Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data.

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses to estimate the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics.

Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.

The effects of age and dysfunction on meibomian gland population dynamics.

PURPOSE: While meibomian gland dysfunction (MGD) is widely recognized as a major cause of evaporative dry eye disease, little is known about normal gland differentiation and lipid synthesis or the mechanism underlying gland atrophy and abnormal lipid secretion. The purpose of this study was to use single-cell and spatial transcriptomics to probe changes in cell composition, differentiation, and gene expression associated with two murine models of MGD: age-related gland atrophy in wild-type mice and altered meibum quality in acyl-CoA wax alcohol acyltransferase 2 (Awat2) knockout (KO) mice. METHODS: Young (6 month) and old (22 month) wild type, C57Bl/6 mice and young (3 month) and old (13 month) Awat2 KO mice were used in these studies. For single-cell analysis, the tarsal plate was dissected from the upper and lower eyelids, and single cells isolated and submitted to the UCI Genomic Core, while for the spatial analysis frozen tissue sections were shipped to Resolve Biosciences on dry ice and sections probed in duplicate using a meibomian gland specific, 100 gene Molecular Chartography panel. RESULTS: Analysis of gene expression patterns identified the stratified expression of lipogenic genes during meibocyte differentiation, which may control the progressive synthesis of meibum lipids; an age-related decrease in meibocytes; and increased immune cell infiltration. Additionally, we detected unique immune cell populations in the Awat2 KO mouse suggesting activation of psoriasis-like, inflammatory pathways perhaps caused by ductal dilation and hyperplasia. CONCLUSION: Together these findings support novel mechanism controlling gland function and dysfunction.

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics.

BACKGROUND: Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development. RESULTS: In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets. CONCLUSIONS: Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.

Application of spatial omics in gastric cancer.

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks.

Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development.

CCL19-producing fibroblasts promote tertiary lymphoid structure formation enhancing anti-tumor IgG response in colorectal cancer liver metastasis.

Tertiary lymphoid structures (TLSs) are associated with enhanced immunity in tumors. However, their formation and functions in colorectal cancer liver metastasis (CRLM) remain unclear. Here, we reveal that intra- and peri-tumor mature TLSs (TLS+) are associated with improved clinical outcomes than TLS- tumors. Using single-cell-RNA-sequencing and spatial-enhanced-resolution-omics-sequencing (Stereo-seq), we reveal that TLS+ tumors are enriched with IgG+ plasma cells (PCs), while TLS- tumors are characterized with IgA+ PCs. By generating TLS-associated PC-derived monoclonal antibodies in vitro, we show that TLS-PCs secrete tumor-targeting antibodies. As the proof-of-concept, we demonstrate the anti-tumor activities of TLS-PC-mAb6 antibody in humanized mouse model of colorectal cancer. We identify a fibroblast lineage secreting CCL19 that facilitates lymphocyte trafficking to TLSs. CCL19 treatment promotes TLS neogenesis and prevents tumor growth in mice. Our data uncover the central role of CCL19+ fibroblasts in TLS formation, which in turn generates therapeutic antibodies to restrict CRLM.

Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are the prominent cell type in the tumor microenvironment (TME), and CAF subsets have been identified in various tumors. However, how CAFs spatially coordinate other cell populations within the liver TME to promote cancer progression remains unclear. METHODS: We combined multi-region proteomics (6 patients, 24 samples), 10X Genomics Visium spatial transcriptomics (11 patients, 25 samples), and multiplexed imaging (92 patients, 264 samples) technologies to decipher the expression heterogeneity, functional diversity, spatial distribution, colocalization, and interaction of fibroblasts. The newly identified CAF subpopulation was validated by cells isolated from 5 liver cancer patients and in vitro functional assays. RESULTS: We identified a liver CAF subpopulation, marked by the expression of COL1A2, COL4A1, COL4A2, CTGF, and FSTL1, and named F5-CAF. F5-CAF is preferentially located within and around tumor nests and colocalizes with cancer cells with higher stemness in hepatocellular carcinoma (HCC). Multiplexed staining of 92 patients and the bulk transcriptome of 371 patients demonstrated that the abundance of F5-CAFs in HCC was associated with a worse prognosis. Further in vitro experiments showed that F5-CAFs isolated from liver cancer patients can promote the proliferation and stemness of HCC cells. CONCLUSIONS: We identified a CAF subpopulation F5-CAF in liver cancer, which is associated with cancer stemness and unfavorable prognosis. Our results provide potential mechanisms by which the CAF subset in the TME promotes the development of liver cancer by supporting the survival of cancer stem cells.

