Biology subjects

Thijssen, R.

Publications and source records attributed to Thijssen, R..

7 recordsLinked to original sources

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2025

Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

Bone marrow mononuclear cells (BMMCs) are a heterogeneous pool of hematopoietic progenitors and mature immune cells that collectively sustain hematopoiesis and coordinate immune responses. The bone marrow serves not only as the primary site for blood cell production but also as a niche for various disorders, including blood cancers. Advances in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have significantly enhanced our understanding of the cellular biology and molecular dynamics within this complex microenvironment. The choice between these two approaches, however, is often shaped or constrained by the study design, such as research objectives, sample type, and preservation conditions. Consequently, methodological differences in library preparation and transcript capture efficiency can introduce systematic biases that complicate downstream analyses and interpretation, underscoring the need to identify and account for method-specific features. In this study, we conducted a comparative analysis of matched snRNA-seq and scRNA-seq datasets from 11 pairs of healthy donor bone marrow mononuclear cell samples, generated using the popular 10x Genomics platform. We evaluated method-specific biases using multiple quality metrics and compared cell type proportions and transcriptomic signatures captured by each approach. Integrative analysis of these datasets is feasible but not advisable due to systematic gene length biases that were observed between these approaches. Our results showed that despite inherent differences in library complexity, both protocols reliably captured all major cell types. This comparative analysis highlights intrinsic differences between snRNA-seq and scRNA-seq data, providing valuable insights into their respective advantages, limitations, and trade-offs. These findings can assist researchers in selecting the optimal method tailored to specific biological questions and sample characteristics, and also enable more method-aware data analysis and interpretation.

bioinformatics↗

bioRxiv2024

GLMsim: a GLM-based single cell RNA-seq simulator incorporating batch and biological effects

With development of the single cell RNA-seq technologies, large numbers of cells can now be routinely sequenced by different platforms. This requires us to choose an efficient integration tool to merge those cells, and computational simulators to help benchmark and assess the performance of these tools. Although existing single cell RNA-seq simulators can simulate library size, biological and batch effects separately, they currently do not capture associations among these three factors. Here we present GLMsim, the first single cell RNA-seq simulator to simultaneously capture the library size, biology and unwanted variation and their associations via a generalized linear model, and to simulate data resembling the original experimental data in these respects. GLMsim is capable of quantitatively benchmarking different single cell integration methods, and assessing their abilities to retain biology and remove library size and batch effects.

bioinformatics↗

bioRxiv2024

Early cytokine-driven adaptation of survival pathways in lymphoid cells during targeted therapies

Venetoclax, a first-in-class BH3 mimetic drug targeting BCL-2, has improved outcomes for patients with chronic lymphocytic leukemia (CLL). Early measurements of the depth of the venetoclax treatment response, assessed by minimal residual disease, are strong predictors of long-term clinical outcomes. Yet, there are limited data concerning the early changes induced by venetoclax treatment that might inform strategies to improve responses. To address this gap, we conducted longitudinal mass cytometric profiling of blood cells from patients with CLL during the first two months of venetoclax monotherapy. At baseline, we resolved CLL heterogeneity at the single-cell level to define multiple subpopulations in all patients distinguished by proliferative, metabolic and cell survival proteins. Venetoclax induced significant reduction in all CLL subpopulations coincident with rapid upregulation of pro-survival BCL-2, BCL-XL and MCL-1 proteins in surviving cells, which had reduced sensitivity to the drug. Mouse models recapitulated the venetoclax-induced elevation of survival proteins in B cells and CLL-like cells that persisted in vivo, with genetic models demonstrating that extensive apoptosis and access to the B cell cytokine, BAFF, were essential. Accordingly, analysis of patients with CLL that were treated with a different targeted therapy, the anti-CD20 antibody obinutuzumab, also exhibited marked elevation of BAFF and increased pro-survival proteins in leukemic cells that persisted. Overall, these data highlight the rapid adaptation of CLL cells to targeted therapies via homeostatic factors and support co-targeting of cytokine signals to achieve deeper and more durable long-term responses. Key pointsO_LILeukaemic cells rapidly adapt to targeted therapy by elevating pro-survival protein expression. C_LIO_LICell attrition and increased bioavailability of homeostatic cytokines drive this heightened survival, highlighting avenues for more potent combination therapies. C_LI

cancer biology↗

bioRxiv2024

Single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) to reliably detect isoform usage and coding mutations in targeted genes at a single-cell level

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrated high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA which allows analysis to be combined with existing short-read RNA-seq datasets. In our investigation of BTK and SF3B1 genes in samples from patients with chronic lymphocytic leukaemia (CLL), we detected SF3B1 isoforms and mutations with high sensitivity. Integration with short-read scRNA-seq data revealed significant gene expression differences in SF3B1-mutated CLL cells, though it did not impact the sensitivity of the anti-cancer drug venetoclax. scRaCH-seqs capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

molecular biology↗

bioRxiv2021

RUV-III-NB: Normalization of single cell RNA-seq Data

Despite numerous methodological advances, the normalization of single cell RNA-seq (scRNA-seq) data remains a challenging task and the performance of different methods can vary greatly across datasets. Part of the reason for this is the different kinds of unwanted variation, including library size, batch and cell cycle effects, and the association of these with the biology embodied in the cells. A normalization method that does not explicitly take into account cell biology risks removing some of the signal of interest. Furthermore, most normalization methods remove the effects of unwanted variation for the cell embedding used for clustering-based analysis but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. Here we propose RUV-III-NB, a statistical method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. RUV-III-NB explicitly takes into account its potential association with biology when removing unwanted variation via the use of pseudo-replicates. The method can be used for both UMI or sequence read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using five publicly available datasets that encompass different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve differential expression analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent across the five datasets and is not sensitive to the number of factors assumed to contribute to the unwanted variation. It also shows promise for removing other kinds of unwanted variation such as platform effects. The method is implemented as a publicly available R package available from https://github.com/limfuxing/ruvIIInb.

bioinformatics↗

bioRxiv2020

Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing

Alternative splicing shapes the phenotype of cells in development and disease. Long-read RNA-sequencing recovers full-length transcripts but has limited throughput at the single-cell level. Here we developed single-cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to overcome these issues and perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, we performed the first comprehensive characterization of the full-length isoform landscape in single cells of different types and species and identified thousands of unannotated isoforms. We found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. Analysis at the transcript-level allowed data integration with scATAC-seq on individual promoters, improved correlation with protein expression data and linked mutations known to confer drug resistance to transcriptome heterogeneity. Our methods reveal previously unseen isoform complexity and provide a better framework for multi-omics data integration.

genomics↗