MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data DOI Creative Commons
Yu Wang, Zaiyi Liu, Xiaoke Ma

et al.

Genome biology, Journal Year: 2024, Volume and Issue: 25(1)

Published: May 23, 2024

Abstract Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail address heterogeneity interpretability data. Here, we present a multi-layer network model for identifying domains data with joint learning. We demonstrate that can be precisely characterized discriminated by topological cell networks, facilitating identification domains, which outperforms state-of-the-art baselines. Furthermore, prove offers effective efficient strategy integrative analysis from various platforms.

Language: Английский

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST DOI Creative Commons
Yahui Long, Kok Siong Ang, Mengwei Li

et al.

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

Published: March 1, 2023

Abstract Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits data to outperform existing methods. It combines neural networks learn informative discriminative spot representations by minimizing the embedding distance between adjacent spots vice versa. demonstrated GraphST on multiple tissue types technology platforms. achieved 10% higher clustering accuracy better delineated fine-grained structures in brain embryo tissues. is also only can jointly analyze slices vertical or horizontal integration while correcting batch effects. Lastly, superior deconvolution capture niches like lymph node germinal centers exhausted tumor infiltrating T cells breast tissue.

Language: Английский

Citations

233
Clinical and translational values of spatial transcriptomics DOI Creative Commons
Linlin Zhang, Dongsheng Chen, Dongli Song

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2022, Volume and Issue: 7(1)

Published: April 1, 2022

Abstract The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes human tissues with molecular alterations, defining in situ intercellular communications knowledge on spatiotemporal medicine. present article overviews development ST aims evaluate clinical translational values for understanding pathogenesis uncovering disease-specific biomarkers. We compare advantages disadvantages sequencing- imaging-based technologies highlight opportunities challenges ST. also describe bioinformatics tools necessary dissecting patterns gene expression cellular interactions potential applications diseases practice one important issues medicine, including neurology, embryo development, oncology, inflammation. Thus, clear objectives, designs, optimizations sampling procedure protocol, repeatability ST, well simplifications analysis interpretation are key translate from bench clinic.

Language: Английский

Citations

120
SODB facilitates comprehensive exploration of spatial omics data DOI
Zhiyuan Yuan, Wentao Pan, Xuan Zhao

et al.

Nature Methods, Journal Year: 2023, Volume and Issue: 20(3), P. 387 - 399

Published: Feb. 16, 2023

Language: Английский

Citations

70
Tumor heterogeneity: preclinical models, emerging technologies, and future applications DOI Creative Commons
Marco Proietto, Martina Crippa,

C. Damiani

et al.

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: April 28, 2023

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to describing variations in morphology, transcriptional profiles, metabolism, metastatic potential. More recently, field has included characterization of tumor immune microenvironment depiction dynamics underlying cellular interactions promoting ecosystem evolution. been found most tumors representing one challenging behaviors ecosystems. As critical factors impairing long-term efficacy solid therapy, heterogeneity leads resistance, more aggressive metastasizing, recurrence. We review role main models emerging single-cell spatial genomic technologies our understanding heterogeneity, its contribution lethal outcomes, physiological challenges consider designing therapies. highlight how dynamically evolve because leverage this unleash recognition through immunotherapy. A multidisciplinary approach grounded novel bioinformatic computational tools will allow reaching integrated, multilayered knowledge required implement personalized, efficient therapies urgently for patients.

Language: Английский

Citations

56
Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope DOI Creative Commons

Xiaomeng Wan,

Jiashun Xiao,

Sindy Sing Ting Tam

et al.

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

Published: Nov. 29, 2023

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding tissue architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either cellular resolution transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data using deep generative models. With innovation model algorithm designs, SpatialScope not only enhances seq-based to achieve single-cell resolution, but also accurately infers expression levels for image-based data. We demonstrate SpatialScope's utility through simulation studies real analysis from both approaches. provides characterization structures at facilitating downstream analysis, including detecting communication ligand-receptor interactions, localizing subtypes, identifying spatially differentially expressed genes.

Language: Английский

Citations

54
Benchmarking spatial clustering methods with spatially resolved transcriptomics data DOI
Zhiyuan Yuan, Fangyuan Zhao, Senlin Lin

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: 21(4), P. 712 - 722

Published: March 15, 2024

Language: Английский

Citations

53
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics DOI
Gunsagar S. Gulati,

Jeremy Philip D’Silva,

Yunhe Liu

et al.

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 26(1), P. 11 - 31

Published: Aug. 21, 2024

Language: Английский

Citations

33
MENDER: fast and scalable tissue structure identification in spatial omics data DOI Creative Commons
Zhiyuan Yuan

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 5, 2024

Abstract Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance notable question the field. Inspired by consistent observation of cellular neighborhood structures across various technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), tissue identification. Applied on datasets 3 brain regions whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern models while automatically aligning labels slices, despite using much less running time than second-fastest. MENDER’s power allows uncovering previously overlooked domains that exhibit strong associations aging. scalability makes it freely appliable million-level atlas. discriminative enables differentiation breast cancer patient subtypes obscured single-cell analysis.

Language: Английский

Citations

21
PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics DOI Creative Commons
Yuchen Liang,

Guowei Shi,

Runlin Cai

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 18, 2024

Abstract Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying variable (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated depicting domains, they technically dissociated most methods. Here, we present framework (PROST) for the quantitative recognition of patterns, consisting (i) quantitatively characterizing variations gene through PROST Index; (ii) unsupervised clustering domains via self-attention mechanism. We demonstrate that performs superior SVG identification domain segmentation various resolutions, multicellular cellular levels. Importantly, Index can be applied prioritize variations, facilitating exploration biological insights. Together, our study provides flexible robust analyzing diverse data.

Language: Английский

Citations

16
Identifying spatial domain by adapting transcriptomics with histology through contrastive learning DOI Open Access
Yuansong Zeng, Rui Yin,

Mai Luo

et al.

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 24(2)

Published: Feb. 13, 2023

Abstract Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the information and histology images tissues. Accurately identifying domains spots is a vital step for various downstream tasks analysis. To remove noises expression, several methods been developed to combine histopathological data analysis transcriptomics. However, these either use image only relations spots, or individually learn embeddings without fully coupling information. Here, we propose novel method ConGI accurately exploit by adapting with through contrastive learning. Specifically, designed three loss functions within between two modalities (the data) common representations. The learned representations are then used cluster on tumor normal datasets. was shown outperform existing domain identification. In addition, also powerful tasks, including trajectory inference, clustering, visualization.

Language: Английский

Citations

28