Comprehensive Multi-omics Integration of Bulk, Single cell and Spatial Transcriptomics Reveals Temporal and Spatial Gene Expression to Cisplatin and 5-Fluorouracil in Colorectal Cancer

Comprehensive Multi-omics Integration of Bulk, Single cell and Spatial Transcriptomics Reveals Temporal and Spatial Gene Expression to Cisplatin and 5-Fluorouracil in Colorectal Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comprehensive Multi-omics Integration of Bulk, Single cell and Spatial Transcriptomics Reveals Temporal and Spatial Gene Expression to Cisplatin and 5-Fluorouracil in Colorectal Cancer Seyed Taleb Hosseini, Kimia Aminian Toosi, Roya BishehKolaei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8003617/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, mainly as outcomes of varying treatment responses and an increase of drug resistance. Although cisplatin and 5-fluorouracil (5-FU) are used in medical treatment widely, it remains unknown exactly molecular pathways explain numerous therapeutic responses. This study aimed to identify genes responsive to these two drugs and to characterize their expression patterns and associated cell populations using an integrative multi-omics approach. We first analyzed bulk RNA-seq datasets from CRC cell lines (HCT116, HT29, and SW480) treated with 5-FU and cisplatin to identify differentially expressed genes (DEGs) and pathways. Next, we assessed the expression levels and cell-type specificity of these DEGs in single-cell RNA-seq data from ten colorectal tissue samples (five tumors and five normal tissues). Finally, spatial transcriptomics from four CRC tumor slides were examined to map the localization of treatment-responsive genes within the tumor microenvironment. Our results revealed that epithelial and fibroblast populations exhibited distinct transcriptional adaptations to chemotherapy. Pseudotime trajectories showed fibroblast enrichment at later transition states and suggesting a role in remodeling during treatment adaptation. Spatial mapping demonstrated that fibroblast-associated genes (SPARC, COL12A1, VCAN) were localized to stromal-rich peripheral regions, while epithelial markers (IFIT3, MYH9, KMT2E-AS1) were concentrated in tumor cores, particularly under high-dose cisplatin. Collectively, these findings demonstrate that epithelial plasticity and fibroblast-mediated remodeling contribute to drug resistance, highlighting possible targets to enhance cancer therapy sensitivity because chemotherapy induces considerable cellular and spatial modifications in the landscape of colorectal tumors. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Spatial Transcriptomics Colorectal Cancer Tumor Landscape Single Cell Cisplatin 5-Fluorouracil Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Author Summary Colorectal cancer is a highly complex disease that often shows varied responses to chemotherapy. To better understand how treatment changes the tumor environment, we analyzed gene expression at both the single-cell and spatial levels. Using colorectal cancer cell lines treated with two common drugs, cisplatin and 5-fluorouracil, we combined data from bulk, single-cell, and spatial transcriptomic analyses. This approach allowed us to map how different cell types, such as epithelial cells, fibroblasts, and immune cells respond to therapy within the tumor’s structure. We discovered that fibroblasts play an important role in the later stages of treatment adaptation, while epithelial cells show distinct gene activity changes under high-dose cisplatin. Certain genes, including SPARC, VCAN, and KMT2E-AS1, were found to have specific expression patterns in particular tumor regions. Overall, our findings show that chemotherapy not only affects individual cancer cells but also reshapes the spatial organization of the tumor. This study highlights the importance of understanding how different cell populations and their locations contribute to treatment response in colorectal cancer. Introduction Colorectal cancer (CRC) is one of the most common cancers in the world ( 1 ). Men and women are approximately equally affected by colorectal cancer, according to latest global cancer data, while its prevalence and mortality are influenced considerably by location and lifestyle factors ( 1 ). In 2020, colorectal cancer was responsible for more than 930,000 fatalities in addition to more 1.9 million cases were reported ( 1 ). While colorectal cancer is frequently undetectable in its initial stages, numerous types of warning symptoms could appear as the cancer spreads ( 2 ). Change in intestinal habits, bleeding from the rectal region, chronic stomach pain, insoluble weight loss and exhaustion are some of symptoms ( 2 ). Utilizing the diagnostic approaches such as colonoscopy, fecal occult blood tests (FOBT), CT scans and genomic profiling are essential for early and accurate detection of colorectal cancer ( 3 ). Innovations in the sequencing process, especially bulk RNA sequencing (bulk RNA-seq), have greatly contributed to our molecular understanding of colorectal cancer ( 4 ). Accurate investigation of gene expression patterns across whole tissues or cell types is made possible by bulk RNA-seq, which provides important information about tumor heterogeneity, gene regulatory processes and prospective treatment strategies ( 5 ). Colorectal cancer cellular heterogeneity and tumor microenvironment complexities can be discovered by utilizing single-cell RNA sequencing (scRNA-seq), which is useful for the study of gene expression at the single-cell level ( 6 ). By establishing the spatial context of gene expression within tissue architecture, spatial transcriptomics improves on this comprehension ( 7 ). Together, these technologies provide a powerful framework for identifying cell-type-specific gene expression patterns and spatially localized molecular signatures that contribute to colorectal cancer progression and treatment resistance ( 7 ). Colorectal cancer cell lines including SW480, HCT116 and HT29 are essential models in molecular oncology and used to investigate tumor biological processes, intervention responses and resistance mechanisms ( 8 ). These cell lines are helpful for comparative research due to their differences in molecular aspects and genetic origin ( 8 ). Chemotherapeutic drugs including 5-fluorouracil (5-FU) and cisplatin have been comprehensively studied to determine cellular flexibility, apoptotic pathways and cytotoxic effects on cells ( 9 ). The insights obtained from these studies contributes enhance personalized medicine in the therapeutic management of colorectal cancer in addition to facilitating the discovery of new drugs ( 10 ). In this research, we conducted a multi-omics analysis to explore the molecular mechanisms underlying the response of colorectal cancer cell lines to two widely used chemotherapeutic drugs: 5-fluorouracil and cisplatin. Initially, we analyzed publicly available bulk RNA-Seq datasets of colorectal cancer cell lines treated with these drugs to identify differentially expressed genes and uncover potential pathways and protein-protein interactions associated with drug responsiveness. To further validate these findings, we investigated the expression patterns of the identified genes in single-cell RNA-Seq data derived from ten colorectal tissue samples (five tumor tissues and five normal tissues), allowing us to assess gene expression heterogeneity across individual cells within the tumor microenvironment. Finally, we examined spatial transcriptomics data from four colorectal cancer tumor tissues to determine the localization of gene expression within tumor architecture and to evaluate their potential roles in cancer progression and spatial dynamics of gene expression. This integrative approach provides a comprehensive framework for understanding the gene expression related to the drug response in colorectal cancer. Material and Methods Data Collections for RNA-Seq Characterization Figure 1 shows the complete steps and general workflow of data processing. To determine RNA-Seq-based CRC gene expression profiling studies in different cell lines and tissues, we explored the PubMed database, the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) (11) and the Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) (12). Bulk RNA-Seq datasets that investigated the use of various antibodies and drug therapies to treat different types of colorectal cancer cell lines such as: SW480, HT29 and HCT116 and single cell RNASeq and spatial transcriptomics datasets related to colorectal cancer tissues were included. The experiment Bulk RNA-Seq data associated with the levels of gene expression in cancer and normal tissue of patients with colorectal cancer, as well as datasets related to studies conducted on animal models such as mice and rat species and systematic review articles, were excluded. Information Related to the RNA-Seq Data Collections SRP345690 (9 samples, Illumina HiSeq 4000), SRP360190 (8 samples, Illumina NovaSeq 6000) (13) and SRP351625 (3 samples, Illumina NovaSeq 6000) (14) are the three original expression bulk-RNA-Seq datasets that we obtained from the SRA database (which is accessible online at https://www.ncbi.nlm.nih.gov/sra) (12). These datasets provided 20 colorectal cancer cell line samples (HCT116 cell line = 7, HT29 = 6 and SW480 = 7) treated with two dose of Cisplatin 30M, Cisplatin 300M and 5-fluorouracil in 8 groups such as: (Group 1: HCT116 Control vs. HCT116 5-fluorouracil), (Group 2: HCT116 Control vs. HCT116 Cisplatin 30M), (Group 3: HCT116 Control vs. HCT116 Cisplatin 300M), (Group 4: HT29 Control vs. HT29 Cisplatin 30M), (Group 5: HT29 Control vs. HT29 Cisplatin 300M), (Group 6: SW480 Control vs. SW480 5-fluorouracil), (Group 7: SW480 Control vs. SW480 Cisplatin 30M), and (Group 8: SW480 Control vs. SW480 Cisplatin 300M). We downloaded the single cell RNASeq dataset with the accession ID GSE201348 (15) from the GEO database (https://www.ncbi.nlm.nih./) was sequenced utilizing the Illumina NovaSeq 6000 (10XGenomics platform) and included 10 human colorectal cancer tissues: 5 normal tissues (23,105 genes and 15,941 cells) and 5 tumor tissues (23,362 genes and 17,713 cells). Following that, we downloaded spatial transcriptomics of Visium gene expression profile from 4 tumor slides in colorectal cancer patients such as: A121573-Rep1, A121573-Rep2, A938797Rep1 and A938797Rep2 (16). Selected details of datasets such as bulk, single cell and spatial transcriptomics were reported in Table 1 . Preprocessing of RNASeq data: Assessment of Quality, Trimming, Alignment and Counting The read accuracy of the sequences was assessed using the FASTQC tool (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (17). For eliminating and trim reads, the TRIMMOMATIC software (V-0.39) was employed (18). The options (LEADING:15, TRAILING:15, SLIDINGWINDOW:4:25 and MINLEN:50) were applied to reduce the sequencing reads. Using the HISAT2 (v2.2.1) alignment tool (19), processed results from RNA-Seq were mapped to the human reference genome GRCH38. Utilizing the HT-Seq software (20), read count for gene expression have been determined. Identification of Differentially Expression Genes To generate an integrated data set of 20 bulk samples, each sample from these three data sets have been merged utilizing the SVA (21) package (v3.50) in the R programming language. The batch effect from the count data was obtained by applying the "ComBat_seq" function. The DESeq2 package (22) in R programming language was used to identify the differentially expressed genes (DEGs) between 8 groups: (Group 1: HCT116 Control vs. HCT116 5-fluorouracil), (Group 2: HCT116 Control vs. HCT116 Cisplatin 30M), (Group 3: HCT116 Control vs. HCT116 Cisplatin 300M), (Group 4: HT29 Control vs. HT29 Cisplatin 30M), (Group 5: HT29 Control vs. HT29 Cisplatin 300M), (Group 6: SW480 Control vs. SW480 5-fluorouracil), (Group 7: SW480 Control vs. SW480 Cisplatin 30M), and (Group 8: SW480 Control vs. SW480 Cisplatin 300M). A statistically significant P value of less than 0.05 was determined to be the threshold for the selection of DEGs, with log2foldchange (FC) ∣ >1. The DESeq2 package (22) ("cor" function) was utilized to normalized counts and co-expression evaluation was performed using Hmisc package (23) and "corplot" function in R programming language. Enrichment Analysis of DEGs GO investigation is an effective method that uses data collected via high-throughput genomics to discover the particular biological capacities of genes and proteins (24). KEGG is a collection of resources developed to communicate genome-related data with higher order biological process pathways and conduct a systematic analysis of gene function (25-27). Consequently, the enrichR package (28) in the R programming language was used to carry out the GO and KEGG pathways enrichment assessment of DEGs. Adj.p-value of less than 0.05 was considered to be the threshold for statistical significance. PPI Network Construction Investigation of protein-protein interactions (PPIs) may assist in discovering the molecular roles of proteins and provide suggestions for such cellular processes as differentiation, development, metabolism and apoptosis (29). To assess the regulatory processes, it is necessary to identify protein-interacting ions on a genome-wide level (30). A web-based application called STRING (Search Tool for the Retrieval of Interacting Genes) has been employed to assess the relationship between PPI networks of common DEGs (31). After that, the PPI network complex of the common DEGs was imported into Cytoscape v3.10.0 (https://cytoscape.org/), a free program for PPI network visualization (32). RNA-seq data was used to confirm our final candidate genes by employing TNMplot (https://tnmplot.com/) (33). Quality Control and Data Integration in Single-Cell Transcriptomics Analysis The 10xGenomice scRNA-seq data (46,467 genes and 33,654 cells) was evaluated utilizing the Seurat (34) package in the R programming language. In order to create a highly accurate scRNA-seq expression matrix, quality control (QC) was performed on the initial matrix and lower-quality cells were eliminated based on the following parameters: 1) In order to generate a Seurat object, cells must express more than 200 different genes, with genes expressed in a minimum of three different cells being appropriate. 2) Only cells with gene expression levels over 200 and below 5000 for normal objects and 5500 for tumor objects have been assessed in terms of overall diversity. 3) The "PercentageFeatureSet" function was used to determine the percentage of genes associated with both ribosome and mitochondrial processes that were actually discovered in each cell. The "LogNormalize" method in the "NormalizeData" function has been used to normalize the scRNA-seq data. The top 2000 highly distinct genes were found and demonstrated by employing the "FindVariableFeatures" and "VariableFeaturePlot" functions following a quality assessment. The CCA algorithm was utilized for merging all Seurat objects (normal and tumor) by applying the "SelectIntegrationFeatures", "FindIntegrationAnchors" and "IntegrateData" functions. After that, data for every gene expression was subsequently adjusted and centered utilizing the "ScaleData" function. Nonlinear Dimension Reduction, Clustering, and Marker Identification The "RunPCA" function in the Seurat (34) package was utilized to evaluate 2000 genes using principal component analysis (PCA). Using the first 20 major components, a complete cellular clustering assessment was performed. Principal component analyses (PCAs) were conducted using the "VizDimLoadings", "DimPlot" and "DimHeatmap" functions to represent gene expression. subsequently with the parameter "resolution" set at 0.5, cellular clustering was detected utilizing the "JackStraw, num.replicate = 100", "ScoreJackStraw, dims = 1:20", "JackStrawPlot", "ElbowPlot", "FindNeighbors, dims = 1:20", and "FindClusters" functions in the Seurat (34) package. Furthermore, the "RunUMAP" function in the uniform manifold approximation and projection (UMAP) method was utilized to find cell clusters and reduce dimension. To identify the genes that showed differential expression (DEGs) in each cluster, the "FindAllMarkers" function was utilized to evaluate the false discovery rates (Adj.Pvalue) and log2foldchange. For all clusters, DEGs with logfc. threshold = 1 and min.pct = 0.25 were regarded as the marker genes. The gene expression patterns of cell lines such as SW480, HT29 and HCT116 throughout several treatments with 5-fluorouracil and cisplatin 30M and 300M were also discovered by using the "FeaturePlot" function with reduction "umap" in the Seurat (34) package. Following that, the "HumanPrimaryCellAtlasData" function was used to computationally cluster and characterize various cell types using the SingleR (35), celldex (35) and SingleCellExperiment (36) R packages. Single Cell Pseudotime Trajectories Analysis To determine the cell transitions in state, single-cell trajectory analysis was carried out using the R package Monocle2 (v2.30.0) (37). Cell types along with additional RDS data were imported into the R programming language. The parameters "expressionFamily = negbinomial.size" and "lowerDetectionLimit = 0.5" were respectively applied to generate a new object using the "newCellDataSet" function. The "reduceDimension" function was utilized to reduce the dimensionality using the parameters "reduction_method = DDRTree" and "max_components = 2". Pseudotime and cell clustering were subsequently utilized to identify the cell lineage trajectories using the standard criteria of the Monocle2 (37) package in R programming language. Following that, the "plot_cell_trajectory" function was used to present the findings. Additionally, the "plot_genes_in_pseudotime" function was used to show dynamic modifications to pseudotime-dependent gene expression over pseudotime. Spatial Gene Expression Profiling Spatial transcriptomics data (ST) was analyzed and revealed utilizing the Seurat (v5.0.1) (34) and SpacexR (38) packages in R programming language. We integrated single-cell RNA seq (scRNA-seq) data with spatial transcriptomics data to investigate gene expression localization within four colorectal cancer tissues such as: A121573-Rep1, A121573-Rep2, A938797Rep1 and A938797Rep2. The spatial transcriptomics dataset was loaded into R programming language using the "Load10X_Spatial" function, which reads 10X Genomics Visium output and initializes a Seurat object for spatial analysis. Subsequent preprocessing steps including: normalization ("NormalizeData"), identification of highly variable genes ("FindVariableFeatures") and data scaling ("ScaleData") functions. Dimensionality reduction is performed using Principal Component Analysis (PCA) via the "RunPCA" function, which captures the major sources of variation in the dataset. The resulting components are used to construct a shared nearest neighbor graph ("FindNeighbors") and perform clustering ("FindClusters"). Uniform Manifold Approximation and Projection (UMAP) approaches were applied with "RunUMAP" function to visualize the spatial organization of clusters in two dimensions. To incorporate single-cell reference data, a SpatialRNA object were generated thought the count matrix and tissue coordinates from the spatial dataset using "SpatialRNA" function. A reference object is constructed from the scRNA-seq data using the "Reference" function, which includes cell type annotations and UMI counts. These objects are then used to initialize an RCTD (Robust Cell Type Decomposition) model with "create.RCTD" function. The "run.RCTD" function executes the deconvolution algorithm, estimating the contribution of each cell type at every spatial location. The results are then added to the Seurat metadata using "AddMetaData" function. Finally, the spatial gene expression of key marker such as (list genes and cell lines) is visualized using "SpatialFeaturePlot" function in R programming language. This integrated approach enables high-resolution mapping of gene expression across colorectal cancer tissues and provides valuable insights into the tissue architecture and gene regulatory programs underlying cancer progression like colon and rectum cancer. Statistical Analysis UBUNTU 22.04 LTS and R programming language (v4.3.2, R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org/) was utilized for statistical analysis and some packages including SRAToolkits, FASTQC, TRIMMOMATIC, HISAT2, HT-Seq, SVA, DESeq2, enrichR, Hmisc, Cytoscape, Seurat, celldex, SingleCellExperiment, SingleR, Monocle2 and SpacexR were utilized for data visualization. The findings of RNASeq-Read counts were analyzed using GraphPad Prism Software version 9.0 (GraphPad Software, San Diego, CA, USA). Next, Unpaired Student’s t-test was used to determine the statistical significance of the difference between normally distributed variables and a p-value of 0.05 or less was considered as statistically significant. Results Supplementary Table S1 provides the overall statistics of transcriptome mapping for colorectal cancer cell lines, showing the sequencing quality and alignment efficiency. Supplementary Table S2 summarizes the number of differentially expressed genes (DEGs) identified between various treatment doses for HCT116, HT29, and SW480 cell lines. Supplementary Table S3 lists the DEGs detected between control and 5-Fluorouracil-treated HCT116 cells. Supplementary Table S4 shows the DEGs identified in HCT116 cells comparing control vs Cisplatin 30M treatment. Supplementary Table S5 presents the DEGs for HCT116 cells under control vs Cisplatin 300M treatment. Supplementary Table S6 includes the DEGs observed in HT29 cells when comparing control and Cisplatin 30M treatment. Supplementary Table S7 displays the DEGs for HT29 cells in control vs Cisplatin 300M conditions. Supplementary Table S8 provides the DEGs identified between control and 5-Fluorouracil-treated SW480 cells. Supplementary Table S9 shows the DEGs in SW480 cells under control vs Cisplatin 30M treatment. Supplementary Table S1 0 lists the DEGs obtained from SW480 cells comparing control with Cisplatin 300M treatment. Supplementary Table S1 1 presents the gene ontology (GO) enrichment analysis results for DEGs identified in the HCT116, HT29 and SW480 cell lines. Supplementary Table S1 2 contains the quality control assessment for both normal and tumor samples. All corresponding results text are included in the Supplementary Results file . Cellular Landscape in Normal and Tumor Tissues The umap plot indicates the number of cell clusters among normal and tumor tissues were analyzed through integrating the samples (Fig. 2 A). Additionally, 22 clusters were generated by clustering cells utilizing the "FindCluster" function (Fig. 2 A). By applying "SingleR", a computational annotation tool based on the Human Primary Cell Atlas database, we subsequently discovered 8 distinct cell types within these clusters. These cell types included: Epithelial cells (Cluster annotated: C0, C1, C2, C3, C5, C6, C7, C9, C11, C12, C13 and C19), Fibroblasts (Cluster annotated: C4, C10, C15 and C20), Tissue stem cells (Cluster annotated: C8), NK cell (Cluster annotated: C14), T cells (Cluster annotated: C16), Endothelial cells (Cluster annotated: C17), B cell (Cluster annotated: C18) and DC (Cluster annotated: C21) (Fig. 2 B, Supplementary Table S13 ) and revealing that the most common cell types in samples of normal and tumor are Epithelial cell, Fibroblasts, Tissue stem cells and NK cell (Fig. 2 B). After comparing the proportions of each population in the tumor and normal samples, it was found that in the immune cell population, the proportion of Fibroblasts (Normal vs. Tumor: 6.34% vs. 19.74%), Tissue stem cells (Normal vs. Tumor: 0.06% vs. 9.20%) and Endothelial cells (Normal vs. Tumor: 1.82% vs. 2.20%) in the tumor tissue samples were significantly increased, while the proportion of Epithelial cells (Normal vs. Tumor: 83.67% vs. 62.67%), NK cell (Normal vs. Tumor: 3.04% vs. 2.99%), T cells (Normal vs. Tumor: 2.90% vs. 1.72%), B cell (Normal vs. Tumor: 1.58% vs. 1.37%) and DC (Normal vs. Tumor: 0.55% vs. 0.075%) was significantly reduced (Fig. 2 C, Supplementary Table S14 ). Also, we revealed that the proportions of 8 cell types for all samples using umap plot (Fig. 2 D). In order to find gene expression profiles, we applied the umap technique for unsupervised clustering and we found several significant genes, such as: (B cell: 97), (DC: 242), (Endothelial cells: 192), (Epithelial cells: 406), (Fibroblasts: 822), (NK cell: 174), (T cells: 93), (Tissue stem cells: 117). The full list of genes for the 8 cell types were reported in Supplementary Table S15. Mapping Treatment Responsive Gene Expression into Single-Cell Landscapes of Colorectal Tumors To further explore the relevance of treatment-induced gene expression changes in colorectal cancer cell lines, we selected differentially expressed genes from the HCT116 cell, HT29 and SW480 cell lines treated with 5-fluorouracil (5-FU) and cisplatin (30 M and 300 M) onto single-cell RNA-seq data derived from colorectal tumor tissues. This allowed us to assess the cell-type specificity of gene expression patterns in a more physiologically relevant context. In the HCT116-5FU group, genes such as KIF1A, IFIT3, OASL and HOXD13 were predominantly expressed in Epithelial cells, while SPARC showed enriched expression in Fibroblasts (Fig. 3 A). For the HCT116-30 M cisplatin group, AKR1B10 and EGFLAM were associated with Epithelial cells and Fibroblasts, respectively, whereas IGHG1 exhibited specific expression in B cells (Fig. 3 B). In the HCT116-300 M cisplatin group, all selected genes including: KMT2E-AS1, LINC01465 and RPL36AL displayed specific expression in Epithelial cells (Fig. 3 C). In the HT29-cisplatin 30 M treatment group, most of the selected genes including: FBXO44, PDZD3, FGF17 and HERC6, showed high expression in Epithelial cells, whereas PHYHIP was predominantly expressed in Fibroblasts. Notably, TSPAN32 exhibited specific expression in T cells (Fig. 3 D). In the SW480 5-fluorouracil (5-FU) group, the majority of responsive genes, such as: KITLG, RTL8A and NUDT6 were expressed in Epithelial cells, while VCAN was specifically expressed in Fibroblasts (Fig. 3 E). In the SW480 cisplatin 30 M group, all examined genes (HCG15, LINC01465, TAT-AS1, KMT2E-AS1 and GGT6) demonstrated expression to Epithelial cells (Fig. 3 F). By contrast, in the SW480 cisplatin 300 M group, gene expression patterns were more heterogeneous. Genes such as FAT4 and SGCD were high expressed in Fibroblasts and several genes such as CACNA1E, LINC01465, OTOG, HCG15 and ADAM7) showed moderate expression in Epithelial cells and DCC gene were moderate expressed in B cell (Fig. 3 G). Cell-Type Expression Patterns of key Hub Genes in Colorectal Tumor Tissues We also evaluated a group of hub genes and treatment-common genes and their expression in the colorectal single-cell transcriptomic data. Within the hub genes group, the majority of genes including: ISG15, IFIT2, MYH9, BRCA2, FGFR2, HSPA4 and MED1 were predominantly expressed in Epithelial cells (Fig. 4 A). Meanwhile, PDGFRA and COL12A1 exhibited enriched expression in Fibroblasts (Fig. 4 A). In the HT29 common group, which includes genes commonly regulated across multiple conditions in HT29 cells, all identified genes (TBCB, STK16, MRPL38 and SMPD4) showed expression in Epithelial cells (Fig. 4 B). For the SW480 common group, SUSD2 was expressed in Epithelial cells, while OLFML2A was enriched in Fibroblasts (Fig. 4 C). Altogether, the integration of bulk RNA-seq derived treatment signatures from colorectal cancer cell lines with single-cell transcriptomic data from colorectal tumors provides a high-resolution view of how distinct chemotherapeutic agents and doses may selectively effects in epithelial, stromal and immune cell populations within the tumor microenvironment. Development of Pseudotime Trajectories Between Normal-Tumor Tissues Pseudotime trajectory evaluation was conducted for every cell type to obtain insight into differentiation developments. After that, individual cells were classified using the clusters of normal-tumor, pseudotime and class in order to generate the tree-like structure of the entire lineage progression trajectory using the Monocle2 package in the R programming language (Fig. 5 A). Epithelial cells and Fibroblasts were detected mainly during the initial point of the pseudotime trajectory (Fig. 5 A). Around the final part of trajectories branch 1, Fibroblasts were most prevalent and the majority of the Fibroblasts and Tissue stem cells were located at the end of trajectory branch 2 (Fig. 5 A). Our findings indicates that Epithelial cells and Fibroblasts may be the initial stages of tumor cells that spread (Fig. 5 A). In the HCT116 5-fluorouracil (5-FU) group, the pseudotime trajectories showed a very low expression of KIF1A in Epithelial cells, while SPARC exhibited high expression in Fibroblasts (Fig. 5 B). Moderate expression levels were observed for IFIT3, OASL and HOXD13 in Epithelial cells along the pseudotime trajectories (Fig. 5 B). In the HCT116 cisplatin 30 M group, EGFLAM and AKR1B10 showed very low expression in Fibroblasts and Epithelial cells, respectively (Fig. 5 C). In contrast, IGHG1 exhibited moderate expression in B cells during pseudotime progression (Fig. 5 C). In the HCT116 cisplatin 300 M group, KMT2E-AS1 showed moderate expression in Epithelial cells, while both LINC01465 and RPL36AL displayed very low expression in the Epithelial cells throughout the pseudotime trajectory (Fig. 5 D). In the HT29 cisplatin 30 M group, FBXO44 and HERC6 showed very low expression in Epithelial cells along