Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Nucleic Acids Research, Год журнала: 2023, Номер 51(20), С. 10934 - 10949

Опубликована: Окт. 16, 2023

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, their target genes, so called TF regulons, can be coupled with computational algorithms to estimate activity TFs. However, interpret these findings accurately, regulons high reliability coverage are needed. In this study, we present evaluate collection created using CollecTRI meta-resource containing signed TF-gene interactions for 1186 context, introduce workflow integrate information from multiple resources assign sign could applied other comprehensive knowledge bases. We find CollecTRI-derived outperform public collections accurately inferring changes activities perturbation experiments. Furthermore, showcase value by examining profiles three different cancer types exploring at level single-cells. Overall, enable accurate estimation thereby help transcriptomics data.

Язык: Английский

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Microfluidic Impedance Cytometry Enabled One‐Step Sample Preparation for Efficient Single‐Cell Mass Spectrometry

Small, Год журнала: 2024, Номер 20(26)

Опубликована: Март 14, 2024

Abstract Single‐cell mass spectrometry (MS) is significant in biochemical analysis and holds great potential biomedical applications. Efficient sample preparation like sorting (i.e., separating target cells from the mixed population) desalting moving off non‐volatile salt solution) urgently required single‐cell MS. However, traditional methods suffer complicated operation with various apparatus, or insufficient performance. Herein, a one‐step strategy by leveraging label‐free impedance flow cytometry (IFC) based microfluidics proposed. Specifically, IFC framework to characterize sort single‐cells adopted. Simultaneously sorting, cell transferred local high‐salinity buffer MS‐compatible solution. In this way, are achieved collected can be directly fed for MS analysis. A high efficiency (>99%), cancer purity (≈87%), whole workflow of impedance‐based separation normal (MCF‐10A) (MDA‐MB‐468) verified. As standalone module, microfluidic chip compatible variety methods, envisioned provide new paradigm efficient preparation, further multi‐modal electrical metabolic) characterization single‐cells.

Язык: Английский

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Transformers in single-cell omics: a review and new perspectives

Nature Methods, Год журнала: 2024, Номер 21(8), С. 1430 - 1443

Опубликована: Авг. 1, 2024

Язык: Английский

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Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Фев. 12, 2024

Abstract Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured fails to undergo normal and assumes proinflammatory profibrotic phenotype that may promote fibrosis chronic kidney disease. The healthy failed change is marked by cell state-specific transcriptomic epigenomic changes. Single nucleus joint RNA- ATAC-seq sequencing offers an opportunity study the gene regulatory networks underpinning these changes in order identify key drivers. We develop regularized regression approach construct genome-wide parametric using multiomic datasets. generate single dataset from seven adult human samples apply our method drivers injury response associated with demonstrate highly effective tool for predicting cis- trans- elements transition use it NFAT5 as driver maladaptive state.

Язык: Английский

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Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Nature Biotechnology, Год журнала: 2024, Номер unknown

Опубликована: Апрель 12, 2024

Abstract Existing methods for gene regulatory network (GRN) inference rely on expression data alone or lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent points still presents a daunting challenge. Here we present LINGER (Lifelong neural regulation), machine-learning method to infer GRNs single-cell paired incorporates atlas-scale external across diverse cellular contexts prior knowledge transcription factor motifs as manifold regularization. achieves fourfold sevenfold relative increase in accuracy over existing reveals landscape genome-wide association studies, enabling enhanced interpretation disease-associated variants genes. Following GRN reference multiome enables estimation activity solely leveraging abundance available identify driver regulators case-control studies.

Язык: Английский

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Current and future directions in network biology

Bioinformatics Advances, Год журнала: 2024, Номер 4(1)

Опубликована: Янв. 1, 2024

Abstract Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions diseases across systems scales. Although been around for two decades, it remains nascent. It witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably growing complexity volume data together with increased diversity types describing different tiers organization. We discuss prevailing research directions network biology, focusing on molecular/cellular networks but also other such as biomedical knowledge graphs, patient similarity networks, brain social/contact relevant to disease spread. In more detail, we highlight areas inference comparison multimodal integration heterogeneous higher-order analysis, machine learning network-based personalized medicine. Following overview recent breakthroughs these five areas, offer a perspective future biology. Additionally, scientific communities, educational initiatives, importance fostering within field. This article establishes roadmap immediate long-term vision Availability implementation Not applicable.

Язык: Английский

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Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

Nature Immunology, Год журнала: 2024, Номер 25(3), С. 405 - 417

Опубликована: Фев. 27, 2024

Язык: Английский

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Gene expression networks regulated by human personality

Molecular Psychiatry, Год журнала: 2024, Номер 29(7), С. 2241 - 2260

Опубликована: Март 4, 2024

Genome-wide association studies of human personality have been carried out, but transcription the whole genome has not studied in relation to humans. We collected genome-wide expression profiles adults characterize regulation and function genes related personality. devised an innovative multi-omic approach network analysis identify key control elements interactions multi-modular networks. identified sets transcribed that were co-expressed specific brain regions with known be associated Then we minimum networks for co-localized using bioinformatic resources. Subjects 459 from Young Finns Study who completed Temperament Character Inventory provided peripheral blood genomic transcriptomic analysis. extrinsic 45 regulatory seed involved self-regulation emotional reactivity extracellular stimuli (e.g., anxiety) intrinsic 43 interpretations meaning production concepts language). discovered between two coordinated by a hub 3 miRNAs protein-coding shared both. Interactions proteins ncRNAs more than 100 overlap directly personality-related another 4000 interact indirectly. conclude six-gene is crux integrative orchestrates information-transfer throughout system over enriched liquid-liquid-phase-separation (LLPS)-related RNAs, diverse factors, hominid-specific lncRNAs. Gene regulate neuronal plasticity, epigenesis, adaptive functioning salience self-awareness.

Язык: Английский

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Single-cell analysis of chromatin accessibility in the adult mouse brain

Nature, Год журнала: 2023, Номер 624(7991), С. 378 - 389

Опубликована: Дек. 13, 2023

Abstract Recent advances in single-cell technologies have led to the discovery of thousands brain cell types; however, our understanding gene regulatory programs these types is far from complete 1–4 . Here we report a comprehensive atlas candidate cis -regulatory DNA elements (cCREs) adult mouse brain, generated by analysing chromatin accessibility 2.3 million individual cells 117 anatomical dissections. The includes approximately 1 cCREs and their across 1,482 distinct populations, adding over 446,000 most recent such annotation genome. are moderately conserved human brain. mouse-specific cCREs—specifically, those identified subset cortical excitatory neurons—are strongly enriched for transposable elements, suggesting potential role emergence new neuronal diversity. Finally, infer networks 260 subclasses develop deep-learning models predict activities different sequence alone. Our results provide resource analysis cell-type-specific regulation both brains.

Язык: Английский

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Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

npj Systems Biology and Applications, Год журнала: 2023, Номер 9(1)

Опубликована: Окт. 19, 2023

Abstract Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these plays critical role understanding underlying crosstalk drives many cellular processes diseases. Recent advances sequencing technology have led development of state-of-the-art GRN inference methods exploit matched single-cell multi-omic data. By employing diverse mathematical statistical methodologies, aim reconstruct more comprehensive precise networks. In this review, we give brief overview on methodological foundations commonly used methods. We then compare contrast latest for multi-omics data, discuss assumptions, limitations opportunities. Finally, challenges future directions hold promise further advancements rapidly developing field.

Язык: Английский

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