The application of next-generation sequencing (NGS) techniques to CRC research has provided significant new insights into the genomic and epigenomic factors driving CRC development and progression (18, 19). These advancements have greatly enhanced our understanding of the molecular mechanisms underlying CRC, such as the transcriptome, leading to the identification of potential therapeutic targets and strategies for effective treatment (20, 21). NGS technology, along with the development of RNA-seq or bulk RNA-seq, has enabled researchers to ask whole-transcriptome questions and explore the molecular differences between healthy and cancerous cells while superficially examining the TME (18–20). Tumor heterogeneity is a key factor in tumor progression, metastasis, and treatment resistance (21). Bulk RNA-seq allows researchers to ask broad, lower-resolution questions about groups of cells or a tissue’s collective transcriptome, facilitating the categorization of tumors by molecular subtypes and providing rough estimates of different cell types. However, this broad overview of tissue transcriptomics has limitations in understanding the TME (22, 23).

One of the significant limitations of bulk RNA-seq is the loss of cell-type-specific and spatial context in understanding gene expression (24). To address these limitations, new technologies and methods to explore heterogeneity in the TME have emerged, such as single-cell RNA-seq (scRNA-seq) (25) and its variant single-nucleus RNA-seq. These methods rely on the physical separation of individual cells or nuclei and incorporating unique cell-specific barcodes to transcriptomic libraries. Thus, scRNA-seq technologies allow researchers to capture the transcriptome of individual cells, enabling the identification of rare cell types and the study of cellular heterogeneity within tumors at a higher resolution (23, 26). Despite its promise, scRNA-seq has its limitations, primarily because it requires tissue dissociation before library construction, resulting in the loss of spatial context. Spatial context provides vital information about each tumor’s unique microenvironment, cell-to-cell interactions, and organization, which are crucial for understanding the finer details of tumor biology.

In recent years, high-throughput spatial transcriptomics (ST) technology has emerged as an ideal approach to obtain the most comprehensive transcriptomic picture of a tumor’s microenvironment and elucidate CRC’s heterogeneity. Spatial transcriptomic methods address the shortcomings of bulk RNA-seq and scRNA-seq, offering a groundbreaking technology that allows cancer researchers to integrate NGS with spatial coordinates within a tumor. This technology combines tissue imaging, biomarker analysis, NGS, and bioinformatics to meticulously map gene expression across a tumor sample, providing unprecedented insights into tumor architecture and the interactions between different cell types. Thus, spatial transcriptomics offers a more complete understanding of the mechanisms behind cancer development and progression and presents new opportunities for CRC diagnosis and precision medicine.

Spatial transcriptomics comes in two main types: image-based methods, including in situ hybridization (ISH), in situ sequencing (ISS), and NGS sequence-based methods. Each category has multiple technologies, each with its own advantages and disadvantages for exploring the TME. While this technology is relatively new, it has already significantly impacted CRC research and is expected to continue to do so in the future.

The earliest applications of spatial transcriptomics in CRC research can be traced back to in situ hybridization (ISH) methods, which were initially used to map mRNAs while preserving spatial context (27). Over time, ISH evolved into single-molecule Fluorescent in Situ Hybridization (smFISH), a technique that allows the detection of single RNA molecules using multiple short probes that bind to different regions of the same RNA molecule. These short probes are fluorescently labeled and visualized with a fluorescence microscope, providing precise quantification of RNA molecules along with spatial context (28). The technology has significantly advanced with the introduction of high-throughput Multiplexed Error Robust Fluorescence in Situ Hybridization (MERFISH) in 2015.