the pseudotime trajectory, whereas PDZD3 exhibited high expression in the same cell type (Fig. 5 E). FGF17 was moderately expressed in Epithelial cells and PHYHIP showed high expression in Fibroblasts (Fig. 5 E). Additionally, TSPAN32 displayed high expression in T cells during pseudotime development (Fig. 5 E). In the SW480 5-fluorouracil (5-FU) group, VCAN exhibited high expression in Fibroblasts, while both KITLG and RTL8A showed moderate expression in Epithelial cells (Fig. 5 F). Notably, NUDT6 showed no detectable expression along the pseudotime trajectory (Fig. 5 F). In the SW480 cisplatin 30 M group, HCG15 displayed high expression in Epithelial cells, whereas LINC01465 and TAT-AS1 had very low expression in the same cell type (Fig. 5 G). KMT2E-AS1 and GGT6 exhibited moderate expression in Epithelial cells throughout the trajectory (Fig. 5 G). In the SW480 cisplatin 300 M group, SGCD showed high expression in Fibroblasts and HCG15 also revealed highly expressed in Epithelial cells (Fig. 5 H). Moderate expression levels were observed for FAT4 in fibroblasts and DCC in B cells (Fig. 5 H). In contrast, CACNA1E was not expressed and LINC01465, OTOG and ADAM7 showed very low expression in Epithelial cells (Fig. 5 H). Spatial Gene Expression Landscape in Colorectal Tumors Spatial transcriptomic analysis was performed on four colorectal tumor tissue samples. These included two replicates from a rectal adenocarcinoma patient with confirmed lymph node involvement and liver metastasis (S5-A121573) and two replicates from a second rectal adenocarcinoma patient without evidence of lymphatic or hepatic spread (S6-A938797). Comparative assessment of gene expression patterns between tumor tissues based on distinct metastatic characteristics was made possible by this sample combination. In HCT116-5FU group, across all four tissue sections, a consistent spatial distribution of gene expression was observed. SPARC exhibited high expression and was enriched in central and upper-right and left regions (Fig. 6 A) and indicating localized fibroblast proliferation and corresponding with stromal-dense regions. In contrast, Epithelial-associated genes displayed varying levels of expression in central tumor regions: IFIT3 and HOXD13 showed moderate expression, OASL exhibited low expression and KIF1A demonstrated very low expression (Fig. 6 A). In the HCT116 30 M group, spatial transcriptomic profiling revealed differential gene expression concentrated primarily in the central regions of tumor tissue. The fibroblast-associated gene EGFLAM showed low expression, while the epithelial gene AKR1B10 exhibited moderate expression within the same regions (Fig. 6 B). Notably, IGHG1, a B-cell marker, demonstrated high expression in both central and upper-right tumor areas (Fig. 6 B). In the HCT116–300 M group, gene expression patterns remained largely confined to central and upper tumor regions. The epithelial gene KMT2E-AS1 showed moderate expression and RPL36AL exhibited high expression in these areas (Fig. 6 C). Conversely, LINC01465 was very weakly expressed (Fig. 6 C). In the HT29 30 M group, a heterogeneous spatial pattern of epithelial gene expression was observed. Genes such as FBXO44, PDZD3 and HERC6 exhibited moderate expression and enriched mainly in central and upper regions (Fig. 7 ). FGF17 and TSPAN32, associated with epithelial cells and T-cells respectively, showed very low expression in the right side of the tissue (Fig. 7 ). PHYHIP, related to the fibroblasts, also showed very low expression in central regions of the tissue (Fig. 7 ). In the SW480 5FU group, gene expression was concentrated in epithelial and fibroblast compartments. VCAN (fibroblast marker) and KITLG (epithelial marker) showed high and moderate expression in the central and upper-left regions (Fig. 8 A). Notably, RTL8A (epithelial marker) demonstrated moderate expression in central and upper-right tissues, while NUDT6 was weakly expressed in central regions of the tissue (Fig. 8 A). In the SW480 30 M group, most genes showed weak to moderate expression in epithelial regions. KMT2E-AS1 and GGT6 exhibited moderate to high expression in central and upper areas (Fig. 8 B). In contrast, LINC01465 and TAT-AS1 showed very low expression (Fig. 8 B). In the SW480 300 M group, spatial gene expression was generally low. Fibroblast-related genes such as FAT4 and SGCD showed low expression in central regions (Fig. 8 C). Epithelial genes including CACNA1E, LINC01465 and OTOG displayed very low expression and no expression, while DCC (B-cell marker) also exhibited very low expression in central regions of the tissue (Fig. 8 C). In the hub-genes panel, several genes demonstrated notable expression patterns. MYH9 (epithelial marker) showed high expression and HSPA4 (epithelial marker), ISG15 (epithelial marker) and PDGFRA (fibroblast marker) were moderately expressed and enriched mainly in central and upper tumor regions (Fig. 9 A). COL12A1 (fibroblast marker), showed high expression in the central and upper regions of the tissue and underscoring significant stromal involvement in these samples (Fig. 9 A). Meanwhile, FGFR2 (epithelial marker), BRCA2 (epithelial marker), MED1 (epithelial marker) and IFIT2 (epithelial marker) had low to very low expression in the central regions of the tumor tissue (Fig. 9 A). In the HT29 common genes, epithelial genes such as MRPL38 and SMPD4 exhibited high to moderate expression, particularly in central and upper-left regions of the tumor tissue (Fig. 9 B). TBCB and STK16 were moderately expressed and enriched in the central regions of the tumor tissue (Fig. 9 B). In the SW480 common genes, both SUSD2 (epithelial marker) and OLFML2A (fibroblast marker) showed moderate to high expression in central and upper tissue regions (Fig. 9 C). Our spatial transcriptomic analysis across multiple colorectal tumor tissues revealed consistent spatial localization. Notably, epithelial-related genes predominantly exhibited moderate to high expression in the central regions of the tumor, while stromal and immune-related genes demonstrated variable expression in peripheral or upper regions. Discussion Successful chemotherapy of colorectal cancer (CRC) is severely restricted by the complexities of tumor heterogeneity and numerous microenvironmental communications, which continue to rank it amongst the worldwide top causes of cancer-related morbidity and mortality ( 39 ). Comprehensive molecular examination of tumor dynamics and treatment responses is crucial, as demonstrated by the prevalence of therapeutic resistance and tumor growth in spite of advancements in chemotherapy, drugs including cisplatin and 5-fluorouracil (5-FU) ( 40 ). In this study, we analyzed the complicated cellular and molecular landscape of colorectal cancer by utilizing a multi-omics approach such as: bulk-RNASeq, single-cell RNA seq (scRNA-seq) and spatial transcriptomics. Utilizing publicly accessible bulk-RNASeq datasets of three studied colorectal cancer cell lines (HCT116, HT29, and SW480) treated with various chemotherapeutic regimens, our goal was to investigate the spatial and temporal evolution of gene expression patterns that underlying tumor development as well as pharmaceutical response. Crucial changes in the composition of cells in the tumor microenvironment have been demonstrated using single-cell transcriptome profiling of colorectal tumors and adjacent normal tissues. By integrating all samples and employing unsupervised clustering, we detected 22 distinct clusters representing 8 major cell types, including epithelial cells, fibroblasts, tissue stem cells, endothelial cells, NK cells, T cells, B cells, and dendritic cells (DCs). Significant changes in the number of different cell types were found when the cellular landscape of tumor and normal tissues was compared. These findings provided insight into the biological mechanisms underlying the development and spread of colorectal cancer (CRC). One significant observation was the major increase in fibroblasts identified in tumor tissues. The accumulation of cancer-associated fibroblasts (CAFs), which are important regulators of tumor growth, correlates with this enrichment ( 41 ). By influencing the extracellular matrix, circulating pro-tumorigenic cytokines (such as TGF-β and IL-6), and facilitating immune defense, CAFs promote tumor growth and metastasis ( 41 ). Similarly, the significant increase in tissue stem cells suggests the activation of stem-like programs within the tumor. These cells are frequently associated with characteristics of cancer stem cells (CSCs), including the capacity for self-replication, resistance to treatment, and metastasis ( 42 ). Tumor tissues demonstrated a substantial decrease in epithelial cells, which might be attributed to epithelial adaptability, including EMT, in which epithelial cells develop mesenchymal characteristics and invasive capacities, resulting in facilitating local invasion and metastasis ( 43 ). In tumor samples, there was a notable decrease in a number of immune cell types. The number of B cells, T cells, NK cells, and particularly dendritic cells decreased. DCs are critical for activating adaptive immune responses by antigen presentation ( 44 ), whereas NK cells and cytotoxic T cells are crucial for suppressing tumors by directly eradicating cancer cells ( 45 ). This modified cellular architecture not only encourages tumor growth and metastatic dissemination but also constructs challenges to efficient immune-mediated elimination and therapeutic response and understanding these modifications is critical for the discovery of specific therapies. Pseudo-temporal trajectory demonstrated that the dynamic evolution of cell states from early tumorigenic fibroblast and epithelial aggregates to more differentiation and therapy-adapted phenotypic. Additionally, epithelial cells and fibroblasts were identified as key participants in the early phases of tumor lineage