Antoine H. C. van Kampen

Sequencing-based spatial transcriptomics (ST) preserves spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) provides single-cell resolution but loses spatial context during tissue dissociation. Given these complementary strengths, computational tools combine scRNA-seq and ST data using deconvolution to identify cell types and estimate their proportions at each spatial location using scRNA-seq reference data. However, these methods are sensitive to missing cell types in the reference data, a problem known as cell type mismatch. Using two reference datasets, we performed extensive simulations to systematically evaluate the robustness to cell type mismatch of six deconvolution methods (CARD, cell2location, RCTD, Seurat, SPOTlight, and Stereoscope) tailored for ST data, and two designed for bulk RNA-seq data (MuSiC, SCDC). At baseline, i.e. with no cell types missing from the reference datasets, cell2location showed the strongest performance, while Seurat performed the worst. By simulating different cell type mismatch scenarios, we found that the performance of deconvolution methods decreases proportionally to the number of cell types missing from the reference. Moreover, compared to baseline, the relative decrease in performance is similar across methods and therefore no method is more robust to cell type mismatch than others. Additionally, methods that perform well at baseline tend to assign the proportions of a missing cell type to the transcriptionally most similar cell types present in the reference data. Our results highlight the adverse effects of cell type mismatch on the performance of deconvolution methods for ST data and stress the need for more robust approaches to this issue.

High-throughput sequencing of B-cell and T-cell immune receptor repertoires provides unprecedented insight into adaptive immune responses. The data produced are structured by clonal relationships and somatic mutation signatures, and yield extremely rich information in sequence-derived features, including physicochemical properties and compositional patterns. However, integrated analysis across datasets, conditions, and time points remains challenging. Current analytical tools typically focus only on certain features within individual repertoires, without enabling integrated, multivariable comparisons across datasets, conditions, and time points to address their diversity and variability. Here we present AbSolution, a user-friendly and flexible interactive application for comprehensive exploration of immune repertoires and their sequence-based properties. AbSolution enables multiscale analysis of thousands of sequence-derived features across receptor regions, while accounting for V(D)J usage, clonal composition and experimental groupings. We demonstrate its utility by identifying distinct sequence-based profiles associated with dominant (highly abundant) and non-dominant B-cell clones in peripheral blood BCR repertoires from patients with idiopathic inflammatory myopathies, and with antigen-responsive T-cell populations over time in a longitudinal in vitro antigen-stimulation dataset. Through interactive, interlinked visualizations, statistical feature selection and multi-sample comparisons, AbSolution facilitates integrated feature profiling that supports the interpretation of immune selection processes and enables systematic analysis of complex repertoire datasets.

Reproducibility and transparency in computational analyses are essential in science, although achieving these goals often requires significant knowledge and systematic organization. Graphical interactive applications simplify the conduct of analyses and make them accessible to a broader audience. However, there is currently no consensus on how to and to which extent implement reproducibility in interactive applications. We recently developed AbSolution, a user-friendly and flexible interactive web-based R Shiny application for exploring immune repertoires and their sequence-based features, and we established the ENCORE framework to enhance transparency and reproducibility by guiding researchers in structuring and documenting computational projects. In this work, as a proof-of-concept we integrate AbSolution, ENCORE and specific R packages to address reproducibility challenges. This enables a single-step export of raw, processed, and meta-data, the software environment, the underlying generated code and a HTML report containing results and figures, operating system, hardware and R session details, and researcher notes. Its reproducibility has been independently validated by the CODECHECK initiative. This paper demonstrates how the combination of several approaches can improve and automate reproducibility of interactive applications.

An interactive framework for the exploration and analysis of adaptive immune receptor repertoire sequencing (AIRR-seq) data. It enables large-scale computation and integrated analysis of sequence-derived features, including physicochemical properties, amino acid descriptor sets, sequence motifs, compositional patterns, and somatic hypermutation metrics. The application supports multiscale analysis across sequences, clones, and repertoires, with interactive visualizations and statistical feature selection. 'AbSolution' also facilitates reproducible research by enabling structured export of data, code, parameters, and computational environments. See for more details.

