Abstract
Tumor research is a fundamental focus of medical science, yet the intrinsic heterogeneity and complexity of tumors present challenges in understanding their biological mechanisms of initiation, progression, and metastasis. Recent advancements in single-cell transcriptomic sequencing have revolutionized the way researchers explore tumor biology by providing unprecedented resolution. However, a key limitation of single-cell sequencing is the loss of spatial information during single-cell preparation. Spatial transcriptomics (ST) emerges as a cutting-edge technology in tumor research that preserves the spatial information of RNA transcripts, thereby facilitating a deeper understanding of the tumor heterogeneity, the intricate interplay between tumor cells and the tumor microenvironment. This review systematically introduces ST technologies and summarizes their latest applications in tumor research. Furthermore, we provide a thorough overview of the bioinformatics analysis workflow for ST data and offer an online tutorial (https://github.com/SiyuanHuang1/ST_Analysis_Handbook). Lastly, we discuss the potential future directions of ST. We believe that ST will become a powerful tool in unraveling tumor biology and offer new insights for effective treatment and precision medicine in oncology.
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Background
Despite ongoing advancements in the field of oncology, cancer remains a significant threat to human health, primarily due to its inherent complexity. There is a broad consensus that cancer cells are not solitary entities; rather, they engage in intricate interactions with adjacent immune and stromal cells. This complex interplay contributes to the formation of an advanced tumor microenvironment that is fundamentally involved in both the onset and progression of cancer [1]. The advent of molecular targeted therapy and immunotherapy has brought hope for improved cancer treatment. However, the heterogeneous nature of tumors results in only a minority of patients responding effectively to these treatments [2]. Consequently, there is an urgent need for cutting-edge research techniques and innovative approaches in current cancer-related studies.
The spatial architecture of a tumor plays a critical role in its development and progression. This structural trait is markedly pronounced within certain biological niches, where tumor cells and the encompassing microenvironment engage in dynamic reciprocal interactions. These interactions often foster an environment conducive to immune suppression, thereby facilitating tumor immune evasion and posing a significant hurdle to effective cancer therapy [3]. Furthermore, the spatial structure of tumors not only influences their growth but also drives their metastasis [20]. BaristaSeq, in contrast to other methods that employed sequencing by ligation, made use of improved gap-filling padlock probes and employed Illumina synthesis sequencing, offering improved signal-to-noise ratio detection [21]. This technique was commonly used for sequencing a single barcode per cell [19]. ExSeq combined ISS or FISSEQ with expansion microscopy, allowing targeted or non-targeted detection of RNA [22].
Both ISH and ISS methods utilize pre-designed probes and high-resolution microscopy for detection, and enable the analysis of mRNA at subcellular resolution. RCA plays a critical role in enhancing the signal-to-noise ratio, enabling ISS to achieve fewer accumulated errors with fewer hybridization rounds and detect larger tissue areas with lower magnification. However, due to the lower efficiency of RCA amplification and its stronger selectivity [9], ISS can detect fewer genes than ISH. Single-molecule microscopy imaging technology is highly complex, requiring precise instruments and procedures. Additionally, the image data generated using this technology often reach terabytes. The incorporation of multiple imaging cycles and multiple hybridization rounds further consume longer experimental periods and numerous sample handling steps. These factors contribute to the complexity and generally higher experimental costs of ISH and ISS technologies.
Laser capture microdissection-based sequencing methods
Laser capture microdissection (LCM) transfer and tissue analysis were proposed in 1996, initially utilized for polymerase chain reaction amplification and enzyme recovery in specific tissue regions [23]. Subsequently, LCM was combined with comprehensive transcriptome analysis to facilitate the detailed examination of specific cell groups. Although this approach generally required a substantial number of cells, it presents a notable advantage by eliminating the need for tissue dissociation. The Tomo-seq technique was developed to attain spatially resolved, genome-wide expression profiles [24]. By combining LCM with full-length mRNA-sequencing, a robust and highly efficient strategy (LCM-seq) was developed for single-cell transcriptomics. This strategy provided biological insights into the characteristics and functions of similar neuronal populations [25]. Geographical position sequencing (Geo-seq) was a technique that fused laser capture microdissection with single-cell RNA-seq technology to investigate cellular diversity and spatial variability simultaneously [26]. Spatial-histopathological examination-linked epitranscriptomics converged to transcriptomics with sequencing (Select-seq) enabled the acquisition of both transcriptomic and epitranscriptomic data [27].