progression, which is accordance with their demonstrated roles in stromal remodeling and tumor formation ( 46 ). In HCT116-5FU group, low KIF1A expression in epithelial cells and high SPARC levels in fibroblasts along the pseudotime trajectories were detected and demonstrating stromal activation and possible drug resistance pathways ( 47 ). In contrast, groups treated with cisplatin showed altered expression of genes such EGFLAM, AKR1B10, and IGHG1 and suggesting that immunological and epithelial components are regulated differentially. These findings emphasize the critical need for considering tumor heterogeneity for developing therapeutics approaches ( 48 ). Among the identified hub genes, FGFR2 and PDGFRA were prominently expressed in epithelial cells and fibroblasts, respectively, during the pseudotime trajectories. These receptor tyrosine kinases have been reported for stabilizing stromal connections, survival of cells, and development and changes expression of FGFR2 during pseudotime trajectory can contribute to the recurrence of colorectal cancer ( 49 ). The crucial role of innate immune signaling pathways in regulating tumor cell activity and reaction to chemotherapy treatment can be further demonstrated by the reasonable expression of interferon-stimulated genes (ISGs) such IFIT2, IFIT3, and MYH9 ( 50 ). Spatial transcriptomic profiling provided complementary insights by localizing gene expression within tumor microenvironments. Epithelial-associated genes such as KMT2E-AS1, AKR1B10, MRPL38, and SMPD4 were found to be more abundant in central tumor regions. This is consistent with earlier findings that tumor cores contained cells that are highly proliferative and active in metabolic processes ( 51 ). Conversely, stromal markers such FAT4, VCAN, COL12A1, and SPARC were mainly detected in higher or peripheral regions, which is indicative of fibroblast infiltration and active extracellular matrix transformation, among other findings which have been related to the encouragement of aggression and metastasis ( 52 ). Based to our pseudotime and spatial analyses, SPARC (Secreted Protein Acidic and Rich in Cysteine) is significantly expressed in fibroblasts and has a detectable localization in stromal-rich regions of the tumor tissue. This spatial distribution is consistent with its widely recognized roles in fibroblast proliferation and extracellular matrix remodeling, which help establish a microenvironment that promotes tumor growth and chemoresistance ( 52 ). Consequently, Toshikatsu Naito and et al ( 53 ), revealed that the expression level of SPARC was substantially increased in CRC tissues comparable to normal tissues, which is consistent with our findings. Furthermore, immune cell markers such as TSPAN32 and IGHG1 demonstrated different temporal expression distinctions and spatial distributions, underscoring the crucial role of immune cell activation status and recruitment in the colorectal cancer microenvironment ( 54 ). IGHG1 (Immunoglobulin Heavy Constant Gamma 1) was predominantly expressed in B cell populations and localized spatially to immune cell-rich niches within the tumor tissue. These particular niches are frequently associated with additional lymphoid structures or peritumoral lymphoid aggregates, which are evidence of an active immunological microenvironment ( 55 ). The presence of IGHG1 indicates its function in humoral immune responses and may help influence tumor-immune interactions ( 56 ). In accordance with our results, Guangjian Yang and colleagues ( 57 ) indicated that the expression level of IGHG1 was significantly higher in CRC tissues compared to normal tissues. RPL36AL (Ribosomal Protein L36a Like) was spatially concentrated in core tumor regions and demonstrated considerable expression mostly in epithelial components in pseudotime trajectories. This region might be indicative of spots within tumor cores with a strong need for synthesis of protein and proliferation activity, which is consistent with the function of ribosomal protein synthesis to facilitate the proliferation of cancer cells ( 58 ). Moderate expression of PDZD3 (PDZ domain containing 3) in epithelial cells and its geographical detection adjacent to tumor-stromal interfaces indicate that it might be involved in cell binding and polarization activities that affect the epithelial mesenchymal transition (EMT) and possibilities for metastasis ( 59 ). PDZD3 expression was significantly elevated in irritable bowel syndrome compared to normal tissues, according to Michael Camilleri and colleagues ( 60 ). HERC6 (HECT And RLD Domain Containing E3 Ubiquitin Protein Ligase Family Member 6) showed low to moderate expression in epithelial populations, with spatial expression confined mostly to central tumor regions and maybe indicating their roles in intracellular immune signaling networks that response to challenges associated with tumors ( 61 ). VCAN (Versican) was abundantly expressed in fibroblasts and spatially localized to stromal compartments surrounding tumor nests. The crucial role of this proteoglycan in encouraging the spread of colorectal cancer is supported by its spatial restriction to tumor margins and its well-established role in supporting cell migration and invasion ( 62 ). Consequently, Shun Chida and colleagues ( 62 ) found that in stage II–III colon cancer, a higher expression level of the Stromal VCAN protein was related to a lower relapse-free survival (RFS). Hosseini and colleagues ( 63 ) subsequently demonstrated that colorectal tumor tissues had much higher VCAN expression levels than normal tissues. RTL8A (Retrotransposon Gag Like 8A) was detected low expression and mostly found in epithelial cells. spatial data showed that it was distributed differently within tumor cores and demonstrating to a variety of expression patterns that could be related to regulatory functions or genomic instability ( 64 ). GGT6 (Gamma-Glutamyltransferase 6) expression was spatially enriched in the core regions of tumors and was moderate in epithelial cells. It might play an influence in oxidative stress adaptability, which is crucial for tumor survival during chemotherapy and provided its involvement in glutathione metabolism ( 65 ). Accordingly, Sristi Anupam and et al ( 65 ), demonstrate that increased GGT levels in CRC patients are associated to worse outcomes, including lymph node invasion, advanced tumor stages, and a lower overall survival rate. FAT4 (FAT Atypical Cadherin 4) demonstrated expressed mainly in fibroblasts and spatial enrichment at the invasive front and margins of tumor. In colorectal cancer, invasive characteristics may be influenced by reduced FAT4 expression or its spatial restriction and suggesting its tumor suppressor role in modulating cell division and development ( 66 ). FAT4 expression was lower in CRC tissues compared to nonmalignant tissues, according to Ran Wei and et al ( 66 ). According to research by Qianyuan Li et al ( 67 ), FAT4 gene expression level were increased when 5-FU concentrations increased in colorectal tumor cells. Additionally, 5-FU treatment decreased angiogenesis, cell migration, invasion, proliferation, and the EMT process while elevating FAT4 expression ( 67 ). SGCD (Sarcoglycan Delta) was spatially localized in the stroma and expressed at low levels in fibroblasts, especially around tumor borders and indicating that it may play a role in preserving the cellular structure of the stromal ( 68 ). Jianling Liu and colleagues ( 69 ) found that the expression of SGCD was lower in CRC tissues compared to normal tissues. The spatial distribution of these genes inside distinct tumor regions demonstrates the interaction of immune components, stromal cells, and tumor cells in constructing the tumor microenvironment for colorectal cancer. Conclusion This research integrates bulk and single-cell RNA-seq with pseudotime and spatial transcriptomics to uncover the cellular complexity and gene expression patterns of colorectal cancer. By utilizing spatial transcriptomics approach, critical gene expression distributions amongst immune cells, fibroblasts, and epithelial cells were identified in each spot and these genes may contribute to tumor progression. Our findings demonstrate the advantages of spatially resolution evaluation in understanding the biology of colorectal cancer and the opportunity of uncovering novel therapeutic targets and biomarkers that can improve patient outcomes in colorectal cancer. Abbreviations CRC, Colorectal Cancer; SPARC, Secreted Protein Acidic and Cysteine Rich; VCAN, Versican; KMT2E-AS1, KMT2E Antisense RNA 1; COL12A1, Collagen Type XII Alpha 1 Chain; IGHG1, Immunoglobulin Heavy Constant Gamma 1; IFIT3, Interferon Induced Protein With Tetratricopeptide Repeats 3; RPL36AL, Ribosomal Protein L36a-Like; GGT6, Gamma-Glutamyltransferase 6; MYH9, Myosin Heavy Chain 9; MRPL38, Mitochondrial Ribosomal Protein L38; SMPD4, Sphingomyelin Phosphodiesterase 4; FOBT, Fecal Occult Blood Test; scRNA-seq, Single-cell RNA Sequencing; ST, Spatial Transcriptomics; 5-FU, 5-Fluorouracil; GEO, Gene Expression Omnibus; SRA, Sequence Read Archive; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, Differentially Expressed Genes; PPIs, Protein–Protein Interactions; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; QC, Quality Control; CCA, Canonical Correlation Analysis; PCA, Principal Component Analysis; UMAP, Uniform Manifold Approximation and Projection; UMI, Unique Molecular Identifier; RCTD, Robust Cell Type Decomposition. Declarations Acknowledgments The authors gratefully acknowledge the researchers at Greenleaf Lab from Stanford University (USA); which made the single cell data available. We would like to thank the Visium HD Spatial Gene Expression datasets and the 10x Leadership Team and the members of the 10x Genomics microscopy, flow, sequencing and histo-pathology core facilities. Funding Information The author(s) received no specific funding for this work. Conflict of Interest The authors have no conflict of interest. Author Contributions Conceptualization, S.T.H. and K.A.T.; Methodology, S.T.H. and K.A.T.; Software, S.T.H. and K.A.T.; Validation, S.T.H.; Formal analysis, S.T.H., K.A.T., R.B., R.A. and F.N.; Investigation, S.T.H.; Resources, S.T.H.; Data curation, S.T.H.; Writing—original draft preparation, S.T.H. and K.A.T.; Writing—review and editing, S.T.H., K.A.T., R.B., R.A. and F.N.; Visualization, S.T.H.; Supervision, F.N.; Project administration, S.T.H. All authors have read and agreed to the published version of the manuscript. Data Availability Statement Publicly available datasets were analyzed in this study. These data can be found at SRP345690 (https://www.ncbi.nlm.nih.gov/Traces/study?acc=SRP345690), SRP360190 (https://www.ncbi.nlm.nih.gov/Traces/study?acc=SRP360190) and SRP351625 (https://www.ncbi.nlm.nih.gov/Traces/study?acc=SRP351625) from Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra), GSE201348 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201348) from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and spatial transcriptomics data were downloaded from https://zenodo.org/records/7760264. All other data supporting the findings of this study are available within the article and the supporting information or from the corresponding author upon reasonable request. 