scholarly journals Highly Multiplexed Single-Cell Full-Length cDNA Sequencing of human immune cells with 10X Genomics and R2C2

2020 ◽  
Author(s):  
Roger Volden ◽  
Christopher Vollmers
Keyword(s):  
B Cell ◽  
Single Cell ◽  
High Throughput ◽  
Cell Biology ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Cell Receptor ◽  
Full Length ◽  
Full Length Cdna ◽  
Wide Range

AbstractSingle cell transcriptome analysis elucidates facets of cell biology that have been previously out of reach. However, the high-throughput analysis of thousands of single cell transcriptomes has been limited by sample preparation and sequencing technology. High-throughput single cell analysis today is facilitated by protocols like the 10X Genomics platform or Drop-Seq which generate cDNA pools in which the origin of a transcript is encoded at its 5’ or 3’ end. These cDNA pools are currently analyzed by short read Illumina sequencing which can identify the cellular origin of a transcript and what gene it was transcribed from. However, these methods fail to retrieve isoform information. In principle, cDNA pools prepared using these approaches can be analyzed with Pacific Biosciences and Oxford Nanopore long-read sequencers to retrieve isoform information but all current implementations rely heavily on Illumina short-reads for the analysis in addition to long reads. Here, we used R2C2 to sequence and demultiplex 9 million full-length cDNA molecules generated by the 10X Chromium platform from ∼3000 peripheral blood mononuclear cells (PBMCs). We used these reads to – independent from Illumina data – cluster cells into B cells, T cells, and Monocytes and generate isoform-level transcriptomes for these cell-types. We also generated isoform-level transcriptomes for all single cells and used this information to identify a wide range of isoform diversity between genes. Finally, we also designed a computational workflow to extract paired adaptive immune receptor – T cell receptor and B cell receptor (TCR and BCR) –sequences unique to each T and B cell. This work represents a new, simple, and powerful approach that –using a single sequencing method – can extract an unprecedented amount of information from thousands of single cells.

scholarly journals High-Throughput Single-Cell Analysis of B Cell Receptor Usage among Autoantigen-Specific Plasma Cells in Celiac Disease

2017 ◽  
Vol 199 (2) ◽  
pp. 782-791 ◽  
Author(s):  
Bishnudeo Roy ◽  
Ralf S. Neumann ◽  
Omri Snir ◽  
Rasmus Iversen ◽  
Geir Kjetil Sandve ◽  
...  
Keyword(s):  
Celiac Disease ◽  
B Cell ◽  
Single Cell ◽  
High Throughput ◽  
Plasma Cells ◽  
Single Cell Analysis ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Cell Analysis ◽  
Receptor Usage

Modelling Single Cell B-Cell Receptor Signaling Reveals Enhanced Activity in Primary CLL Cells Compared to Non-Malignant Cells While Fundamental Network Circuit Topology Remains Stable Even with Novel Therapeutic Inhibitors

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4275-4275
Author(s):  
Daniel Mertens ◽  
Christine Wolf ◽  
Carsten Maus ◽  
Michael Persicke ◽  
Katharina Filarsky ◽  
...  
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Network Topology ◽  
Research Funding ◽  
Single Cells ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Malignant Cells ◽  
Network Learning ◽  
Bcr Signaling

