scholarly journals DALI (Diversity AnaLysis Interface): a novel tool for the integrated analysis of multimodal single cell RNAseq data and immune receptor profiling.

2021 ◽  
Author(s):  
Kevin Verstaen ◽  
Ines Lammens ◽  
Jana Roels ◽  
Yvan Saeys ◽  
Bart N Lambrecht ◽  
...  
Keyword(s):  
Single Cell ◽  
Surface Protein ◽  
Cell Receptor ◽  
Integrated Analysis ◽  
Diversity Analysis ◽  
Sequencing Data ◽  
Seamless Integration ◽  
Immune Receptor ◽  
Technological Advances ◽  
Wet Lab

Single-cell RNA sequencing is instrumental to unravel the cellular and transcriptomic heterogeneity of T and B cells in health and disease. Recent technological advances add additional layers of information allowing researchers to simultaneously explore the transcriptomic, surface protein and immune receptor diversity during adaptive immune responses. The increasing data complexicity poses a burden on the workload for bioinformaticians, who are often not familiar with the specificities and biology of immune receptor profiling. The wet-lab modalities and sequencing capabilities currently have outpaced bioinformatics solutions, which forms an ever-increasing barrier for many biologists to analyze their datasets. Here, we present DALI (Diversity AnaLysis Interface), a software package to identify and analyze T cell and B cell receptor diversity in high-throughput single-cell sequencing data. DALI aims to support bioinformaticians with a functional toolbox, allowing seamless integration of multimodel scRNAseq and immune receptor profiling data. The R-based package builds further on workflows using the Seurat package and other existing tools for BCR/TCR analyses. In addition, DALI is designed to engage immunologists having limited coding experience with their data, using a browser-based interactive graphical user interface. The implementation of DALI can effectively lead to a two-way communication between wet-lab scientists and bioinformaticians to advance the analysis of complex datasets.

scholarly journals Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

2020 ◽  
Author(s):  
Gregor Sturm ◽  
Tamas Szabo ◽  
Georgios Fotakis ◽  
Marlene Haider ◽  
Dietmar Rieder ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Seamless Integration ◽  
T Cell Phenotypes ◽  
Cell Phenotypes

AbstractSummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

scholarly journals Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

2020 ◽  
Vol 36 (18) ◽  
pp. 4817-4818 ◽  
Author(s):  
Gregor Sturm ◽  
Tamas Szabo ◽  
Georgios Fotakis ◽  
Marlene Haider ◽  
Dietmar Rieder ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Cell Receptor ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Seamless Integration ◽  
T Cell Phenotypes ◽  
Cell Phenotypes

Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

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.

scholarly journals rCASC: reproducible classification analysis of single-cell sequencing data

GigaScience ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Luca Alessandrì ◽  
Francesca Cordero ◽  
Marco Beccuti ◽  
Maddalena Arigoni ◽  
Martina Olivero ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Heterogeneity ◽  
Cell Subpopulation ◽  
Integrated Analysis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Cell Stability ◽  
Reproducible Analysis

Abstract Background Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g., ATAC-seq. Many related algorithms and tools have been developed, but few computational workflows provide analysis flexibility while also achieving functional (i.e., information about the data and the tools used are saved as metadata) and computational reproducibility (i.e., a real image of the computational environment used to generate the data is stored) through a user-friendly environment. Findings rCASC is a modular workflow providing an integrated analysis environment (from count generation to cell subpopulation identification) exploiting Docker containerization to achieve both functional and computational reproducibility in data analysis. Hence, rCASC provides preprocessing tools to remove low-quality cells and/or specific bias, e.g., cell cycle. Subpopulation discovery can instead be achieved using different clustering techniques based on different distance metrics. Cluster quality is then estimated through the new metric "cell stability score" (CSS), which describes the stability of a cell in a cluster as a consequence of a perturbation induced by removing a random set of cells from the cell population. CSS provides better cluster robustness information than the silhouette metric. Moreover, rCASC's tools can identify cluster-specific gene signatures. Conclusions rCASC is a modular workflow with new features that could help researchers define cell subpopulations and detect subpopulation-specific markers. It uses Docker for ease of installation and to achieve a computation-reproducible analysis. A Java GUI is provided to welcome users without computational skills in R.

scholarly journals Integrated analysis of bulk multi omic and single-cell sequencing data confirms the molecular origin of hemodynamic changes in Covid-19 infection explaining coagulopathy and higher geriatric mortality

2020 ◽  
Author(s):  
Shreya Johri ◽  
Deepali Jain ◽  
Ishaan Gupta
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Older Patients ◽  
Cell Types ◽  
Integrated Analysis ◽  
Molecular Evidence ◽  
Sequencing Data ◽  
Phagocytic Cells ◽  
Hemodynamic Changes ◽  
Single Cell Sequencing