Since 2021, Vizgen has commercialized this ST technology with their MERSCOPE and newly released MERSCOPE ULTRA platforms. MERFISH is an imaging-based ST method that enables single-cell imaging of the expression of up to 1,000 genes and thousands of RNA species through combinatorial FISH labeling, providing the ability to measure the copy number and spatial distribution of those RNA species. Despite the potential of this technology, it has not yet gained significant traction among CRC researchers. However, a notable study utilized Vizgen’s MERSCOPE public colon cancer dataset, comprising approximately 800,000 cells, to observe the expression pattern of secreted frizzled-related protein 2 (SFRP2) in fibroblast cells. This study identified SFRP2, a gene involved in the WNT signaling pathway, via scRNA-seq differential expression analysis. Through the use of Lactobacillus acidophilus postbiotics, they determined that they could target the differentially expressed genes to induce cell cycle arrest and promote anti-proliferative and anti-migration effects on a CRC cell line, thereby providing a new therapeutic avenue to explore (29).

The CosMx Spatial Molecular Imager (SMI) is NanoString’s high-plex in situ platform for spatial multiomics, released in 2022. Similar to MERSCOPE, this platform leverages FISH-based probes that are designed to bind to specific RNA or protein targets, allowing for the capture of spatial context for approximately 6,000 genes and over 64 proteins at a single-cell and subcellular resolution on a tissue sample (30). CosMx SMI provides a unique platform to study RNA and protein expression simultaneously with spatial context. This capability allows researchers to integrate transcriptomics and proteomics data for a more comprehensive understanding of the molecular landscape. However, our extensive literature review found no peer-reviewed studies that have used CosMx SMI specifically in CRC research, suggesting that this technology is still in its early stages of adoption for CRC studies.

In 2022, 10x Genomics introduced its own imaging-based ST platform, Xenium, which leverages ISS alongside NGS to spatially resolve RNA transcripts at the single-cell and subcellular levels. Additionally, 10x Genomics acquired Cartana technology and integrated it into the Xenium platform. Xenium can detect up to 5,000 genes in cells and tissues with spatial context at high resolution (31). One limitation of Xenium compared to CosMx SMI is the lack of protein detection; however, 10x Genomics has stated that this feature is in development and will be available in the future. Xenium is a relatively new platform, and peer-reviewed studies using this technology in CRC research have not yet been published. Nevertheless, a CRC Xenium dataset is available for analysis on the 10x Genomics website.

Prior to its acquisition by 10x Genomics, Cartana technology was used in two key publications focusing on CRC research. Sallinger et al. addressed the challenge of deciding whether to administer adjuvant chemotherapy for stage II colon cancer patients by investigating the spatial tissue composition of relapsed versus non-relapsed patients. Using ST Cartana technology, the researchers analyzed a panel of 176 genes related to key cancer processes such as apoptosis, proliferation, angiogenesis, and the tumor microenvironment. They identified a tumor gene signature that enabled the spatial classification of neoplastic and non-neoplastic tissue compartments. Additionally, they identified three differentially expressed genes (FGFR2, MMP11, and OTOP2) in the neoplastic tissue compartments, offering potential biomarkers for predicting relapse and guiding treatment decisions (32).

Andersson et al. aimed to understand the fibrotic rim formed in the desmoplastic histopathologic growth pattern (DHGP) of colorectal cancer liver metastasis (CRLM) using Cartana’s ISS technology. By analyzing 10 chemo-naïve liver metastasis samples, the researchers uncovered molecular and cellular diversity within the rim, confirming the presence of the ductular reaction and cancer-associated fibroblasts (CAFs). These findings offer insights into the origin of the fibrotic rim and propose potential targeted therapies to improve patient survival (33).

Imaging-based ST platforms offer several advantages for studying gene expression in CRC tumor samples while maintaining spatial context. These platforms provide single-cell and subcellular resolution for the direct visualization of RNA transcripts and tumor architecture, offering novel insights into the spatial organization of gene expression and allowing for the characterization of the TME and cell-to-cell interactions. The CosMx platform provides further insights through its simultaneous protein detection, enabling a more integrated analysis of transcriptomics and proteomics. These platforms are particularly well-suited for targeted spatial transcriptomic analysis once genes of interest have been identified, providing a robust platform for detailed analysis of specific genes.

