Biology subjects

Colubri, A.

Publications and source records attributed to Colubri, A..

7 recordsLinked to original sources

bioRxiv2026

scSketch: Interactive Sketch-based Trajectory Exploration and Pathway-Aware Analysis of Single-Cell Data

Interactively exploring gene expression gradients across low-dimensional cell embeddings is central to single-cell RNA sequencing analysis, yet there arent tools that allow users to sketch trajectories and interactively compute pathway-level interpretation. We present scSketch, a tool that enables users to iteratively explore and test trajectory hypotheses in single-cell data while maintaining statistical validity and biological interpretability. Specifically, users apply interactive directional sketching to draw trajectories across embeddings and probe continuous processes such as cellular differentiation and cell state transitions. scSketch automatically computes gene-trajectory correlations and applies online false discovery rate (FDR) control to maintain statistical validity during iterative exploration. Significant genes are grouped into Reactome pathways for contextual interpretation. Applied to human oral keratinocytes infected with human cytomegalovirus, scSketch revealed infection-associated gradients involving interferon responses, metabolic remodeling and autophagy. Together, these features position scSketch as a bridge between exploratory visualization and mechanistic insight in single-cell biology.

bioinformatics↗

bioRxiv2025

Transmission network for foot-and-mouth disease epidemic in Mar Chiquita - Argentina: A spatial modeling analysis

Foot-and-mouth disease (FMD) is a highly contagious viral disease with major implications for livestock production, food security, and economic stability. In this study, we investigate the transmission dynamics of the 2001 FMD outbreak in Mar Chiquita, Argentina, using a spatially explicit modeling framework. Transmission kernels based on inter-farm Euclidean distances are developed to estimate the probability of between-farm transmission and to reconstruct the likely transmission network. Several kernel formulations are evaluated, and parameters are estimated using maximum likelihood estimation techniques. We further assess the influence of isolation interventions by incorporating time-dependent kernel modifications that reflect reduced transmission risk following farm visits. Local transmission potential is quantified through farmspecific reproduction numbers, and transmission pathways are inferred using Markov Chain Monte Carlo (MCMC) simulations. Our results show that controlling geographic proximity alone does not reliably mitigate transmission risk and that delayed intervention can sustain epidemic spread. The reconstructed transmission network reveals spatial clusters of infections and temporal patterns consistent with field observations. These findings highlight the importance of timely isolation, active surveillance, and spatial risk assessment for effective outbreak control. HighlightsO_LISpatial transmission modeling captures FMD spread across 65 farms in Mar Chiquita, Argentina. C_LIO_LIDistance-based kernel functions estimated from epidemic data quantify between-farm infection risk. C_LIO_LIMCMC simulations reconstruct high-resolution farm-to-farm transmission networks. C_LIO_LIIntervention strategies (e.g., isolation) effectively reduce transmission probability and local R0. C_LI

ecology↗

bioRxiv2025

Human Cytomegalovirus Infection of Primary Human Oral Keratinocytes Induces Intermediate Keratinocyte Differentiation and an Altered Innate Immune Response

The oral mucosa is central for human cytomegalovirus (HCMV) acquisition. However, the initial infection events in the oral cavity, including how HCMV modulates oral cells to establish infection and spread, are supported by limited experimental data and therefore remain poorly understood. We investigated HCMV infection in cultures of primary human oral keratinocytes (HOKs) as a model to study HCMV infection in the oral mucosa using the TB40E-Gfp and MOLD strains of HCMV. Both viral strains successfully infected and replicated in HOKs as demonstrated by the appearance of cytopathic effects, full expression of viral transcripts, and the shedding of infectious virus. To investigate how HCMV modulates the transcriptome of primary HOKs, we performed single cell RNA sequencing (scRNA-seq) which revealed 11 subtypes of cells. Furthermore, infection resulted in significant changes in host gene expression. HCMV upregulated genes involved in immune responses, cell cycle regulation, cancer-related pathways, neuronal and synaptic functions, metabolism, stress responses, molecular chaperones, and vesicular trafficking which might be critical for viral protein expression and assembly. HCMV also increased the expression of genes related to the extracellular matrix, cell adhesion, microtubules, signal transduction, kinases, and transcriptional regulation. Conversely, HCMV downregulated genes associated with inflammation and immune responses, cell cycle control and apoptosis, cell adhesion and migration, as well as signaling pathways, growth factors, ion channels, transporters, and transcription factors when compared to uninfected cells. These findings suggest that HCMV modulates a wide range of host cellular pathways to create a favorable environment for its replication. HCMV also induced changes in HOK differentiation genes downregulating basal state genes and upregulating intermediate genes and select terminal differentiation genes, indicating HCMV might be driving HOK differentiation towards a dysregulated intermediate phenotype while inhibiting their terminal differentiation to maintain a cell state capable of sustaining viral replication. Using SciViewer, our recently developed scRNA-seq data visualization tool, we determined that as the infection progressed from low to high viral transcript accumulation, HCMV infection upregulated E2F targeted genes, some antiviral and immune responses as well as autophagy and cellular stress responses, while downregulating interferon-stimulated genes, immune and antiviral response genes, pyroptosis, inflammatory cell death, and membrane remodeling and viral entry genes. Overall, our study shows that primary human HOKs can be used as a model to study HCMV infection in the oral mucosa and that HCMV drives dysregulated intermediate oral keratinocyte differentiation and altered immune responses which likely facilitate viral replication in the oral mucosa and spread into the rest of the host.

molecular biology↗

bioRxiv2024

Real-Time Spatiotemporal Tracking of Infectious Outbreaks in Confined Environments with a Host-Pathogen Agent-Based System