Several ST technologies employed light-controlled methods to select the region of interest (ROI) instead of involving tissue sectioning. These techniques could be categorized as LCM-like ST technologies. NICHE-seq enabled the isolation and examination of cells from visually chosen specific areas in transgenic mice expressing a photoactivatable green fluorescent protein [28]. This approach was utilized to pinpoint distinct niches for T and B cells within the lymph nodes and spleens of mice following viral infection. ZipSeq used patterned illumination and photocaged oligonucleotides to serially print barcodes onto live cells in intact tissues, allowing real-time and on-the-fly selection of patterns [29]. Digital Spatial Profiling (DSP) was an advanced technique well-suited for formalin-fixed, paraffin-embedded (FFPE) samples, providing detailed spatial analysis of proteins or RNAs [30]. Light-Seq was designed for the in situ spatial labeling of target molecules within specified ROIs, enabling ex situ next-generation sequencing without damaging the sample [31]. Although LCM methods had notable advantages, they typically offered limited spatial resolution and throughput compared to other ST techniques.
ST technology based on spatial barcode
The concept of ST technology based on spatial barcode was first proposed in 2016 [32]. This method identified messenger RNA transcripts by transferring RNA molecules onto a glass slide. The slide was coated with reverse-transcription oligo(dT) primers that contained a unique molecular identifier (UMI) and a spatial barcode, enabling the retrieval of original transcript locations. Although this technique has innovated the approach to transcriptome research, it faced challenges in terms of low RNA capturing efficiency and limited spatial resolution, with a spot diameter of 100 μm and a spot-to-spot distance of 200 μm. To address this limitation, 10 × Genomics reformed and commercialized the method. achieving a higher resolution with a spot diameter of about 55 μm and a spot-to-spot distance of approximately 100 μm. Another approach, Slide-seq, utilized polystyrene beads randomly distributed on a slide, with each bead carrying a unique barcode and attains a spatial resolution of 10 μm [33, 34]. High-definition spatial transcriptomics (HDST) employed a silicon wafer as the base, further improving the spatial resolution to 2 μm [35]. However, the advanced spatial resolution achieved by both Slide-seq and HDST hinges on the random allocation of spatially barcoded beads. Consequently, pinpointing their precise spatial arrangement necessitated labor-intensive imaging-based in situ sequencing techniques. The DBiT-seq (deterministic barcoding in tissue for spatial omics sequencing) method enabled the simultaneous map** of mRNAs and proteins in formaldehyde-fixed tissue sections by combining microfluidic barcoding with next-generation sequencing [36]. Seq-Scope was a two-phase sequencing technology that first created a spatially barcoded oligonucleotide array for mRNA capture and then used Illumina NGS to sequence captured mRNAs, linking each to precise array coordinates for high-resolution spatial transcriptomics.[40].
In recent developments in the field of ST based on spatial barcode, two significant breakthroughs emerged. Russell et al. developed Slide-tags, a novel method enabling single-cell (nuclei) resolution [41]. This technique involved 'tagging' cellular nuclei from tissue sections with spatial barcode oligonucleotides, which were derived from DNA-barcoded beads, under ultraviolet exposure. Slide-tags also offered the advantage of enabling the profiling using existing single-cell methods with the addition of spatial positions. Another significant advancement in ST technology based on spatial barcode was the introduction of the Visium HD spatial gene expression assay by 10 × Genomics. This technique employed a whole transcriptome probe panel and achieves single-cell scale resolution within intact tissue sections. The HD array was composed of approximately 12 million 2 µm × 2 µm spatially-barcoded areas without gaps. This significantly enhances spatial resolution compared to the previous Visium platform.