Despite their advantages, these imaging-based ST methods also face several challenges. MERFISH and other similar techniques require complex, expensive, and labor-intensive protocols due to multiple rounds of hybridization and imaging, which limit throughput and sensitivity. Moreover, the limited tissue size and the need for extensive sample profiling routines make these methods further time-consuming and labor-intensive. For example, while CosMx provides a high level of multiplexing for both RNA and protein detection, the cost and scalability of such high-throughput approaches are often prohibitive for routine clinical application. Other challenges include increasing errors and signal ratios with multiple rounds of hybridization, and optical crowding. Future advancements in automation, cost reduction, and scalability will be essential for wider adoption of these technologies in CRC research and personalized medicine.

The earliest use of NGS technology for spatial context in tissue samples was demonstrated by Stahl et al. in 2016 using a technique called “Spatial Transcriptomics” (34). This method involved annealing fixed tissue samples directly onto an array with wells spaced 100 μm apart, each containing spatially barcoded reverse transcription primers. These primers capture RNA transcripts for subsequent RNA sequencing, allowing the measurement of gene expression across large regions of tissue sections. However, this method’s resolution was limited to the spatial distribution of groups of cells (3–30 cells per well), which restricted its ability to capture gene expression at the single-cell level (34).

To overcome this limitation, the Stahl group introduced the “High-Definition Spatial Transcriptomics” (HDST) methodology, which uses wells spaced 2 μm apart. This advancement allows for subcellular resolution, capturing gene expression data at a higher resolution and facilitating the study of both intercellular and intracellular interactions (35). HDST provides the foundational technology for 10x Genomics’ NGS ST platforms, including Visium and Visium HD, which have become pivotal in CRC research.

Slide-seq, a subsequent iteration of the Spatial Transcriptomics method, was developed to achieve near single-cell resolution (36). This ST method has since been commercialized under Curio Biosciences Seeker platform. This method involves randomly distributing spatially and DNA-barcoded beads on a slide with beads spaced 10 μm apart. By reducing the bead size and increasing the density, Slide-seq provides finer spatial mapping of gene expression than the original Spatial Transcriptomics. The introduction of Slide-seqV2 further enhanced this method’s efficiency by an order of magnitude (37). Currently, one limitation of Curio’s Seeker platform is its inability to process formalin-fixed paraffin-embedded (FFPE) samples. However, they have announced plans to modify the technology to address this.

In CRC research, Slide-seq has been employed alongside Slide-DNA-seq, a related technology that captures genomic mutations. Together, these methods enable high-resolution, multi-omic characterization of intratumoral heterogeneity. For example, Zhao et al. used Slide-seq to investigate whether genetically distinct CRC tumor clones also have distinct spatial transcriptomes, revealing that different clones exhibit unique gene expression profiles associated with tumor progression and immune infiltration (38). This capability demonstrates the potential of Slide-seq combined with Slide-DNA-seq for spatially characterizing tumor evolution and cell states in CRC.

Building on the advancements of HDST, 10x Genomics released the Visium platform in 2020 and its higher-resolution counterpart, Visium HD, in 2024. The Visium platform uses a hexagonal array with a capture spot size of 55 μm, enabling the capture of gene expression from 1 to 10 cells per spot (24). Visium HD further improves resolution with 2 × 2 μm barcoded squares with no gaps between squares, allowing for single-cell and subcellular resolution (24). This technology provides a comprehensive view of cell, tumor, and tissue architecture and transcriptomics, enabling the visualization of how a cell’s transcriptome affects cell-to-cell interactions within their native tissue environment (23).

