Promise of spatially resolved omics for tumor research

Abstract

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Keywords: Spatially resolved metabolomics; Spatially resolved transcriptomics; Tumor.

Graphical abstract

Fig. 1

Schematic diagram of the spatial omics approach. (A) Laser capture microdissection (LCM)-based spatial imaging, (B) in situ hybridization (ISH)-based RNA imaging, (C) mass spectrometry imaging (MSI)-based spatial metabolomics, and (D) spatial barcode sequencing. NGS: next-generation sequencing; LC-MS: liquid chromatography-mass spectrometry; mRNA: messenger RNA.

Fig. 2

Mass spectrometry imaging (MSI) platform combining ion source (probe) with various mass analyzers. (A) Matrix-assisted laser desorption ionization (MALDI)-time-of-flight (TOF) MSI platform, (B) desorption electrospray ionization (DESI) SYNAPT G2-Si high resolution MSI platform, and (C) airflow-assisted desorption electrospray ionization (AFADESI) Orbitrap MSI platform. HCD: Higher energy collisioninduced dissociation.

Fig. 3

In situ visualization of crucial metabolites and metabolic enzyme in the uridine metabolism pathway. (A) Mass spectrometry (MS) images of uridine and uracil. (B) Uridine and uracil levels in cancer and paired epithelium and muscle tissues from 256 esophageal squamous cell carcinoma (ESCC) patients (means ± standard deviation). ^∗∗∗P < 0.001. (C) Uridine phosphorylase 1 (UPase1)-mediated metabolic process of converting uridine to uracil. (D) The newly constructed MS image based on the ion-intensity ratio of uracil to uridine. (E) Scanning path of airflow-assisted desorption electrospray ionization (AFADESI)-mass spectrometry imaging (MSI). (F) Plot of the intensity changes of uridine and uracil occurring during the transition from cancer, muscle, to epithelial tissue. (G) Expression of UPase1 in different regions of an ESCC tissue section. m/z: mass to charge ratio; MT: muscular tissue; CT: cancer tissue; ET: epithelial tissue. Reprinted from Ref. [40] with permission.

Fig. 4

Spatial gene expression heterogeneity within the cancer tissue sample. (A) Factor activity maps for selected factors corresponding to epithelial, stromal, cancerous, prostatic intraepithelial neoplasia (PIN), or inflamed regions. (B) Annotated brightfield image of hematoxylin and eosin (H&E)-stained tissue section. (C) Heatmap of 20 most variable genes between cancer, PIN, and normal gland regions. Arrows highlight genes of interest validated by immunohistochemistry (IHC). (D) First two principal components of spot sets from C separate cancer, PIN, and normal regions. (E) Array dot plots for serine peptidase inhibitor kazal type 1 (SPINK1) and NPY. Circle size in array dot plots indicates normalized spatial transcriptomics (ST) counts. (F) IHC staining for SPINK1 and neuropeptide Y (NPY) of an adjacent section on the ST array. Nuclei are stained with 4',6-diamidino-2-phenylindole (DAPI; blue). Scale bar indicates 1 mm. UBC: ubiquitin C; NR4A1: nuclear receptor subfamily 4 group A member 1; IER2: immediate early response protein 2. Reprinted from Ref. [112] with permission.

Fig. 5

Application of the virtual calibration quantitative mass spectrometry imaging strategy to visualize antitumor drugs on whole-body animal sections. (A) The machine learning method to predict the relative calibration factor based on the endogenous metabolites. (B) The imaging of relative calibration factors of different organs (top) and the comparison between predicted and true values of the relative calibration factor (bottom). (C) The non-calibration and virtual calibration standard curves constructed with the drug amount versus the non-calibrated (top) and calibrated (bottom) drug ion intensities, respectively. (D) The image of whole-body sample segmentation by automatic pixel labelling using K-means and t-distributed stochastic neighbor embedding (t-SNE) clustering analysis. (E) The result of drug quantitative visualization and the optical image of the sample. Reprinted from Ref. [128] with permission.