Sequencing-based spatial transcriptomics (ST) approaches preserve spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) techniques provide single-cell resolution but lose spatial context during tissue dissociation. Given these complementary strengths, computational tools have been developed to combine scRNA-seq and ST data. These methods use deconvolution techniques to identify cell types and estimate their proportions at each spatial location in ST data, using scRNA-seq reference data. However, these methods are sensitive to missing cell types in the scRNA-seq reference, a problem known as cell type mismatch. Using two reference datasets, we performed extensive simulations to systematically evaluate the robustness to cell type mismatch of six deconvolution methods (CARD, cell2location, RCTD, Seurat, SPOTlight, Stereoscope) tailored for ST data, and two designed for bulk RNA-seq data (MuSiC, SCDC). At baseline, that is, with no cell types missing from the reference datasets, cell2location showed the strongest performance, while Seurat performed the worst. By simulating different cell type mismatch scenarios, we found that the performance of deconvolution methods decreases proportionally to the number of cell types missing from the reference. Moreover, compared to baseline, for most methods the relative decrease in performance is similar. Additionally, methods that perform well at baseline tend to assign the proportions of a missing cell type to the transcriptionally most similar cell types present in the reference data. Our results highlight the adverse effects of cell type mismatch on the performance of deconvolution methods for ST data and stress the need for more robust approaches to this issue.

Reproducibility of computational research is often challenging despite established guidelines and best practices. Translating these guidelines into practical applications remains difficult. Here, we present ENCORE, an approach to enhance transparency and reproducibility by guiding researchers in how to structure and document a computational project. ENCORE builds on previous efforts in computational reproducibility and integrates all project components into a standardized file system structure. It utilizes pre-defined files as documentation templates, leverages GitHub for software versioning, and includes an HTML-based navigator. ENCORE is designed to be agnostic to the type of computational project, data, programming language, and ICT infrastructure, and does not rely on specific software tools. We also share our group's experience using ENCORE, highlighting that the most significant challenge to the routine adoption of approaches like ours is the lack of incentives to motivate researchers to dedicate sufficient time and effort to ensure reproducibility.

The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.

Outcomes of computational approaches are occasionally difficult to reproduce despite ongoing (open science) initiatives that resulted in guidelines and best practices to improve reproducibility. However, their translation and application in practice still is an obstacle. We propose ENCORE (ENhancing COmputational REproducilbity) to improve reproducibility and transparency through integration of all project components (e.g., data, code, results, concepts, documentation) in a standardized file system structure. The approach makes use of pre-defined (template) files to guide documentation, a GitHub repository for software versioning, a HTML-based navigator, and ENCORE user documentation. ENCORE is agnostic to the type of computational project, data, programming language, and ICT infrastructure and does not depend on any software tool. ENCORE improved reproducibility and transparency of computational projects in our group. Perhaps the most significant challenge to overcome for routine usage of initiatives such as ENCORE is the lack of incentives to sufficiently motivate researchers to spend sufficient time and effort on reproducibility. We expect ENCORE to contribute to the further development of approaches increasing transparency and reproducibility of computational research.

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.

Sequencing of B cell and T cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes and their frequencies. Generally, additional time-consuming or expensive experiments are required to further characterize the identified (dominant) clones by measuring, for example, their affinity or function. Here, we present a multiscale model of the germinal center (GC) to gain general insight in B cell repertoires, to establish the relationship between clonal abundance and affinity, to establish the variability of affinity within a clone, and to establish the extent that plasma cells (PCs) with high B cell receptor (BcR) mRNA content may disturb the identification of dominant clones. Since we simulate B cell repertoires generated from a single GC we also compare the extent that these repertoires deviate from experimental repertoires established from single GCs, blood or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor subclones. Our simulations suggest that low abundant (sub)clones might also be of interest since they may have high affinity for the Ag. We show that the presence of PCs does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. As expected, immune repertoires generated from our single GC model deviate in several aspects from experimental repertoires. At the same time, results from these simulations guide data interpretation and the design of follow-up experiments.