B-cell receptor (BCR) signalling is central for the pathomechanism of chronic lymphocytic leukemia (CLL). Novel inhibitors of BCR signalling have recently substantially improved treatment of CLL, and a better characterization of the molecular circuitry of leukemic BCR signalling will allow a more refined targeting of this Achilles heel. In order to model malignant and non-malignant BCR signalling, we quantified after stimulation 5 components of BCR signaling (ZAP70/SYK, BTK, PLCy2, AKT, ERK1/2) in single cells from primary human leukemic and non-malignant tissue via phospho-specific flow cytometry over 6 time points. We stimulated cells from 11 patients and non-malignant CD19 negative enriched B-cells from 5 healthy donors by crosslinking the BCR with anti-IgM and/or anti-CD19 and synchronous inhibition of phosphatases with H2O2. As expected, we found more phosphorylation of all BCR signalling components after stimulation in malignant vs non-malignant cells and in IGHV non-mutated CLL cells compared to IGHV mutated CLL cells. Intriguingly, inhibition of phosphatases with H2O2 led to higher phosphorylation of BCR components in CLL cells with mutated IGHV genes compared to CLL cells with non-mutated IGHV genes, suggesting a stronger dampening of signalling activity in mutated IGHV CLL by phosphatases. In order to characterize the signalling circuitry, we modelled the connectivity of the cascade components by correlating signal intensities across single cells of the cell populations of single samples (Figure 1). Surprisingly, upon stimulation no substantial differences in network topology were observed between malignant and non-malignant cells. To additionally test for changes in network topology, we challenged the BCR signaling cascade with inhibitors for BTK (ibrutinib), PI3K (idelalisib). Ibrutinib and idelalisib acted complementary, but not synergistic, and were similarly effective in IGHV mutated and non-mutated CLL. Effects of idelalisib were the same on malignant and non-malignant cells, whereas ibrutinib was mostly active on CLL cells, not on non-malignant B-cells. Upon stimulation with combinations of IgM and CD19 crosslinking augmented with H2O2, phosphorylation of PLCy2 could not be significantly inhibited by idelalisib or ibrutinib on a timescale of 28mins. We therefore aimed to identify central activating nodes of the BCR signalling cascade using targeted inhibitors. In fact, we found that inhibition of LYN with dasatinib and inhibition of SYK with entospletinib could substantially reduce phosphorylation of PLCy2, BTK and ERK but not AKT after all combinations of BCR stimulation. This suggests additional signalling cascades modulating AKT and a strong impact of SYK/LYN activity on the regulation of PLCy2. In summary, our findings underline the importance of single cell analysis of the dynamic circuitry of B-cell receptor signalling to understand development of resistance mechanisms and potential vulnerabilities. Figure 1: Workflow scheme of the Bayesian network learning and averaging approach. After discretizing the continuous single cell data, an optimal network is derived from each of R bootstrap samples. The Bayesian network learning strategy uses the BDe scoring function and a greedy hill-climbing algorithm to find the network model that represents the resampled data best. An average arc strength for each connection between nodes is derived from the number of occurrences of the respective connection in the set of R best scoring networks. Further averaging among networks derived from different data sets was applied for identifying conditional, temporal, and group-specific differences. Figure 1 Disclosures Döhner: Novartis: Consultancy, Honoraria, Research Funding; Astex: Consultancy, Honoraria; Bristol Myers Swuibb: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Arog: Research Funding; Seattle Genetics: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Pfizer: Research Funding; Celgene Corporation: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria. Stilgenbauer:GSK: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pharmacyclics: Other: Travel support; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Hoffmann La-Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau.

scholarly journals High-Throughput BCR Sequencing and Single-Cell Transcriptomics Reveal Distinct Transcriptional Profiles Associated with Subclonal Evolution of Follicular Lymphoma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 298-298
Author(s):  
Cornelis A.M. van Bergen ◽  
Marvyn T. Koning ◽  
Edwin Quinten ◽  
Agnieszka Mykowiecka ◽  
Julieta Sepulveda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lymph Node ◽  
Follicular Lymphoma ◽  
B Cell ◽  
Single Cell ◽  
High Throughput ◽  
Conflicts Of Interest ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Full Length

