bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data.

Bulk sequencing experiments (single- and multi-omics) are essential for exploring wide-ranging biological questions. To facilitate interactive, exploratory tasks, coupled with the sharing of easily accessible information, we present bulkAnalyseR, a package integrating state-of-the-art approaches using an expression matrix as the starting point (pre-processing functions are available as part of the package). Static summary images are replaced with interactive panels illustrating quality-checking, differential expression analysis (with noise detection) and biological interpretation (enrichment analyses, identification of expression patterns, followed by inference and comparison of regulatory interactions). bulkAnalyseR can handle different modalities, facilitating robust integration and comparison of cis-, trans- and customised regulatory networks.

Microfluidic antibody profiling after repeated SARS-CoV-2 vaccination links antibody affinity and concentration to impaired immunity and variant escape in patients on anti-CD20 therapy.

Background: Patients with autoimmune/inflammatory conditions on anti-CD20 therapies, such as rituximab, have suboptimal humoral responses to vaccination and are vulnerable to poorer clinical outcomes following SARS-CoV-2 infection. We aimed to examine how the fundamental parameters of antibody responses, namely, affinity and concentration, shape the quality of humoral immunity after vaccination in these patients. Methods: We performed in-depth antibody characterisation in sera collected 4 to 6 weeks after each of three vaccine doses to wild-type (WT) SARS-CoV-2 in rituximab-treated primary vasculitis patients (n = 14) using Luminex and pseudovirus neutralisation assays, whereas we used a novel microfluidic-based immunoassay to quantify polyclonal antibody affinity and concentration against both WT and Omicron (B.1.1.529) variants. We performed comparative antibody profiling at equivalent timepoints in healthy individuals after three antigenic exposures to WT SARS-CoV-2 (one infection and two vaccinations; n = 15) and in convalescent patients after WT SARS-CoV-2 infection (n = 30). Results: Rituximab-treated patients had lower antibody levels and neutralisation titres against both WT and Omicron SARS-CoV-2 variants compared to healthy individuals. Neutralisation capacity was weaker against Omicron versus WT both in rituximab-treated patients and in healthy individuals. In the rituximab cohort, this was driven by lower antibody affinity against Omicron versus WT [median (range) KD: 21.6 (9.7-38.8) nM vs. 4.6 (2.3-44.8) nM, p = 0.0004]. By contrast, healthy individuals with hybrid immunity produced a broader antibody response, a subset of which recognised Omicron with higher affinity than antibodies in rituximab-treated patients [median (range) KD: 1.05 (0.45-1.84) nM vs. 20.25 (13.2-38.8) nM, p = 0.0002], underpinning the stronger serum neutralisation capacity against Omicron in the former group. Rituximab-treated patients had similar anti-WT antibody levels and neutralisation titres to unvaccinated convalescent individuals, despite two more exposures to SARS-CoV-2 antigen. Temporal profiling of the antibody response showed evidence of affinity maturation in healthy convalescent patients after a single SARS-CoV-2 infection, which was not observed in rituximab-treated patients, despite repeated vaccination. Discussion: Our results enrich previous observations of impaired humoral immune responses to SARS-CoV-2 in rituximab-treated patients and highlight the significance of quantitative assessment of serum antibody affinity and concentration in monitoring anti-viral immunity, viral escape, and the evolution of the humoral response.

noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise.

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.