Biology subjects

Trull, A.

Publications and source records attributed to Trull, A..

3 recordsLinked to original sources

bioRxiv2026

nf_xpatial: A Reproducible Framework for Standardized Preprocessing and Clustering of Xenium Data

Recent advances in spatial transcriptomics have enabled the profiling of increasingly larger numbers of genes while retaining single-cell and subcellular resolution in situ. However, standardized bioinformatics workflows for analyzing these datasets have lagged behind, with existing pipelines focusing primarily on image processing and cell segmentation. To address this gap, we present nf_xpatial, a best-practices Nextflow pipeline for the downstream analysis of 10x Genomics Xenium data. The pipeline performs quality control, filtering, log and cell area normalization, multi-sample integration, and both expression-driven and spatially informed clustering across systematic parameter sweeps, allowing users to evaluate and compare clustering resolutions and spatial modeling parameters within a single reproducible run. Overall, nf_xpatial streamlines the processing of Xenium data from platform outputs to integrated single-cell and spatial clustering datasets, providing a standardized starting point from which biologists can fine-tune parameters and proceed to hypothesis-driven spatial analyses.

bioinformatics↗

bioRxiv2026

A conserved population of genetically defined striatal neurons gates opioid reward

A longstanding paradox in striatal circuit architecture is that opioid reward depends on -opioid receptors ({micro}ORs) in nucleus accumbens medium spiny neurons (MSNs), yet {micro}OR function is not explained by the canonical D1/direct and D2/indirect pathway organization. Here, we identify a rare MSN population marked by Chst9 that exhibits exceptionally high expression of the {micro}OR and is conserved across species. Notably, Chst9-MSNs comprise a specialized indirect pathway striatal neuron subtype that is molecularly and spatially distinct from canonical striatal populations. Opioids robustly silence Chst9-MSNs, and selective deletion of Oprm1 from this population abolishes fentanyl-conditioned place preference. These findings establish Chst9-MSNs as a critical substrate for opioid reward and define a new cellular framework for therapies targeting opioid use disorder.

neuroscience↗

bioRxiv2025

scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing

Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement. Though these methods now exist, the limited availability of modular and portable workflows remains a challenge. Here we present, nf-core/scnanoseq, a secondary analysis pipeline for long-read single-cell and single-nuclei RNA that delivers gene and transcript-level quantification. The scnanoseq pipeline is implemented using Nextflow and is built upon the nf-core framework, enabling portability across computational environments, scalability and reproducibility of results across pipeline runs. The nf-core/scnanoseq workflow follows best practices for analyzing single-cell and single-nuclei data, performing barcode detection and correction, genome and transcriptome read alignment, unique molecular identifier deduplication, gene and transcript quantification, and extensive quality control reporting. AVAILABILITY OF DATA AND MATERIALSnf-core/scnanoseq is available at https://github.com/nf-core/scnanoseq under the MIT License and the documentation is available at https://nf-co.re/scnanoseq. The downstream analytical code for validation analysis is available at https://github.com/U-BDS/scnanoseq_analysis and all dataset sources have been disclosed under the methods section for each respective dataset.

bioinformatics↗