Objectives: Follicular lymphoma (FL) typically originates from premalignant mature B cells that carry the founder t(14;18) BCL2 translocation. Mutations in epigenetic modifiers and acquisition of N-glycosylation sites in CDR regions of the B-cell receptor (BCR) are recurrent secondary events in FL pathogenesis. Despite these oncogenic drivers, FL can remain indolent and clinically stable for years. The molecular events driving subclonal evolution into symptomatic progression and eventual transformation to aggressive lymphoma are insufficiently understood. FL cells are frozen in their B-cell development at the germinal center stage and undergo continuous somatic hypermutation mediated by expression of activation-induced deaminase (AID). We aim to identify crucial drivers of subclonal FL evolution by high-throughput mapping at single-cell resolution. Methods: Viable FL cells were isolated and cryopreserved from 23 histologically or immunocytologically confirmed FL samples from 13 patients with informed consent. Full-length VDJ/VJ transcripts were isolated by unbiased template-switching ARTISAN PCR and massive parallel NGS sequencing on the PacBio platform. The clonal primordial FL BCR (pBCR) was reconstructed from unmutated IGV/IGJ sequences with the CDR3 of the least mutated BCR. Since the IgTree program was unable to process the obtained numbers of BCR sequences, we developed the WILLOW algorithm for analysis of BCR evolution based on the principle of maximum parsimony and on distance from the pBCR. Intraclonal BCR variability was quantified by Shannon's diversity index. 5' single cell transcriptomics and VDJ/VJ sequencing was performed on 2 pools of highly purified FL cells from 5 lymph node biopsies on the 10x Genomics platform. Data were deconvoluted based on expressed variants by the Single Cell Sample Matcher (SCSM) algorithm. Clustering based on gene expression profiles was performed by shared nearest neighbour (SNN) modularity optimization within the R Seurat package. Genes whose expression differed significantly (adjusted p<0.05) between clusters were assigned to gene ontology terms. Results: ARTISAN PCR/PacBio NGS yielded a median of 743 full-length VDJ and VJ sequences (range 62-12782) per BCR chain with expected high intraclonal diversity (median 200 subclones, range 15-3301). WILLOW revealed dominant FL subclones with a subclonal hierarchy wherein multiple routes converged to offspring nodes with identical additional mutations rather than tree-like branching (Figure). In serial samples of 4 patients, lymph node biopsies had only marginally higher subclonal diversity than blood or bone marrow samples (p=0,055; Wilcoxon's matched-pairs signed rank test). Overall BCR mutational burden increased over time in sequential biopsies. Two cases of histological FL transformation were dominated by a single subclone (65% and 80% of all VDJ/VJ sequences, respectively) that was rare in the preceding FL BCR network (0.2% and 1.8%). Pooled transcriptomics data from 6050-6500 cells were assigned to individual samples by SCSM and revealed up to seven transcriptional clusters per FL. In 9 of 10 FL, genes assigned to immune function strongly contributed to separation into one or more clusters. Single cell VDJ/VJ sequencing yielded combined heavy and light chain BCR sequences for a median of 502 FL cells per biopsy (range 22 - 1919) that permitted mapping of subclonal evolution by WILLOW based on complete BCR information. Transcriptome clusters were not distributed evenly throughout the WILLOW FL BCR networks but rather statistically associated with distinct major FL subclones. Vice versa, major FL subclones within the same biopsy were distinguished by particular gene expression profiles. Conclusions: WILLOW facilitates mapping of subclonal FL evolution based on high-throughput BCR sequencing. FL evolution proceeds in networks rather than tree-like branching, whereby acquisition of certain combinations of several BCR mutations can occur in parallel in different trajectories. Transcriptomic profiling of single FL cells identifies distinct clusters within a single biopsy. Mapping of these clusters to the FL cell position in the subclonal FL evolutionary network identifies putative mechanisms that are associated with subclonal progression. These mechanisms involve physiological B-cell signalling pathways. Figure Disclosures No relevant conflicts of interest to declare.

