Biology subjects

Robinson, M. D.

Publications and source records attributed to Robinson, M. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Addressing technical variations in ATAC-seq data and improving motif accessibility analyses

Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages to facilitate bulk and single-cell epigenomic data analysis, with a special focus on motif accessibility analysis. The weightedMotifAccess package uses weight models to enable motif accessibility analysis, including transcription factor footprint information. The betterChromVAR package provides a novel, analytical re-implementation of the popular chromVAR method that offers substantial speed improvements, eliminates stochasticity, and offers additional features. Based on this, we also propose a method, CVnorm, that outperforms alternatives in normalizing technical bias in peak count data. The computational efficiency of these tools further enables a new framework for systematically investigating synergistic and antagonistic interactions between transcription factor motifs. Finally, the epiwraps package streamlines the visualization, normalization, and summarization of epigenomic data.

bioinformatics↗

bioRxiv2026

Co-option of CENP-A for activity-induced neuronal plasticity

Neuronal activation drives activity-dependent gene expression that underlies experience-associated synaptic modifications, learning and memory. Here we show that the histone variant CENP-A, best known for specifying centromere identity, is dynamically regulated by synaptic activity at both the RNA and protein levels in postmitotic neurons. Neuronal activation increases a non-centromeric nuclear pool of CENP-A while leaving centromeric CENP-A levels unchanged. Downregulation of CENP-A selectively reduces the activity-associated non-centromeric pool, impairs activity-dependent induction of immediate-early genes such as FOS and ARC, and disrupts hippocampus-dependent learning and memory. Furthermore, we find that activity-dependent neuronal responses in human embryonic stem cell-derived forebrain organoids similarly require CENP-A. Our results reveal a mitosis-independent, conserved role of CENP-A for driving plasticity in mammalian neurons.

neuroscience↗

bioRxiv2026

Large-scale prediction of transcription factor binding across human cell types informs regulatory genomics and reveals promiscuous occupancy associated with chromatin contacts

Our understanding of the mechanisms regulating gene expression has been hampered by our limited knowledge of which transcription factors (TFs) bind where in the genome, which is highly cell type-specific. While genome-wide TF binding can be experimentally assayed for individual TFs in individual cell types, profiling the full combinations of over 1600 TFs in hundreds of cell types is beyond practical reach. In this work, we developed a streamlined platform, TFBlearner, to train TF-specific models and predict bindings based on ATAC-seq data. We focused on biologically-motivated feature engineering and harnessed TF cooperativity and binding similarity across cell types to achieve state of the art binding predictions in unseen cell types in a scalable fashion. This enabled us to generate a compendium of binding predictions for 1108 Chromatin-associated proteins, of which 960 TFs, across 43 human cell types including widely-used cell lines and 36 physiological cell types representing all major human cell lineages. We show how the models additionally provide biological insights on the TFs, and show how the binding predictions can be used in downstream tasks such as TF activity inference. Our study additionally led to the observation of high promiscuity in TF occupancy. To investigate aspecific occupancy, we characterized crowded or high-occupancy (HOT) regions across cell types, providing evidence of their functionality, and reporting important cell type-specificity. Finally, we show that, across cell types, crowded regions engage in more 3D contacts, and that most TF occupancy at crowded promoters can be explained as tethered bindings from distal regulatory elements.

genomics↗

bioRxiv2026

Building computational benchmarks: an Omnibenchmark reimplementation of a single-cell preprocessing pipeline evaluation

In the past few years, we have seen a veritable surge in single-cell (e.g., RNA sequencing) techniques and datasets, enabling increasingly detailed characterization of cellular heterogeneity across tissues and conditions. This surge in single-cell techniques has been complemented by a large number of analysis frameworks and pipelines, and a large parameter space and researcher degrees of freedom to use them. Many neutral benchmarks have been presented for various computational tasks, but most make design decisions that render them incompatible with each other, e.g., different datasets and metrics, or parameter sets used. In this work, we showcase a recently developed framework, Omnibenchmark, to build reproducible, extensible and standardized method comparisons. This not only facilitates the broad investigation of pipelines used in single-cell data analysis, but also highlights how the process of building benchmarks can be streamlined and unified. We do this as an initial proof-of-principle for an arms-length benchmark that evaluates five single-cell RNA sequencing pipelines (filtering to normalization to dimensionality reduction to clustering) on three datasets. This standardization enables benchmarks to be easily extended in several directions, including broader parameter sweeps, comparisons across software versions and architectures, isolation of pipeline steps, and integration of additional pipelines, datasets, and metrics.

