  
Clonal Selection ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Immune Receptor ◽  
Single Cell Sequencing ◽  
Wide Range

Single-cell sequencing now enables the recovery of full-length immune repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate a comprehensive ground truth single-cell dataset that incorporates both immune receptor repertoires and gene expression. Therefore, we developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution. Our simulation tool generates annotated single-cell sequencing data with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, and transcriptional phenotypes. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. Finally, we demonstrate the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals Single Cell Atlas of Human Dura Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response

2021 ◽  
Author(s):  
Anthony Z Wang ◽  
Jay Bowman-Kirigin ◽  
Rupen Desai ◽  
Pujan Patel ◽  
Bhuvic Patel ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Copy Number Variant ◽  
Cell Types ◽  
Cell Receptor ◽  
Immune Microenvironment ◽  
Clinical Models ◽  
Insight Into

Recent investigation of the meninges, specifically the dura layer, has highlighted its importance in CNS immune surveillance beyond a purely structural role. However, most of our understanding of the meninges stems from the use of pre-clinical models rather than human samples. In this study, we use single cell RNA-sequencing to perform the first characterization of both non-tumor-associated human dura and meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, through T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. We also identify a functionally heterogeneous population of non-immune cell types and report copy-number variant heterogeneity within our meningioma samples. Our comprehensive investigation of both the immune and non-immune cell landscapes of human dura and meningioma at a single cell resolution provide new insight into previously uncharacterized roles of human dura.

scholarly journals Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Wen Wen ◽  
Wenru Su ◽  
Hao Tang ◽  
Wenqing Le ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Recovery Stage ◽  
Single Cell Sequencing

scholarly journals Applications of Single-Cell Omics to Dissect Tumor Microenvironment

2020 ◽  
Vol 11 ◽  
Author(s):  
Tingting Guo ◽  
Weimin Li ◽  
Xuyu Cai
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Immune Checkpoint Blockade ◽  
Lineage Tracing ◽  
Great Success ◽  
Novel Technologies ◽  
Single Cell Sequencing

The recent technical and computational advances in single-cell sequencing technologies have significantly broaden our toolkit to study tumor microenvironment (TME) directly from human specimens. The TME is the complex and dynamic ecosystem composed of multiple cell types, including tumor cells, immune cells, stromal cells, endothelial cells, and other non-cellular components such as the extracellular matrix and secreted signaling molecules. The great success on immune checkpoint blockade therapy has highlighted the importance of TME on anti-tumor immunity and has made it a prime target for further immunotherapy strategies. Applications of single-cell transcriptomics on studying TME has yielded unprecedented resolution of the cellular and molecular complexity of the TME, accelerating our understanding of the heterogeneity, plasticity, and complex cross-interaction between different cell types within the TME. In this review, we discuss the recent advances by single-cell sequencing on understanding the diversity of TME and its functional impact on tumor progression and immunotherapy response driven by single-cell sequencing. We primarily focus on the major immune cell types infiltrated in the human TME, including T cells, dendritic cells, and macrophages. We further discuss the limitations of the existing methodologies and the prospects on future studies utilizing single-cell multi-omics technologies. Since immune cells undergo continuous activation and differentiation within the TME in response to various environmental cues, we highlight the importance of integrating multimodal datasets to enable retrospective lineage tracing and epigenetic profiling of the tumor infiltrating immune cells. These novel technologies enable better characterization of the developmental lineages and differentiation states that are critical for the understanding of the underlying mechanisms driving the functional diversity of immune cells within the TME. We envision that with the continued accumulation of single-cell omics datasets, single-cell sequencing will become an indispensable aspect of the immune-oncology experimental toolkit. It will continue to drive the scientific innovations in precision immunotherapy and will be ultimately adopted by routine clinical practice in the foreseeable future.

