Biology subjects

Bosdriesz, E.

Publications and source records attributed to Bosdriesz, E..

10 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↗

bioRxiv2025

Cell-state specific drug-responses are associated with differences in signaling network wiring.

Intracellular signaling pathways form networks through which information is transmitted, often in the form of kinase-mediated phosphorylation events, to interpret extracellular signals and elicit appropriate cellular responses. Yet, even isogenic cells in a homogenous environment show heterogeneity in their intracellular "cell-state", as well as their response to extracellular signals. Here, we aimed to better understand this relation between these phenomena by investigating how information flows through the EGF-receptor centered network upon targeted drug treatment, and how this is affected by cell-to-cell-state differences. Using single-cell ID-seq, we profiled the cell-state and signaling activity of primary human epidermal stem cells by measuring 69 (phospho-)proteins upon inhibition of the Erk/MAPK (p90RSK) and Akt/mTOR (p70S6K) routes downstream of the EGF pathway. We found that the effects of drug treatment propagated from the EGF-signaling pathway to other connected parts of the cellular signaling network, indicating altered signaling flow. We identified nine distinct cell-states that show pervasive state-dependent drug-responses for many (phospho-)proteins. Computational modeling of the signaling network using single-cell Comparative Network Reconstruction showed that many interactions between phospho-proteins (i.e. network wiring) were quantitatively different between cell-states. Furthermore, (phospho-)proteins with a cell-state dependent drug response, were more likely to be involved in interactions that showed a cell-state dependent strength. Overall, our results indicate that drug treatment response and signaling interactions between proteins are closely related and modulated by cell-state.

systems biology↗

bioRxiv2024

Reconstructing and comparing signal transduction networks from single cell protein quantification data

MotivationSignal transduction networks regulate a multitude of essential biological processes and are frequently aberrated in diseases such as cancer. Developing a mechanistic understanding of such networks is essential to understand disease or cell population specific signaling and to design effective treatment strategies. Typically, such networks are computationally reconstructed based on systematic perturbation experiments, followed by quantification of signaling protein activity. Recent technological advances now allow for the quantification of the activity of many (signaling) proteins simultaneously in single cells. This makes it feasible to reconstruct signaling networks from single cell data. ResultsHere we introduce single cell Comparative Network Reconstruction (scCNR) to derive signal transduction networks by exploiting the heterogeneity of single cell (phospho)protein measurements. scCNR treats stochastic variation in total protein abundances as natural perturbation experiments, whose effects propagate through the network. scCNR reconstructs cell population specific networks of the same underlying topology for cells from diverse populations. We extensively validated scCNR on simulated single cell data, and we applied it to a dataset of EGFR-inhibitor treated keratinocytes to recover signaling differences downstream of EGFR and in protein interactions associated with proliferation. scCNR will help to unravel the mechanistic signaling differences between cell populations by making use of single-cell data, and will subsequently guide the development of well-informed treatment strategies. Availability and implementationscCNR is available as a python module at https://github.com/ibivu/scmra. Additionally, code to reproduce all figures is available at https://github.com/tstohn/scmra_analysis. Supplementary informationSupplementary information and data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Identifying selective drug combinations using Comparative Network Reconstruction

Inhibition of aberrant signaling with target inhibitors is an important treatment strategy in cancer, but unfortunately responses are often short-lived. Multi-drug combinations have the potential to mitigate this, but to avoid toxicity such combinations must be selective and the dosage of the individual drugs should be as low as possible. Since the search space of multi-drug combinations is enormous, an efficient approach to identify the most promising drug combinations and dosages is needed. Here, we present a pipeline to prioritize promising multi-drug combinations. We performed a limited set of drug perturbations in an isogenic cell line pair with and without an activating PI3K mutation, and recorded their signaling states and cell viability. We used these data to reconstruct mutant specific signaling networks and map the short term signaling response to longer term changes in cell viability. The resulting models then allowed us to predict the effect of unseen multi-drug combinations, at arbitrary drug-concentrations, on cell viability. Our initial aim was to find combinations that selectively reduce the viability of the PI3K mutant cells, but our models indicated that such combinations do not exist for this cell line pair. However, we were able to validate 25 of the 30 low-dose multi-drug combinations that we predicted to be anti-selective. Our pipeline thus enables a powerful strategy to rapidly map the efficacy and possible selectivity of drug combinations, hence significantly speeding up the pace at which we can explore the vast space of combination therapies.

systems biology↗

bioRxiv2019

Multiple Low Dose therapy: an effective strategy to treat EGFR inhibitor-resistant NSCLC tumours