bioinformatics↗

bioRxiv2026

Systematic benchmarking of small variant calling pipelines for long-read RNA sequencing data

Background: Long-read RNA sequencing (lrRNA-seq) enables transcript-resolved variant detection, but systematic and neutral evaluations of small variants calling pipelines remain limited. The performance of existing tools across sequencing technologies, alignment strategy, variant caller choice, genomic contexts and downstream haplotype phasing is not fully understood. Results: Here, we systematically benchmark four lrRNA-seq variant callers (Clair3-RNA, DeepVariant, longcallR, and longcallR-nn), along with a widely used short-read RNA-seq variant caller (GATK HaplotypeCaller) as a baseline, using Genome in a Bottle (GIAB) datasets comprising three cell lines sequenced with four Oxford Nanopore Technologies (ONT) and two PacBio library preparation protocols. We further evaluate the impact of upstream alignment strategies, including aligner choice and alignment transformation, on variant-calling performance. Accuracy is assessed across sequencing depths and genomic contexts. Additionally, we compare haplotype phasing tools (WhatsHap, LongPhase, HapCUT2, HiPhase and longcallR) using variant calls generated by different callers to identify optimal pipeline combinations. Finally, we extend our evaluation of variant-calling performance to more recent LongBench datasets. Conclusions: Our benchmark shows that sequencing quality is the primary determinant of lrRNA-seq variant-calling performance, followed by variant caller and alignment strategy, with additional effects from genomic context. In GIAB datasets, all lrRNA-seq-specific callers performed reasonably well, with Clair3-RNA (across both ONT and PacBio) and DeepVariant (for PacBio) ranking among the top-performing methods. In more recent LongBench datasets of cancer cell lines, DeepVariant and longcallR showed higher sensitivity, whereas Clair3-RNA and longcallR-nn were more conservative, yielding fewer variant calls. For downstream haplotype phasing, we recommend WhatsHap or HapCUT2 for most libraries, owing to their high phasing coverage and accuracy, respectively, while longcallR performs better on ONT dRNA004 datasets across both metrics.

bioinformatics↗

bioRxiv2026

Differential co-localisation analysis of multi-sample and multi-condition experiments with spatialFDA

Advances in spatial omics data generation have led to an explosion in new datasets that record the spatial location of transcripts and proteins. However, challenges remain in the analysis of spatial omics data. One important analysis is differential cellular co-localisation (CCoL): the quantification of the clustering, or spacing, of one or more cell types across multiple conditions. Our framework spatialFDA combines methodology from spatial statistics with functional data analysis to accurately quantify and test for differences between conditions in CCoL across spatial scales. Using two simulation studies, we show that spatialFDA performs well in controlled settings. Furthermore, spatialFDA recovers known biological processes in type-1 diabetes and adds insights about the CCoL strength in space. spatialFDA is readily available as an open-source Bioconductor R package.

bioinformatics↗

bioRxiv2026

Synovial fibroblast niche shapes the efficacy - safety dynamics of JAK inhibition in rheumatoid arthritis