AbstractBesides severe respiratory distress, recent reports in Covid-19 patients have found a strong association between platelet counts and patient survival. Along with hemodynamic changes such as prolonged clotting time, high fibrin degradation products and D-dimers, increased levels of monocytes with disturbed morphology have also been identified. In this study, through an integrated analysis of bulk RNA-sequencing data from Covid-19 patients with data from single-cell sequencing studies on lung tissues, we found that most of the cell-types that contributed to the altered gene expression were of hematopoietic origin. We also found that differentially expressed genes in Covid-19 patients formed a significant pool of the expressing genes in phagocytic cells such as Monocytes and platelets. Interestingly, while we observed a general enrichment for Monocytes in Covid-19 patients, we found that the signal for FCGRA3+ Monocytes was depleted. Further, we found evidence that age-associated gene expression changes in Monocytes and platelets, associated with inflammation, mirror gene expression changes in Covid-19 patients suggesting that pro-inflammatory signalling during aging may worsen the infection in older patients. We identified more than 20 genes that change in the same direction between Covid-19 infection and aging cells that may act as potential therapeutic targets. Of particular interest were IL2RG, GNLY and GMZA expressed in platelets, which facilitates cytokine signalling in Monocytes through an interaction with platelets. To understand whether infection can directly manipulate the biology of Monocytes and platelets, we hypothesize that these non-ACE2 expressing cells may be infected by the virus through the phagocytic route. We observed that phagocytic cells such as Monocytes, T-cells, and platelets have a significantly higher expression of genes that are a part of the Covid-19 viral interactome. Hence these cell-types may have an active rather than a reactive role in viral pathogenesis to manifest clinical symptoms such as coagulopathy. Therefore, our results present molecular evidence for pursuing both anti-inflammatory and anticoagulation therapy for better patient management especially in older patients.

scholarly journals Comparison of high-throughput single-cell RNA sequencing data processing pipelines

2020 ◽  
Author(s):  
Mingxuan Gao ◽  
Mingyi Ling ◽  
Xinwei Tang ◽  
Shun Wang ◽  
Xu Xiao ◽  
...  
Keyword(s):  
Data Processing ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Evaluation Framework ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Experiment ◽  
Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

scholarly journals Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

2020 ◽  
Author(s):  
Mingxuan Gao ◽  
Mingyi Ling ◽  
Xinwei Tang ◽  
Shun Wang ◽  
Xu Xiao ◽  
...  
Keyword(s):  
Data Processing ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Evaluation Framework ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Experiment ◽  
Single Cell Rna Sequencing

AbstractWith the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

scholarly journals Single Cell Transcriptomics Reveals Cell Type Specific Diversification in Human Heart Failure

2021 ◽  
Author(s):  
Andrew L Koenig ◽  
Irina Shchukina ◽  
Prabhakar S Andhey ◽  
Konstantin Zaitsev ◽  
Lulu Lai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Human Heart ◽  
Integrated Analysis ◽  
Common Disease ◽  
Cell Type ◽  
Sequencing Data ◽  
Human Heart Failure ◽  
Cell Composition ◽  
Cell Type Specific

Heart failure represents a major cause of morbidity and mortality worldwide. Single cell transcriptomics have revolutionized our understanding of cell composition and associated gene expression across human tissues. Through integrated analysis of single cell and single nucleus RNA sequencing data generated from 45 individuals, we define the cell composition of the healthy and failing human heart. We identify cell specific transcriptional signatures of heart failure and reveal the emergence of disease associated cell states. Intriguingly, cardiomyocytes converge towards a common disease associated cell state, while fibroblasts and myeloid cells undergo dramatic diversification. Endothelial cells and pericytes display global transcriptional shifts without changes in cell complexity. Collectively, our findings provide a comprehensive analysis of the cellular and transcriptomic landscape of human heart failure, identify cell type specific transcriptional programs and states associated with disease, and establish a valuable resource for the investigation of human heart failure.

scholarly journals An ultra-sensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data

2020 ◽  
Vol 36 (15) ◽  
pp. 4255-4262
Author(s):  
Si-Yi Chen ◽  
Chun-Jie Liu ◽  
Qiong Zhang ◽  
An-Yuan Guo
Keyword(s):  
T Cell ◽  
Single Cell ◽  
High Performance ◽  
Cell Receptor ◽  
Computational Method ◽  
Read Length ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Rna Seq ◽  
Sequencing Data

Abstract Motivation T-cell receptors (TCRs) function to recognize antigens and play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) is a key issue. Due to the high diversity of CDR3 and technological limitation, accurate characterization of CDR3 repertoires remains a great challenge. Results We propose a computational method named CATT for ultra-sensitive and precise TCR CDR3 sequences detection. CATT can be applied on TCR sequencing, RNA-Seq and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated de Bruijn graph-based micro-assembly algorithm, data-driven error correction model and Bayesian inference algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high performance. Benchmark results of datasets from in silico and experimental data demonstrated that CATT showed superior recall and precision compared with existing tools, especially for data with short read length and small size and single-cell sequencing data. Thus, CATT will be a useful tool for TCR analysis in researches of cancer and immunology. Availability and implementation http://bioinfo.life.hust.edu.cn/CATT or https://github.com/GuoBioinfoLab/CATT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shadi Darvish Shafighi ◽  
Szymon M. Kiełbasa ◽  
Julieta Sepúlveda-Yáñez ◽  
Ramin Monajemi ◽  
Davy Cats ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
B Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Tumor Heterogeneity ◽  
Single Cells ◽  
Clonal Evolution ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Sequencing Data

Abstract Background Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. Methods Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. Results We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. Conclusions The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS).

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