Biology subjects

Gerber, R.

Publications and source records attributed to Gerber, R..

8 recordsLinked to original sources

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

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

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↗

bioRxiv2022

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

bioinformatics↗

bioRxiv2022

A comprehensive single-cell atlas of freshly dissociated human synovium in inflammatory arthritis with an optimized dissociation protocol for prospective fresh synovial biopsy collection

Single-cell RNA-sequencing is advancing our understanding of synovial pathobiology in inflammatory arthritis. Here, we optimized the protocol for the dissociation of fresh synovial biopsies and created a reference single-cell map of fresh human synovium in inflammatory arthritis. We utilized the published method for dissociating cryopreserved synovium and optimized it for dissociating small fresh synovial biopsies. The optimized protocol enabled the isolation of a good yield of consistently highly viable cells, minimizing the dropout rate of prospectively collected biopsies. Our reference synovium map comprised over 100000 unsorted single-cell profiles from 25 synovial tissues of patients with inflammatory arthritis. Synovial cells formed 11 lymphoid, 15 myeloid and 16 stromal cell clusters, including IFITM2+ synovial neutrophils. Using this reference map, we successfully annotated published synovial scRNA-seq datasets. Our dataset uncovered endothelial cell diversity and identified SOD2highSAA1+SAA2+ and SERPINE1+COL5A3+ fibroblast clusters, expressing genes linked to cartilage breakdown (SDC4) and extracellular matrix remodelling (LOXL2, TGFBI, TGFB1), respectively. We broadened the characterization of tissue resident FOLR2+COLEC12high and LYVE1+SLC40A1+ macrophages, inferring their extracellular matrix sensing and iron recycling activities. Our research brings an efficient synovium dissociation protocol and a reference annotation resource of fresh human synovium, while expanding the knowledge about synovial cell diversity in inflammatory arthritis.

genomics↗

bioRxiv2022

Heat and humidity sensors that alert mosquitoes to nearby human hosts

Mosquito-borne diseases sicken >500,000,000 people annually, killing >500,0001. Mosquito host-seeking is guided by multiple host-associated cues, which combine to drive blood feeding in a manner that remains poorly understood2,3. While heat is a powerful mosquito attractant, recent studies indicate that disruption of heat seeking has little impact on host detection by the malaria vector Anopheles gambiae4, suggesting other cues act alongside heat in the complex sensory environment of a human host. Here we show mosquitoes require Ir93a (an Ionotropic Receptor5) to maintain attraction to a human host and feed on warmed blood. Using Ir93a, we uncover the previously uncharacterized mosquito hygrosensory system, and show Ir93a is required for humidity detection by humidity sensors (hygrosensors) as well as temperature detection by thermosensors, and for attraction to each stimulus. These findings indicate that hygrosensation and thermosensation function in parallel, driving host proximity detection in response to the overlapping heat and humidity gradients humans produce6,7. These host cue sensors appear to have arisen by co-opting existing sensors of physical cues rather than de novo, as Ir93a-dependent thermo- and hygro-sensors support physiological homeostasis in non-blood-feeding insects8-11. While Ir93a is conserved among arthropods, reliance on heat and humidity evolved independently in multiple blood-feeding lineages, suggesting multiple, vector-specific implementations of this common host-seeking strategy.

neuroscience↗

bioRxiv2020

censcyt: censored covariates in differential abundance analysis in cytometry

Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable. To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show high error control and decent sensitivity for a subset of the methods. The tested methods are implemented in the R package censcyt as an extension of diffcyt and are available at https://github.com/retogerber/censcyt. Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.

bioinformatics↗