Visium has become one of the most frequently utilized ST platforms in CRC research, with over 34 studies employing this technology to date. For instance, Wu et al. used Visium ST combined with single-cell RNA sequencing (scRNA-seq) to create an immune microenvironment atlas of colorectal liver metastasis (CRLM), revealing a high concentration of immune-suppressive M2-like macrophages in CRC metastatic tumors (39). Similarly, Peng et al. explored cancer-associated fibroblasts (CAFs) in the CRC TME, identifying distinct CAF subtypes associated with poor prognosis and tumor progression (40). Liu H et al. investigated the local invasion process, the initial step in metastasis and leading cause of death in CRC, by using Visium ST to analyze four colorectal cancer tissue samples. By comparing pre-cancerous regions, cancer centers, and invasive margins, researchers identified 13 genes were overexpressed in invasive clusters, including CSTB and TM4SF1 correlating with poor progression-free survival (41). Using Visium ST alongside a cell line-derived xenograft (CDX) model, Martinez-Marin et al. explores how the loss of Helicase-like transcription factor (HLTF), a tumor suppressor, leads to metabolic reprogramming within specific regions of the TME, particularly in lymphatic intravascular metastatic niches (42). These studies highlight Visium’s power in uncovering molecular and cellular dynamics in CRC, advancing precision medicine strategies.

The Visium platform’s versatility is further showcased when integrated with multi-omic approaches, such as bulk RNA-seq, proteomics, and scRNA-seq. Studies like those conducted by Liu et al. and Yuan et al. have leveraged Visium ST to identify key molecular features of CRC, such as endoplasmic reticulum (ER) stress-related gene patterns and tumor heterogeneity markers, to understand tumor biology better and develop targeted therapies (43, 44). Similarly, Feng et al. integrated CRISPR screening with Visium ST data to identify potential therapeutic targets for immune checkpoint inhibitors (ICIs), underscoring the platform’s utility in immuno-oncology research (45).

NanoString’s GeoMx Digital Spatial Profiler (DSP) is another prominent NGS-based ST platform utilized in CRC research. The DSP platform combines spatial gene expression analysis with high multiplexing capabilities, providing subcellular resolution of 10 μm (46). Unlike most ST platforms, DSP is non-destructive to tissue samples, enabling researchers to perform subsequent analyses on the same sample. The technology employs oligo-labeled probes or antibodies linked to unique DSP barcodes via UV-cleavable linkers, which are hybridized to mRNA targets on a tissue slide. After staining with fluorescent antibodies, regions of interest (ROIs) are selected for barcode release, followed by sequencing to quantify gene or protein expression within a spatially defined context (47).

DSP has been applied in multiple CRC studies to investigate gene expression patterns and molecular interactions. For example, Galeano et al. utilized Visium ST and DSP to map intratumoral microbiota interactions in CRC, revealing how microbial niches influence immune and epithelial cell functions in the TME (48). Tanjak et al. performed a DSP analysis of TME regions within KRASmut CMS3 tissues revealed up regulation of specific genes, CD40, CTLA4, ARG1, STAT3, IDO, CD274, that may contribute to immune suppression in the TME. Lastly, Pennel et al. revealed higher expression of checkpoint and hypoxia-associated genes in tumor regions (pan-cytokeratin positive) and reduced lymphocyte receptor signaling in the TME (pan-cytokeratin-/αSMA-) and αSMA-positive areas (49). Such applications illustrate DSP’s potential for providing detailed insights into the spatial molecular landscape of CRC and facilitating translational research.

Spatial enhanced resolution omics sequencing (Stereo-seq) utilizes a patterned array of DNA nanoballs (DNBs) generated through rolling circle amplification to create spatially resolved barcode arrays on a chip surface (50). Each DNB contains a unique spatial barcode that, when combined with next-generation sequencing (NGS), allows for the identification of gene expression profiles at subcellular resolution, reaching up to 220 nm (50). This enables highly detailed spatial mapping of RNA transcripts within tissue samples, surpassing the resolution of earlier ST methods like Visium by 10x Genomics and Slide-seq.

In the context of CRC, Stereo-seq’s ultra-high resolution facilitates an unprecedented level of detail in analyzing tumor architecture, cellular interactions, and the TME. By capturing spatial transcriptomics data at a single-cell or even subcellular level, researchers can dissect the complex cellular and molecular landscapes that govern tumor growth, immune evasion, and metastasis (51). For example, the integration of Stereo-seq with scRNA-seq was used to characterize the spatial distribution of cancer-associated fibroblast (CAF) subtypes in CRC before and after neoadjuvant chemotherapy (NAC). Revealing CAFs distinct roles in modulating the immune microenvironment and promoting tumor progression through pathways such as TGF-β and CXCL12-CXCR4 signaling (51). By identifying specific regions where CAFs interact with immune cells, researchers can better understand how these interactions contribute to immune evasion and resistance to therapies like immune checkpoint inhibitors.

