Biology subjects

Robles, M.

Publications and source records attributed to Robles, M..

6 recordsLinked to original sources

bioRxiv2026

Single Cell Transcriptomics of Refractory Epilepsy patients in Colombia

Maintaining electrical signaling homeostasis in the human neocortex relies on cell-type specific gene expression programs. However, when these programs are disrupted, the resulting imbalances can contribute to the pathogenesis of neurological disorders like epilepsy. Genetic factors are particularly implicated in a specific subtype of epilepsy known as refractory epilepsy or drug-resistant epilepsy (RE/DRE). This study shows the main results of the analysis of single cell transcriptomics for five pediatric RE patients in Colombia. A total of six samples obtained through surgical resection were analyzed by single-nuclei RNA sequencing (snRNA-seq). The genome of one patient was sequenced using high fidelity long-read sequencing. Functional enrichment of differentially expressed genes (DEGs) revealed glia-driven dysregulation of synaptic signaling, impaired glial-neuronal communication, and altered expression of genes related to neurotransmitter transport and calcium signaling. Activation of taste receptors in neurons was associated with neuroinflammatory processes. Structural variants were detected in genes associated with alterations of expression in specific cell types. This new data resource increases the diversity of information needed to develop new strategies for diagnosis of refractory epilepsy.

neuroscience↗

bioRxiv2025

Acute in vivo proximity labeling for membrane targeted proteomics in neuronal circuits

MOTIVATIONDespite the growing use of proximity labeling tools, there are limited techniques available to identify subcellularly localized proteins within targeted neuronal circuits on acute timescales in vivo. We engineered membrane targeted versions of the proximity labeling enzyme TurboID and validated their use for proteomic discovery in the mouse brain. We optimized an in vivo protocol to identify proteins in cell bodies and long range neuronal projections, and proteins differentially detected during acute drug labeling windows. SUMMARYA major goal within molecular systems neuroscience is to bridge the study of neuronal circuit function with changes in protein expression and localization in awake behaving animals. However, there are limited tools for capturing changes in subcellularly-defined proteomes within neuronal circuits during activity-gated timescales in vivo. Here, we engineered targeted versions of the proximity labeling enzyme TurboID, to tag proteins at the neuronal membrane during a user-delivered biotin injection. We optimized a labeling strategy that enables a one-to-two-hour labeling window and tagged proteins in medial prefrontal cortex (mPFC) cell bodies and corresponding axons in a downstream projection. We performed proteomics to identify proteins enriched in mPFC cell bodies and terminals, and upregulated in mPFC cell bodies following an acute cocaine injection. These advancements enable the detection of proteins at the subcellular level within short labeling windows, allowing identification of stimulus-specific proteomes in behaving mice.

neuroscience↗

bioRxiv2024

New algorithms for unsupervised cell clusteringfrom scRNA-seq data

The identification of cell types is a basic step of the pipeline for Single-Cell RNA sequencing data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: the high dimensional nature of the data, the sparse nature of the gene expression matrix, and the presence of technical noise that can introduce false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds a k-MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps in which each step calculates and stores the edges of minimum spanning trees over different subgraphs obtained removing edges selected in previous iterations. The Louvain algorithm is executed on the k-MST graph for cell clustering. We also explored alternatives based on neural networks in which an autoencoder is used to learn the parameters of a Gaussian mixture model, aiming to improve the handling of clusters with different shapes and sizes. Benchmark experiments with simulated data and public datasets show that the algorithms proposed in this work have competitive accuracy, compared to previous solutions, but also that sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Moreover, we performed further experiments with scRNA-data taken from a patient with refractory epilepsy. The AE-GMM model achieved the best accuracy for this dataset, and the k-MST ranked first among methods that do not require previous information on the expected number of clusters.

bioinformatics↗

bioRxiv2022

The Yeast Lifespan Machine: a microfluidic platform for automated replicative lifespan measurements