Synovial fibroblasts (SF) drive joint pathology in rheumatoid arthritis (RA). Difficult-to-treat RA frequently exhibits a fibroblast-rich synovial pathotype, enriched in DKK3{square} and CD34{square} SF, highlighting a critical therapeutic gap. Through multicohort transcriptomic analysis of synovial tissues and mechanistic in vitro studies, we identified SF as principal targets of Janus kinase (JAK) inhibition in RA. We demonstrated that JAK inhibitors (JAKi) can target multiple core aspects of fibroblast pathobiology - therapeutic refractoriness, cartilage destruction, and inflammation - offering a mechanistic rationale for JAKi superiority in difficult-to-treat RA. JAK1 was the dominantly expressed JAK across synovial pathotypes and SF subsets, including DKK3{square} and CD34{square} populations. A STAT1-interferon type I gene program was enriched in matrix-destructive PRG4{square} SF, consistent with JAKi efficacy in erosive RA. In contrast, canonical IL-6 signaling predominated in IL6-expressing inflammatory CXCL12high and HLA-DR+ SF, and was reproduced in cytokine-stimulated cultured SF, underscoring the autocrine nature of synovial IL-6 signaling. These data inferred a heightened JAKi sensitivity of PRG4{square}, CXCL12high, and HLA-DR+ SF subsets, informing precision therapeutic strategies. We uncovered a strong synergy between TNF and IL-6 trans-signaling, profoundly amplifying fibroblast inflammation. In high and synergistic cytokine milieu, STAT1/3 phosphorylation and IL-6 secretion persisted in SF despite tofacitinib treatment, revealing tofacitinibs functional ceiling. This could explain reduced tofacitinib efficacy and adherence in patients with high baseline arthritis activity. Finally, inflamed SF partially uncoupled STAT3 activation from sustained JAK1 phosphorylation, limiting inflammatory output. Similar uncoupling in tofacitinib-treated SF, likely drove rapid STAT1/3 reactivation following tofacitinib washout. These data aligned with JAKi withdrawal complications and clinical recommendations for gradual JAKi tapering. Collectively, our study identifies SF as key cellular targets of JAK inhibition and delineates cytokine- and drug-driven mechanisms that may constrain the efficacy and safety profiles of JAKi in RA.

molecular biology↗

bioRxiv2026

scprocess: a pipeline for processing, integrating and visualising atlas-scale single cell data

MotivationThe transition toward "atlas-scale" single cell research has resulted in datasets comprising millions of cells across hundreds of samples, creating significant challenges for data management, computational efficiency, and reproducibility. While numerous methods are available for individual steps in single cell data processing, the highly complex nature of the analysis makes it challenging to maintain a clear record of every tool and parameter used. This makes final results difficult to reproduce, highlighting the need for a unified workflow that integrates multiple steps into a cohesive framework. Resultsscprocess is a Snakemake pipeline designed to streamline and automate the complex steps involved in processing single cell RNA sequencing data. Specifically optimized for data generated using the 10x Genomics technology, it provides a comprehensive solution that transforms raw sequencing files into standardized outputs suitable for a variety of downstream tasks. The pipeline is built to support the analysis of datasets comprising multiple (e.g. 100+) samples via a simple CLI, allowing researchers to efficiently explore their datasets while ensuring reproducibility and scalability in their workflows. Availability and implementationscprocess can be installed via GitHub (https://github.com/marusakod/scprocess) under the MIT license. Documentation, including setup instructions and tutorials on example datasets is available at https://marusakod.github.io/scprocess/.

bioinformatics↗

bioRxiv2026

Resolving sialylated N Glycans and immune cell landscapes using a unified same-section IMC-MSI workflow

Integrating antibody-based imaging with mass spectrometry imaging (MSI) on the same formalin-fixed paraffin-embedded (FFPE) tissue section offers powerful opportunities for multimodal spatial analysis but remains analytically challenging due to cross-platform chemical and physical interference. In particular, chemically aggressive on-tissue derivatization strategies required for isomer-resolved glycan MSI may compromise downstream antibody detection. Here, we systematically evaluate the analytical compatibility and acquisition order of imaging mass cytometry (IMC) and sialic-acid-linkage-resolving N-glycan MALDI-MSI using an Amidation-Activation-X-Linkage (AAXL) derivatization strategy on the same FFPE tissue section. Two same-section workflows were compared: AAXL-MALDI MSI followed by IMC (MALDI-first) and IMC followed by AAXL-MALDI MSI (IMC-first). We find that AAXL-first processing results in severe and widespread loss of IMC anti-body signal across epithelial, immune, and nuclear markers, rendering subsequent antibody-based analysis unreliable. In contrast, IMC-first acquisition preserves quantitative antibody performance while maintaining spatial glycan distributions, relative abundance structure, and isomer-specific signal integrity in downstream AAXL-MALDI MSI. Using high-precision co-registration, we further demonstrate that IMC-first sequencing enables analytically robust integration of IMC and MSI data at both domain and pixel levels. These results establish IMC-first acquisition as the preferred same-section strategy for workflows combining antibody imaging with chemically intensive, isomer-resolved glycan MSI and provide generalizable guidance for the design of multimodal spatial mass spectrometry experiments.