While NGS-based ST technologies have transformed spatial transcriptomics and multi-omics research, they present certain challenges. These include the high cost of sequencing and high-volume data storage, the need for sophisticated computational tools to analyze complex datasets, and the requirement for specialized equipment and expertise. However, the continued optimization and integration of these platforms with advanced bioinformatics and machine learning models offer exciting opportunities for developing more precise and personalized therapies in CRC and other cancers (42).

Overall, NGS-based spatial transcriptomics has expanded the horizons of cancer research, providing high-resolution spatial data that can elucidate the complexities of tumor biology and the tumor microenvironment. As these technologies continue to evolve, they promise to play an increasingly central role in advancing precision oncology.

One high-throughput fluorescence microscopy–based method, known as multi-epitope-ligand cartography (MELC), subjects a CRC tissue section to serial rounds of incubation using up to 100 antibody-coupled fluorescent tags, followed by a gentle bleaching step to increase fluorophore detection for each cycle and to map and morphologically position groups of cell types and their connections (52). The cost of performing MELC varies according to the experimental design, allowing the visualization of up to 50 proteins on the same sample. This technique has been reported to profile 20 tissue sections from different tumor types, each with a size of 1.5 cm × 1.5 cm and a thickness of 5 μm, and each stained with 32 antibodies in 32 cycles, consuming a total of 8 hours per slide (53).

MELC’s application in CRC research has been instrumental in mapping the spatial organization of immune cells, stromal cells, and cancer-associated fibroblasts (CAFs) within the TME. It enables the detection of low-abundance proteins, which enhances the understanding of key signaling pathways involved in CRC progression, such as the Wnt/β-catenin and TGF-β pathways. However, MELC is limited by the availability of specific antibodies, the need for a single microscopic medium-to-high-power field, a lengthy sampling time compared to other methods, and the generation of complex output data that require advanced computational analysis (52, 53).

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) is a powerful technique used to spatially resolve, detect, quantify, and map hundreds of biomolecules, including proteins, peptides, lipids, and metabolites. This method works by directing a laser beam at a tissue sample, causing the embedded biomolecules to desorb and ionize. The ionized molecules are then analyzed by mass spectrometry, creating an image that maps the distribution of these biomolecules within the tissue. MALDI MSI is highly sensitive and label-free, avoiding the limitations associated with fluorescence-based methods. An additional advantage is its ability to integrate multi-omics data, providing comprehensive insights into the molecular landscape of the tissue. However, this technique does have limitations, including high costs, potential background noise due to its sensitivity, and the need for specialized equipment and bioinformatic expertise for data analysis (54, 55).

In cancer research, MALDI-MSI has primarily been used to study metabolomics within the TME following treatment. However, our literature review identified a study where MALDI-MSI was combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to examine the spatial proteomic landscape in colorectal cancer (CRC) liver metastases. This study analyzed peptide abundance across three comparisons: tumor versus stroma, male versus female, and among three patient groups categorized by overall survival (0–3 years, 4–6 years, and 7+ years). A total of 471 peptides were identified, and survival analysis revealed that three peptides (Histone H4, Hemoglobin subunit alpha, and Inosine-5’-monophosphate dehydrogenase 2) were significantly elevated in patients with shorter survival. Additionally, several protein biomarkers were identified as potential targets for drug development (56).

