IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer

Nature, Journal Year: 2023, Volume and Issue: 623(7986), P. 415 - 422

Published: Nov. 1, 2023

Language: Английский

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Modeling intercellular communication in tissues using spatial graphs of cells

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 41(3), P. 332 - 336

Published: Oct. 27, 2022

Abstract Models of intercellular communication in tissues are based on molecular profiles dissociated cells, limited to receptor–ligand signaling and ignore spatial proximity situ. We present node-centric expression modeling, a method graph neural networks that estimates the effects niche composition gene an unbiased manner from profiling data. recover signatures processes known underlie cell communication.

Language: Английский

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A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: March 21, 2023

Abstract Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction tissue architecture. Due the existence low-resolution spots in recent technologies, uncovering cellular heterogeneity is crucial for disentangling cell types, many related methods have been proposed. Here, we benchmark 18 existing resolving a deconvolution task with 50 real-world simulated datasets by evaluating accuracy, robustness, usability methods. We compare these comprehensively using different metrics, resolutions, spot numbers, gene numbers. In terms performance, CARD, Cell2location, Tangram best conducting task. To refine our comparative results, provide decision-tree-style guidelines recommendations method selection their additional features, will help users easily choose fulfilling concerns.

Language: Английский

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Spatial biology of cancer evolution

Nature Reviews Genetics, Journal Year: 2022, Volume and Issue: 24(5), P. 295 - 313

Published: Dec. 9, 2022

Language: Английский

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An integrated single cell and spatial transcriptomic map of human white adipose tissue

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: March 15, 2023

Abstract To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis publicly available newly generated single-cell, single-nucleus, spatial transcriptomic results from subcutaneous, omental, perivascular WAT. Our high-resolution map is built data ten allowed us to robustly identify >60 subpopulations adipocytes, fibroblast adipogenic progenitors, vascular, immune cells. Using these results, deconvolved bulk nine additional cohorts provide clinical dimensions the map. This identified cell-cell interactions as well relationships between specific cell subtypes insulin resistance, dyslipidemia, adipocyte volume, lipolysis upon long-term weight changes. Altogether, our meta-map provides rich resource defining microarchitectural landscape WAT describes associations types metabolic states.

Language: Английский

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Lymphatics act as a signaling hub to regulate intestinal stem cell activity

Cell stem cell, Journal Year: 2022, Volume and Issue: 29(7), P. 1067 - 1082.e18

Published: June 20, 2022

Language: Английский

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Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation

Nature Plants, Journal Year: 2023, Volume and Issue: 9(4), P. 515 - 524

Published: April 13, 2023

Language: Английский

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Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Cell, Journal Year: 2024, Volume and Issue: 187(10), P. 2485 - 2501.e26

Published: April 22, 2024

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, proteomics, and computational approaches to define glioma cellular states uncover their organization. We find three prominent modes of First, gliomas are composed small local environments, each typically enriched with one major state. Second, specific pairs preferentially reside proximity across multiple scales. This pairing is consistent tumors. Third, these pairwise interactions collectively a global architecture five layers. Hypoxia appears drive the layers, as it associated long-range organization that includes all cancer cell Accordingly, tumor regions distant from any hypoxic/necrotic foci tumors lack hypoxia such low-grade IDH-mutant less organized. In summary, provide conceptual framework for glioma, highlighting tissue organizer.

Language: Английский

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A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

Cells, Journal Year: 2023, Volume and Issue: 12(15), P. 1970 - 1970

Published: July 30, 2023

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of heterogeneity, identification rare but significant cell types, and exploration cell-cell communications interactions. Its broad applications span both basic clinical research domains. In this comprehensive review, we survey current landscape scRNA-seq analysis methods tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, inference communication. We review challenges encountered in analysis, issues sparsity or low expression, reliability assumptions discuss potential impact suboptimal clustering differential expression tools downstream analyses, particularly identifying subpopulations. Finally, recent advancements future directions enhancing analysis. Specifically, highlight development novel annotating single-cell data, integrating interpreting multimodal datasets covering epigenomics, proteomics, inferring communication networks. By elucidating latest progress innovation, provide overview rapidly advancing field

Language: Английский

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Deep learning applications in single-cell genomics and transcriptomics data analysis

Biomedicine & Pharmacotherapy, Journal Year: 2023, Volume and Issue: 165, P. 115077 - 115077

Published: July 1, 2023

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding complex biological systems diseases, such as cancer, immune system, chronic diseases. However, technologies generate massive amounts data that often high-dimensional, sparse, complex, thus making analysis with traditional computational approaches difficult unfeasible. To tackle these challenges, many turning deep learning (DL) potential alternatives conventional machine (ML) algorithms for studies. DL is branch ML capable extracting high-level features from raw inputs multiple stages. Compared ML, models have provided significant improvements across domains applications. In this work, we examine applications genomics, transcriptomics, spatial multi-omics integration, address whether techniques will prove be advantageous or if omics domain poses unique challenges. Through systematic literature review, found has not yet revolutionized most pressing challenges field. using shown promising results (in cases outperforming previous state-of-the-art models) preprocessing downstream analysis. Although developments generally been gradual, recent advances reveal can offer valuable resources fast-tracking advancing research single-cell.

Language: Английский

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