Biology subjects

Restrepo, P.

Publications and source records attributed to Restrepo, P..

3 recordsLinked to original sources

bioRxiv2025

A single-cell spatial transcriptomic census of human skin anatomy

The skin is the largest human organ and a site of significant disease burden, yet its cellular and molecular organization across the body are largely undefined. Here, we construct a spatially-resolved single-cell atlas of 1.2 million cells from normal adult human skin to localize 45 cell types across 15 anatomic sites. We define principles of organ-wide cell composition, including axes of cell diversity and specialization, and distinguish site-enriched cell types. Each body site is comprised of 10 multicellular neighborhoods that define cell-cell communication. Notably, we identify a perivascular neighborhood enriched for immune-stromal crosstalk with features resembling a homeostatic immune niche similar to skin-associated lymphoid tissue. Finally, mapping these neighborhoods onto skin disease reveals pathogenic neighborhood disruptions, including pan-disease immune alterations in the perivascular neighborhood. We present a framework charting the skins multiscale spatial organization across a molecular to macroanatomic scale. This work advances our understanding of organ-wide skin cellular organization and communication, and its architectural disruption in disease. HIGHLIGHTSO_LIHuman skin MERFISH spatial atlas of 1.2 million cells from 114 samples and 22 donors C_LIO_LICellular diversity varies along a central to peripheral body site spatial axis C_LIO_LI10 multicellular neighborhoods define skin microanatomy and homeostatic interactions C_LIO_LIHuman skin diseases feature spatial and transcriptional neighborhood remodeling C_LI

genomics↗

bioRxiv2020

Unannotated small RNA clusters in circulating extracellular vesicles detect early stage liver cancer

BackgroundHepatocellular carcinoma (HCC) is among the deadliest malignancies and surveillance tools for early detection are suboptimal. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumor initiation and metastasis, however, most extracellular RNA (exRNA) biomarker studies are limited to annotated genomic regions. MethodsEVs were isolated with ultracentrifugation and nanoDLD and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking, and deconvolution analysis. We performed genome-wide small exRNA sequencing, including unannotated transcripts. We identified small RNA clusters (smRCs) and delineated their key genomic features across biospecimens (blood, urine, tissue) and EV isolation techniques. A 3-smRC signature for early HCC detection was trained and validated in two independent cohorts. ResultsEV-derived smRCs were dominated by uncharacterized, unannotated small RNA and uniformly tiled across the genome with a consensus sequence of 20bp. A 3-smRC signature was significantly overexpressed in circulating EVs of HCC patients compared to controls at risk or patients with non-HCC malignancies (p<0.01, n=157). An independent validation in a phase 2 biomarker study revealed 86% sensitivity and 91% specificity for the detection of early HCC from controls at risk (i.e. cirrhosis or chronic liver disease, n=209) (positive predictive value (PPV): 89%, area under the ROC curve [AUC]: 0.87). The 3-smRC signature was independent of alpha-fetoprotein (p<0.0001) and a composite model yielded an increased AUC of 0.93 (sensitivity: 85%, specificity: 94%, PPV: 95%). ConclusionAn exRNA-based 3-smRC signature from plasma detects early stage HCC, which directly leads to the prospect of a minimally-invasive, blood-only, operator-independent surveillance biomarker. One sentence summaryWe employ a novel, data-driven approach to identify and characterize small RNA clusters from unannotated loci in extracellular vesicle-derived RNA across different cancer types, isolation techniques, and biofluids, facilitating discovery of a robust biomarker for detection of early stage liver cancer.

cancer biology↗

bioRxiv2018

GeTallele: a mathematical model and a toolbox for integrative analysis and visualization of DNA and RNA allele frequencies

BackgroundAsymmetric allele expression typically indicates functional and/or structural features associated with the underlying genetic variants. When integrated, RNA and DNA allele frequencies can reveal patterns characteristic of a wide-range of biological traits, including ploidy changes, genome admixture, allele-specific expression and gene-dosage transcriptional response. ResultsTo assess RNA and DNA allele frequencies from matched sequencing datasets, we introduce a method for generating model distributions of variant allele frequencies (VAF) with a given variant read probability. In contrast to other methods, based on whole sequences or single SNV, proposed methodology uses continuous multi-SNV genomic regions. The methodology is implemented in a GeTallele toolbox that provides a suite of functions for integrative analysis, statistical assessment and visualization of Genome and Transcriptome allele frequencies. Using model VAF probabilities, GeTallele allows estimation and comparison of variant read probabilities (VAF distributions) in a sequencing dataset. We demonstrate this functionality across cancer DNA and RNA sequencing datasets. ConclusionBased on our evaluation, variant read probabilities can serve as a dependable indicator to assess gene and chromosomal allele asymmetries and to aid calls of genomic events in matched sequencing RNA and DNA datasets. ContactP.M.Slowinski@exeter.ac.uk

bioinformatics↗