Biology subjects

Wessels, L. F. A.

Publications and source records attributed to Wessels, L. F. A..

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↗

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↗

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↗

bioRxiv2023

Enhancer plasticity in endometrial tumorigenesis demarcates non-coding driver mutations and alterations in 3D genome organization to stimulate oncogene expression

The incidence and mortality of Endometrial Cancer (EC) is on the rise. 85% of ECs depend on Estrogen Receptor alpha (ER) for proliferation, but little is known about its transcriptional regulation in these tumors. We generated epigenomics, transcriptomics and Hi-C datastreams in healthy and tumor endometrial tissues, identifying robust ER reprogramming and profound alterations in 3D genome organization that lead to a gain of tumor-specific enhancer activity during EC development. Integration with endometrial cancer risk single-nucleotide polymorphisms, as well as WGS data from primary tumors and metastatic samples revealed a striking enrichment of risk variants and non-coding somatic mutations at tumor-enriched ER sites. Through machine learning-based predictions and interaction proteomics analyses, we identified an enhancer mutation which alters 3D genome conformation, impairing recruitment of the transcriptional repressor EHMT2/G9a/KMT1C, thereby alleviating transcriptional repression of ESR1 in EC. In summary, we identified a complex genomic-epigenomic interplay in EC development and progression, altering 3D genome organization to enhance expression of the critical driver ER.

cancer biology↗

bioRxiv2023

Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors

Immune checkpoint inhibitors (ICI) currently achieve remarkable clinical results in urothelial cancer (UC). However, the relationship between the tumor microenvironment (TME), usually characterized by immune cell density, and response to ICI is unclear. We quantified the TME immune cell densities and spatial relationships (SRs) using the multiplex immunofluorescence data of 24 UC pre-treatment tumor resections. We described SRs by approximating the 1-NN distance distribution with a Weibull distribution and evaluated the association between TME metrics (spatial and density parameters) and ipilimumab+nivolumab response. Immune cell density did not discriminate between response groups. However, the Weibull SR metrics of CD8+ T-cells or macrophages to their closest cancer cell were positively associated with response. CD8+ T-cells close to B-cells were characteristic of non-response. The G- function, a threshold dependent alternative SR metric, yielded variable effect sizes and statistical power in association studies with response. We validated our SR response associations in a cohort of head and neck tumors with a comparable treatment design. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to ICIs, a finding with significant translational relevance.

cancer biology↗

bioRxiv2022

Multi-omics analysis reveals distinct non-reversion mechanisms of PARPi resistance in BRCA1- versus BRCA2-deficient mammary tumors

BRCA1 and BRCA2 both function in DNA double-strand break repair by homologous recombination (HR). Due to their HR-defect, BRCA1/2-deficient cancers are sensitive to poly(ADP-ribose) polymerase inhibitors (PARPi) but they eventually acquire resistance. Preclinical studies yielded several PARPi resistance mechanisms that do not involve BRCA1/2 reactivation, but their relevance in the clinic remains elusive. To investigate which BRCA1/2-independent mechanisms drive spontaneous resistance in vivo, we combined molecular profiling with functional analysis of the HR status of matched PARPi-naive and PARPi-resistant mouse mammary tumors harboring large intragenic deletions that prevent functional restoration of BRCA1/2. We observed restoration of HR in 64% of PARPi-resistant BRCA1-deficient tumors but none in the PARPi-resistant BRCA2-deficient tumors. Moreover, we found that 53BP1 loss is the prevalent resistance mechanism in HR-proficient BRCA1-deficient tumors, whereas resistance in BRCA2-deficient tumors is mainly induced by the loss of PARG. Our combined multi-omics analysis catalogued additional genes and pathways potentially involved in modulating PARPi response.

cancer biology↗

bioRxiv2021

γδ T cells are effectors of immune checkpoint blockade in mismatch repair-deficient colon cancers with antigen presentation defects

DNA mismatch repair deficient (MMR-d) cancers present an abundance of neoantigens that likely underlies their exceptional responsiveness to immune checkpoint blockade (ICB)1,2. However, MMR-d colon cancers that evade CD8+ T cells through loss of Human Leukocyte Antigen (HLA) class I-mediated antigen presentation3-6, frequently remain responsive to ICB7 suggesting the involvement of other immune effector cells. Here, we demonstrate that HLA class I-negative MMR-d cancers are highly infiltrated by {gamma}{delta} T cells. These {gamma}{delta} T cells are mainly composed of V{delta}1 and V{delta}3 subsets, and express high levels of PD-1, activation markers including cytotoxic molecules, and a broad repertoire of killer-cell immunoglobulin-like receptors (KIRs). In vitro, PD-1+ {gamma}{delta} T cells, isolated from MMR-d colon cancers, exhibited a cytolytic response towards HLA class I-negative MMR-d colon cancer cell lines and {beta}2-microglobulin (B2M)-knockout patient-derived tumor organoids (PDTOs), which was enhanced as compared to antigen presentation-proficient cells. This response was diminished after blocking the interaction between NKG2D and its ligands. By comparing paired tumor samples of MMR-d colorectal cancer patients obtained before and after dual PD-1 and CTLA-4 blockade, we found that ICB profoundly increased the intratumoral frequency of {gamma}{delta} T cells in HLA class I-negative cancers. Taken together, these data indicate that {gamma}{delta} T cells contribute to the response to ICB therapy in patients with HLA class I-negative, MMR-d colon cancers, and illustrate the potential of {gamma}{delta} T cells in cancer immunotherapy.

cancer biology↗

bioRxiv2020

PRECISE+ predicts drug response in patients by non-linear subspace-based transfer from cell lines and PDX models

Pre-clinical models have been the workhorse of cancer research for decades. While powerful, these models do not fully recapitulate the complexity of human tumors. Consequently, translating biomarkers of drug response from pre-clinical models to human tumors has been particularly challenging. To explicitly take these differences into account and enable an efficient exploitation of the vast pre-clinical drug response resources, we developed TRANSACT, a novel computational framework for clinical drug response prediction. First, TRANSACT employs non-linear manifold learning to capture biological processes active in pre-clinical models and human tumors. Then, TRANSACT builds predictors on cell line response only and transfers these to Patient-Derived Xenografts (PDXs) and human tumors. TRANSACT outperforms four competing approaches, including Deep Learning approaches, for a set of 15 drugs on PDXs, TCGA cohorts and 226 metastatic tumors from the Hartwig Medical Foundation data. For only four drugs Deep Learning outperforms TRANSACT. We further derived an algorithmic approach to interpret TRANSACT and used it to validate the approach by identifying known biomarkers to targeted therapies and we propose novel putative biomarkers of resistance to Paclitaxel and Gemcitabine.

bioinformatics↗

bioRxiv2018

iTOP: Inferring the Topology of Omics Data

MotivationIn biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets.\n\nResultsWe present iTOP, a methodology to infera topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics.\n\nAvailabilityAn implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary Material.\n\nContacta.k.smilde@uva.nl and l.wessels@nki.nl\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

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↗