Spatial barcode-based spatial transcriptomics technologies stand out due to their innovative approach to map** mRNA transcripts by translocating them onto slides with reverse-transcription primers that include UMIs and spatial barcodes. This enables precise map** of each transcript's original location, providing a high-resolution spatial context to transcriptome data. Advancements in this domain, such as the development of Slide-seq and HDST, have dramatically enhanced spatial resolution down to 2 µm, enabling the detailed visualization of transcript distribution at near single-cell resolution. The deterministic nature of DBiT-seq and the single-cell spatial resolution of Slide-tags further underscore the rapid evolution of this field, offering unparalleled insights into the cellular composition of tissues while maintaining the integrity of spatial information. These methods surpass other spatial transcriptomics techniques by allowing for extensive multiplexing and the ability to handle whole transcriptomes, which is a significant leap forward in capturing comprehensive cellular behavior within intact tissue environments. Despite challenges such as lower RNA capture efficiency and the need for complex imaging to pinpoint bead arrangements, the benefits of these techniques, particularly their enhanced resolution and capacity for detailed spatial map**, position them as powerful tools for advancing our understanding of complex biological systems.
Application of ST in cancer research
Understanding the TME and tumor heterogeneity is crucial for effective cancer treatment and prognosis. Traditional single-cell techniques allow researchers to examine and compare genetic and functional features at the single-cell level, unraveling the identities of distinct cell types within complex tissues. This is vital for revealing the intricacies of tumor heterogeneity and the multifaceted complexity of the TME. In order to overcome the limitations of spatial resolution in single-cell RNA sequencing, ST has emerged as a valuable tool for obtaining spatially distributed transcriptomic data from tissue slices. By generating a cell atlas with spatial resolution for solid tumors, this technique facilitates the inference of cell interactions based on co-localization patterns [42], the exploration of tumor infiltration regions and boundaries of the stroma [43], the analysis of spatial immune niches [44], and the identification of specific cell subpopulations with unique distributions within the tissue structure [45]. These insights are crucial for understanding spatial patterns of cell interactions, as well as the distribution of cell types and tumor heterogeneity within the TME. A summary of the applications of ST technology in cancer research is provided in Table 1.
Applications of ST technology in TME
The TME is primarily composed of a diverse range of cell types, including malignant cells, immune cells, stromal cells, neurons, smooth muscle cells, lymphatic vessels, and blood vessels. These cell types can be further classified into various subtypes based on their specific gene expression patterns. Within the TME, these diverse cell types interact spatially, occupying ecological niches to form a complex ecosystem [87]. ST has emerged as a powerful technique for elucidating the ecological structure of the TME by revealing cell types, cell states, and the interactions between different cell types.
ST elucidates the complex interactions within the immune microenvironment by revealing cell-type-specific interactions and structures that are critical for the immune response to cancer. For instance, in a study on HER2-positive breast cancer (HER2 + BC), Andersson et al. explored the spatial gene expression patterns and discovered specific interactions between macrophages and T cell subtypes during the type I interferon response. Furthermore, they identified co-localization between B cells and T cells, unveiling tertiary lymphoid structures (TLS) in a spatial context [46]. Liu et al. combined ST with single-cell RNA sequencing and multi-immunofluorescence to uncover the tumor immune barrier (TIB) structure—a spatial niche composed of SPP + macrophages and cancer-associated fibroblasts (CAFs) located near the tumor boundary. The study demonstrated that the impact of spatial structures on the immunotherapeutic efficacy in hepatocellular carcinoma (HCC) patients receiving anti-PD-1 treatment [53]. Spatial organizational information provided by ST has assisted researchers to propose TLS-50 features for precise spatial localization of TLS, highlighting their unique composition determined by their proximity to tumor cells [51].