Multiplexed Ion Beam Imaging (MIBI) is a platform commercialized by Ionpath through their MIBIscope 3.0. Originally used for imaging human breast tumors by Dr. Michael Angelo and his team, MIBI is a mass spectrometry-based imaging technology that incorporates mass spectral imaging and secondary ion mass spectrometry. This enables the detection of up to 40 protein biomarkers at subcellular resolution. The technology uses metal-labeled antibody cocktails for tissue staining, followed by imaging with a primary ion beam in a time-of-flight (TOF) mass spectrometer. The imaging process is followed by bioinformatic analysis. One advantage of MIBI is that it avoids signal fading, spectral overlap, and autofluorescence since it does not use fluorophores (57, 58).

MIBI has been utilized in colorectal cancer (CRC) research to examine the metabolic characteristics of cell subsets within tumors. Researchers using this technique have uncovered the spatial organization of metabolically repressed cytotoxic T cells and identified the exclusion of clinically relevant CD8+ T cell subsets from the tumor–immune boundary (59). However, MIBI is limited in the number of markers that can measured simultaneously is limited to existing heavy metals, high cost, high level training required and low throughput speed (60).

The Hyperion XTI imaging system from Standard Biotools, formerly Fluidigm, is a multiplexed tissue imaging platform that utilizes Imaging Mass Cytometry (IMC). Initially introduced by Giesen et al., IMC combines immunohistochemical and immunocytochemical techniques with laser ablation and CyTOF (Cytometry by Time of Flight) mass cytometry to achieve subcellular resolution for analyzing over 40 protein or RNA biomarkers in tissue samples. This is accomplished using a strategy similar to that of MIBI, employing metal-tagged antibodies instead of fluorescent labels for tissue staining. Imaging is performed through laser ablation, followed by detection using a mass cytometer and TOF mass spectrometer. The platform shares the same advantages as MIBI, including avoiding signal fading, spectral overlap, and autofluorescence. Additionally, it can process up to 40 samples per run. However, IMC also has certain limitations similar to those of MIBI (60–62).

IMC has been employed to characterize the tumor microenvironment (TME) and uncover the spatial interactions between tumor and immune cell populations in CRC tissue. This has provided a framework for analyzing both intra- and intercellular signaling within the TME, which can ultimately guide precision treatment strategies for CRC patients (63). IMC has also been applied in clinical CRC research to spatially profile tumors before and after pembrolizumab treatment. This analysis revealed that granulocytic cells and cytotoxic T cells were significantly closer in proximity in patients experiencing progressive disease compared to those without progressive events (64).

Co-Detection by Indexing (CODEX), available via Akoya Biosciences’ PhenoCycler-Fusion platform, relies on the use of a multiplex panel of antibodies conjugated to unique oligonucleotide barcodes specific to three or fewer oligonucleotide reporters linked to fluorophores (65). The hybridization process occurs cyclically, so that each new cycle begins right after the washing step responsible for erasing the remaining signals generated in the previous round. Images are obtained in subcellular layers until all protein targets have been detected (66).

In CRC research, CODEX has been applied to characterize the spatial heterogeneity of immune cells and their interactions with tumor cells. For example, CODEX has been used to map the localization of T cell subsets, macrophages, and dendritic cells in different CRC regions, such as the invasive front and the tumor core. This spatial resolution provides critical insights into the organization of immune niches that contribute to immune evasion and metastasis. Furthermore, CODEX data, like other spatial profiling methods, can be integrated with multi-omics data, such as single-cell RNA sequencing (scRNA-seq), to provide a comprehensive view of the TME and identify potential biomarkers for immunotherapy (66, 67).

PhenoCycler-Fusion analyzes one or two slides at a time with an image area of 35 mm × 18 mm, and the cost depends on the experimental design as the panel size and the custom antibody conjugation process vary according to the study. This method allows for rapid, deep, and unbiased spatial phenotyping of complex CRC tissue sections and provides super-resolution imaging offering multiomic profiling as it can analyze both proteins and RNA biomarkers in situ within 24 hours. However, a significant limitation of this assay is data management, storage, and interpretation due to the large amount of information generated, and the assay is also limited by the number of biomarkers that can be detected simultaneously (66, 67).

