Pipeline release! nf-core/scnanoseq v1.2.2 - nf-core/scnanoseq v1.2.2 - Thallium Tiger!
Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.2.2
#10xgenomics #longreadsequencing #nanopore #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics
Our new pre‑print is out!
scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians
https://wellcomeopenresearch.org/articles/11-43
@haessar.bsky.social @fionan-a.bsky.social @yiyicheng
Pipeline release! nf-core/scnanoseq v1.2.1 - nf-core/scnanoseq v1.2.1 - Zinc Turtle!
Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.2.1
#10xgenomics #longreadsequencing #nanopore #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics
Multiplets-droplets that capture more than one cell-are a known artefact in droplet-based single-cell RNA sequencing (scRNA-seq), yet their prevalence and impact remain underestimated. In this study, we assess the frequency of multiplets across diverse publicly available datasets and evaluate how well commonly used detection tools are able to identify them. Using cell hashing data to determine a lower bound of the true multiplet rate, we demonstrate that commonly used heuristic estimations systematically underestimate multiplet rates, and that existing tools-despite optimized parameters-detect only a small subset of cell-hashing multiplets. We further refine a Poisson-based model to estimate the true multiplet rate, revealing that actual rates can exceed heuristic predictions by more than twofold. Downstream analyses are significantly affected by multiplets: they are not confined to isolated clusters but are distributed throughout the transcriptional landscape, where they distort clustering and cell type annotation. Using both quantitative and qualitative approaches, we visualize these effects and show that cell-hashing-informed multiplet removal eliminates artefactual clusters and improves annotation clarity, whereas computationally detected multiplets fail to fully remove artefacts in the most common experimental contexts. Our findings confirm that multiplet contamination remains a pervasive and under-addressed issue in scRNA-seq analysis. Since most datasets lack multiplexing, researchers must often rely on heuristics and limited tools, leaving many multiplets unidentified. We advocate for more robust multiplet-detection strategies, including multimodal validation, to ensure more accurate and interpretable scRNA-seq results. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, HO 1582/12
Pipeline release! nf-core/scnanoseq v1.2.0 - nf-core/scnanoseq v1.2.0 - Copper Rhinoceros!
Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.2.0
#10xgenomics #longreadsequencing #nanopore #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics
How do diverse brain cells change over evolution? By comparing single-cell transcriptomic atlases from ecologically-distinct drosophilid species, this work identifies changes in the composition and gene expression patterns of different cell types, revealing higher divergence in glia than neurons.
How does transcriptional patterning regulate salivary gland morphogenesis? In this study, the authors provide a detailed scRNA-seq characterization of transcriptional changes during early morphogenesis of the Drosophila salivary gland placode, revealing that this process is regulated by both the induction, but also exclusion of regulatory factors, such as Tollo.
Our new preprint is now out!
Dynamic transcriptional heterogeneity in pituitary corticotrophs
https://www.biorxiv.org/content/10.1101/2025.04.04.645979v1
We analysed publicly available single-cell RNA sequencing data of pituitary gland tissue and looked at corticotrophs, cells that are central to mediate stress responses.
We identified several transcriptional states in these cells that are related to how they respond to stress. Cells are able to transition between these states and this might be helpful for them to respond to stress coming at unpredictable times.
We also highlight issues related to using scRNAseq to look at functional subpopulations of cells.
#scrnaseq #stress #physiology #cellbiology #bioinformatics #corticotrophs #pituitary
A large body of evidence has shown that corticotrophs, the anterior pituitary cells central to the generation of hormonal stress responses, exhibit heterogeneous functional behavior, suggesting the presence of functional sub-populations of corticotrophs. We investigated whether this was the case at the transcriptomic level by conducting a comprehensive analysis of scRNA-seq datasets from rodent pituitary cells. We envisaged two alternative scenarios, one where robust subtypes of corticotrophs exist, and the other where these subpopulations were only transient states, possibly transitioning into one another. Our findings suggest that corticotrophs transition between multiple transcriptional states rather than existing as rigidly defined subpopulations. We employed marker gene-based comparisons and whole transcriptome label transfer approaches to analyze transcriptional signatures across datasets. Marker-based clustering revealed strikingly low similarity in the identified subpopulations across datasets. This analysis evidenced the presence of transcriptional states with different functional relevance, related to different stages of hormonal signalling. Similarly, the label transfer approach, which considers non-linear interactions across the entire transcriptome showed that transcriptional states could be detected across independent datasets. This classification relied on broader gene expression patterns rather than conventional marker genes, reinforcing the notion of continuous rather than discrete cell states. Furthermore, trajectory analysis by RNA velocity indicated dynamic transitions between transcriptional states, suggesting the presence of transcriptional mechanisms facilitating rapid recruitment of corticotrophs in response to physiological demands. Our findings align with evidence from other endocrine cell types, such as lactotrophs and pancreatic β-cells, where hormone secretion is linked to fluctuating transcriptional activity. The observed transitions in corticotroph states suggest a mechanism allowing flexible hormonal responses to unpredictable and time-varying stressful events. Additionally, this study highlights the challenges associated with scRNA-seq methodologies, including data sparsity, batch effects, and pseudoreplication, underscoring the need for rigorous experimental design and reproducibility in single-cell transcriptomics research. These insights contribute to a broader understanding of pituitary cell plasticity and endocrine adaptation mechanisms. ### Competing Interest Statement The authors have declared no competing interest.
nf-core
Dom Somma
PLOS Biology
Marc Robinson-Rechavi
Daniel Hoffmann 🌼
Nicola Romanò