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Collected RNA-Seq Datasets for Transcriptomic Profiling in Colorectal Cancer Dataset Type Accession ID Platform Samples Description Bulk RNA-seq SRP345690 Illumina HiSeq 4000 9 Colorectal cancer cell lines (HCT116, HT29, SW480) treated with Cisplatin (30M & 300M) and 5-Fluorouracil Bulk RNA-seq SRP360190 Illumina NovaSeq 6000 8 Bulk RNA-seq SRP351625 Illumina NovaSeq 6000 3 Bulk RNA-seq (Total) - - 20 HCT116 = 7, HT29 = 6, SW480 = 7 Single-cell RNA-seq GSE201348 Illumina NovaSeq 6000 (10XGenomics) 10 tissues 5 normal (23,105 genes - 15,941 cells) 5 tumors (23,362 genes - 17,713 cells) Spatial transcriptomics - 10X Visium 4 Tumor slides: A121573-Rep1, A121573-Rep2 A938797-Rep1, A938797-Rep2 Additional Declarations No competing interests reported. Supplementary Files SupplementaryResults.docx SupplementaryTables.xlsx SupplementaryFigures.docx Supportinginformationcaptions.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8003617","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538367074,"identity":"197284dd-055f-4e0e-ae60-3f3af60468a4","order_by":0,"name":"Seyed Taleb Hosseini","email":"","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Taleb","lastName":"Hosseini","suffix":""},{"id":538367075,"identity":"8cda9763-912c-43d8-88bf-dc1d38b39766","order_by":1,"name":"Kimia Aminian Toosi","email":"","orcid":"","institution":"Ghaem Hospital, Mashhad University of 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FASTQC was used to assess read quality, and TRIMMOMATIC was applied to trim low-quality reads. Clean reads were aligned to the human reference genome (GRCh38) using HISAT2, followed by gene-level quantification with HTSeq. Differential gene expression analysis was conducted using DESeq2. Co-expression were evaluated using Hmisc, and gene enrichment analyses (GO and KEGG pathways) were performed with enrichR. Protein-protein interaction (PPI) networks were constructed using Cytoscape, and gene expression patterns in colorectal tissues were validated via the TNMplot database. \u003cstrong\u003e(B)\u003c/strong\u003e Single-cell RNA-seq analysis: Preprocessing, quality control, normalization, dimensionality reduction (using the CCA algorithm), clustering, and UMAP visualization were performed using Seurat. Automated cell-type annotation was achieved using SingleR with reference data from Celldex (HumanPrimaryCellAtlasData). The SingleCellExperiment algorithm was used to manage single-cell data structures. Pseudotime trajectory analysis was performed using Monocle2 with the DDRTree method to track gene expression dynamics over inferred cell transitions. \u003cstrong\u003e(C)\u003c/strong\u003e Spatial transcriptomics analysis: Spatially resolved transcriptomic data from four colorectal tumor tissue sections (A121573-Replicate 1, A121573-Replicate 2, A938797-Replicate 1, and A938797-Replicate 2) were analyzed using SpacexR and enabling the detection of gene expression patterns at spot-level resolution.\u003c/p\u003e","description":"","filename":"image1.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/7a6c13e5246a50d95c99817d.jpg"},{"id":95085775,"identity":"87e661bc-a2d1-4abc-8011-4101745ed284","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":930675,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic profiling of normal and tumor colorectal tissues.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e UMAP plot showing 22 cell clusters identified from integrated scRNA-seq data. \u003cstrong\u003e(B)\u003c/strong\u003eAnnotation of clusters into eight cell types using SingleR, including epithelial cells, fibroblasts, tissue stem cells, NK cells, T cells, endothelial cells, B cells, and dendritic cells. \u003cstrong\u003e(C)\u003c/strong\u003e Comparison of cell type proportions reveals increased fibroblasts, tissue stem cells, and endothelial cells in tumors, with decreased epithelial and immune cell populations. \u003cstrong\u003e(D)\u003c/strong\u003e UMAP visualization of cell type distribution across all samples with distinct gene signatures for each type.\u003c/p\u003e","description":"","filename":"image2.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/fe47108ce12e8350f8670d4c.jpg"},{"id":95085773,"identity":"64d64cdd-e872-4685-ad96-a73afc68c503","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":967428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMapping of treatment-responsive DEGs from CRC cell lines onto single-cell RNA-seq data from colorectal tumors.\u003c/strong\u003e \u003cstrong\u003e(A–C)\u003c/strong\u003eSelected DEGs from HCT116 cells treated with 5-FU \u003cstrong\u003e(A)\u003c/strong\u003e, 30M cisplatin \u003cstrong\u003e(B)\u003c/strong\u003e, and 300M cisplatin \u003cstrong\u003e(C)\u003c/strong\u003e show predominant expression in epithelial cells, with specific genes also enriched in fibroblasts and B cells. \u003cstrong\u003e(D)\u003c/strong\u003e In HT29 cells treated with 30M cisplatin, most DEGs are expressed in epithelial cells, with PHYHIP enriched in fibroblasts and TSPAN32 in T cells. \u003cstrong\u003e(E–F)\u003c/strong\u003eIn SW480 cells treated with 5-FU \u003cstrong\u003e(E)\u003c/strong\u003e and 30M cisplatin \u003cstrong\u003e(F)\u003c/strong\u003e, most genes are expressed in epithelial cells, while VCAN and KMT2E-AS1 are enriched in fibroblasts. \u003cstrong\u003e(G)\u003c/strong\u003e SW480 cells treated with 300M cisplatin show a more diverse expression pattern across epithelial cells, fibroblasts, and B cells.\u003c/p\u003e","description":"","filename":"image3.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/3694786289db6b49b1089bba.jpg"},{"id":95224210,"identity":"c2aff368-8748-446d-941c-24d4ab31423d","added_by":"auto","created_at":"2025-11-05 16:23:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":561169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of hub genes and treatment-common genes in colorectal tumor single-cell transcriptomic data.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Hub genes identified from CRC cell lines show predominant expression of ISG15, IFIT2, MYH9, BRCA2, FGFR2, HSPA4, and MED1 in epithelial cells, while PDGFRA and COL12A1 are enriched in fibroblasts. \u003cstrong\u003e(B)\u003c/strong\u003e Common genes regulated across HT29 treatment conditions (TBCB, STK16, MRPL38, SMPD4) are mainly expressed in epithelial cells. \u003cstrong\u003e(C)\u003c/strong\u003eIn the SW480 common gene set, SUSD2 is expressed in epithelial cells and OLFML2A in fibroblasts.\u003c/p\u003e","description":"","filename":"image4.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/08b03f3087b5ea03da185934.jpg"},{"id":95085781,"identity":"d070c0f4-26b6-4160-b82a-9d040bff6564","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1052139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePseudotime trajectory analysis of cell differentiation and gene expression patterns in colorectal tumor microenvironment.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eTrajectory tree constructed using Monocle2 shows epithelial cells and fibroblasts primarily located at the early pseudotime stage, while fibroblasts and tissue stem cells accumulate at later stages in different trajectory branches, suggesting their roles in tumor progression. \u003cstrong\u003e(B–H)\u003c/strong\u003e Expression dynamics of selected genes along pseudotime in different treatment groups and cell types: \u003cstrong\u003e(B)\u003c/strong\u003e HCT116–5-FU group: Low KIF1A in epithelial cells; high SPARC in fibroblasts; moderate IFIT3, OASL, HOXD13 in epithelial cells. \u003cstrong\u003e(C)\u003c/strong\u003eHCT116–cisplatin 30M: Low EGFLAM (fibroblasts) and AKR1B10 (epithelial); moderate IGHG1 (B cells). \u003cstrong\u003e(D)\u003c/strong\u003e HCT116–cisplatin 300M: Moderate KMT2E-AS1; low LINC01465 and RPL36AL in epithelial cells. \u003cstrong\u003e(E)\u003c/strong\u003e HT29–cisplatin 30M: Low FBXO44, HERC6; high PDZD3 in epithelial cells; moderate FGF17 (epithelial), high PHYHIP (fibroblasts); high TSPAN32 (T cells). \u003cstrong\u003e(F)\u003c/strong\u003e SW480–5-FU: High VCAN (fibroblasts); moderate KITLG, RTL8A (epithelial); no NUDT6 expression. \u003cstrong\u003e(G)\u003c/strong\u003eSW480–cisplatin 30M: High HCG15; low LINC01465, TAT-AS1; moderate KMT2E-AS1, GGT6 in epithelial cells. \u003cstrong\u003e(H)\u003c/strong\u003e SW480–cisplatin 300M: High SGCD (fibroblasts) and HCG15 (epithelial); moderate FAT4 (fibroblasts) and DCC (B cells); no CACNA1E and low LINC01465, OTOG, ADAM7 expression in epithelial cells.