Biology subjects

Mallona, I.

Publications and source records attributed to Mallona, I..

10 recordsLinked to original sources

bioRxiv2026

Co-option of CENP-A for activity-induced neuronal plasticity

Neuronal activation drives activity-dependent gene expression that underlies experience-associated synaptic modifications, learning and memory. Here we show that the histone variant CENP-A, best known for specifying centromere identity, is dynamically regulated by synaptic activity at both the RNA and protein levels in postmitotic neurons. Neuronal activation increases a non-centromeric nuclear pool of CENP-A while leaving centromeric CENP-A levels unchanged. Downregulation of CENP-A selectively reduces the activity-associated non-centromeric pool, impairs activity-dependent induction of immediate-early genes such as FOS and ARC, and disrupts hippocampus-dependent learning and memory. Furthermore, we find that activity-dependent neuronal responses in human embryonic stem cell-derived forebrain organoids similarly require CENP-A. Our results reveal a mitosis-independent, conserved role of CENP-A for driving plasticity in mammalian neurons.

neuroscience↗

bioRxiv2026

fastder: fast, annotation-agnostic detection of expressed regions from RNA-seq coverage and splicing data

Background.Features from RNA-seq experiments are usually counted using a curated annotation (GENCODE, Ensembl, etc) as reference. This approach constrains RNA-seq counts to what the annotation considers a valid gene, transcript or exon, and misses transcription outside them. Moreover, reference annotations are known to be incomplete, and inadequate in some experiments altering splicing, and some diseases. To circumvent these constraints, annotation-free detection of expressed regions aims to detect the actual regions being expressed in a sample or samples. For large scale usage, the recount3 resource holds uniformly processed coverage tracks and splice junctions for over 8,000 human and over 10,000 mouse RNA-seq studies, with several hundred thousand samples in total. This opens an opportunity for scalable tools to call expressed regions that are annotation free. Results.We present fastder, a C++ tool to detect expressed regions directly from recount3 coverage and splice junctions files, with no read alignment steps. It also can run on local raw reads, after aligning them with STAR, making it suitable to analyze species beyond human and mouse. fastder calls expressed regions by bump hunting expression coverage files, and stitches them into spliced multi-exon structures using the splice junction data. On simulated RNA-seq with unannotated expression features, fastder calls exons at a base-level accuracy on par with derfinder and golden standard for the task. Exon precision improves with sequencing depth. We compare fastders performance against a coverage-only baseline, derfinder and groHMM. fastder extends the functionality of those by assigning strands to the expressed regions, and by also producing multi-exon regions. It runs about 15 to 20 times faster than derfinder and groHMM, within bounded memory. We showcase its use with two recount3-derived examples, including the recovery of cryptic exons from a TDP-43 knockdown experiment; and the GTEx data clustering based on the topology of the expressed regions alone, without taking the expression levels into account. Conclusions.fastder makes annotation-agnostic detection of expressed-regions fast enough to run at recount3 scale. As limitation, it detects expressed regions and the junctions between them, not different isoforms from the same gene.

genomics↗

bioRxiv2026

Building computational benchmarks: an Omnibenchmark reimplementation of a single-cell preprocessing pipeline evaluation

In the past few years, we have seen a veritable surge in single-cell (e.g., RNA sequencing) techniques and datasets, enabling increasingly detailed characterization of cellular heterogeneity across tissues and conditions. This surge in single-cell techniques has been complemented by a large number of analysis frameworks and pipelines, and a large parameter space and researcher degrees of freedom to use them. Many neutral benchmarks have been presented for various computational tasks, but most make design decisions that render them incompatible with each other, e.g., different datasets and metrics, or parameter sets used. In this work, we showcase a recently developed framework, Omnibenchmark, to build reproducible, extensible and standardized method comparisons. This not only facilitates the broad investigation of pipelines used in single-cell data analysis, but also highlights how the process of building benchmarks can be streamlined and unified. We do this as an initial proof-of-principle for an arms-length benchmark that evaluates five single-cell RNA sequencing pipelines (filtering to normalization to dimensionality reduction to clustering) on three datasets. This standardization enables benchmarks to be easily extended in several directions, including broader parameter sweeps, comparisons across software versions and architectures, isolation of pipeline steps, and integration of additional pipelines, datasets, and metrics.

bioinformatics↗

bioRxiv2026

rhapsodist: a reproducible Snakemake workflow for BD~Rhapsody single-cell RNA-seq data

