Biology subjects

Gonzalez-Velasco, O.

Publications and source records attributed to Gonzalez-Velasco, O..

4 recordsLinked to original sources

bioRxiv2025

Somatic mutations in ALS genes in the motor cortex of sporadic ALS patients

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive degeneration of cortical and spinal motor neurons. Mendelian germline mutations often cause familial ALS (fALS) but only approximately ten percent of sporadic ALS cases (sALS). We leveraged DNA and single cell RNA-sequencing data from autopsy tissue to explore the presence of somatic mosaic variants in sALS cases. Deep targeted panel sequencing of known ALS disease genes in motor cortex tissue revealed an enrichment of low allele frequency variants in sALS, but not in fALS with an identified monogenic cause. In silico analysis predicted increased pathogenicity of mosaic mutations in various known ALS mutational hot spots. In particular, we identified the somatic FUS variant p.E516X, located in an established hot spot for germline ALS mutations, which leads to nucleo-cytoplasmic mislocalization and aggregation typical for ALS FUS pathology. Additionally, we performed somatic variant calling on single cell RNA-sequencing data from sALS tissue and revealed a specific accumulation of somatic variants in excitatory neurons, reinforcing a neuron-autonomous disease initiation. Collectively, this study indicates that somatic mutations within the motor cortex, especially in excitatory neurons, may contribute to sALS development.

genetics↗

bioRxiv2024

Identifying similar populations across independent single cell studies without data integration

Supervised and unsupervised methods have emerged to address the complexity of single cell data analysis in the context of large pools of independent studies. Here, we present ClusterFoldSimilarity (CFS), a novel statistical method design to quantify the similarity between cell groups acroos any number of independent datasets, without the need for data correction or integration. By bypassing these processes, CFS avoids the introduction of artifacts and loss of information, offering a simple, efficient, and scalable solution. This method match groups of cells that exhibit conserved phenotypes across datasets, including different tissues and species, and in a multimodal scenario, including single-cell RNA-Seq, ATAC-Seq, single-cell proteomics, or, more broadly, data exhibiting differential abundance effects among groups of cells. Additionally, CFS performs feature selection, obtaining cross-dataset markers of the similar phenotypes observed, providing an inherent interpretability of relationships between cell populations. To showcase the effectiveness of our methodology we generated single-nuclei RNA-Seq data from the motor cortex and spinal cord of adult mice. By using CFS, we identified three distinct sub-populations of astrocytes conserved on both tissues. CFS includes various visualization methods for the interpretation of the similarity scores and similar cell populations.

bioinformatics↗

bioRxiv2024

LotOfCells: data visualization and statistics of single cell metadata

Single-cell sequencing unveils a treasure trove into the biological and molecular characteristics of samples. Yet, within this flood of data, the challenge to draw meaningful conclusions sometimes can be time consuming and a tortuous process. Here we introduce LotOfCells: a simple R package designed to explore the intricate landscape of phenotypic data within single-cell studies. Normally, we are interested in visualizing and measuring if the differences in the proportion of number of cells across various covariates is significant or biologically relevant. As an example, one of the most common questions is the proportion of different cell types across conditions in our experiment, or the cluster composition before and after treatment (e.g.: difference in cell type proportions between wild type and mutant). LotOfCells helps with the interpretation and visualization of meta-data of these recurrent scenarios, including the test of proportion changes across multiple ordered stages. Additionally, it computes a symmetric divergence score to measure global deregulation of cell proportions due to a condition. Code repositoryR package, manual and relevant examples can be accessed on the GitHub repository: https://github.com/OscarGVelasco/LotOfCells

bioinformatics↗

bioRxiv2022

A single-cell clusters similarity measure for different batches, datasets, and samples

SummarySince the inception of single-cell level measuring techniques, identification of distinct cell stages, phenotypes and populations has been a challenge. Cell clustering and dimensionality reduction methods are the most popular approaches to identify heterogeneity of single-cell data. But, as public repositories continue to grow in number, integrative analyses and merging of large pools of samples from different and heterogeneous datasets becomes a difficult challenge, which showcases the impossibility of scalability of some of the existing methods. Here we present ClusterFoldSimilarity, an R package that calculates a measure of similarity between clusters from different datasets/batches, without the need of correcting for batch effect or normalizing and merging the data, thus avoiding artifacts and the loss of information derived from these kinds of techniques. The similarity metric is based on the average vector module and sign of the product of logarithmic fold-changes. ClusterFoldSimilarity compares every single pair of clusters from any number of different samples/datasets, including different number of clusters for each sample. Additionally, the algorithm is able to select the top genes which contribute the most to the similarity of two specific clusters, serving also as a feature selection tool. Availability and implementationThe algorithm is freely available as an R package at: https://github.com/OscarGVelasco/ClusterFoldSimilarity Contactoscargvelasco@gmail.com

bioinformatics↗