Biology subjects

Alquicira-Hernandez, J.

Publications and source records attributed to Alquicira-Hernandez, J..

10 recordsLinked to original sources

bioRxiv2026

Efficient genome-wide mapping of reproducible, context-dependent eQTLs at single-cell resolution

Single-cell technologies enable linking disease-risk variants to gene regulatory effects in specific cell-state contexts. However, most so called "single-cell eQTL" studies use a "pseudobulking" strategy to identify expression Quantitative Trait Loci (eQTLs), obscuring subtle dynamic regulatory effects of disease alleles. Here, we propose Dynema (Dynamic eQTL mapping in single cells) for fast and accurate genome-wide mapping of context-dependent and independent eQTL effects at true single-cell resolution. To identify eQTLs, Dynema uses a Poisson model with cluster robust variance estimators (CRVEs) to account for correlation of single-cell profiles from the same individual. In contrast to other common methods, Dynema achieves statistical calibration and scales to genome-wide analysis in large single-cell datasets in realistic timeframes. We applied Dynema to two independent T cell datasets and identified reproducible cell-state-dependent eQTL effects. Some cell-state-dependent eQTLs are missed by pseudobulking approaches, and many others are conditionally independent from lead eQTL effects. We show that TSPAN32 and other autoimmune loci colocalize with cell-state-dependent eQTLs. Mapping context-dependent eQTLs at single-cell resolution enables the definition of the molecular effects of complex disease alleles.

genetics↗

bioRxiv2025

The impact of sex on the immune system explored at the single-cell level

Sex has a key role in disease susceptibility, in particular autoimmunity. Sex differences of the immune system stems from genes and their interactions with intrinsic and external factors. However, the cellular-level factors influencing sexual dimorphism is not fully understood. We thus examined immune sex differences at single-cell resolution to dissect the genetic impacts. Female-biased sex-differentially expressed genes (DEGs) in multiple immune cells were involved in TNF[a] signalling, whereas male DEGs were enriched for ribosomal-related functions. While cis-eQTLs were less common on sex chromosomes, we identified over 1000 sex-specific eQTLs and 51 sex-interacting eQTLs on autosomes. When we examined the effect of genetic control on sex-DEGs, we found genetic variants affecting the female-biased expression FCGR3A in NK cells (rs2099684) and ITGB2 in Monocytes (rs760462), both of which are associated with systemic lupus erythematosus. Our work reveals novel biases masked in bulk analyses and highlights sexually dimorphic genes and pathways at baseline.

immunology↗

bioRxiv2024

Single-cell atlas of the human immune system reveals sex-specific dynamics of immunosenescence

Immunosenescence, or immune aging, is characterized by both changes in cell type abundance and a decline in cellular function, leading to increased susceptibility to immune-related diseases. Yet, the extent to which sex influences the dynamic composition of immune cells during immunosenescence remains unknown. Here, we use single-cell RNA sequencing in peripheral blood mononuclear cells from 982 donors to uncover the sex-specific immune aging dynamics. We reveal that aging drives sexually dimorphic compositional and transcriptional shifts, with females showing stronger immune remodeling. Female-specific shifts include the expansion of three cytotoxic CD8+ T effector memory subpopulations and inflammatory monocytes. In addition, female CD4+ central memory T cells exhibit abundance shifts in subpopulations involved in autoimmunity alongside age-associated transcriptional signatures enriched for autoimmune-related pathways. Conversely, males show an age-related expansion of a B cell subpopulation associated with an asymptomatic precursor state to chronic lymphocytic leukemia. These sex-biased, functionally distinct immune subpopulations represent sex-specific hallmarks of immune aging. Our work underscores the complexity and sexual dimorphism of immunosenescence and supports the development of sex-specific strategies to promote healthy aging. Highlights- A single-cell transcriptomic atlas of >1 million PBMCs from 982 donors uncovers sex-specific immune aging trajectories. - Aging females show greater cellular remodeling and stronger transcriptional shifts than males. - Female-specific expansions with age of cytotoxic CD8+ T effector memory cells, inflammatory monocytes, and CD4+ T helper 22 cells reflect a systemic shift toward a pro-inflammatory, self-reactive immune state. - Male-specific B cell shifts reveal expansion of CD5+ clonal subpopulations linked to leukemic precursor states. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/622096v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@161b6b1org.highwire.dtl.DTLVardef@214d55org.highwire.dtl.DTLVardef@5c3ee8org.highwire.dtl.DTLVardef@263227_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Identifying genetic variants that influence the abundance of cell states in single-cell data

Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1 GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96x10-11) associates with increased abundance of NK cells expressing TNF- response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

genetics↗

bioRxiv2022

Clonal dynamics of SARS-CoV-2-specific T cells in children and adults with COVID-19

Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV- 2-specific naive T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. More naive interferon-activated CD4+ T cells were recruited into the memory compartment and recovery was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection. HIGHLIGHTSO_LIChildren have diverse polyclonal SARS-CoV-2-specific naive T cells C_LIO_LIAdults have clonally expanded exhausted SARS-CoV-2-specific memory T cells C_LIO_LIInterferon-activated naive T cells differentiate into memory T cells in adults but not children C_LIO_LIAdults but not children develop robust memory T cell responses to SARS-CoV-2 C_LI O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/478400v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e9586org.highwire.dtl.DTLVardef@17aaf37org.highwire.dtl.DTLVardef@18575e0org.highwire.dtl.DTLVardef@fde4ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets

Kidney organoids provide a valuable resource to understand kidney development and disease. Clustering algorithms and marker genes fail to accurately and robustly classify cellular identity between human pluripotent stem cell (hPSC)-derived organoid datasets. Here we present a new method able to accurately classify kidney cell subtypes, a hierarchical machine learning model trained using comprehensive reference data from single cell RNA-sequencing of human fetal kidney (HFK). We demonstrate the tools (DevKidCC) performance by application to all published kidney organoid datasets and a novel dataset. DevKidCC is available on Github and can be used on any kidney single cell RNA-sequence data.

bioinformatics↗

bioRxiv2020

No evidence that plasmablasts transdifferentiate into developing neutrophils in severe COVID-19 disease

A recent study by Wilk et al. of the transcriptome of peripheral blood mononuclear cells (PBMCs) in seven patients hospitalized with COVID-19 described a population of "developing neutrophils" that were "phenotypically related by dimensionality reduction" to plasmablasts, and that these two cell populations represent a "linear continuum of cellular phenotype"1. The authors suggest that, in the setting of acute respiratory distress syndrome (ARDS) secondary to severe COVID-19, a "differentiation bridge from plasmablasts to developing neutrophils" connected these distantly related cell types. This conclusion is controversial as it appears to violate several basic principles in cell biology relating to cell lineage identity and fidelity. Correctly classifying cells and their developmental history is an important issue in cell biology and we suggest that this conclusion is not supported by the data as we show here that: (1) regressing out covariates such as unique molecular identifiers (UMIs) can lead to overfitting; and (2) that UMAP embeddings may reflect the expression of similar genes but not necessarily direct cell lineage relationships.

genomics↗

bioRxiv2020

Programmatic access to bacterial regulatory networks with regutools

SummaryRegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and Implementationregutools is an R package available through Bioconductor at bioconductor.org/packages/regutools. Contactgithub.com/ComunidadBioInfo/regutools, lcolladotor@gmail.com, alejandro.reyes.ds@gmail.com.

bioinformatics↗

bioRxiv2020

Comprehensive benchmarking of computational deconvolution of transcriptomics data

Many computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking. Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference. Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

bioinformatics↗

bioRxiv2018

scPred: Single cell prediction using singular value decomposition and machine learning classification

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many human tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an unknown cell based on its transcriptional profile; and clearly, the ability to accurately predict a cell type and any pathologic-related state will play a critical role in the early diagnosis of disease and decisions around the personalized treatment for patients. Here we present a new generalizable method (scPred) for prediction of cell type(s), using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning classification. scPred solves several problems associated with the identification of individual gene feature selection, and is able to capture subtle effects of many genes, increasing the overall variance explained by the model, and correspondingly improving the prediction accuracy. We validate the performance of scPred by performing experiments to classify tumor versus non-tumor epithelial cells in gastric cancer, then using independent molecular techniques (cyclic immunohistochemistry) to confirm our prediction, achieving an accuracy of classifying the disease state of individual cells of 99%. Moreover, we apply scPred to scRNA-seq data from pancreatic tissue, colorectal tumor biopsies, and circulating dendritic cells, and show that scPred is able to classify cell subtypes with an accuracy of 96.1-99.2%. Collectively, our results demonstrate the utility of scPred as a single cell prediction method that can be used for a wide variety of applications. The generalized method is implemented in software available here: https://github.com/IMB-Computational-Genomics-Lab/scPred/

genomics↗