\u003c/p\u003e","description":"","filename":"image5.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/d429951c573adde7650b9e87.jpg"},{"id":95085798,"identity":"c3ced62c-13a5-4940-8db3-acadc08810f8","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2187374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial gene expression in HCT116 tumor sections under treatments.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e HCT116–5FU: High SPARC expression in central and upper-left/right regions (fibroblasts). IFIT3 and HOXD13 moderate, OASL low, KIF1A very low in epithelial cells. \u003cstrong\u003e(B)\u003c/strong\u003e HCT116–30M cisplatin: Low EGFLAM (fibroblasts), moderate AKR1B10 (epithelial), high IGHG1 (B cells) in central and upper-right tumor regions. \u003cstrong\u003e(C)\u003c/strong\u003e HCT116–300M cisplatin: Moderate KMT2E-AS1, high RPL36AL, very low LINC01465 in epithelial cells at central and upper tumor areas.\u003c/p\u003e","description":"","filename":"image6.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/89cf375395b8df8cb27f9b2e.jpg"},{"id":95085787,"identity":"cfa20753-03e3-42d0-8a3a-d1e340b6053a","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1159731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial gene expression in HT29–30M cisplatin group.\u003c/strong\u003e FBXO44, PDZD3, and HERC6 showed moderate expression mainly in central and upper regions (epithelial cells). FGF17 (epithelial) and TSPAN32 (T-cells) had very low expression on the right side. PHYHIP (fibroblasts) showed very low expression in central tissue areas.\u003c/p\u003e","description":"","filename":"image7.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/08510bff8b63a7716811bab2.jpg"},{"id":95085790,"identity":"27346826-aca6-44c8-b45e-60f9d1873a2b","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1471102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial gene expression in SW480 treatment groups.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e5FU group: VCAN (fibroblast) highly expressed; KITLG and RTL8A (epithelial) moderate in central and upper-left/right regions; NUDT6 weak in central area. \u003cstrong\u003e(B)\u003c/strong\u003e30M group: KMT2E-AS1 and GGT6 moderate to high in central and upper regions; LINC01465 and TAT-AS1 very low. \u003cstrong\u003e(C)\u003c/strong\u003e 300M group: Low overall expression; FAT4 and SGCD (fibroblast) low in central regions; CACNA1E, LINC01465, OTOG (epithelial) very low or absent; DCC (B-cell) very low.\u003c/p\u003e","description":"","filename":"image8.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/0a0f13436b556c3541c3ca38.jpg"},{"id":95085785,"identity":"14292ba2-067f-4ab4-a610-c89a9eb2e3f5","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1751657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial expression of hub and common genes in colorectal tumor tissues.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Hub genes: MYH9 (epithelial) highly expressed; HSPA4, ISG15 (epithelial), and PDGFRA (fibroblast) moderately expressed, mainly in central and upper tumor regions. COL12A1 (fibroblast) highly expressed, highlighting stromal involvement. FGFR2, BRCA2, MED1, IFIT2 (epithelial) showed low to very low expression in central regions. \u003cstrong\u003e(B)\u003c/strong\u003e HT29 common genes: MRPL38 and SMPD4 (epithelial) high to moderate in central and upper-left areas. TBCB and STK16 moderately expressed in central tumor regions. \u003cstrong\u003e(C)\u003c/strong\u003e SW480 common genes: SUSD2 (epithelial) and OLFML2A (fibroblast) moderate to high expression in central and upper tissue regions.\u003c/p\u003e","description":"","filename":"image9.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/d634a61933ce06f9d20da313.jpg"},{"id":95230269,"identity":"9721d610-0338-4365-9887-99f4286e00f6","added_by":"auto","created_at":"2025-11-05 16:37:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12783188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/cb0649b7-9b8d-437f-81c7-78b1b24993ac.pdf"},{"id":95085778,"identity":"58ab7d73-be85-4d59-8dfd-76ddb87545b9","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23306,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryResults.docx","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/894b22bff47a9edcb232a27d.docx"},{"id":95085783,"identity":"ea294afe-b208-4a35-bbb3-dc6d77cdec65","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1567652,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/5541285f90d41073e47ba7fa.xlsx"},{"id":95085810,"identity":"a1ee8178-e813-4eb1-973f-df9beee1ce31","added_by":"auto","created_at":"2025-11-04 07:20:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5382661,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/39213137bb38b40b2641c022.docx"},{"id":95085780,"identity":"d62fc9f7-9bbe-4659-834c-17e50a0e1daa","added_by":"auto","created_at":"2025-11-04 07:20:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19871,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformationcaptions.docx","url":"https://assets-eu.researchsquare.com/files/rs-8003617/v1/711705dc6ac33447f92a9091.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Multi-omics Integration of Bulk, Single cell and Spatial Transcriptomics Reveals Temporal and Spatial Gene Expression to Cisplatin and 5-Fluorouracil in Colorectal Cancer","fulltext":[{"header":"Author Summary","content":"\u003cp\u003eColorectal cancer is a highly complex disease that often shows varied responses to chemotherapy. To better understand how treatment changes the tumor environment, we analyzed gene expression at both the single-cell and spatial levels. Using colorectal cancer cell lines treated with two common drugs, cisplatin and 5-fluorouracil, we combined data from bulk, single-cell, and spatial transcriptomic analyses. This approach allowed us to map how different cell types, such as epithelial cells, fibroblasts, and immune cells respond to therapy within the tumor\u0026rsquo;s structure. We discovered that fibroblasts play an important role in the later stages of treatment adaptation, while epithelial cells show distinct gene activity changes under high-dose cisplatin. Certain genes, including SPARC, VCAN, and KMT2E-AS1, were found to have specific expression patterns in particular tumor regions. Overall, our findings show that chemotherapy not only affects individual cancer cells but also reshapes the spatial organization of the tumor. This study highlights the importance of understanding how different cell populations and their locations contribute to treatment response in colorectal cancer.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most common cancers in the world (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Men and women are approximately equally affected by colorectal cancer, according to latest global cancer data, while its prevalence and mortality are influenced considerably by location and lifestyle factors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2020, colorectal cancer was responsible for more than 930,000 fatalities in addition to more 1.9\u0026nbsp;million cases were reported (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While colorectal cancer is frequently undetectable in its initial stages, numerous types of warning symptoms could appear as the cancer spreads (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Change in intestinal habits, bleeding from the rectal region, chronic stomach pain, insoluble weight loss and exhaustion are some of symptoms (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Utilizing the diagnostic approaches such as colonoscopy, fecal occult blood tests (FOBT), CT scans and genomic profiling are essential for early and accurate detection of colorectal cancer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Innovations in the sequencing process, especially bulk RNA sequencing (bulk RNA-seq), have greatly contributed to our molecular understanding of colorectal cancer (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Accurate investigation of gene expression patterns across whole tissues or cell types is made possible by bulk RNA-seq, which provides important information about tumor heterogeneity, gene regulatory processes and prospective treatment strategies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Colorectal cancer cellular heterogeneity and tumor microenvironment complexities can be discovered by utilizing single-cell RNA sequencing (scRNA-seq), which is useful for the study of gene expression at the single-cell level (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). By establishing the spatial context of gene expression within tissue architecture, spatial transcriptomics improves on this comprehension (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Together, these technologies provide a powerful framework for identifying cell-type-specific gene expression patterns and spatially localized molecular signatures that contribute to colorectal cancer progression and treatment resistance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Colorectal cancer cell lines including SW480, HCT116 and HT29 are essential models in molecular oncology and used to investigate tumor biological processes, intervention responses and resistance mechanisms (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These cell lines are helpful for comparative research due to their differences in molecular aspects and genetic origin (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Chemotherapeutic drugs including 5-fluorouracil (5-FU) and cisplatin have been comprehensively studied to determine cellular flexibility, apoptotic pathways and cytotoxic effects on cells (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The insights obtained from these studies contributes enhance personalized medicine in the therapeutic management of colorectal cancer in addition to facilitating the discovery of new drugs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this research, we conducted a multi-omics analysis to explore the molecular mechanisms underlying the response of colorectal cancer cell lines to two widely used chemotherapeutic drugs: 5-fluorouracil and cisplatin. Initially, we analyzed publicly available bulk RNA-Seq datasets of colorectal cancer cell lines treated with these drugs to identify differentially expressed genes and uncover potential pathways and protein-protein interactions associated with drug responsiveness. To further validate these findings, we investigated the expression patterns of the identified genes in single-cell RNA-Seq data derived from ten colorectal tissue samples (five tumor tissues and five normal tissues), allowing us to assess gene expression heterogeneity across individual cells within the tumor microenvironment. Finally, we examined spatial transcriptomics data from four colorectal cancer tumor tissues to determine the localization of gene expression within tumor architecture and to evaluate their potential roles in cancer progression and spatial dynamics of gene expression. This integrative approach provides a comprehensive framework for understanding the gene expression related to the drug response in colorectal cancer.