Deadly infection outbreaks in confined spaces, whether it is a COVID-19 outbreak on a cruise ship or at a veteran home, or measles and stomach flu outbreaks in schools, can be characterized by their rapid spread due to the abundance of common spaces, shared airways, and high population density. Preventing future infectious outbreaks and developing efficient mitigation protocols can benefit from advanced computational modeling approaches. Here, we developed an agent-based modeling approach to study the spatiotemporal dynamics of an infection outbreak in a confined environment caused by a specific pathogen and to determine effective infection containment protocols. The approach integrates the 3D geographic information system of a confined environment, the behavior of the hosts, key biological parameters about the pathogen obtained from the experimental data, and the general mechanics of host-pathogen and pathogen-fomite interactions. To assess our approach, we applied it to the historical data of infectious outbreak caused by norovirus, H1N1 influenza A, and SARS-CoV-2 viruses. As a result our model was able to accurately predict the number of infections per day, correctly identify the day when the CDC vessel sanitation protocol would be triggered, single out key biological parameters affecting the infection spread, and propose important changes to existing containment protocols, specific for different pathogens. This research not only contributes to our understanding of infection spread and containment in cruise ships but also offers insights applicable to other similar confined settings, such as nursing homes, schools, and hospitals. By providing a robust framework for real-time outbreak modeling, this study proposes new, more effective containment protocols and enhances our preparedness for managing infectious diseases and emerging pathogens in confined environments.

bioinformatics↗

bioRxiv2021

Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the Python programming environment

SummaryVisualizing two-dimensional (2D) embeddings (e.g. UMAP or tSNE) is a key step in interrogating single-cell RNA sequencing (scRNA-Seq) data. Subsequently, users typically iterate between programmatic analyses (e.g. clustering and differential expression) and visual exploration (e.g. coloring cells by interesting features) to uncover biological signals in the data. Interactive tools exist to facilitate visual exploration of embeddings such as performing differential expression on user-selected cells. However, the practical utility of these tools is limited because they dont support rapid movement of data and results to and from the programming environments where the bulk of data analysis takes place, interrupting the iterative process. Here, we present the Single-cell Interactive Viewer (Sciviewer), a tool that overcomes this limitation by allowing interactive visual interrogation of embeddings from within Python. Beyond differential expression analysis of user-selected cells, Sciviewer implements a novel method to identify genes varying locally along any user-specified direction on the embedding. Sciviewer enables rapid and flexible iteration between interactive and programmatic modes of scRNA-Seq exploration, illustrating a useful approach for analyzing high-dimensional data. Availability and implementationCode and examples are provided at https://github.com/colabobio/sciviewer

bioinformatics↗

bioRxiv2020

Deployable CRISPR-Cas13a diagnostic tools to detect and report Ebola and Lassa virus cases in real-time

Viral hemorrhagic fevers (VHFs) remain some of the most devastating human diseases, and recent outbreaks of Ebola virus disease (EVD) 1,2 and Lassa fever (LF) 3,4 highlight the urgent need for sensitive, field-deployable tests to diagnose them 5,6. Here we develop CRISPR-Cas13a-based (SHERLOCK) diagnostics targeting Ebola virus (EBOV) and Lassa virus (LASV), with both fluorescent and lateral flow readouts. We demonstrate on laboratory and clinical samples the sensitivity of these assays and the capacity of the SHERLOCK platform to handle virus-specific diagnostic challenges. Our EBOV diagnostic detects both the L and NP genes, thereby eliminating the potential for false positive results caused by the rVSV{Delta}G-ZEBOV-GP live attenuated vaccine. Our two LASV diagnostics together capture 90% of known viral diversity and demonstrate that CRISPR-RNAs (crRNAs) can be effectively multiplexed to provide greater coverage of known viral diversity. We performed safety testing to demonstrate the efficacy of our HUDSON protocol in heat-inactivating and chemically treating VHF viruses before SHERLOCK testing, eliminating the need for an extraction. We developed a user-friendly field protocol and mobile application (HandLens) to report results, facilitating SHERLOCKs use in endemic regions. Finally, we successfully deployed our tests in Sierra Leone and Nigeria in response to recent outbreaks.

molecular biology↗

bioRxiv2018

Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization

BackgroundWe created a family of prognostic models for Ebola virus disease from the largest dataset of EVD patients published to date. We incorporated these models into an app, \"Ebola Care Guidelines\", that provides access to recommended, evidence-based supportive care guidelines and highlights the signs/symptoms with the largest contribution to prognosis.\n\nMethodsWe applied multivariate logistic regression on 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone during the 2014-16 outbreak. We validated the models with two independent datasets from Sierra Leone.\n\nFindingsViral load and age were the most important predictors of death. We generated a parsimonious model including viral load, age, body temperature, bleeding, jaundice, dyspnea, dysphagia, and referral time recorded at triage. We also constructed fallback models for when variables in the parsimonious model are unavailable. The performance of the parsimonious model approached the predictive power of observational wellness assessments by experienced health workers, with Area Under the Curve (AUC) ranging from 0.7 to 0.8 and overall accuracy of 64% to 74%.\n\nInterpretationMachine-learning models and mHealth tools have the potential for improving the standard of care in low-resource settings and emergency scenarios, but data incompleteness and lack of generalizable models are major obstacles. We showed how harmonization of multiple datasets yields prognostic models that can be validated across different cohorts. Similar performance between the parsimonious model and those incorporating expert wellness assessments suggests that clinically-guided machine learning approaches can recapitulate clinical expertise, and thus be useful when such expertise is unavailable. We also demonstrated with our guidelines app how integration of those models with mobile technologies enables deployable clinical management support tools that facilitate access to comprehensive bodies of medical knowledge.\n\nFundingHoward Hughes Medical Institute, US National Institutes of Health

bioinformatics↗