The application of ST in cancer research has provided insights into the dynamic remodeling of the TME in response to cancer progression and metastasis, highlighting the evolving landscape of cellular populations and the formation of specialized niches. Hwang et al. conducted single-nucleus RNA sequencing and Digital Spatial Profiling (DSP) analysis on 43 primary pancreatic ductal adenocarcinoma (PDAC) tumor specimens. This approach enabled the construction of a high-resolution spatial map of cell community distribution within the PDAC microenvironment. Through their analysis, they identified expression programs prevalent in both PDAC malignant cells and fibroblasts, unveiling three multicellular communities comprising various cell subtypes within the TME [52]. Qi et al. explored the spatial landscape of primary and metastatic tumors in non-small cell lung cancer (NSCLC) brain metastases (BrMs), and revealed extensive remodeling of the TME in the brain, leading to the formation of an immunosuppressive and fibrotic niche for BrMs [49]. In another study of invasive TME in lung adenocarcinoma (LUAD), Zhu et al. conducted an integrated analysis using single-cell RNA-sequencing (scRNA-seq) and ST to characterize the cellular atlas of LUAD invasion trajectories. They observed a continuous increase in the UBE2C + cancer cell subpopulation during LUAD invasion, while mast cells, monocytes, and lymphatic endothelial cells decreased [92]. This probabilistic model provides improved estimates of UMI counts for every gene at each spot by removing contamination from spot swap**. Furthermore, SpotClean has demonstrated significant improvements in marker gene identification and spatial domain detection.
The shallow depth of ST sequencing is associated with lower capture efficiency, consequently leading to a higher rate of dropouts. Recently, some computational tools have been developed to impute the missing expression levels due to dropouts by aggregating the expression data of the spatially neighbor spots. Furthermore, some of these methods consider the similarity of histological structures observed in histology images when selecting the spatially neighbor spots. One such method, Sprod, employs latent graph learning techniques to integrate gene expression data and imaging data, allowing for the precise imputation and denoising of ST gene expression [108], involves spatial smoothing of gene expression based on morphological similarity and spatial location. Subsequently, it employs a standard Louvain clustering procedure to detect spatial domains. The second category of methods is developed based on the standard Hidden Markov Random Field (HMRF) framework, allowing latent states representing domain categories to be spatially continuous. Representative tools include BayesSpace [97], Giotto [109], SC-MEB [110], DR-SC [111] and BASS [112]. Taking Giotto as an example, it first selects spatially differentially expressed genes and then utilizes HMRF clustering to detect spatial domains. The third category employs graph neural networks (GNNs) to process graphs derived from spatial locations, expression data, and potentially histology image information. This approach enhances spot embeddings through learning the complex relationships between spots, ultimately leading to improved detection of spatial domains and gene expression patterns. Representative algorithms include SpaGCN [113], STAGATE [114], CCST [115], and GraphST [116]. For example, GraphST integrates graph neural networks with self-supervised contrastive learning to improve the representations of spots. It optimizes the embedding distances among spatially neighboring or distant spots to acquire informative and discriminative spot representations, finally enabling spatially informed clustering, integration, and deconvolution.
Deconvolution
Deconvolution, a widely employed method in bulk RNA-seq to differentiate mixed expression signals into identifiable cell types, becomes exceedingly useful in the realm of spatial transcriptomics, such as with Visium platforms, where each spot aggregates transcripts from several cells. This method typically uses a cell-type-annotated single-cell sequencing data as a reference, facilitating accurate estimation of cell type proportions within each spot, thereby illuminating the intricacies of tissue microenvironments and the underlying cellular architecture. Several tools in this field have been developed to perform deconvolution effectively, such as SPOTlight [117], SpatialDWLS [118], DSTG [130], cell2location [119], CARD [120], RCTD [123], destVI [121] and STRIDE [122]. SPOTlight utilizes seeded non-negative matrix factorization (NMF) regression to deconvolute the ST information with scRNA-seq data. DSTG generates pseudo-ST data by simulating cell mixtures from scRNA-seq data and utilizes graph-based convolutional networks for deconvolution. Cell2location, a Bayesian model, demonstrates the ability to resolve fine-grained cell types in ST data and integrate single-cell and ST with high sensitivity and resolution. To assess and compare the performance of these algorithms, benchmarking studies have been conducted, providing valuable insights for selecting appropriate methods for cell type deconvolution of spots [143, 144].
