Biology subjects

Vallejos, C. A.

Publications and source records attributed to Vallejos, C. A..

7 recordsLinked to original sources

bioRxiv2026

Benchmarking computational tools for locus-specific analysis of transposable elements in single-cell RNA-seq datasets

BackgroundTransposable elements (TEs) are increasingly recognized as regulators of gene expression and cellular identity in development and disease. Single-cell RNA-sequencing (scRNA-seq) enables the analysis of their transcription at cellular resolution, but the repetitive nature of TEs and their frequent overlap with genes create substantial mapping ambiguity. Although several tools quantify TE expression, few support locus-specific analysis, and their performance in single-cell data has not been systematically evaluated. ResultsWe present a comprehensive benchmarking framework for locus-level TE quantification in short-read scRNA-seq, combining real datasets with simulations that provide read-level ground truth. TE-derived reads constitute a considerable fraction of the transcriptome and capture meaningful biological structure. Our simulations reveal that older, sequence-diverged insertions can be quantified with relatively high accuracy, whereas young TEs remain intrinsically difficult to resolve due to unreliable assignment of multi-mapping reads. We observe pronounced family-specific biases and identify gene-TE disambiguation as a major unresolved challenge. Among evaluated methods, SoloTE (unique-mapper mode) and Stellarscope (with an expectation-maximization-based reallocation of multi-mappers) showed comparable performance, while including multi-mappers generally increased false positives without substantially improving locus-level accuracy. ConclusionsOur benchmark delineates the fundamental limits imposed by short-read scRNA-seq on locus-specific TE quantification, providing practical guidance for prospective users. Suggested best practices include focusing locus-level analyses on older insertions, applying unique-mapper strategies to improve precision, aggregating counts at the subfamily level for young TEs, and explicitly checking for gene-TE overlaps. Our workflow is fully reproducible and extensible, providing a foundation for evaluating emerging methods aimed at resolving TE transcription at single-locus resolution.

bioinformatics↗

bioRxiv2025

A Human Single-cell Atlas Identifies OLR1+ Scar-associated Macrophages as a Therapeutic Target for Chronic Liver Disease

Chronic liver disease (CLD) is a major global healthcare problem. Irrespective of cause, chronic damage to the liver results in fibrosis, which is associated with adverse clinical outcomes. Immune cells, in particular monocyte-derived macrophages (MDMs), are key regulators of fibrosis and represent an attractive therapeutic target for CLD. However, it has remained unclear which specific subpopulation of MDMs drives pro-inflammatory and pro-fibrotic functions in human CLD and how they might be selectively modulated. Here, we generate an annotated human liver single cell atlas from 42 healthy and 35 CLD patients, identifying 125 transcriptionally distinct cellular states. Leveraging large patient and cell numbers, our atlas resolves rare liver cell states and distinguishes two types of disease-expanded scar-associated macrophages (SAMac), including a specific subpopulation with a pro-inflammatory pro-fibrotic phenotype. The scavenger receptor OLR1 was enriched in pro-inflammatory SAMacs and high hepatic OLR1 expression was associated with increased mortality in CLD patients. A corresponding monocyte-derived OLR1+ SAMac subpopulation expanded in a mouse model of CLD and exhibited a pro-inflammatory phenotype based on single-cell RNA-seq, single-cell ATAC-seq, and flow cytometry analyses. Primary human OLR1+ MDMs promoted fibrogenic signalling in multilineage liver spheroid cultures, whilst specific targeting of OLR1 reduced IL-1{beta} production by human macrophages and attenuated myofibroblast activation. Overall, our annotated human liver single-cell atlas provides a valuable reference to study disease-associated cell states in CLD. We utilise this resource to identify a distinct pro-inflammatory subpopulation of SAMacs and highlight OLR1 as a potential therapeutic target to specifically modulate SAMac function and attenuate liver fibrosis.

immunology↗

bioRxiv2023

Inter individual variability in neuronal expression of heat shock protein genes predicts stress survival in Caenorhabditis elegans.