BD Rhapsody is a widely used single-cell RNA-seq (scRNA-seq) platform to profile gene expression. Based on barcoded beads, processing BD Rhapsody experiments is demanding because of the multiple barcode versions and layouts, which makes it difficult to locate barcodes by scanning reads positionally. We introduce rhapsodist, a reproducible and scalable Snakemake workflow to process BD Rhapsody whole transcriptome analysis (WTA) data from raw FASTQ reads. As a result, rhapsodist generates count tables in a HDF5-backed SingleCellExperiment format. It supports all generations of BD Rhapsody beads, regardless of the barcode allowlist version and the presence of a variable-length diversity inset. rhapsodist orchestrates STARsolo, kallisto/bustools, salmon/alevin, and the official BD Rhapsody CWL pipeline, enabling direct cross-aligner comparison. Optionally, sample tag demultiplexing, read downsampling, tunable linker-mismatch tolerance, and per-sample reports are provided. We showcase a remarkable consistency across aligner results, and highlight their differences in performance (e.g., quantification, clustering, or speed). On simulated data all four aligners recovered true barcodes with perfect precision and recall and equivalent UMI counts. On HeLa cells (a cell line; enhanced beads), both per-cell correlations across aligners and pseudobulk correlations were remarkably high on every comparison (Pearson r of 0.89-0.96 and 0.94-0.98 per cell and pseudobulk, respectively). On a mouse epidermis dataset with a more complex transcriptional profile (e.g., with celltypes; on legacy beads), cell type pseudobulk correlations showed a similar consistency (Pearson r 0.92-0.98), pointing to calling robustness. As expected, pseudoaligners (kallisto/bustools and salmon/alevin) were best performers speed-wise, and BDs workflow recovered a higher number of barcodes and UMIs. rhapsodist is available at https://github.com/imallona/rhapsodist under GPLv3, tried on Linux with conda, and includes unit and integration tests.

bioinformatics↗

bioRxiv2025

BiCLUM: Bilateral Contrastive Learning for Unpaired Single-Cell Multi-Omics Integration

The integration of single-cell multi-omics data provides a powerful approach for understanding the complex interplay between different molecular modalities, such as RNA expression, chromatin accessibility and protein abundance, measured through assays like scRNA-seq, scATAC-seq and CITE-seq, at single-cell resolution. However, most existing single-cell technologies focus on individual modalities, limiting a comprehensive understanding of their interconnections. Integrating such diverse and often unpaired datasets remains a challenging task due to unknown cell correspondences across distinct feature spaces and limited insights into cell- type-specific activities in non-scRNA-seq modalities. In this work, we propose BiCLUM, a Bilateral Contrastive Learning approach for Unpaired single-cell Multi-omics integration, which simultaneously enforces cell-level and feature-level alignment across modalities. BiCLUM first transforms one modality, such as scATAC-seq, into the data space of another modality, such as scRNA-seq, using prior genomic knowledge. It then learns cell and gene embeddings simultaneously through a bilateral contrastive learning framework, incorporating both cell-level and feature-level contrastive losses. We evaluated BiCLUM on aligning gene expression with chromatin accessibility via three paired RNA-ATAC multi-omics datasets, as well as gene expression with protein expression via three CITE-seq datasets. The results demonstrate that BiCLUM either outperforms or is at least comparable to existing integration methods, excelling in both visualization and quantitative metrics. Furthermore, BiCLUM preserves the biological relevance of the integrated data, making it a potential powerful tool for downstream biological analysis, such as cell type identification and pathway exploration.

bioinformatics↗

bioRxiv2024

RoCK and ROI: Single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing

Various tools have been developed to reliably identify, trace and analyze single cells in complex tissues. In recent years, these technologies have been combined with transcriptomic profiling approaches to explore molecular mechanisms that drive development, health, and disease. However, current methods still fall short of profiling single cell transcriptomes comprehensively, with one major challenge being high non-detection rates of specific transcripts and transcript regions. Such information is often crucial to understanding the biology of cells or tissues and includes lowly expressed transcripts, sequence variations and exon junctions. Here, we developed a scRNAseq workflow, RoCK and ROI (Robust Capture of Key transcripts and Regions Of Interest), that tackles these limitations. RoCKseq uses targeted capture to enrich for key transcripts, thereby supporting the detection and identification of cell types and complex phenotypes in scRNAseq experiments. ROIseq directs a subset of reads to a specific region of interest via selective priming to ensure detection. Importantly, RoCK and ROI guarantees efficient retrieval of specific sequence information without compromising overall single cell transcriptome information and our workflow is supported by a novel bioinformatics pipeline to analyze the multimodal information. RoCK and ROI represents a significant enhancement over non-targeted single cell sequencing, particularly when cell categorization depends on transcripts that are missed in standard scRNAseq experiments. In addition, it also allows exploration of biological questions that require assessment of specific sequence elements along the targets to be addressed.