scholarly journals Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

2021 ◽  
Author(s):  
Congmin Xu ◽  
Junkai Yang ◽  
Astrid Kosters ◽  
Benjamin R Babcock ◽  
Peng Qiu ◽  
...  
Keyword(s):  
Immune System ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Receptor ◽  
Surface Proteins ◽  
Cell Type ◽  
Human Immune System ◽  
Receptor Repertoire ◽  
Human Immune

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissue and disease contexts. Still, deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in our multi-omics single-cell datasets, we developed SuPERR-seq, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery and characterization of previously hidden cell subsets. We show that by incorporating information from cell-surface proteins and immunoglobulin transcript counts, we accurately remove cell doublets and prevent widespread cell-type misclassification. This approach uniquely improves the identification of heterogeneous cell types in the human immune system, including a novel subset of antibody-secreting cells in the bone marrow.

scholarly journals Immune Cell Profiling of COVID-19 Patients in the Recovery Stage by Single-Cell Sequencing

2020 ◽  
Author(s):  
Wen Wen ◽  
Wenru Su ◽  
Hao Tang ◽  
Wenqing Le ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
T Cells ◽  
B Cells ◽  
B Cell ◽  
Single Cell ◽  
Hospital Discharge ◽  
Immune Cell ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Early Recovery ◽  
Recovery Stage

AbstractCOVID-19, caused by SARS-CoV-2, has recently affected over 300,000 people and killed more than 10,000. The manner in which the key immune cell subsets change and their states during the course of COVID-19 remain unclear. Here, we applied single-cell technology to comprehensively characterize transcriptional changes in peripheral blood mononuclear cells during the recovery stage of COVID-19. Compared with healthy controls, in patients in the early recovery stage (ERS) of COVID-19, T cells decreased remarkably, whereas monocytes increased. A detailed analysis of the monocytes revealed that there was an increased ratio of classical CD14++ monocytes with high inflammatory gene expression as well as a greater abundance of CD14++IL1B+ monocytes in the ERS. CD4+ and CD8+ T cells decreased significantly and expressed high levels of inflammatory genes in the ERS. Among the B cells, the plasma cells increased remarkably, whereas the naïve B cells decreased. Our study identified several novel B cell-receptor (BCR) changes, such as IGHV3-23 and IGHV3-7, and confirmed isotypes (IGHV3-15, IGHV3-30, and IGKV3-11) previously used for virus vaccine development. The strongest pairing frequencies, IGHV3-23-IGHJ4, indicated a monoclonal state associated with SARS-CoV-2 specificity. Furthermore, integrated analysis predicted that IL-1β and M-CSF may be novel candidate target genes for inflammatory storm and that TNFSF13, IL-18, IL-2 and IL-4 may be beneficial for the recovery of COVID-19 patients. Our study provides the first evidence of an inflammatory immune signature in the ERS, suggesting that COVID-19 patients are still vulnerable after hospital discharge. Our identification of novel BCR signaling may lead to the development of vaccines and antibodies for the treatment of COVID-19.Highlights-The immune response was sustained for more than 7 days in the early recovery stage of COVID-19, suggesting that COVID-19 patients are still vulnerable after hospital discharge.-Single-cell analysis revealed a predominant subset of CD14++ IL1β+ monocytes in patients in the ERS of COVID-19.-Newly identified virus-specific B cell-receptor changes, such as IGHV3-23, IGHV3-7, IGHV3-15, IGHV3-30, and IGKV3-11, could be helpful in the development of vaccines and antibodies against SARS-CoV-2.-IL-1β and M-CSF were discovered as novel mediators of inflammatory cytokine storm, and TNFSF13, IL-2, IL-4, and IL-18 may be beneficial for recovery.

scholarly journals SCOPIT: sample size calculations for single-cell sequencing experiments

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alexander Davis ◽  
Ruli Gao ◽  
Nicholas E. Navin
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Multinomial Distribution ◽  
R Package ◽  
Cell Type ◽  
Single Cell Sequencing ◽  
Link Type ◽  
Dna And Rna ◽  
Sample Size Calculations ◽  
Number Of Cells

Abstract Background In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). Results We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. Conclusions Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.

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