Single-cell sequencing combined with spatial transcriptomics reveals that the IRF7 gene in M1 macrophages inhibits the occurrence of pancreatic cancer by regulating lipid metabolism-related mechanisms.

AIM: The main focus of this study is to explore the molecular mechanism of IRF7 regulation on RPS18 transcription in M1-type macrophages in pancreatic adenocarcinoma (PAAD) tissue, as well as the transfer of RPS18 by IRF7 via exosomes to PAAD cells and the regulation of ILF3 expression. METHODS: By utilising single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data from the Gene Expression Omnibus database, we identified distinct cell types with significant expression differences in PAAD tissue. Among these cell types, we identified those closely associated with lipid metabolism. The differentially expressed genes within these cell types were analysed, and target genes relevant to prognosis were identified. Flow cytometry was employed to assess the expression levels of target genes in M1 and M2 macrophages. Cell lines with target gene knockout were constructed using CRISPR/Cas9 editing technology, and cell lines with target gene knockdown and overexpression were established using lentiviral vectors. Additionally, a co-culture model of exosomes derived from M1 macrophages with PAAD cells was developed. The impact of M1 macrophage-derived exosomes on the lipid metabolism of PAAD cells in the model was evaluated through metabolomics analysis. The effects of M1 macrophage-derived exosomes on the viability, proliferation, division, migration and apoptosis of PAAD cells were assessed using MTT assay, flow cytometry, EdU assay, wound healing assay, Transwell assay and TUNEL staining. Furthermore, a mouse PAAD orthotopic implantation model was established, and bioluminescence imaging was utilised to assess the influence of M1 macrophage-derived exosomes on the intratumoural formation capacity of PAAD cells, as well as measuring tumour weight and volume. The expression of proliferation-associated proteins in tumour tissues was examined using immunohistochemistry. RESULTS: Through combined analysis of scRNA-seq and ST technologies, we discovered a close association between M1 macrophages in PAAD samples and lipid metabolism signals, as well as a negative correlation between M1 macrophages and cancer cells. The construction of a prognostic risk score model identified RPS18 and IRF7 as two prognostically relevant genes in M1 macrophages, exhibiting negative and positive correlations, respectively. Mechanistically, it was found that IRF7 in M1 macrophages can inhibit the transcription of RPS18, reducing the transfer of RPS18 to PAAD cells via exosomes, consequently affecting the expression of ILF3 in PAAD cells. IRF7/RPS18 in M1 macrophages can also suppress lipid metabolism, cell viability, proliferation, migration, invasion and intratumoural formation capacity of PAAD cells, while promoting cell apoptosis. CONCLUSION: Overexpression of IRF7 in M1 macrophages may inhibit RPS18 transcription, reduce the transfer of RPS18 from M1 macrophage-derived exosomes to PAAD cells, thereby suppressing ILF3 expression in PAAD cells, inhibiting the lipid metabolism pathway, and curtailing the viability, proliferation, migration, invasion of PAAD cells, as well as enhancing cell apoptosis, ultimately inhibiting tumour formation in PAAD cells in vivo. Targeting IRF7/RPS18 in M1 macrophages could represent a promising immunotherapeutic approach for PAAD in the future.

Precise detection of cell-type-specific domains in spatial transcriptomics.

Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.

Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer.

BACKGROUND: High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor. METHODS: Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups. RESULTS: By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses. CONCLUSIONS: This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

A single-cell transcriptomic map of the developing Atoh1 lineage identifies neural fate decisions and neuronal diversity in the hindbrain.