Targeted cancer drugs often elicit powerful initial responses, but generally fail to deliver long-term benefit due to the emergence of resistant cells1,2. This is thought to be the consequence of strong selective pressure exerted on the cancer drug target by a Maximum Tolerated Dose (MTD) of a drug3-5. We hypothesized that partial inhibition of multiple components in the same oncogenic signalling pathway might add up to complete pathway inhibition, while at the same time decreasing the selective pressure on each individual component to acquire a resistance mutation. We report here testing of this Multiple Low Dose (MLD) model of drug administration in Epidermal Growth Factor Receptor (EGFR) mutant non-small cell lung cancer (NSCLC). We show that as little as 20% of the individual drug doses required for full inhibition of cell viability is sufficient to completely block MAPK signalling and proliferation when used in 3D (RAF+MEK+ERK inhibitors) or 4D (EGFR+RAF+MEK+ERK inhibitors) combinations. Importantly, EGFR mutant NSCLC cells treated with EGFR inhibitors at a high dose rapidly developed resistance in vitro, but the cells treated with 3D or 4D MLD therapy did not. Moreover, NSCLC cells that had gained resistance to high dose anti-EGFR therapies were still sensitive to MLD therapy. Using several animal models, including patient derived xenografts of NSCLC tumours that are resistant to EGFR inhibitors erlotinib and osimertinib6,7, we found durable responses to MLD therapy without associated toxicity. These data support the notion that partial inhibition of multiple components of cancer-activated signalling pathways is difficult to circumvent and suggest that MLD therapy could deliver clinical benefit. We propose that MLD strategy could be an effective treatment option for EGFR mutant NSCLC patients, especially those having acquired resistance to even third generation EGFR inhibitor therapy.

cancer biology↗

bioRxiv2018

Identifying biomarkers of anti-cancer drug synergy using multi-task learning

Combining anti-cancer drugs has the potential to increase treatment efficacy. Because patient responses to drug combinations are highly variable, predictive biomarkers of synergy are required to identify which patients are likely to benefit from a drug combination. To aid biomarker identification, the DREAM challenge consortium has recently released data from a screen containing 85 cell lines and 167 drug combinations. The main challenge of these data is the low sample size: per drug combination, a median of 14 cell lines have been screened. We found that widely used methods in single drug response prediction, such as Elastic Net regression per drug, are not predictive in this setting. Instead, we propose to use multi-task learning: training a single model simultaneously on all drug combinations, which we show results in increased predictive performance. In contrast to other multi-task learning approaches, our approach allows for the identification of biomarkers, by using a modified random forest variable importance score, which we illustrate using artificial data and the DREAM challenge data. Notably, we find that mutations in MYO15A are associated with synergy between ALK / IGFR dual inhibitors and PI3K pathway inhibitors in triple-negative breast cancer.\n\nAuthor summaryCombining drugs is a promising strategy for cancer treatment. However, it is often not known which patients will benefit from a particular drug combination. To identify patients that are likely to benefit, we need to identify biomarkers, such as mutations in the tumors DNA, that are associated with favorable response to the drug combination. In this work, we identified such biomarkers using the drug combination data released by the DREAM challenge consortium, which contain 85 tumor cell lines and 167 drug combinations. The main challenge of these data is the extremely low sample size: a median of 14 cell lines have been screened per drug combination. We found that traditional methods to identify biomarkers for monotherapy response, which analyze each drug separately, are not suitable in this low sample size setting. Instead, we used a technique called multi-task learning to jointly analyze all drug combinations in a single statistical model. In contrast to existing multi-task learning algorithms, which are black-box methods, our method allows for the identification of biomarkers. Notably, we find that, in a subset of breast cancer cell lines, MYO15A mutations associate with response to the combination of ALK / IGFR dual inhibitors and PI3K pathway inhibitors.

bioinformatics↗

bioRxiv2018

Modelling Signalling Networks from Perturbation Data

MotivationIntracellular signalling is realized by complex signalling networks which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts.\n\nResultsWe developed a derivative of MRA that is suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using data from a knock out and parental colon cancer cell line. We find that SHP2 is required for MAPK signalling, whereas AKT signalling only partially depends on SHP2.\n\nAvailabilityAn R-package is available at https://github.com/MathurinD/STASNet\n\nContactnils.bluethgen@charite.de

bioinformatics↗

bioRxiv2018

Comparative Network Reconstruction using Mixed Integer Programming

New anti-cancer drugs that specifically target oncogenes involved in signalling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. Addressing this problem requires an understanding of these networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells. Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on incomplete perturbation data, and to identify which edges differ quantitatively between two or more signalling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wildtype MAPK signaling, possibly through an alternative RAF-isoform.

systems biology↗

bioRxiv2017

Low affinity membrane transporters can increase net substrate uptake rate by reducing efflux

Cells require membrane-located transporter proteins to import nutrients from the environment. Many organisms have several similar transporters for the same nutrient, which differ in their affinity. Typically, high affinity transporters are expressed when substrate is scarce and low affinity ones when substrate is more abundant. The benefit of using low affinity transporters when high affinity ones are available has so far remained unclear. Here, we investigate two hypotheses. First, it was previously hypothesized that a trade-off between the affinity and the maximal catalytic rate explains this phenomenon. We find some theoretical and experimental support for this hypothesis, but no conclusive evidence. Secondly, we propose a new hypothesis: for uptake by facilitated diffusion, at saturating extracellular substrate concentrations, lowering the affinity enhances the net uptake rate by reducing the substrate efflux rate. As a consequence, there exists an optimal, external substrate concentration dependent transporter affinity. An in silico analysis of glycolysis in Saccharomyces cerevisiae shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis using yeast strains expressing a single glucose transporter that was modified to have either a high or a low affinity. Due to the intimate and reciprocal link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive in our hands. Still, our theoretical results provide a novel reason for the presence of low affinity transport systems which might have more general implications for enzyme catalyzed conversions.

microbiology↗

bioRxiv2017

A system-wide approach to monitor responses to synergistic BRAF and EGFR inhibition in colorectal cancer cells

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread upregulation of receptors tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally reactivating the MAPK pathway.

cancer biology↗