Another category of deconvolution methods, known as reference-free deconvolution methods, does not require single-cell transcriptomic data as a reference. Representative methods in this category include CARDfree [120], STdeconvolve [124], and SMART [125]. In spatial transcriptomic samples of tumors, the estimation of cancer cell abundance can be particularly challenging due to the heterogeneity of tumor cells. SpaCET [126] addresses this challenge by integrating a gene pattern dictionary of copy number alterations and expression changes to estimate cancer cell abundance. Furthermore, it employs a constrained regression model to determine the proportions of immune and stromal cell lineages. However, these methods have a limitation in that they can only estimate the proportions of cell types in a given spot without providing single-cell level deconvolution.
An alternative approach is to enhance ST data to achieve single-cell resolution. Tools like CellTrek [127] and CytoSPACE [128] assign the most probable spatial location in ST data to each single cell and subsequently estimate the cellular composition within the tissue. SpatialScope [145]. Consequently, it only tests combinations of clusters if they coexist within the same microenvironment.
SpaOTsc uses optimal transport map** between scRNA-seq data and spatial data to construct a spatial metric for cells in scRNA-seq data, and reconstruct space-constrained cell–cell communication networks [134]. The main author of SpaOTsc later proposed COMMOT, which considers the spatial distances between cells and aids in deducing the competitive dynamics between different ligands and receptors [135]. This approach provides a new perspective for examining the spatial interactions between immune cells and tumor cells in the tumor ecosystem.
Alignment and integration of multiple slices
The alignment and integration of multi-slice ST data represents a current challenge in spatial data analysis, which can be broadly categorized into three types: reconstructing three-dimensional (3D) structures, identifying spatial shared domain, and revealing dynamics in biological processes.
For the reconstruction of 3D structures, several methods such as PASTE [138], PASTE2 [139], SLAT [86]. However, some ST technologies, such as Slide-seq and HDST, are not yet applicable to FFPE samples [148]. In the future, spatial omics technologies adapted for FFPE samples will accelerate research related to tumors, especially in longitudinal studies on tumor progression or disease relapse.
Spatial multi-omics and full-length technology
Following the pioneering development of ST, other spatial omics technologies have increasingly garnered attention. Spatial proteomics technologies have emerged as powerful tools capable of detecting the levels of dozens of proteins in situ. Spatial proteomics primarily fall into two categories: mass spectrometry-based [149,150,151] and antibody-based approaches [152, 153]. Additionally, spatial metabolomics technologies enable non-targeted detection of metabolites and lipids on tissue sections using imaging mass spectrometry [154]. In recent years, several other types of spatial omics technologies have been developed. Spatial ATAC-seq enables in situ sequencing of chromatin to understand chromatin accessibility related to tissue structure and spatial position [86]. This approach may introduce heterogeneity among the sections and pose challenges in data analysis, particularly in aligning multiple sections. Another strategy involves performing multiple omics sequencing on the same section. Compared to the first approach, this method conserves samples and eliminates the necessity for aligning sections. However, it presents greater technical challenges, which is why such technologies are still relatively rare. For instance, spatial CITE-seq [159] and STARmap PLUS [160] add a protein dimension to the capture of spatial transcripts. Slide-TCR-seq enables simultaneous sequencing of TCR and RNA on a single tissue Sect. [161]. In the future, the integration of spatial multi-omics will revolutionize our understanding of how cellular programs are coordinated and the intricate mechanisms underlying tumor biology.
