The future of rapid and automated single-cell data analysis using reference mapping

Cell, Journal Year: 2024, Volume and Issue: 187(10), P. 2343 - 2358

Published: May 1, 2024

As the number of single-cell datasets continues to grow rapidly, workflows that map new data well-curated reference atlases offer enormous promise for biological community. In this perspective, we discuss key computational challenges and opportunities reference-mapping algorithms. We how mapping algorithms will enable integration diverse across disease states, molecular modalities, genetic perturbations, species eventually replace manual laborious unsupervised clustering pipelines.

Language: Английский

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Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS

Nature Biotechnology, Journal Year: 2024, Volume and Issue: 42(10), P. 1594 - 1605

Published: Jan. 23, 2024

Abstract Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework mosaic integration knowledge transfer (MIDAS) multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation correction data using self-supervised information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods reliability evaluating performance trimodal tasks. also constructed atlas human peripheral blood mononuclear cells tailored learning reciprocal reference mapping schemes enable flexible accurate from new Applications pseudotime analysis cross-tissue on bone marrow versatility MIDAS. available at https://github.com/labomics/midas .

Language: Английский

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LIANA+ provides an all-in-one framework for cell–cell communication inference

Nature Cell Biology, Journal Year: 2024, Volume and Issue: 26(9), P. 1613 - 1622

Published: Sept. 1, 2024

The growing availability of single-cell and spatially resolved transcriptomics has led to the development many approaches infer cell-cell communication, each capturing only a partial view complex landscape intercellular signalling. Here we present LIANA+, scalable framework built around rich knowledge base decode coordinated inter- intracellular signalling events from single- multi-condition datasets in both data. By extending unifying established methodologies, LIANA+ provides comprehensive set synergistic components study communication via diverse molecular mediators, including those measured multi-omics is accessible at https://github.com/saezlab/liana-py with extensive vignettes ( https://liana-py.readthedocs.io/ ) an all-in-one solution inference.

Language: Английский

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Trigeminal ganglion neurons are directly activated by influx of CSF solutes in a migraine model

Science, Journal Year: 2024, Volume and Issue: 385(6704), P. 80 - 86

Published: July 4, 2024

Classical migraine patients experience aura, which is transient neurological deficits associated with cortical spreading depression (CSD), preceding headache attacks. It not currently understood how a pathological event in cortex can affect peripheral sensory neurons. In this study, we show that cerebrospinal fluid (CSF) flows into the trigeminal ganglion, establishing nonsynaptic signaling between brain and cells. After CSD, ~11% of CSF proteome altered, up-regulation proteins directly activate receptors ganglion. collected from animals exposed to CSD activates neurons naïve mice part by CSF-borne calcitonin gene-related peptide (CGRP). We identify communication pathway central nervous system might explain relationship migrainous aura headache.

Language: Английский

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Current and future directions in network biology

Bioinformatics Advances, Journal Year: 2024, Volume and Issue: 4(1)

Published: Jan. 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.

Language: Английский

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

Nature Immunology, Journal Year: 2024, Volume and Issue: 25(3), P. 405 - 417

Published: Feb. 27, 2024

Language: 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, Journal Year: 2025, Volume and Issue: 27, P. 265 - 277

Published: Jan. 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.

Language: Английский

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Revealing microRNA regulation in single cells

Trends in Genetics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 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

Language: Английский

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Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 22, 2025

Abstract Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, efficacy these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies been conducted to unravel cell-specific responses ICB treatment. However, are limited in sample sizes require advanced coding skills exploration. Here, we compiled eight scRNA-seq datasets from nine types, encompassing 223 patients, 90,270 cells, 265,671 other cell This compilation forms unique resource tailored investigate how cells respond We meticulously curated, quality-checked, pre-processed, analyzed data, ensuring easy access researchers. Moreover, designed user-friendly interface seamless By sharing code data creating interfaces, aim assist fellow These resources offer valuable support those interested leveraging exploring diverse facilitating comprehensive understanding responses.

Language: Английский

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Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Science, Journal Year: 2025, Volume and Issue: 387(6735)

Published: Jan. 2, 2025

Combinations of transcription factors govern the identity cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these codes and devised three metrics compare types in telencephalon across amniotes. To this end, we generated single-cell multiome spatially resolved transcriptomics data chicken telencephalon. Enhancer orthologous nonneuronal γ-aminobutyric acid–mediated (GABAergic) show a high degree similarity amniotes, whereas excitatory neurons mammalian neocortex avian pallium exhibit varying degrees similarity. mesopallial are most similar those deep-layer neurons. With study, present generally applicable approaches on basis regulatory sequences.

Language: Английский

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