Chananchida Sang-Aram

Summary: Many single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. Availability: anndataR’s source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241.

Secondary lymphoid organs (SLO) are prototypic sites of antibody production, yet mucosal sites also generate and maintain mucosal antibodies during continued inflammation. Using a murine model of house dust mite–induced airway inflammation, we show that prolonged allergen exposure induces tertiary lymphoid organs (TLO) within para-bronchial adventitial cuffs. Within these regions, IL-1 signaling instructs fibroblasts to form niches that recruit, retain, and sustain lung antibody-secreting cells (ASCs) through chemokine induction. These mucosal ASCs share immunoglobulin repertoires with TLO-derived germinal center B cells. Thus, continued allergen exposure reshapes the lung microenvironment by converting para-bronchial fibroblasts into IL-1–dependent supportive niches for non-IgE antibody production, revealing a fibroblast-mediated mechanism for local immune regulation in chronic inflammation. One sentence summary: Continued allergen exposure drives lung TLO to generate ASCs that home to IL1-instructed para-bronchial cuffs.

Summary: Many single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages. Availability: anndataR’s source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241. Contact: robrecht.cannoodt@gmail.com

Ligand–receptor interactions constitute a fundamental mechanism of cell–cell communication and signaling. NicheNet is a well-established computational tool that infers ligand–receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a ‘sender-agnostic’ approach that considers ligands from the entire microenvironment and a ‘sender-focused’ approach that considers ligands only from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. We include further developments made in NicheNet v2, in which we have updated the data sources and implemented a downstream procedure for prioritizing cell type–specific ligand–receptor pairs. Although a standard NicheNet analysis takes <10 min to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell-differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency.

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Spatial transcriptomics (ST) is an emerging field that aims to profile the transcriptome of a cell while keeping its spatial context. Although the resolution of non-targeted ST technologies has been rapidly improving in recent years, most commercial methods do not yet operate at single-cell resolution. To tackle this issue, computational methods such as deconvolution can be used to infer cell type proportions in each spot by learning cell type-specific expression profiles from reference single-cell RNA-sequencing (scRNA-seq) data. Here, we benchmarked the performance of 11 deconvolution methods using 63 silver standards, three gold standards, and two case studies on liver and melanoma tissues. The silver standards were generated using our novel simulation engine synthspot, where we used seven scRNA-seq datasets to create synthetic spots that followed one of nine different biological tissue patterns. The gold standards were generated using imaging-based ST technologies at single-cell resolution. We evaluated method performance based on the root-mean-squared error, area under the precision-recall curve, and Jensen-Shannon divergence. Our evaluation revealed that method performance significantly decreases in datasets with highly abundant or rare cell types. Moreover, we evaluated the stability of each method when using different reference datasets and found that having sufficient number of genes for each cell type is crucial for good performance. We conclude that while cell2location and RCTD are the top-performing methods, a simple off-the-shelf deconvolution method surprisingly outperforms almost half of the dedicated spatial deconvolution methods. Our freely available Nextflow pipeline allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

Dysregulated cell-cell communication is a hallmark of many disease phenotypes. Due to recent advances in single-cell transcriptomics and computational approaches, it is now possible to study intercellular communication on a genome- and tissue-wide scale. However, most current cell-cell communication inference tools have limitations when analyzing data from multiple samples and conditions. Their main limitation is that they do not address inter-sample heterogeneity adequately, which could lead to false inference. This issue is crucial for analyzing human cohort scRNA-seq datasets, complicating the comparison between healthy and diseased subjects. Therefore, we developed MultiNicheNet ( https://github.com/saeyslab/multinichenetr ), a novel framework to better analyze cell-cell communication from multi-sample multi-condition single-cell transcriptomics data. The main goals of MultiNicheNet are inferring the differentially expressed and active ligand-receptor pairs between conditions of interest and predicting the putative downstream target genes of these pairs. To achieve this goal, MultiNicheNet applies the principles of state-of-the-art differential expression algorithms for multi-sample scRNA-seq data. As a result, users can analyze differential cell-cell communication while adequately addressing inter-sample heterogeneity, handling complex multifactorial experimental designs, and correcting for batch effects and covariates. Moreover, MultiNicheNet uses NicheNet-v2, our new and substantially improved version of NicheNet’s ligand-receptor network and ligand-target prior knowledge model. We applied MultiNicheNet to patient cohort data of several diseases (breast cancer, squamous cell carcinoma, multisystem inflammatory syndrome in children, and lung fibrosis). For these diseases, MultiNicheNet uncovered known and novel aberrant cell-cell signaling processes. We also demonstrated MultiNicheNet’s potential to perform non-trivial analysis tasks, such as studying between- and within-group differences in cell-cell communication dynamics in response to therapy. As a final example, we used MulitNicheNet to elucidate dysregulated intercellular signaling in idiopathic pulmonary fibrosis while correcting batch effects in integrated atlas data. Given the anticipated increase in multi-sample scRNA-seq datasets due to technological advancements and extensive atlas-building integration efforts, we expect that MultiNicheNet will be a valuable tool to uncover differences in cell-cell communication between healthy and diseased states.

Spatial transcriptomics (ST) is an emerging field that aims to profile the transcriptome of a cell while keeping its spatial context. Although the resolution of non-targeted ST technologies has been rapidly improving in recent years, most commercial methods do not yet operate at single-cell resolution. To tackle this issue, computational methods such as deconvolution can be used to infer cell type proportions in each spot by learning cell type-specific expression profiles from reference single-cell RNA-sequencing (scRNA-seq) data. Here, we benchmarked the performance of 11 deconvolution methods using 63 silver standards, three gold standards, and two case studies on liver and melanoma tissues. The silver standards were generated using our novel simulation engine synthspot , where we used seven scRNA-seq datasets to create synthetic spots that followed one of nine different biological tissue patterns. The gold standards were generated using imaging-based ST technologies at single-cell resolution. We evaluated method performance based on the root-mean-squared error, area under the precision-recall curve, and Jensen-Shannon divergence. Our evaluation revealed that method performance significantly decreases in datasets with highly abundant or rare cell types. Moreover, we evaluated the stability of each method when using different reference datasets and found that having sufficient number of genes for each cell type is crucial for good performance. We conclude that while cell2location and RCTD are the top-performing methods, a simple off-the-shelf deconvolution method surprisingly outperforms almost half of the dedicated spatial deconvolution methods. Our freely available Nextflow pipeline allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset ( https://github.com/saeyslab/spotless-benchmark ).