Proneural transcription factors establish molecular cascades to orchestrate neuronal diversity. One such transcription factor, Atonal homolog 1 (Atoh1), gives rise to cerebellar excitatory neurons and over 30 distinct nuclei in the brainstem critical for hearing, breathing, and balance. Although Atoh1 lineage neurons have been qualitatively described, the transcriptional programs that drive their fate decisions and the full extent of their diversity remain unknown. Here, we analyzed single-cell RNA sequencing and ATOH1 DNA binding in Atoh1 lineage neurons of the developing mouse hindbrain. This high-resolution dataset identified markers for specific brainstem nuclei and demonstrated that transcriptionally heterogeneous progenitors require ATOH1 for proper migration. Moreover, we identified a sizable population of proliferating unipolar brush cell progenitors in the mouse Atoh1 lineage, previously described in humans as the origin of one medulloblastoma subtype. Collectively, our data provide insights into the developing mouse hindbrain and markers for functional assessment of understudied neuronal populations.

aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph.

Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.

Spatial transcriptomics reveals organized and distinct immune activation in cutaneous granulomatous disorders.

BACKGROUND: Non-infectious (inflammatory) cutaneous granulomatous disorders include cutaneous sarcoidosis (CS), granuloma annulare (GA), necrobiosis lipoidica (NL), and necrobiotic xanthogranuloma (NXG). These disorders share macrophage predominant inflammation histologically, but the inflammatory architecture and the pattern of extracellular matrix alteration varies. The underlying molecular explanations for these differences remain unclear. OBJECTIVE: To understand spatial gene expression characteristics in these disorders. METHODS: We performed spatial transcriptomics in cases of CS, GA, NL, and NXG to compare patterns of immune activation and other molecular features in a spatially resolved fashion. RESULTS: CS is characterized by a polarized, spatially organized T helper (Th) 1 predominant response with classical macrophage activation. GA is characterized by a mixed, but spatially organized pattern of Th1 and Th2 polarization with both classical and alternative macrophage activation. NL showed concomitant activation of Th1, Th2, and Th17 immunity with a mixed pattern of macrophage activation. Activation of type 1 immunity was shared among, CS, GA, and NL and included upregulation of IL-32. NXG showed upregulation of CXCR4-CXCL12/14 chemokine signaling and exaggerated alternative macrophage polarization. Histologic alteration of extracellular matrix correlated with hypoxia and glycolysis programs and type 2 immune activation. CONCLUSIONS: Inflammatory cutaneous granulomatous disorders show distinct and spatially organized immune activation that correlate with hallmark histologic changes.

Open-source, high-throughput targeted in-situ transcriptomics for developmental and tissue biology.

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in-situ expression maps of hundreds of genes. We exploit direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high throughput and large multiplexing. We validate the method across a number of species, and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental and tissue biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to high-throughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

Spatial transcriptome reveals the region-specific genes and pathways regulated by Satb2 in neocortical development.

BACKGROUND: It is known that the neurodevelopmental disorder associated gene, Satb2, plays important roles in determining the upper layer neuron specification. However, it is not well known how this gene regulates other neocortical regions during the development. It is also lack of comprehensive delineation of its spatially regulatory pathways in neocortical development. RESULTS: In this work, we utilized spatial transcriptomics and immuno-staining to systematically investigate the region-specific gene regulation of Satb2 by comparing the Satb2+/+ and Satb2-/- mice at embryonic stages, including the ventricle zone (VZ) or subventricle zone (SVZ), intermediate zone (IZ) and cortical plate (CP) respectively. The staining result reveals that these three regions become moderately or significantly thinner in the Satb2-/- mice. In the cellular level, the cell number increases in the VZ/SVZ, whereas the cell number decreases in the CP. The spatial transcriptomics data show that many important genes and relevant pathways are dysregulated in Satb2-/- mice in a region-specific manner. In the VZ/SVZ, the key genes involved in neural precursor cell proliferation, including the intermediate progenitor marker Tbr2 and the lactate production related gene Ldha, are up-regulated in Satb2-/- mice. In the IZ, the key genes in regulating neuronal differentiation and migration, such as Rnd2, exhibit ectopic expressions in the Satb2-/- mice. In the CP, the lineage-specific genes, Tbr1 and Bcl11b, are abnormally expressed. The neuropeptide related gene Npy is down-regulated in Satb2-/- mice. Finally, we validated the abnormal expressions of key regulators by using immunofluorescence or qPCR. CONCLUSIONS: In summary, our work provides insights on the region-specific genes and pathways which are regulated by Satb2 in neocortical development.

