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 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 Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V’DJer

2016 ◽  
Vol 32 (24) ◽  
pp. 3729-3734 ◽  
Author(s):  
Lisle E. Mose ◽  
Sara R. Selitsky ◽  
Lisa M. Bixby ◽  
David L. Marron ◽  
Michael D. Iglesia ◽  
...  
Keyword(s):  
B Cell ◽  
Rna Sequencing ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Sequencing Data ◽  
Short Read

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 BraCeR: Reconstruction of B-cell receptor sequences and clonality inference from single-cell RNA-sequencing

2017 ◽  
Author(s):  
Ida Lindeman ◽  
Guy Emerton ◽  
Ludvig M. Sollid ◽  
Sarah A. Teichmann ◽  
Michael J.T. Stubbington
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
B Lymphocytes ◽  
Antigen Receptor ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Supplementary Note ◽  
Single Cell Rna Sequencing ◽  
Repertoire Sequencing

Reconstruction of antigen receptor sequences from single-cell RNA-sequencing (scRNA-seq) data allows the linking of antigen receptor usage to the full transcriptomic identity of individual B lymphocytes, without having to perform additional targeted repertoire sequencing (Rep-seq). Here we report BraCeR (freely available at https://github.com/teichlab/bracer/), an extension of TraCeR [1], for reconstruction of paired full-length B-cell receptor sequences and inference of clonality from scRNA-seq data (Supplementary Note 1).

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

2018 ◽  
Author(s):  
Shaked Afik ◽  
Gabriel Raulet ◽  
Nir Yosef
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Read Length ◽  
Short Read ◽  
Long Reads ◽  
Single Cell Rna Sequencing ◽  
The Cost

ABSTRACTRNA-sequencing of single B cells provides simultaneous measurements of the cell state and its binding specificity. However, in order to uncover the latter further reconstruction of the B cell receptor (BCR) sequence is needed. We present BRAPeS, 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 (25bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared to long reads. BRAPeS is publicly available in the following link: https://github.com/YosefLab/BRAPeS.

scholarly journals Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

2019 ◽  
Author(s):  
Imad Abugessaisa ◽  
Shuhei Noguchi ◽  
Melissa Cardon ◽  
Akira Hasegawa ◽  
Kazuhide Watanabe ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Assessment Method ◽  
Poor Quality ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Gene Coverage ◽  
The Impact

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.

scholarly journals Abstract 4697: Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

2019 ◽  
Author(s):  
Emily F. Davis-Marcisak ◽  
Pranay Orugunta ◽  
Genevieve Stein-O'Brien ◽  
Sidharth V. Puram ◽  
Evanthia Roussos Torres ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Tumor Heterogeneity ◽  
Variation Analysis ◽  
Sequencing Data ◽  
Expression Variation ◽  
Single Cell Rna Sequencing

scholarly journals Signatures of B Cell Receptor Repertoire Following Pneumocystis Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Han Sun ◽  
Hu-Qin Yang ◽  
Kan Zhai ◽  
Zhao-Hui Tong
Keyword(s):  
B Cells ◽  
B Cell ◽  
Single Cell ◽  
Plasma Cells ◽  
Somatic Hypermutation ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Receptor Repertoire ◽  
Elevated Expression ◽  
Pneumocystis Infection

B cells play vital roles in host defense against Pneumocystis infection. However, the features of the B cell receptor (BCR) repertoire in disease progression remain unclear. Here, we integrated single-cell RNA sequencing and single-cell BCR sequencing of immune cells from mouse lungs in an uninfected state and 1–4 weeks post-infection in order to illustrate the dynamic nature of B cell responses during Pneumocystis infection. We identified continuously increased plasma cells and an elevated ratio of (IgA + IgG) to (IgD + IgM) after infection. Moreover, Pneumocystis infection was associated with an increasing naïve B subset characterized by elevated expression of the transcription factor ATF3. The proportion of clonal expanded cells progressively increased, while BCR diversity decreased. Plasma cells exhibited higher levels of somatic hypermutation than naïve B cells. Biased usage of V(D)J genes was observed, and the usage frequency of IGHV9-3 rose. Overall, these results present a detailed atlas of B cell transcriptional changes and BCR repertoire features in the context of Pneumocystis infection, which provides valuable information for finding diagnostic biomarkers and developing potential immunotherapeutic targets.

Sign in / Sign up

Export Citation Format

Share Document