Spatial transcriptomics: Technologies, applications and experimental considerations

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Abstract

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

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Most cited references 58

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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

Patrik L. Ståhl, Fredrik Salmén, Sanja Vickovic … (2016)

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

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B cells are associated with survival and immunotherapy response in sarcoma

Florent Petitprez, Aurélien de Reyniès, Emily Z Keung … (2020)

Soft-tissue sarcomas represent a heterogeneous group of cancer, with more than 50 histological subtypes1,2. The clinical presentation of patients with different subtypes is often atypical, and responses to therapies such as immune checkpoint blockade vary widely3,4. To explain this clinical variability, here we study gene expression profiles in 608 tumours across subtypes of soft-tissue sarcoma. We establish an immune-based classification on the basis of the composition of the tumour microenvironment and identify five distinct phenotypes: immune-low (A and B), immune-high (D and E), and highly vascularized (C) groups. In situ analysis of an independent validation cohort shows that class E was characterized by the presence of tertiary lymphoid structures that contain T cells and follicular dendritic cells and are particularly rich in B cells. B cells are the strongest prognostic factor even in the context of high or low CD8+ T cells and cytotoxic contents. The class-E group demonstrated improved survival and a high response rate to PD1 blockade with pembrolizumab in a phase 2 clinical trial. Together, this work confirms the immune subtypes in patients with soft-tissue sarcoma, and unravels the potential of B-cell-rich tertiary lymphoid structures to guide clinical decision-making and treatments, which could have broader applications in other diseases.

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RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.

Kok Chen, Alistair N Boettiger, Jeffrey R. Moffitt … (2015)

Knowledge of the expression profile and spatial landscape of the transcriptome in individual cells is essential for understanding the rich repertoire of cellular behaviors. Here, we report multiplexed error-robust fluorescence in situ hybridization (MERFISH), a single-molecule imaging approach that allows the copy numbers and spatial localizations of thousands of RNA species to be determined in single cells. Using error-robust encoding schemes to combat single-molecule labeling and detection errors, we demonstrated the imaging of 100 to 1000 distinct RNA species in hundreds of individual cells. Correlation analysis of the ~10(4) to 10(6) pairs of genes allowed us to constrain gene regulatory networks, predict novel functions for many unannotated genes, and identify distinct spatial distribution patterns of RNAs that correlate with properties of the encoded proteins.

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Author and article information

Journal

Journal ID (nlm-journal-id): 8800135

Journal ID (pubmed-jr-id): 4142

Journal ID (nlm-ta): Genomics

Journal ID (iso-abbrev): Genomics

Title: Genomics

ISSN (Print): 0888-7543

ISSN (Electronic): 1089-8646

Publication date Nihms-submitted: 9 October 2023

Publication date (Print): September 2023

Publication date (Electronic): 21 June 2023

Publication date PMC-release: 13 October 2023

Volume: 115

Issue: 5

Page: 110671

Affiliations

[a ] Clinical Laboratory, The Affiliated Qingdao Central Hospital of Medical College of Qingdao University, Qingdao 266042, China

[b ] Departments of Medical Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, China

[c ] UCLA Technology Center for Genomics & Bioinformatics, Department of Pathology & Laboratory Medicine, 650 Charles E Young Dr., Los Angeles, CA 90095, USA

[d ] Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia

[e ] Moscow Institute of Physics and Technology, Moscow Region, 141701, Russia

[f ] World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia

[g ] Heilongjiang Academy of Traditional Chinese Medicine, No. 142, Sanfu Street, Xiangfang District, Harbin City, Heilongjiang Province 150036, China

Author notes

[1]

Contributed equally: Ye Wang and Bin Liu.

Author contributions

YW, BL, HC and XL designed and wrote the manuscript. GZ and YL prepared figures and table, AB, XM and JZ wrote the part of manuscript and edited the entire manuscript. All authors reviewed and approved the submitted version.

Table 1 summarizes 5 key parameters of 8 discussed technologies to assist in choosing a suitable platform. As a general guideline, sequencing-based technologies are preferably used for an unbiased analysis for hypothesis generation, and imaging-based technologies are more suitable for a focused in-depth analysis for hypothesis testing.

[* ] Corresponding authors. yewang@ 123456qdu.edu.cn (Y. Wang), xinminli@ 123456mednet.ucla.edu (X. Li).

Article

Manuscript ID: NIHMS1933876

DOI: 10.1016/j.ygeno.2023.110671

PMC ID: 10571167

PubMed ID: 37353093

SO-VID: e0656f70-8b0f-459e-9d36-74c119d0cc46

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

Spatial transcriptomics: Technologies, applications and experimental considerations

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Abstract

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Most cited references 58

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

B cells are associated with survival and immunotherapy response in sarcoma

RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.

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