genomics↗

bioRxiv2024

Targeted CRISPR-Cas9 screening identifies transcription factor network controlling murine haemato-endothelial fate commitment

Haematopoiesis is a tightly coordinated process that forms and maintains all blood cells. During development blood generation begins in the yolk sac with the differentiation of haemato-endothelial mesoderm giving rise to haematopoietic progenitors. Which molecular regulators are crucial for haemato-endothelial mesoderm formation remains unclear and has not been studied in an unbiased way. Here we employ a mouse embryonic stem cell model that recapitulates embryonic blood development and perform targeted CRISPR-Cas9 knock out screens focusing on transcription factors and chromatin regulators. Focusing on the transition of primitive towards haematoendothelial mesoderm we identified the known master regulator Etv2 and novel transcription factors including Smad1, Ldb1, Six4 and Zbtb7b acting as crucial drivers or repressors of mesodermal commitment. Our transcriptome analysis highlights that each factor has a precise impact on the gene expression signature of the developing mesoderm resulting in the formation of mesodermal subsets with a defined lineage differentiation bias. Our study reveals novel molecular pathways governing mesodermal development crucial to allow endothelial and haematopoietic lineage specification.

developmental biology↗

bioRxiv2022

TDP-43 oligomerization and RNA binding are codependent but their loss elicits distinct pathologies

Aggregation of the RNA-binding protein TDP-43 is the main common neuropathological feature of TDP-43 proteinopathies. In physiological conditions, TDP-43 is predominantly nuclear and contained in biomolecular condensates formed via liquid-liquid phase separation (LLPS). However, in disease, TDP-43 is depleted from these compartments and forms cytoplasmic or, sometimes, intranuclear inclusions. How TDP-43 transitions from physiological to pathological states remains poorly understood. Here, we show that self-oligomerization and RNA binding cooperatively govern TDP-43 stability, functionality, LLPS and cellular localization. Importantly, our data reveal that TDP-43 oligomerization is connected to, and conformationally modulated by, RNA binding. Mimicking the impaired proteasomal activity observed in patients, we found that TDP-43 forms nuclear aggregates via LLPS and cytoplasmic aggregates via aggresome formation. The favored aggregation pathway depended on the TDP-43 state -monomeric/oligomeric, RNA-bound/-unbound- and the subcellular environment -nucleus/cytoplasm. Our work unravels the origins of heterogeneous pathological species occurring in TDP-43 proteinopathies.

cell biology↗

bioRxiv2020

Flanking sequence preference modulates de novo DNA methylation in the mouse genome

Mammalian de novo DNA methyltransferases (DNMT) are responsible for the establishment of cell-type-specific DNA methylation in healthy and diseased tissues. Through genome-wide analysis of de novo methylation activity in murine stem cells we uncover that DNMT3A prefers to methylate CpGs followed by cytosines or thymines, while DNMT3B predominantly methylates CpGs followed by guanines or adenines. These signatures are further observed at non-CpG sites, resembling methylation context observed in specialised cell types, including neurons and oocytes. We further show that these preferences are not mediated by the differential recruitment of the two de novo DNMTs to the genome but are resulting from structural differences in their catalytic domains. Molecular dynamics simulations suggest that, in case of DNMT3A, the preference is due to favourable polar interactions between the flexible Arg836 side chain and the guanine that base-pairs with the cytosine following the CpG. This context-dependent de novo DNA methylation provides additional insights into the complex regulation of methylation patterns in different cell types.

molecular biology↗

bioRxiv2018

Modular dynamics of DNA co-methylation networks exposes the functional organization of colon cancer cells’ genome

Epigenomic plasticity is interconnected with chromatin structure and gene regulation. In tumor progression, orchestrated remodeling of genome organization accompanies the acquisition of malignant properties. DNA methylation, a key epigenetic mark extensively altered in cancer, is also linked to genome architecture and function. Based on this association, we postulate that the dissection of long-range co-methylation structure unveils cancer cells genome architecture remodeling. We applied network-modeling of DNA methylation co-variation in two colon cancer cohorts and found abundant and consistent transchromosomal structures in both normal and tumor tissue. Normal-tumor comparison indicated substantial remodeling of the epigenome covariation and revealed novel genomic compartments with a unique signature of DNA methylation rank inversion.

genomics↗