It is worth noting that the current ST approach employed by Visium can only detect information at the 3’ end of transcripts, thus neglecting full-length exploration. This limitation considerably restricts the investigation of immune cell receptor repertoire and alternative splicing events. To improve the accuracy and continuity of sequencing reads, long-read sequencing technologies from Pacific Biosciences (PacBio) [162] and Oxford Nanopore Technologies (ONT) [163] emerge as pivotal tools. Innovative sequencing strategies that integrate long-read sequencing with either bulk or single-cell sequencing [164, 165], along with computational methods [Analysis platforms suitable for clinical practitioners As mentioned previously, the analysis of ST data remains complex due to the diversity of data types, lacking a unified paradigm similar to single-cell transcriptomic analysis. This poses significant challenges for biologists and clinical practitioners. To alleviate this hurdle, several online platforms have emerged to streamline the showcase and exploration of spatial transcriptomic data from various sequencing platforms. Two noteworthy databases, Spatial Omics DataBase (SODB) [174] and Spatial TranscriptOmics DataBase (STOmicsDB) [175] stand out in providing comprehensive resources. In the realm of spatial omics, SODB is a pivotal online platform meticulously designed to provide researchers with an extensive collection of data resources and diverse interactive modules for sophisticated data analysis. Encompassing over 2,400 experiments from more than 25 different spatial omics technologies, SODB maintains data in a standardized format that is compatible with numerous computational tools, ensuring versatility and accessibility. Notably, SODB features a range of interactive modules for data analysis. Another key resource is STOmicsDB, a comprehensive database designed as a central resource for ST research. STOmicsDB contains 218 carefully curated datasets from 17 species, with detailed annotations of cell types, spatial regions, genes, and analyses of cell-to-cell interactions. Its user-friendly interface enables quick visualization of data involving millions of cells. In summary, ST technology presents several areas for improvement and refinement in the future. As these advancements unfold, it is anticipated to emerge as a powerful tool for biologists and clinical scientists, enabling in-depth exploration of the intricate mechanisms underlying tumors.
Conclusions and perspectives
In spite of the clinical implementation and notable successes of targeted and immunotherapy, cancer remains one of the major health challenges faced by humanity. The effectiveness of cancer treatment is primarily hindered by the heterogeneity of tumors and the intricate nature of the TME. Factors such as tumor metastasis, the development of drug resistance, and the presence of an immunosuppressive microenvironment often contribute to treatment failures.
ST, an emerging technology in recent years, offers resolution comparable to single-cell transcriptomics while addressing the lack of spatial context in single-cell sequencing. ST has gained increasing attention from researchers as a powerful tool for studying tumor heterogeneity, the TME, and the spatial structure of tumors, thereby laying an important foundation for research in tumor biology. This review aims to provide a comprehensive overview of the developmental trajectory and recent applications of ST in cancer research. To facilitate comprehension among clinical practitioners and biomedical professionals, a thorough description of the ST data analysis process is included, supplemented by an online guide.
With ST advancing towards enhanced resolution, higher sequencing throughput, multimodality, and reduced costs, its significance in cancer biology research is anticipated to grow substantially. We believe that ST will establish a theoretical foundation for personalized precision medicine, making significant contributions to the development of targeted and individualized therapeutic approaches in the field of oncology.
Availability of data and material
Not applicable.
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This work was supported by the National Key R&D Program of China [2020YFE0204200 to R.X.], the National Natural Science Foundation of China [12371286, 11971039 to R.X., 12201219 to J.M.], Sino-Russian Mathematics Center, Foundation of Qinglonghu laboratory, Shanghai Sailing Program (No. 21YF1410600 to J.M.), and Shanghai Key Program of Computational Biology (No. 23JS1400500, 23JS1400800 to J.M.).
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Conceptualization: **gsi Ming, Ruibin **; Writing-Original draft preparation: Siyuan Huang, Linkun Ouyang; Supervision: **gsi Ming, Ruibin **; Writing-Reviewing and Editing: Junjie Tang, Kun Qian, Xuanwei Chen, Zijie Xu, **gsi Ming, Ruibin **.
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Huang, S., Ouyang, L., Tang, J. et al. Spatial transcriptomics: a new frontier in cancer research. CCB 3, 13 (2024). https://doi.org/10.1007/s44272-024-00018-8
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DOI: https://doi.org/10.1007/s44272-024-00018-8