Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives DOI

Felipe Segato Dezem,

Wani Arjumand,

Hannah DuBose

et al.

Annual Review of Biomedical Data Science, Journal Year: 2024, Volume and Issue: 7(1), P. 131 - 153

Published: May 20, 2024

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative organization of molecular microenvironment tissue samples in normal disease states. Spatial can be categorized three major modalities: (a) next-generation sequencing–based assays, (b) imaging-based spatially resolved transcriptomics approaches including situ hybridization/in sequencing, (c) proteomics. These modalities allow assessment transcripts proteins at level, generating large computationally challenging datasets. The lack standardized computational pipelines analyze integrate these nonuniform structured made it necessary apply artificial intelligence machine learning strategies best visualize translate their complexity. In this review, we summarize currently available techniques strategies, highlight advantages limitations, discuss future prospects scientific field.

Language: Английский

Spatially aware dimension reduction for spatial transcriptomics DOI Creative Commons
Lulu Shang, Xiang Zhou

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 23, 2022

Abstract Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing data is computationally challenging, as the collected from various often noisy and display substantial correlation across tissue locations. Here, we develop spatially-aware dimension reduction method, SpatialPCA, can extract low dimensional representation biological signal preserved structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis transcriptomics. We illustrate benefits SpatialPCA domain detection explores its utility trajectory inference high-resolution map construction. In real applications, identifies key molecular immunological signatures detected tumor surrounding microenvironment, including tertiary lymphoid structure shapes gradual transition during tumorigenesis metastasis. addition, detects past neuronal developmental history underlies current landscape locations cortex.

Language: Английский

Citations

136
Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas DOI Creative Commons
Yanming Ren,

Zongyao Huang,

Lingling Zhou

et al.

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

Published: Feb. 23, 2023

Abstract Diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) are the most lethal brain tumors that primarily occur in pediatric adult patients, respectively. Both exhibit significant heterogeneity, shaped by distinct genetic/epigenetic drivers, transcriptional programs including RNA splicing, microenvironmental cues glioma niches. However, spatial organization of cellular states niche-specific regulatory remain to be investigated. Here, we perform a profiling DMG GBM combining short- long-read transcriptomics, single-cell transcriptomic datasets. We identify clinically relevant programs, isoform diversity, multi-cellular ecosystems across different find while tumor core enriches for oligodendrocyte precursor-like cells, radial glial stem-like (RG-like) cells enriched neuron-rich invasive niche both GBM. Further, RG-like functionally confirm FAM20C mediates growth microenvironment human neural stem cell derived orthotopic model. Together, our results provide blueprint understanding architecture vulnerabilities

Language: Английский

Citations

86
BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies DOI Creative Commons
Zheng Li, Xiang Zhou

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Aug. 4, 2022

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for resolution transcriptomics. BASS performs cell type clustering at the scale domain detection regional scale, two tasks carried out simultaneously within Bayesian hierarchical modeling framework. We illustrate benefits of through comprehensive simulations applications to three datasets. The substantial power gain brought by allows us reveal accurate cellular landscape in both cortex hypothalamus.

Language: Английский

Citations

76
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
CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity DOI
Marco Varrone, Daniele Tavernari, Albert Santamaria‐Martínez

et al.

Nature Genetics, Journal Year: 2023, Volume and Issue: 56(1), P. 74 - 84

Published: Dec. 8, 2023

Language: Английский

Citations

49
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics DOI Creative Commons
Jiaqiang Zhu, Lulu Shang, Xiang Zhou

et al.

Genome biology, Journal Year: 2023, Volume and Issue: 24(1)

Published: March 3, 2023

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing SRT data poorly documented, hard to reproduce, or unrealistic. Single-cell simulators not directly applicable for simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator scalable, reproducible, realistic simulations. SRTsim only maintains various expression characteristics of but also preserves patterns. illustrate the benefits benchmarking clustering, pattern detection, cell-cell communication identification.

Language: Английский

Citations

46
Spatiotemporal omics for biology and medicine DOI
Longqi Liu, Ao Chen, Yuxiang Li

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(17), P. 4488 - 4519

Published: Aug. 1, 2024

Language: Английский

Citations

29
Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases DOI Creative Commons
Paul Kießling, Christoph Kuppe

Genome Medicine, Journal Year: 2024, Volume and Issue: 16(1)

Published: Jan. 18, 2024

Abstract Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome parallel or even same tissue section. This review focuses on recent advancements spatial multi-omics technologies, including novel computational approaches. We discuss low-resolution high-resolution methods which can resolve up 10,000 individual molecules at subcellular level. By applying integrating these techniques, researchers recently gained valuable insights into molecular circuits mechanisms govern cell biology along cardiovascular disease spectrum. provide an overview current approaches, with focus integration datasets, highlighting strengths weaknesses various pipelines. These tools play crucial role analyzing interpreting facilitating discovery new findings, enhancing translational research. Despite nontrivial challenges, such need for standardization experimental setups, analysis, improved tools, application holds tremendous potential revolutionizing our understanding human processes identification biomarkers therapeutic targets. Exciting opportunities lie ahead field will likely contribute advancement personalized medicine diseases.

Language: Английский

Citations

24
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
Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics DOI Creative Commons
Lulu Shang, Peijun Wu, Xiang Zhou

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 26, 2025

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)—a subset of exhibiting distinct expression patterns within specific types. Celina utilizes varying coefficient model to accurately capture each gene's pattern relation the distribution types across tissue locations, ensuring effective type I error control and high power. proves powerful compared existing methods single-cell resolution stands as only solution spot-resolution transcriptomics. Applied five real datasets, uncovers ct-SVGs associated with tumor progression patient survival lung cancer, identifies metagenes unique linked proliferation immune response kidney detects preferentially expressed near amyloid-β plaques an Alzheimer's model. The authors develop detect (ct-SVGs) These exhibit types, offering insights into transcriptomic mechanism underlying cellular heterogeneity.

Language: Английский

Citations

2