immunology↗

bioRxiv2025

Spatially-smoothed quantification improves cell typing in imaging mass cytometry datasets

Accurate cell type annotation in imaging mass cytometry (IMC) and related technologies critically depends on preprocessing steps such as normalization, segmentation, and marker aggregation. More distinct separation between negative and positive signals enables more precise cell boundary inference and more robust marker assignment to single cells. However, inherent limitations in spatial resolution and uncertain cell boundaries can lead to spillover, where signal leaks from one cell to a neighboring cell, distorting marker intensities and leading to incorrect cell type annotation. To address these challenges, we first systematically investigated the impact of spatial resolution and segmentation variability on per-cell marker aggregation using simulated IMC datasets, establishing upper limits for reliable marker separation in cell type annotation. We further analyzed technical biases in large scale studies, demonstrating that appropriate normalization approaches can significantly reduce batch effects without compromising biological variability. Finally, we benchmarked multiple spillover correction strategies across both (semi-)simulated and real IMC datasets. Our results revealed two simple methods: spatial smoothing of intensity images followed by mean marker aggregation, or resampling of cell masks followed by mean marker aggregation and median calculation, both of which improved annotation performance. Other methods often performed worse than the baseline of simple mean aggregation. Together, these findings underscore the central importance of spatial resolution, normalization, and marker aggregation in IMC data preprocessing for accurate single-cell annotation.

bioinformatics↗

bioRxiv2025

Sample-specific haplotype-resolved protein isoform characterization via long-read RNA-seq-based proteogenomics

Protein isoform inference from bottom-up mass spectrometry (MS) relies on database search strategies that assume the reference protein database accurately reflects the full repertoire of genetic and transcriptomic states present in the sample being analyzed. Long-read RNA sequencing (lrRNA-seq) now enables simultaneous recovery of complete transcript (splice) structures and the genetic variants present on each molecule, offering a direct route to allele-specific isoforms. Yet, this capability has not been fully leveraged to improve MS-based proteogenomics workflows. Here, we develop an end-to-end workflow for constructing and searching haplotype-resolved, sample-specific proteomes using matched lrRNA-seq and MS data. We benchmark phasing algorithms on PacBio lrRNA-seq from Genome-in-a-Bottle samples and identify methods that achieve high phasing accuracy and completeness. Our Snakemake pipeline leverages existing methods to perform variant calling, read-based phasing, transcript discovery, haplotype-resolved proteome construction, MS search, and downstream annotation. To demonstrate its utility, we apply the workflow to an induced pluripotent stem cell line (WTC11) and to an osteoblast differentiation time course. We show that sample-specific haplotype-resolved databases enable the detection of variant and splice peptides, allele-specific protein isoforms, and linked variants not detectable with reference proteomes. Together, our results demonstrate that lrRNA-seq-based phasing is feasible and effective for proteogenomics and provide a practical framework for allele-resolved proteome characterization in dynamic or disease-relevant settings.

bioinformatics↗

bioRxiv2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

bioRxiv2025

Hybrid untargeted and targeted RNA sequencing facilitates genotype-phenotype associations at single-cell resolution

Long-read scRNA-seq enables simultaneous, unbiased identification of transcriptomic variants and gene expression but is often limited by low read coverage, restricting genotype-phenotype analyses at single-cell resolution. We systematically evaluated short-read whole-transcriptome amplification (SR-WTA) via Illumina, long-read whole-transcriptome amplification (LR-WTA), and long-read targeted sequencing (LR-Twist) via PacBio for cell typing and genotyping performance. Based on these comparisons, we propose a hybrid strategy and accompanying Snakemake pipeline that integrates SR-WTA with LR-Twist to leverage the strengths of both approaches. SR-WTA provides broad transcriptome coverage, while LR-Twist enriches a 50-gene panel for deeper variant detection. This hybrid approach improves power to link mutational profiles with transcriptional programs at single-cell resolution.

