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Review
. 2024 Sep 13:12:e17860.
doi: 10.7717/peerj.17860. eCollection 2024.

The burgeoning spatial multi-omics in human gastrointestinal cancers

Weizheng Liang #  1 , Zhenpeng Zhu #  2   3 , Dandan Xu  1 , Peng Wang  2   3 , Fei Guo  2 , Haoshan Xiao  2   3 , Chenyang Hou  2   3 , Jun Xue  2 , Xuejun Zhi  4 , Rensen Ran  1   5
Affiliations
Review

The burgeoning spatial multi-omics in human gastrointestinal cancers

Weizheng Liang et al. PeerJ. .

Abstract

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Keywords: Esophageal cancer; Gastric cancer and colorectal cancer; Spatial metabolomics; Spatial proteomics; Spatial transcriptomics.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Flow chart of the review.
Figure 2
Figure 2. Spatial multi-omics techniques.
Created with Figdraw, https://www.figdraw.com.
Figure 3
Figure 3. Schematic diagrams of spatial transcriptomic and matrix-assisted laser desorption/ionization (MALDI).
(A) Procedures of NGS-based method of spatial transcriptomic. (B) Procedures of imaging-based method of spatial transcriptomic. (C) Schematic diagram of MALDI. The process begins with the preparation of the MALDI sample, followed by the application of MALDI for the matrix deposition. Subsequently, laser scanning technology is employed for the collection, processing, and analysis of signals. Finally, the signals and multi-image data are integrated for visualization. Created with Figdraw, https://www.figdraw.com.
Figure 4
Figure 4. Schematic diagrams of MIBI, MIBI and DSP.
(A) Schematic diagram of MIBI. Commence by incubating the sample with isotope labeling and antibodies. Subsequently, conduct imaging and isotope quantification using mass spectrometry (MS). Finally, integrate and analyze the data to elucidate spatial organization at the single-cell resolution. (B) Schematic diagram of CODEX. To begin, conjugate antibodies with barcode tags and incubate them with the sample along with fluorescent tricolor probes for specific hybridization. Next, remove excess fluorescent dyes, and proceed to the cycling reaction and imaging steps. Finally, overlay the images at single-cell resolution and conduct spatial information analysis. (C) Schematic diagram of DSP. Initially, incubate the sample with a mixed antibody population. Subsequently, designate specific regions of interest (ROIs) for scanning imaging, succeeded by UV light exposure to liberate oligonucleotides. Finally, utilize microcapillaries for collection and integrate the data for comprehensive analysis. Created with Figdraw, https://www.figdraw.com.
Figure 5
Figure 5. Schematic diagrams of MADLI and DESI and AFADESI.
Sample preparation can involve both frozen or formalin-fixed paraffin-embedded (FFPE) specimens. In the processing phase, matrix-assisted laser desorption ionization (MALDI) primarily entails matrix-assisted laser desorption ionization for matrix-assisted laser desorption ionization (MALDI) for sample matrix deposition. On the other hand, desorption electrospray ionization (DESI) and air flow-assisted desorption electrospray ionization (AFADESI) primarily involve desorption electrospray ionization for ionization. AFADESI benefits from air flow-assisted transfer tubing to enhance ionization efficiency. Ultimately, all three techniques converge towards comprehensive mass spectrometry imaging analysis. Created with Figdraw, https://www.figdraw.com.
Figure 6
Figure 6. The flow chart of space group to learn more explanatory as a flowchart of spatial multiomics.
The spatial multi-omics technology was used to analyze the tissues at the single-cell resolution, and spatial clustering was performed through spatial reconstruction to analyze the interaction between cells. Finally, the collected spatial multi-omics information was integrated and analyzed. Created with Figdraw, https://www.figdraw.com.
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Grants and funding

This work was supported by the Zhangjiakou City Key R&D Plan Project (Grant No. 2322088D and 2311038D), the Hebei Provincial Natural Science Foundation (H2022405033) and the Hebei Province Key R&D Plan Project (22377784D). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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