Biology subjects

Thijssen, B.

Publications and source records attributed to Thijssen, B..

5 recordsLinked to original sources

bioRxiv2026

ESGI: Efficient splitting of generic indices in single-cellsequencing data

Single-cell sequencing technologies increasingly rely on complex nucleotide barcoding schemes to encode cellular identities, experimental conditions, and multiple molecular modalities within a single experiment. While demultiplexing, alignment, and UMI-based quantification form the core preprocessing steps that transform raw sequencing reads into analyzable single-cell data, existing pipelines are often tightly coupled to specific experimental designs and typically assume fixed barcode positions and substitution-only error models. As a result, many emerging assays employing combinatorial, variablelength, or multimodal barcoding designs require custom, hard-coded preprocessing solutions that are difficult to generalize and maintain. Here, we present ESGI (Efficient Splitting of Generic Indices), a flexible and extendable framework for demultiplexing and processing single-cell sequencing data with arbitrary barcode architectures. ESGI operates directly on raw FASTQ files using a generic barcode pattern specification, supports barcode matching with insertions and deletions via Levenshtein distance, accommodates variable-length barcodes, and provides detailed quality metrics for barcode assignment. ESGI optionally integrates genome alignment via STAR and performs feature quantification and UMI collapsing to generate cellby-feature count matrices. ESGI is well documented and readily applicable to novel single-cell experiments. We demonstrate the versatility of ESGI across six datasets spanning four distinct single-cell technologies, including combinatorial indexing-based transcriptomic and multimodal assays, feature barcode-based protein measurements, and spatial barcoding data. Across these applications, ESGI robustly demultiplexes complex barcode designs that are not natively supported by existing pipelines, while producing results comparable to established workflows where applicable. Together, ESGI provides a general and future-proof solution for preprocessing single-cell sequencing data, enabling rapid adoption and analysis of emerging experimental designs.

bioinformatics↗

bioRxiv2024

Mitochondrial damage triggers therapy-induced senescence

Glioblastoma (GBM) is a fatal brain tumor with a critical need for better therapies. It is known that the PI3K, MAPK, and CDK4/6 signaling pathways are hyper-activated in these tumors; however, previous studies have used very high concentration of inhibitors to assess their importance, with mixed results. Here we developed PMCi, a combination approach that targets all three pathways simultaneously, at clinically-relevant doses. PMCi effectively suppresses GBM cell proliferation in vitro and in vivo, and outperforms monotherapies and dual combinations. PMCi acts by inducing cellular senescence, which is mediated solely by the mitochondria, and, unlike other forms of senescence, is independent of nuclear damage. This phenotype is caused by a reactive oxygen species (ROS)\cGAS-STING\senescence-associated secretory phenotype (SASP) signaling cascade, that acts in a paracrine manner to establish and maintain senescence. Our results demonstrate that mitochondrial damage is sufficient to drive senescence, and that this can be leveraged to target GBM cells.

cell biology↗

bioRxiv2023

Inferring single-cell protein levels and cell cycle behavior in heterogeneous cell populations

Individual cells in a genetically identical population can show highly variable behavior. Single-cell measurements allow us to study this variability, but the available measurement techniques have limitations: live-cell microscopy is typically restricted to one or a few molecular markers, while techniques that simultaneously measure large numbers of molecular markers are destructive and cannot be used to follow cells over time. To help overcome these limitations, we present here scMeMo (single cell Mechanistic Modeler): a mechanistic modeling framework that can leverage diverse sets of measurements in order to infer unobserved variables in heterogeneous single cells. We used this framework to construct a model describing cell cycle progression in human cells, and show that it can predict the levels of several proteins in individual cells, based on live-cell microscopy measurements of only one marker and information learned from other experiments. The framework incorporates an uncertainty calibration step that makes the posterior distributions robust against partial model misspecification. Our modeling framework can be used to integrate information from separate experiments with diverse readouts, and to infer single cell variables that may be difficult to measure directly.

systems biology↗

bioRxiv2021

Combined SHP2 and ERK inhibition for the treatment of KRAS-driven Pancreatic Ductal Adenocarcinoma

Mutant KRAS is present in over 90% of pancreatic as well as 30-40% of lung and colorectal cancers and is one of the most common oncogenic drivers. Despite decades of research and the recent emergence of isoform-specific KRASG12C-inhibitors, most mutant KRAS isoforms, including the ones frequently associated with pancreatic ductal adenocarcinoma (PDAC), cannot be targeted directly. Moreover, targeting single RAS downstream effectors induces adaptive mechanisms leading to tumor recurrence or resistance. We report here on the combined inhibition of SHP2, a non-receptor tyrosine phosphatase upstream of KRAS, and ERK, a serine/threonine kinase and a key molecule downstream of KRAS in PDAC. This combination shows synergistic anticancer activity in vitro, superior disruption of the MAPK pathway, and significantly increased apoptosis induction compared to single-agent treatments. In vivo, we demonstrate good tolerability and efficacy of the combination. Concurrent inhibition of SHP2 and ERK induces significant tumor regression in multiple PDAC mouse models. Finally, we show evidence that 18F-FDG PET scans can be used to detect and predict early drug responses in animal models. Based on these compelling results, we will investigate this drug combination in a clinical trial (SHERPA, SHP2 and ERK inhibition in pancreatic cancer, NCT04916236), enrolling patients with KRAS-mutant PDAC.

cancer biology↗

bioRxiv2018

Delineating feedback activity in the MAPK and AKT pathways using feedback-enabled Inference of Signaling Activity

An important aspect of cellular signaling networks is the existence of feedback mechanisms. However, due to the complexity of signaling networks, as well as the presence of multiple interrelated feedback events, it can be difficult to identify which signaling routes are active in any particular context. We have previously shown that Inference of Signaling Activity (ISA) can be a useful method to study steady-state oncogenic signaling across different cell lines and inhibitor treatments. However, ISA did not explicitly include feedback signaling events. Incorporating feedback will increase the complexity and computational cost of the model, and more data is likely to be needed to infer feedback activities. Here, we developed feedback-ISA (f-ISA), an extension of the ISA modeling approach which incorporates feedback signaling events. It also includes integrated batch correction in order to fit the models to multiple, independent datasets simultaneously. We find that the identifiability of feedback activities can be counter-intuitive, which shows the importance of analyzing the full, joint uncertainty in model parameters. By iteratively adapting the model and including multiple datasets, including both steady state and intervention data, we constructed a model that can explain a large part of the phosphorylation levels of several signaling molecules in the MAPK and AKT pathways, across many breast cancer cell lines and across various conditions. The resulting model delineates which routes in the signaling network are likely to be active in each cell line and condition, given all of the data. Additionally, such models can indicate whether datasets agree with each other, and identify which parts of the data cannot be explained, thereby highlighting gaps in the current knowledge. We conclude that this modeling approach can be useful to quantitatively understand how complex cellular signaling networks behave across different cell lines and conditions.

systems biology↗