For many years, the tumor microenvironment remained a mysterious frontier. Tumor's diverse cellular landscape, composed of mutated cancer cells, healthy cells, blood vessels, and a dynamic immune response, has hindered the development of targeted cell therapies. Tumor microenvironments are complex systems where normal and cancer cells interact, and chemical concentrations fluctuate due to factors like molecular transport, biochemical reactions, metabolic activity, and drug exposure. These fluctuations significantly influence tumor cell behavior and response to therapy.



Investigating tumor microenvironments requires the dissociation of tissue samples into single cells and the subsequent characterization of their molecular composition through single-cell omics technologies. These techniques, particularly for transcriptome (RNA) analysis, are relatively accessible. Many omics platforms can visualize gene expression across millions of cells at single cell scale. While single-cell analysis offers invaluable insights into the molecular makeup of tumor cells, it presents a significant limitation: the loss of spatial context. The effectiveness of immunotherapy, for instance, hinges not only on the presence of specific immune cells but also on their precise location within the tumor microenvironment.



To address these limitations, both sequencing based and imaging based platforms have emerged to map RNA molecules, proteins and metabolites in spatial context within a tissue. DNA barcoding to preserve the spatial origin of RNA molecules within tissue samples can reveal precisely their location within the tissue. This technique involves barcoding tissue sections, extracting RNA, converting it to cDNA, and sequencing the resulting library. Advancements in antibody-based imaging technologies like IMC-Cytof (Imaging Mass Cytometry) and MIBI-TOF (Multiplexed ion beam imaging by time-of-flight) have revolutionized our ability to simultaneously visualize multiple proteins at subcellular resolution within tumor tissue.

These techniques have unveiled the intricate spatial organization of cells, including the formation of cellular neighborhoods, which significantly impact therapeutic outcomes. Techniques like Spatial CITE-seq (spatial co-indexing of transcriptomes and epitopes for multi-omics mapping by NGS), with a wide range of applications in biomedical research, has the potential to detect both proteome and transcriptome in situ.



Understanding the inherent heterogeneity of the TME by combining spatial transcriptomics and proteomics, offers a powerful approach to investigate the three-dimensional structure of the TME, overcoming the limitations of conventional two-dimensional sequencing methods. Spatial organization within the TME, characterized by distinct hierarchies and varying structures across different tumors, plays a crucial role in determining tumor cell fate. This spatial organization is dynamically regulated by tumor-intrinsic factors and intercellular communication, and it can be significantly altered in response to external stimuli, such as chemotherapy. Spatial metabolomics, particularly through techniques like imaging mass spectrometry (IMS), provides crucial insights into this dynamic interplay between immune cells, cancer cells, signaling molecules and the unique metabolic processes.



By mapping the distribution and abundance of metabolites within the TME, researchers can gain a deeper understanding of how tumor cells acquire nutrients, produce energy, and evade immune responses. This spatial information reveals localized metabolic shifts, such as nutrient gradients and the accumulation of metabolic byproducts, which can significantly impact tumor growth, progression, and response to therapies.

In essence, spatial metabolomics offers a powerful tool to decipher the intricate metabolic landscape of the TME. Recent advancements in spatial transcriptomics, proteomics and metabolomics have significantly improved our understanding of the tumor microenvironment in various cancers, including skin, lung, digestive, breast, blood, and prostate cancer.



Although several platforms offer spatial multiomics, they often capture only a single aspect within a tissue either proteome, genome, transcriptome or metabolome. To overcome this, same slide spatial multi omics approaches have emerged. One such innovation by scientists at Harvard is IN-DEPTH (IN-situ Detailed Phenotyping To High-resolution transcriptomics), a technology combining spatial proteomics and transcriptomics on the same tissue section, preserving both biomolecule types and is compatible with various commercial platforms. This, coupled with k-bandlimited Spectral Graph Cross-Correlation (SGCC) analysis, enables precise single-cell phenotyping and cell-type specific transcriptome capture. Researchers successfully applied this integrated approach to dissect the tumor microenvironment of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma, identifying a distinct tumor-macrophage-CD4 T-cell immunomodulatory axis differentially regulated between these subtypes.



Integration of spatial multi-omics datasets across the biomolecular scale is crucial for understanding the complex relationships between multiple molecular layers, but the key challenge is the lack of softwares that handles large data sets coming from different platforms. Recently researchers have developed a novel pipeline called SMOx (spatial multi-omics data integration pipeline), and applied it to interpret multilayered spatial omics data from the heterogenous Prostate Cancer (PCa) Tissue to study TME. SMOx approach, integrating Spatial Transcriptomics, Mass Spectrometry Imaging, and snRNA-seq data on prostate cancer (PCa) samples into a single, powerful analysis pipeline, uncovered intricate correlations between lipid and transcript profiles, linking them to specific histological states and individual cell populations within the native tumor microenvironment.



By revealing previously hidden complexities, these technologies provide valuable insights into the spatial distribution of various cell types and their molecular profiles within the TME, enabling a more comprehensive understanding of tumor biology and paving the way for personalized and targeted cancer therapies.



Cross-institutional and international research initiatives, such as the Human Tumor Atlas Network (HTAN), an NCI funded Cancer MoonshotSM initiative and the global Human Cell Atlas (HCA), are leveraging single-cell and spatial multiomics analysis to create comprehensive maps of human cells and tissues. These efforts, including the HTAN's focus on understanding cancer development and progression across diverse cancer types and HCA's, global collaborations to map all cell types in the human body, from development to old age, by utilizing cutting-edge technologies and AI to uncover the intricacies of how genes shape life, are ambitious projects, larger in scope than the Human Genome Project and are revolutionizing our understanding of the human body by providing a comprehensive "atlas" of cellular function and dysfunction.