The budding yeast, Saccharomyces cerevisiae, has emerged as a model system for studying the aging processes in eukaryotic cells. However, the full complement of tools available in this organism has not been fully applied, in part because of limitations in throughput that restrict the ability to carry out detailed analyses. Recent advances in microfluidics have provided direct longitudinal observation of entire yeast lifespans, but have not yet achieved the normal scale of operation possible in this model system. Here we present a microfluidic platform, called the Yeast Lifespan Machine, where we combine improvements in microfluidics, image acquisition, and image analysis tools to increase robustness and throughput of lifespan measurements in aging yeast cells. We demonstrate the platforms ability to measure the lifespan of large populations of cells and distinguish long- and short-lived mutants, all with minimal involvement of the experimenter. We also show that environmental pH is capable of significantly modulating lifespan depending on the growth media, highlighting how microfluidic technologies reveal determinants of lifespan that are otherwise difficult to ascertain.

cell biology↗

bioRxiv2021

A TREM2-derived circRNA is upregulated in the entorhinal cortex of Alzheimer's disease patients

Circular RNAs (circRNAs) are a novel class of noncoding RNAs characterized by a covalent and stable closed loop structure. circRNAs are enriched in neural tissues, particularly at synapses, where they are involved in synaptic plasticity. Alzheimers disease (AD) is considered a synaptopathy since neurodegeneration causes loss or dysfunction of synapses. Microglia participate in synaptic pruning and also play a crucial role in developing AD. For instance, genetic variants in TREM2, a microglia-related gene, are risk factors for AD. Alterations in circRNAs expression have been described in different neurological diseases, including AD. However, no TREM2-derived circRNAs have been described so far. TREM2 has 3 linear RNA variants due to alternative splicing. We hypothesized that alternative splicing of exon 4 might be favoring circRNAs originating from TREM2 (circTREM2s), which in turn might be involved in AD pathogenesis. First, divergent primers (overlapping exons 3-4 and 4-5) were designed to amplify circRNAs by RT-PCR, which were confirmed by Sanger sequencing. Three candidate TREM2-derived circRNAs were identified on control human entorhinal samples. Then, additional primer sets were used to confirm back-splicing junctions. One of the circRNAs, circTREM2_1, was consistently amplified with all primer sets. In addition, circTREM2_1 was also present in AD entorhinal cortex samples and in HMC3 cells. We observed that circTREM2_1 is up-regulated in AD entorhinal cortex samples compared to controls, particularly at early stages of the disease, when we performed RT-qPCR. In conclusion, we have identified a novel circRNA derived from the TREM2 gene that could play a role in AD pathogenesis.

genetics↗

bioRxiv2018

The Miniature-chemostat Aging Device: A new experimental platform facilitates assessment of the transcriptional and chromatin landscapes of replicatively aging yeast

Replicative aging of Saccharomyces cerevisiae is an established model system for eukaryotic cellular aging. A major limitation in yeast lifespan studies has been the difficulty of separating old cells from young cells in large quantities for in-depth comparative analyses. We engineered a new platform, the Miniature-chemostat Aging Device (MAD), that enables purification of aged cells at sufficient quantities to enable genomic and biochemical characterization of aging yeast populations. Using the MAD platform, we measured DNA accessibility (ATAC-Seq) and gene expression (RNA-Seq) changes in aging cells. Our data highlight an intimate connection between aging, growth rate, and stress, as many (but not all) genes that change with age have altered expression in cells that are subjected to stress. Stress-independent genes that change with age are highly enriched for targets of the signal recognition particle (SRP). By obtaining pure populations of old cells, we find that nucleosome occupancy does not change significantly with age; however, significant age-dependent changes in accessibility at ~12% of genomic loci reflect decreased replication and changing activities of cell cycle and metabolic regulators. Finally, ATAC-seq revealed that upregulating the proteasome by deleting UBR2 reduces rDNA instability usually observed in aging cells, demonstrating a connection between proteasome activity and genomic stability.

systems biology↗