Despite being isogenic and grown under controlled conditions, C. elegans populations exhibit widespread inter-individual variability in many traits, making it an ideal model organism to investigate non-genetic influences on phenotypic diversity. Our particular interest is to study the consequences of inter-individual variability in genes encoding heat shock proteins, which are expressed at low levels under non-stimulated conditions. To robustly quantify inter-individual gene expression, we developed a novel pipeline that combines a highly efficient cDNA extraction method with a high-throughput qPCR nanofluidics technology with a bespoke computational analysis. We validated our approach by benchmarking against in vivo reporters. We also screened among hundreds of stress inducible genes, and identified a regulon formed by transcripts belonging to the inducible heat shock protein family. We demonstrate, using a bipartite in vivo fluorescent reporter, that the inter-individual variability in the stress regulon stems mostly from anterior neurons. Our studies demonstrate for the first time that, under physiological and unstimulated conditions, the variable expression of neural stress responses has cross-tissue consequences for fitness at the individual worm level, suggesting an adaptive role under variable environmental conditions.

molecular biology↗

bioRxiv2023

Multimodal decoding of human liver regeneration

The liver has a unique ability to regenerate1,2, however in the setting of acute liver failure (ALF) this regenerative capacity is often overwhelmed and emergency liver transplantation is the only curative option3-5. To advance our understanding of human liver regeneration and to inform design of pro-regenerative therapies, we use paired single-nuclei RNA sequencing (snRNA-seq) combined with spatial profiling of healthy and ALF explant human livers to generate the first single-cell, pan-lineage atlas of human liver regeneration. We uncover a novel ANXA2+ migratory hepatocyte subpopulation which emerges during human liver regeneration, and a corollary migratory hepatocyte subpopulation in a mouse model of acetaminophen (APAP)-induced liver regeneration. Importantly, interrogation of necrotic wound closure and hepatocyte proliferation across multiple timepoints following APAP-induced liver injury in mice demonstrates that wound closure precedes hepatocyte proliferation. 4-D intravital imaging of APAP-induced mouse liver injury identifies motile hepatocytes at the edge of the necrotic area, enabling collective migration of the hepatocyte sheet to effect wound closure. Depletion of hepatocyte ANXA2 expression reduces HGF-induced human and mouse hepatocyte migration in vitro, and abrogates necrotic wound closure following APAP-induced mouse liver injury. Taken together, our work dissects unanticipated aspects of liver regeneration, demonstrating an uncoupling of wound closure and hepatocyte proliferation and uncovering a novel migratory hepatocyte subpopulation which mediates wound closure following liver injury. Therapies designed to promote rapid reconstitution of normal hepatic microarchitecture and reparation of the gut-liver barrier may open up new areas of therapeutic discovery in regenerative medicine.

cell biology↗

bioRxiv2022

Marked regional glial heterogeneity in the human white matter of the central nervous system

The myelinated white matter tracts of the central nervous system (CNS) are essential for fast transmission of electrical impulses and are commonly affected in neurodegenerative diseases. However, these often uniquely human diseases differentially affect white matter regions, at various ages and between males and females, and we hypothesised that this is secondary to physiological variation in white matter glia with region, age and sex. Using single nucleus RNA sequencing of healthy human post-mortem samples, we find marked glial heterogeneity with tissue region (primary motor cortex, cerebellum, cervical spinal cord), with tissue-specific cell populations of oligodendrocyte precursor cells and astrocytes, and a spinal cord-enriched oligodendrocyte type that appears human-specific. Spinal cord microglia but not astrocytes show a more activated phenotype compared to brain. These regional effects, with additional differentially expressed genes with age and sex in all glial lineages, help explain pathological patterns of disease - essential knowledge for therapeutic strategies.

neuroscience↗

bioRxiv2021

SCRaPL: hierarchical Bayesian modelling of associations in single cell multi-omics data

Single-cell multi-omics assays offer unprecedented opportunities to explore gene regulation at cellular level. However, high levels of technical noise and data sparsity frequently lead to a lack of statistical power in correlative analyses, identifying very few, if any, significant associations between different molecular layers. Here we propose SCRaPL, a novel computational tool that increases power by carefully modelling noise in the experimental systems. We show on real and simulated multi-omics single-cell data sets that SCRaPL achieves higher sensitivity and better robustness in identifying correlations, while maintaining a similar level of false positives as standard analyses based on Pearson correlation.

bioinformatics↗

bioRxiv2017

Robust expression variability testing reveals heterogeneous T cell responses

Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays a crucial role in tissue function and development. Single-cell RNA sequencing can characterise this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinders meaningful comparison of expression variability between cell populations. To address this problem, we introduce a novel analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. Moreover, we introduce a new and robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronisation of the translational machinery in immune cells upon activation. Additionally, our approach identifies new patterns of variability across CD4+ T cell differentiation.

bioinformatics↗