Building upon cyclic immunofluorescent imaging, Akoya Biosciences’ PhenoCycler platform employs typical hybridization cycles to accurately detect fluorophores and distribute protein targets at high resolution. PhenoCycler utilizes a photobleaching cocktail to clean out traces of fluorescently tagged antibody staining left by a previous cycle to upgrade the signal-to-noise ratio (68). PhenoCycler slides can contain multiple tissue sections, and the system is capable of processing up to four slides at a time.

PhenoCycler has enabled the mapping of diverse cellular populations and their interactions at high resolution, facilitating a deeper understanding of the TME’s spatial architecture. For instance, PhenoCycler has been used to characterize the localization and interaction of immune cells, such as regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs), within CRC tissues. This has provided valuable insights into the mechanisms of immune suppression and tumor progression, potentially guiding the development of combination therapies targeting both the immune checkpoint pathways and the TME’s stromal components (69).

However, like other cyclic immunofluorescent imaging platforms, PhenoCycler requires specialized equipment and reagents that may not be affordable for all research settings. Additionally, the technique may generate artifacts or damage the tissue and is not effective in detecting low-abundance or weakly expressed markers (68).

Lunaphore’s COMET platform is a multiplex immunofluorescence-based system that employs sequential ImmunoFluorescence (seqIF) technology, similar technology to Phenocycler. It can perform 40-plex protein panel spatial proteomic experiments and supports applications such as TME profiling, biomarker discovery, validation, treatment evaluation, and clinical use. However, it shares the same limitations as the Phenocycler platform. As a relatively new technology, there are currently no peer-reviewed CRC studies that have utilized COMET in their analyses (70).

The MACSima system by Miltenyi is a multiplex immunofluorescence platform that utilizes Miltenyi’s REAfinity recombinant antibodies along with REAdye Lease fluorochrome technology. Although it shares similarities with other multiplex immunofluorescence systems, the MACSima system stands out for its use of photobleaching or fluorochrome release through REAdye Lease fluorochromes following staining and imaging, providing a gentler approach to treating tissue samples. This technology allows the MACSima system to accommodate protein panels with over 120 different antibodies. Nonetheless, the platform encounters challenges such as significant data management and storage demands and limitations due to the available selection of REAfinity recombinant antibodies provided by Miltenyi (71, 72).

In colorectal cancer (CRC) research, MACSima has been utilized to examine human γδ T cell subsets within the spatial context of CRC tumor tissue. This study served as a proof of concept, enabling researchers to explore cell heterogeneity and the tumor microenvironment (TME) to identify clinically relevant characteristics. Another CRC study employing MACSima for spatial proteomic analysis revealed NECTIN2 expression on cancer-associated fibroblasts (CAFs), but not on tumor cells. Additionally, this study correlated NECTIN2 expression with poor prognosis in CRC patients (73, 74).

The application of spatial proteomics technologies in CRC research has revolutionized our understanding of the tumor microenvironment by providing spatial context to cellular and molecular interactions that drive cancer progression and therapeutic resistance ( Figure 3 ).

While MELC, CODEX, and PhenoCycler offer powerful tools for spatial proteomics in CRC research, there are several limitations. A major challenge is the need for validated antibodies and probes that are compatible with the cyclic staining and imaging processes. Additionally, these technologies generate vast amounts of data that require advanced computational tools and expertise for accurate analysis and interpretation (66, 68).

Future developments in these platforms may focus on increasing throughput, improving antibody and probe validation processes, and enhancing data analysis capabilities through machine learning and artificial intelligence. Integration with other omics platforms, such as spatial transcriptomics and metabolomics, could provide a more comprehensive understanding of CRC biology at multiple molecular levels, further advancing the field of precision oncology (69).

Despite the transformative potential of ST and SP technologies in CRC research ( Figure 4 ), several challenges must be addressed to fully harness their capabilities for clinical and translational applications. These challenges span technical, biological, computational, and practical domains, and overcoming them is crucial for advancing our understanding of CRC and improving patient outcomes.

To address these challenges, several strategic recommendations can be made to advance the application of spatial biology platforms in CRC research and its integration into precision medicine.