bioinformatics↗

bioRxiv2025

Analysis of anatomical multi-cellular structures from spatial omics data using sosta

Spatial omics technologies enable high-resolution, large-scale quantification of molecular features while preserving the spatial context within tissues. Existing analysis methods largely focus on spatial arrangements of single cells, whereas biological function often emerges from multicellular arrangements. Here, we introduce structure-based analysis of spatial omics data, which focuses on the direct analysis of multicellular, anatomical structures. We illustrate this type of analysis using two publicly available datasets and provide sosta, an open-source Bioconductor package for broad community use.

bioinformatics↗

bioRxiv2025

MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments

Spatial omics is transforming our ability to interrogate local tissue microenvironments by enabling spatially resolved measurement of biomolecules such as transcripts, proteins, and metabolites. However, capturing the full biological complexity of tissues often requires combining multiple modalities, which introduces both experimental as well as computational challenges. To address computational difficulties due to differences in resolution, noise levels, and available channels, we present MIMIC: a reproducible, semi-automated workflow that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry for joint downstream analysis. MIMIC incorporates rigorous quality control, including registration error assessment, and supports pixel-level modeling to delineate analyte-cell type associations. We demonstrate the power of our approach with a proof-of-concept study on artificial tissue and apply it to human liver tissue affected by metabolic dysfunction-associated steatotic liver disease. Despite integration challenges, MIMIC provides a robust framework that successfully recovers known molecular associations and reveals novel spatial relationships across modalities.

bioinformatics↗

bioRxiv2025

DESpace2: detection of differential spatial patterns in spatial omics data

Spatially resolved transcriptomics (SRT) enables the investigation of mRNA expression in a spatial context. While several SRT analysis frameworks have been developed, the vast majority of them focus on analyzing individual samples, and do not allow for comparisons of spatial gene expression patterns across experimental conditions, such as healthy vs. diseased states. Here, we present an approach to identify so-called differential spatial patterns (DSP), i.e., genes that exhibit changes in spatial expression between groups of samples across conditions. Our framework processes diverse SRT data types and detects DSP by performing differential gene expression testing across conditions. Notably, this comparison is not currently available in any other spatial omics method. In addition to detecting DSP across conditions, our framework includes two key features. First, it can identify tissue regions where expression changes across conditions. Second, it can detect spatial gene expression pattern changes across more than two experimental conditions, with flexible models. With ad hoc simulations, we demonstrate that our approach has good true positive rates and well-calibrated false discovery rates. Applied to experimental data, our method identifies biologically relevant DSP genes while maintaining computational efficiency. Our framework has been implemented within the DESpace Bioconductor R package (from version 2.0.0).

bioinformatics↗

bioRxiv2025

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

genomics↗

bioRxiv2025

Quantitative isoform profiling using deep coverage long-read RNA sequencing across early endothelial differentiation

PacBio long-read RNA sequencing resolves transcripts with greater clarity than short-read technologies, yet its quantitative performance remains under-evaluated at scale. Here, we benchmark the high-throughput PacBio Kinnex platform against Illumina short-read RNA-seq using matched, deeply sequenced datasets across a time course of endothelial cell differentiation. Compared to Illumina, Kin-nex achieved comparable gene-level quantification and more accurate transcript discovery and transcript quantification. While Illumina detected more transcripts overall, many reflected potentially unstable or ambiguous estimates in complex genes. Kinnex largely avoids these issues, producing more reliable differential transcript expression (DTE) calls, despite a mild bias against short transcripts (shorter than 1.25 kb). When correcting Illumina for inferential variability, Kinnex and Illumina quantifications were highly concordant, demonstrating equivalent performance. We also benchmarked long-read tools, nominating Oarfish as the most efficient for our Kinnex data. Together, our results establish Kinnex as a reliable platform for full-length transcript quantification.

bioinformatics↗