Spatial transcriptomics: recent developments and insights in respiratory research DOI Creative Commons
Wenjia Wang, Liuxi Chu,

Liyong He

et al.

Military Medical Research, Journal Year: 2023, Volume and Issue: 10(1)

Published: Aug. 17, 2023

Abstract The respiratory system’s complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell (scRNA-seq) have provided insights into cell types the system, relevant specific spatial localization interactions not been clearly elucidated. Spatial transcriptomics (ST) has filled gap widely used studies. This review focuses on latest iterative technology of ST recent years, summarizing how can be applied physiological pathological processes with emphasis lungs. Finally, current potential development directions are proposed, including high-throughput full-length transcriptome, integration multi-omics, temporal omics, bioinformatics analysis, etc. These viewpoints expected advance study systematic mechanisms,

Language: Английский

Methods and applications for single-cell and spatial multi-omics DOI Open Access
Katy Vandereyken, Alejandro Sifrim, Bernard Thienpont

et al.

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 494 - 515

Published: March 2, 2023

Language: Английский

Citations

606
Unsupervised spatially embedded deep representation of spatial transcriptomics DOI Creative Commons
Hang Xu, Huazhu Fu, Yahui Long

et al.

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

Published: Jan. 12, 2024

Abstract Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting to dissect tissue heterogeneity map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with masked self-supervised learning mechanism construct low-dimensional latent representation gene expression, then simultaneously embedded the corresponding through variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets better scalability high-resolution than existing methods. Additionally, we show SEDR’s ability impute denoise expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

Language: Английский

Citations

126
Spatial transcriptomics: Technologies, applications and experimental considerations DOI Creative Commons
Ye Wang, Bin Liu, Gexin Zhao

et al.

Genomics, Journal Year: 2023, Volume and Issue: 115(5), P. 110671 - 110671

Published: June 21, 2023

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in given type, one needs understand how much, when where the is expressed. Classic bulk RNA sequencing popular single destroy structural fail provide spatial information. However, location expression or complex tissue provides key clues comprehend neighboring genes cells cross talk, transduce signals work together as team complete job. functional requirement for content has been driving force rapid development transcriptomics technologies past few years. Here, we present overview current with special focus on commercially available currently being commercialized technologies, highlight applications by category discuss experimental considerations first experiment.

Language: Английский

Citations

80
Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology DOI
Daiwei Zhang,

Amelia Schroeder,

Hanying Yan

et al.

Nature Biotechnology, Journal Year: 2024, Volume and Issue: 42(9), P. 1372 - 1377

Published: Jan. 2, 2024

Language: Английский

Citations

68
Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA DOI Creative Commons
Jian Hu, Kyle Coleman, Daiwei Zhang

et al.

Cell Systems, Journal Year: 2023, Volume and Issue: 14(5), P. 404 - 417.e4

Published: May 1, 2023

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances transcriptomics (ST) have enabled comprehensive characterization gene expression TME. However, popular ST platforms, such as Visium, only measure low-resolution spots large tissue areas that not covered by any spots, which limits usefulness studying detailed structure Here, we present TESLA, a machine learning framework for annotation with pixel-level resolution ST. TESLA integrates histological information annotate heterogeneous immune cells directly on histology image. further detects unique TME features tertiary lymphoid structures, represents promising avenue understanding architecture Although mainly illustrated applications cancer, can also be applied other diseases.

Language: Английский

Citations

54
SpatialData: an open and universal data framework for spatial omics DOI Creative Commons
Luca Marconato, Giovanni Palla, Kevin A. Yamauchi

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: unknown

Published: March 20, 2024

Abstract Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling uni- and multimodal spatial datasets remains a challenge owing to large data volumes, heterogeneity types lack flexible, spatially aware structures. Here we introduce SpatialData, framework that establishes unified extensible multiplatform file-format, lazy representation larger-than-memory data, transformations alignment common coordinate systems. SpatialData facilitates annotations cross-modal aggregation analysis, utility which is illustrated in context multiple vignettes, including integrative analysis on Xenium Visium breast cancer study.

Language: Английский

Citations

53
Stabilized mosaic single-cell data integration using unshared features DOI Creative Commons
Shila Ghazanfar, Carolina Guibentif, John C. Marioni

et al.

Nature Biotechnology, Journal Year: 2023, Volume and Issue: 42(2), P. 284 - 292

Published: May 25, 2023

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured technologies, onto a common embedding facilitate downstream analytical tasks. Current horizontal data techniques use set of features, thereby ignoring non-overlapping and losing information. Here we introduce StabMap, mosaic technique that stabilizes mapping by exploiting the features. StabMap first infers topology based on shared then projects all cells supervised or unsupervised reference coordinates traversing shortest paths along topology. We show performs well in various simulation contexts, facilitates 'multi-hop' where some datasets do not share any enables spatial gene expression for dissociated transcriptomic reference.

Language: Английский

Citations

50
Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues DOI Creative Commons
Huan Wang, Ruixu Huang,

Jack Nelson

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 8, 2023

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, gene signatures associated with pathological features, are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance three commercial iST on serial sections from tissue microarrays (TMAs) containing 23 tumor normal types both relative technical biological performance. On matched found that 10x Xenium shows higher transcript counts per without sacrificing specificity, but all concord to orthogonal RNA-seq datasets perform resolved cell typing, albeit different false discovery rates, segmentation error frequencies, varying degrees sub-clustering downstream analyses. Taken together, our analyses provide a comprehensive benchmark guide choice method as researchers design studies precious samples this rapidly evolving field.

Language: Английский

Citations

46
Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows DOI Creative Commons
Sergio Marco Salas, Paulo Czarnewski,

Louis B. Kuemmerle

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 14, 2023

Abstract The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10X Genomics capable of mapping hundreds transcripts in situ at subcellular resolution. Given the multitude commercially available technologies, recommendations choice and analysis guidelines are increasingly important. Herein, we explore eight preview datasets mouse brain two human breast cancer comparing scalability, resolution, data quality, capacities limitations with other spatially resolved technologies. addition, benchmarked performance multiple open source computational tools when applied to tasks including cell segmentation, segmentation-free analysis, selection variable genes domain identification, among others. This study serves as first independent Xenium, provides best-practices for such datasets.

Language: Английский

Citations

45
A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms DOI Open Access
David P. Cook, Kirk B. Jensen,

Kellie Wise

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 14, 2023

Abstract Spatial transcriptomics is a rapidly evolving field, overwhelmed by multitude of technologies. This study aims to offer comparative analysis datasets generated from leading in situ imaging platforms. We have spatial data serial sections prostate adenocarcinoma using the 10x Genomics Xenium and NanoString CosMx SMI Additionally, orthogonal single-nucleus RNA sequencing (snRNA-seq) was performed on same FFPE tissue establish reference for tumor’s transcriptional profiles. assessed various technical aspects, such as reproducibility, sensitivity, dynamic range, cell segmentation, type annotation, congruence with single-cell profiling. The practicality assessing cellular organization biomarker localization evaluated. Although fewer genes are measured (CosMx: 960, Xenium: 377, an overlap 125), consistently demonstrates higher broader better alignment Conversely, CosMx’s out-of-the-box segmentation outperformed Xenium’s, resulting noticeable transcript misassignment within certain areas. However, impact this cells’ profile minimal. Together, comprehensive comparison two commercial platforms provides essential metrics their performance, offering invaluable insights future research technological advancements field.

Language: Английский

Citations

43