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).

scholarly journals High-throughput chromatin accessibility profiling at single-cell resolution

2018 ◽  
Author(s):  
Anja Mezger ◽  
Sandy Klemm ◽  
Ishminder Mann ◽  
Kara Brower ◽  
Alain Mir ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Mononuclear Cells ◽  
De Novo ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Peripheral Blood Mononuclear ◽  
Whole Cells ◽  
Chip Processing ◽  
On Chip

We have developed a high-throughput single-cell ATAC-seq (assay for transposition of accessible chromatin) method to measure physical access to DNA in whole cells. Our approach integrates fluorescence imaging and addressable reagent deposition across a massively parallel (5184) nano-well array, yielding a nearly 20-fold improvement in throughput (up to ~1800 cells/chip, 4-5 hour on-chip processing time) and cost (~98¢ per cell) compared to prior microfluidic implementations. We applied this method to measure regulatory variation in Peripheral Blood Mononuclear Cells (PBMCs) and show robust,de-novoclustering of single cells by hematopoietic cell type.

scholarly journals Multiplexed PCR-Free Detection of MicroRNAs in Single Cancer Cells Using a DNA-Barcoded Microtrough Array Chip

Micromachines ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 215 ◽  
Author(s):  
Nayi Wang ◽  
Yao Lu ◽  
Zhuo Chen ◽  
Rong Fan
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Pcr Amplification ◽  
Intratumor Heterogeneity ◽  
Rna Molecules ◽  
Multiplexed Pcr ◽  
Microrna Detection ◽  
Wide Range ◽  
Health And Disease

MicroRNAs are a class of small RNA molecules that regulate the expression of mRNAs in a wide range of biological processes and are implicated in human health and disease such as cancers. How to measure microRNA profiles in single cells with high throughput is essential to the development of cell-based assays for interrogating microRNA-mediated intratumor heterogeneity and the design of new lab tests for diagnosis and monitoring of cancers. Here, we report on an in situ hybridization barcoding workflow implemented in a sub-nanoliter microtrough array chip for high-throughput and multiplexed microRNA detection at the single cell level. The microtroughs are used to encapsulate single cells that are fixed, permeabilized, and pre-incubated with microRNA detection probes, each of which consists of a capture strand complementary to specific microRNA and a unique reporter strand that can be photocleaved in the microtroughs and subsequently detected by an array of DNA barcodes patterned on the bottom of the microtroughs. In this way, the measurement of reporter strands released from single cells is a surrogate for detecting single-cell microRNA profiles. This approach permits direct measurement of microRNAs without PCR amplification owing to the small volume (<1 nL) of microtroughs. It offers high throughput and high multiplexing capability for evaluating microRNA heterogeneity in single cells, representing a new approach toward microRNA-based diagnosis and monitoring of complex human diseases.

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

2020 ◽  
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 ◽  
Single Cell Rna Sequencing

ABSTRACTBackgroundDrawing 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 align exome-derived mutations with B-cell receptor clonotypes as an independent measure for clonal evolution.ResultsHere, 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. We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome sequencing, single cell RNA sequencing, and B-cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B-cell receptor clonotypes to the tumor clones.ConclusionsThe 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.

scholarly journals Reconstructing B-cell receptor sequences from short-read single-cell RNA sequencing with BRAPeS

2019 ◽  
Vol 2 (4) ◽  
pp. e201900371 ◽  
Author(s):  
Shaked Afik ◽  
Gabriel Raulet ◽  
Nir Yosef
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Read Length ◽  
Antigen Specificity ◽  
Short Read ◽  
Single Cell Rna Sequencing

RNA sequencing of single B cells provides simultaneous measurements of the cell state and its antigen specificity as determined by the B-cell receptor (BCR). However, to uncover the latter, further reconstruction of the BCR sequence is needed. We present BRAPeS (“BCR Reconstruction Algorithm for Paired-end Single cells” ), an algorithm for reconstructing BCRs from short-read paired-end single-cell RNA sequencing. BRAPeS is accurate and achieves a high success rate even at very short (25 bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared with long reads. BRAPeS is publicly available at the following link: https://github.com/YosefLab/BRAPeS.

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.

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