Revealing microRNA regulation in single cells

Trends in Genetics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

MicroRNAs (miRNAs) are key regulators of gene expression and control cellular functions in physiological pathophysiological states. miRNAs play important roles disease, stress, development, now being investigated for therapeutic approaches. Alternative processing during biogenesis results the generation miRNA isoforms (isomiRs) which further diversify regulation. Single-cell RNA-sequencing (scsRNA-seq) technologies, together with computational strategies, enable exploration miRNAs, isomiRs, interacting RNAs at level. By integration other miRNA-associated single-cell modalities, can be resolved different stages In this review we discuss (i) experimental assays that measure isomiR abundances, (ii) methods their analysis to investigate mechanisms post-transcriptional

Язык: Английский

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AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 265 - 277

Опубликована: Янв. 1, 2025

Despite the wealth of single-cell multi-omics data, it remains challenging to predict consequences novel genetic and chemical perturbations in human body. It requires knowledge molecular interactions at all biological levels, encompassing disease models humans. Current machine learning methods primarily establish statistical correlations between genotypes phenotypes but struggle identify physiologically significant causal factors, limiting their predictive power. Key challenges modeling include scarcity labeled generalization across different domains, disentangling causation from correlation. In light recent advances data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale framework tackle these issues. This will integrate organism hierarchies, species genotype-environment-phenotype relationships under various conditions. AI inspired by biology may targets, biomarkers, pharmaceutical agents, personalized medicines for presently unmet medical needs.

Язык: Английский

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Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology

The Plant Journal, Год журнала: 2022, Номер 111(6), С. 1527 - 1538

Опубликована: Июль 13, 2022

SUMMARY Advances in high‐throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role systems because its excellent performance and wide application analysis However, to achieve ideal performance, supervised ML algorithms require large numbers labeled samples as training In some cases, it is impossible or prohibitively expensive obtain enough data; here, paradigms unsupervised (UL) semi‐supervised (SSL) play indispensable role. this review, we first introduce basic concepts techniques, well representative UL SSL algorithms, including clustering, dimensionality reduction, self‐supervised (self‐SL), positive‐unlabeled (PU) transfer learning. We then review recent advances applications both phenotyping research. Finally, discuss limitations highlight significance challenges strategies biology.

Язык: Английский

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Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

BioEssays, Год журнала: 2022, Номер 44(11)

Опубликована: Сен. 6, 2022

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what observed at this macroscopic level can be the result of many interactions several different single cells. Thus, observable 'average' cannot outright used as representative 'average cell'. Rather, resulting emerging behaviour actions and Single-cell RNA sequencing (scRNA-Seq) enables comparison transcriptomes individual This provides high-resolution maps dynamic cellular programmes allowing us answer fundamental biological questions on their function evolution. It also allows address medical such role rare populations contributing disease progression therapeutic resistance. Furthermore, an understanding context-specific dependencies, namely a in specific context, which crucial understand some complex diseases, diabetes, cardiovascular cancer. Here, we provide overview scRNA-Seq, including comparative review technologies computational pipelines. We discuss current applications focus tumour heterogeneity clear example how scRNA-Seq new disease. Additionally, limitations highlight need powerful pipelines reproducible protocols for broader acceptance technique basic clinical research.

Язык: Английский

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A unified computational framework for single-cell data integration with optimal transport

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Дек. 1, 2022

Abstract Single-cell data integration can provide a comprehensive molecular view of cells. However, how to integrate heterogeneous single-cell multi-omics as well spatially resolved transcriptomic remains major challenge. Here we introduce uniPort, unified framework that combines coupled variational autoencoder (coupled-VAE) and minibatch unbalanced optimal transport (Minibatch-UOT). It leverages both highly variable common dataset-specific genes for handle the heterogeneity across datasets, it is scalable large-scale datasets. uniPort jointly embeds datasets into shared latent space. further construct reference atlas gene imputation Meanwhile, provides flexible label transfer deconvolute spatial using an plan, instead embedding We demonstrate capability by applying variety including transcriptomics, chromatin accessibility, data.

Язык: Английский

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