The spatial distribution of intermediate fibroblasts and myeloid-derived cells dictate lymph node metastasis dynamics in oral cancer.

BACKGROUND: Oral cancer poses a significant health challenge due to limited treatment protocols and therapeutic targets. We aimed to investigate the invasive margins of gingivo-buccal oral squamous cell carcinoma (GB-OSCC) tumors in terms of the localization of genes and cell types within the margins at various distances that could lead to nodal metastasis. METHODS: We collected tumor tissues from 23 resected GB-OSCC samples for gene expression profiling using digital spatial transcriptomics. We monitored differential gene expression at varying distances between the tumor and its microenvironvent (TME), and performed a deconvulation study and immunohistochemistry to identify the cells and genes regulating the TME. RESULTS: We found that the tumor-stromal interface (a distance up to 200 µm between tumor and immune cells) is the most active region for disease progression in GB-OSCC. The most differentially expressed apex genes, such as FN1 and COL5A1, were located at the stromal ends of the margins, and together with enrichment of the extracellular matrix (ECM) and an immune-suppressed microenvironment, were associated with lymph node metastasis. Intermediate fibroblasts, myocytes, and neutrophils were enriched at the tumor ends, while cancer-associated fibroblasts (CAFs) were enriched at the stromal ends. The intermediate fibroblasts transformed into CAFs and relocated to the adjacent stromal ends where they participated in FN1-mediated ECM modulation. CONCLUSION: We have generated a functional organization of the tumor-stromal interface in GB-OSCC and identified spatially located genes that contribute to nodal metastasis and disease progression. Our dataset might now be mined to discover suitable molecular targets in oral cancer.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

Molecular analysis of human tick-bitten skin yields signatures associated with distinct spatial and temporal trajectories - A proof-of-concept study.

Tick-associated diseases present challenges due to tridirectional interactions among host-specific responses, tick toxins and salivary proteins as well as microbes. We aimed to uncover molecular mechanisms in tick-bitten skin samples (cases) and contralateral skin samples (controls) collected simultaneously from the same participants, using spatial transcriptomics. Cases and controls analysed using NanoString GeoMx Digital Spatial Profiler identified 274 upregulated and 840 downregulated differentially expressed genes (DEGs), revealing perturbations in keratinization and immune system regulation. Samples of skin biopsies taken within 72 h post tick-bite DEGs had changes in protein metabolism and viral infection pathways as compared to samples taken 3 months post tick-bite, which instead displayed significant perturbations in several epigenetic regulatory pathways, highlighting the temporal nature of the host response following tick bites. Within-individual signatures distinguished tick-bitten samples from controls and identified between-individual signatures, offering promise for future biomarker discovery to guide prognosis and therapy.

High CIB1 expression in colorectal cancer liver metastases correlates with worse survival and the replacement histopathological growth pattern.

To date, nearly one-quarter of colorectal cancer (CRC) patients develop liver metastases (CRCLM), and its aggressiveness can be correlated to defined histopathological growth patterns (HGP). From the three main HGPs within CRCLM, the replacement HGP emerges as particularly aggressive, characterized by heightened tumor cell motility and vessel co-option. Here, we investigated the correlation between the expression of calcium- and integrin-binding protein 1 (CIB1), a ubiquitously expressed gene involved in various cellular processes including migration and adhesion, and disease-free (DFS) and overall survival (OS) in primary CRC patients. Additionally, we explored the correlation between CIB1 expression and different HGPs of CRCLM. Proteomic analysis was used to evaluate CIB1 expression in a cohort of 697 primary CRC patients. Additionally, single-cell and spatial RNA-sequencing datasets, along with publicly available bulk sequencing data were used to evaluate CIB1 expression in CRCLM. In silico data were further validated by formalin-fixed paraffin-embedded immunohistochemical stainings. We observed that high CIB1 expression is independently associated with worse DFS and OS, regardless of Union Internationale Contre le Cancer stage, gender, or age. Furthermore, the aggressive replacement CRCLM HGP is significantly associated with high CIB1 expression. Our findings show a correlation between CIB1 levels and the clinical aggressiveness of CRC. Moreover, CIB1 may be a novel marker to stratify HGP CRCLM.