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Soto, L. M.

Publications and source records attributed to Soto, L. M..

6 recordsLinked to original sources

Decoy-seq unlocks scalable genetic screening for regulatory small noncoding RNAs

Small noncoding RNAs (smRNAs) play critical roles in regulating various cellular processes, including development, stress response, and disease pathogenesis. However, functional characterization of smRNAs remains limited by the scale and simplicity of phenotypic readouts. Recently, single-cell perturbation screening methods, which link CRISPR-mediated genetic perturbations to rich transcriptomic profiling, have emerged as foundational and scalable approaches for understanding gene functions, mapping regulatory networks, and revealing genetic interactions. However, a comparable approach for probing the regulatory consequences of smRNA perturbations is lacking. Here, we present Decoy-seq as an extension of this approach for high-content, single-cell perturbation screening of smRNAs. This method leverages U6-driven tough decoys (TuD), which form stable duplexes with their target smRNAs, for inhibition in the cell. Lentiviral-encoded TuDs are compatible with conventional single-cell RNA-sequencing (scRNA-seq) technologies, allowing joint identification of the smRNA perturbation in each cell and its associated transcriptomic profile. We applied Decoy-seq to 336 microRNAs (miRNAs) and 196 tRNA-derived fragments (tRFs) in a human breast cancer cell line, demonstrating its ability to uncover complex regulatory pathways and novel functions of these smRNAs. Notably, we show that tRFs influence mRNA polyadenylation and regulate key cancer-associated processes, such as cell cycle progression and proliferation. Therefore, Decoy-seq provides a powerful framework for exploring the functional roles of smRNAs in normal physiology and disease, and holds promise for accelerating future discoveries.

systems biology↗

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

BackgroundA significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimers disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals. MethodsWe analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on {beta}-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution. ResultsTranscriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience. ConclusionsWe identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

neuroscience↗

Accurate isoform quantification by joint short- and long-read RNA-sequencing

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQTs potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

bioinformatics↗

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data

Analysis of single cells across multiple samples and/or conditions encompasses a series of interrelated tasks, which range from normalization and inter-sample harmonization to identification of cell state shifts associated with experimental conditions. Other downstream analyses are further needed to annotate cell states, extract pathway-level activity metrics, and/or nominate gene regulatory drivers of cell-to-cell variability or cell state shifts. Existing methods address these analytical requirements sequentially, lacking a cohesive framework to unify them. Moreover, these analyses are currently confined to specific modalities where the biological quantity of interest gives rise to a singular measurement. However, other modalities require joint consideration of dual measurements; for example, modeling the latent space of alternative splicing involves joint analysis of exon inclusion and exclusion reads. Here, we introduce a generative model, called GEDI, to identify latent space variations in multi-sample, multi-condition single cell datasets and attribute them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with the state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states in the form of transcriptomic vector fields, and machine learning-based prediction of sample characteristics from single-cell data. By incorporating gene-level prior knowledge, it can further project pathway and regulatory network activities onto the cellular state space, enabling the computation of the gradient fields of transcription factor activities and their association with the transcriptomic vector fields of sample covariates. Finally, we demonstrate that GEDI surpasses the gene-centric approach by extending all these concepts to the study of alternative cassette exon splicing and mRNA stability landscapes in single cells.

bioinformatics↗

scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

Recent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application. AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

systems biology↗

Fungal effector Jsi1 hijacks plant JA/ET signaling through Topless

Ustilago maydis (U. maydis) is the causal agent of maize smut disease. During the colonization process, the fungus secretes effector proteins which suppress immune responses and redirect the host-metabolism in favor of the pathogen. Here we describe a novel strategy by which U. maydis induces plant jasmonate/ethylene (JA/ET) hormone signaling and thereby biotrophic susceptibility. The U. maydis effector Jasmonate/Ethylene signaling inducer 1 (Jsi1) possesses an ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motif, DLNxxP, which interacts with the second WD40 domain of the conserved plant co-repressor family Topless/Topless related (TPL/TPR). Jsi1 interaction with TPL/TPRs leads to derepression of the ethylene response factor (ERF) branch of the JA/ET signaling pathway, supporting biotrophic susceptibility. Jsi1 likely activates the ERF branch by interfering with the binding of endogenous DLNxxP motif-containing ERF transcription factors to TPL/TPR proteins. The identification of effector proteins possessing a DLNxxP motif in different fungal species with biotrophic and hemibiotrophic lifestyles together with the validation of the interaction between such effectors from Magnaporthe oryzae (M. oryzae), Sporisorium scitamineum (S. scitamineum), and S. reilianum with TPL proteins indicates the convergent evolution of effectors for modulating the TPL/TPR co-repressor hub.

plant biology↗