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collections for RNA-Seq Characterization \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e shows the complete steps and general workflow of data processing. To determine RNA-Seq-based CRC gene expression profiling studies in different cell lines and tissues, we explored the PubMed database, the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) (11) and the Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) (12). Bulk RNA-Seq datasets that investigated the use of various antibodies and drug therapies to treat different types of colorectal cancer cell lines such as: SW480, HT29 and HCT116 and single cell RNASeq and spatial transcriptomics datasets related to colorectal cancer tissues were included. The experiment Bulk RNA-Seq data associated with the levels of gene expression in cancer and normal tissue of patients with colorectal cancer, as well as datasets related to studies conducted on animal models such as mice and rat species and systematic review articles, were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformation Related to the RNA-Seq Data Collections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSRP345690 (9 samples, Illumina HiSeq 4000), SRP360190 (8 samples, Illumina NovaSeq 6000) (13) and SRP351625 (3 samples, Illumina NovaSeq 6000) (14) are the three original expression bulk-RNA-Seq datasets that we obtained from the SRA database (which is accessible online at https://www.ncbi.nlm.nih.gov/sra) (12). These datasets provided 20 colorectal cancer cell line samples (HCT116 cell line = 7, HT29 = 6 and SW480 = 7) treated with two dose of Cisplatin 30M, Cisplatin 300M and 5-fluorouracil in 8 groups such as: (Group 1: HCT116 Control vs. HCT116 5-fluorouracil), (Group 2: HCT116 Control vs. HCT116 Cisplatin 30M), (Group 3: HCT116 Control vs. HCT116 Cisplatin 300M), (Group 4: HT29 Control vs. HT29 Cisplatin 30M), (Group 5: HT29 Control vs. HT29 Cisplatin 300M), (Group 6: SW480 Control vs. SW480 5-fluorouracil), (Group 7: SW480 Control vs. SW480 Cisplatin 30M), and (Group 8: SW480 Control vs. SW480 Cisplatin 300M). We downloaded the single cell RNASeq dataset with the accession ID GSE201348 (15) from the GEO database (https://www.ncbi.nlm.nih./) was sequenced utilizing the Illumina NovaSeq 6000 (10XGenomics platform) and included 10 human colorectal cancer tissues: 5 normal tissues (23,105 genes and 15,941 cells) and 5 tumor tissues (23,362 genes and 17,713 cells). Following that, we downloaded spatial transcriptomics of Visium gene expression profile from 4 tumor slides in colorectal cancer patients such as: A121573-Rep1, A121573-Rep2, A938797Rep1 and A938797Rep2 (16). Selected details of datasets such as bulk, single cell and spatial transcriptomics were reported in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing of RNASeq data: Assessment of Quality, Trimming, Alignment and Counting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe read accuracy of the sequences was assessed using the FASTQC tool (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (17). For eliminating and trim reads, the TRIMMOMATIC software (V-0.39) was employed (18). The options (LEADING:15, TRAILING:15, SLIDINGWINDOW:4:25 and MINLEN:50) were applied to reduce the sequencing reads. Using the HISAT2 (v2.2.1) alignment tool (19), processed results from RNA-Seq were mapped to the human reference genome GRCH38. Utilizing the HT-Seq software (20), read count for gene expression have been determined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Differentially Expression Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate an integrated data set of 20 bulk samples, each sample from these three data sets have been merged utilizing the SVA (21) package (v3.50) in the R programming language. The batch effect from the count data was obtained by applying the \u0026quot;ComBat_seq\u0026quot; function. The DESeq2 package (22) in R programming language was used to identify the differentially expressed genes (DEGs) between 8 groups: (Group 1: HCT116 Control vs. HCT116 5-fluorouracil), (Group 2: HCT116 Control vs. HCT116 Cisplatin 30M), (Group 3: HCT116 Control vs. HCT116 Cisplatin 300M), (Group 4: HT29 Control vs. HT29 Cisplatin 30M), (Group 5: HT29 Control vs. HT29 Cisplatin 300M), (Group 6: SW480 Control vs. SW480 5-fluorouracil), (Group 7: SW480 Control vs. SW480 Cisplatin 30M), and (Group 8: SW480 Control vs. SW480 Cisplatin 300M). A statistically significant P value of less than 0.05 was determined to be the threshold for the selection of DEGs, with log2foldchange (FC) ∣ \u0026gt;1. The DESeq2 package (22) (\u0026quot;cor\u0026quot; function) was utilized to normalized counts and co-expression evaluation was performed using Hmisc package (23) and \u0026quot;corplot\u0026quot; function in R programming language. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment Analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO investigation is an effective method that uses data collected via high-throughput genomics to discover the particular biological capacities of genes and proteins (24). KEGG is a collection of resources developed to communicate genome-related data with higher order biological process pathways and conduct a systematic analysis of gene function (25-27). Consequently, the enrichR package (28) in the R programming language was used to carry out the GO and KEGG pathways enrichment assessment of DEGs. Adj.p-value of less than 0.05 was considered to be the threshold for statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI Network Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigation of protein-protein interactions (PPIs) may assist in discovering the molecular roles of proteins and provide suggestions for such cellular processes as differentiation, development, metabolism and apoptosis (29). To assess the regulatory processes, it is necessary to identify protein-interacting ions on a genome-wide level (30). A web-based application called STRING (Search Tool for the Retrieval of Interacting Genes) has been employed to assess the relationship between PPI networks of common DEGs (31). After that, the PPI network complex of the common DEGs was imported into Cytoscape v3.10.0 (https://cytoscape.org/), a free program for PPI network visualization (32). RNA-seq data was used to confirm our final candidate genes by employing TNMplot (https://tnmplot.com/) (33).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Control and Data Integration in Single-Cell Transcriptomics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 10xGenomice scRNA-seq data (46,467 genes and 33,654 cells) was evaluated utilizing the Seurat (34) package in the R programming language. In order to create a highly accurate scRNA-seq expression matrix, quality control (QC) was performed on the initial matrix and lower-quality cells were eliminated based on the following parameters: 1) In order to generate a Seurat object, cells must express more than 200 different genes, with genes expressed in a minimum of three different cells being appropriate. 2) Only cells with gene expression levels over 200 and below 5000 for normal objects and 5500 for tumor objects have been assessed in terms of overall diversity. 3) The \u0026quot;PercentageFeatureSet\u0026quot; function was used to determine the percentage of genes associated with both ribosome and mitochondrial processes that were actually discovered in each cell. The \u0026quot;LogNormalize\u0026quot; method in the \u0026quot;NormalizeData\u0026quot; function has been used to normalize the scRNA-seq data. The top 2000 highly distinct genes were found and demonstrated by employing the \u0026quot;FindVariableFeatures\u0026quot; and \u0026quot;VariableFeaturePlot\u0026quot; functions following a quality assessment. The CCA algorithm was utilized for merging all Seurat objects (normal and tumor) by applying the \u0026quot;SelectIntegrationFeatures\u0026quot;, \u0026quot;FindIntegrationAnchors\u0026quot; and \u0026quot;IntegrateData\u0026quot; functions. After that, data for every gene expression was subsequently adjusted and centered utilizing the \u0026quot;ScaleData\u0026quot; function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNonlinear Dimension Reduction, Clustering, and Marker Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;RunPCA\u0026quot; function in the Seurat (34) package was utilized to evaluate 2000 genes using principal component analysis (PCA). Using the first 20 major components, a complete cellular clustering assessment was performed. Principal component analyses (PCAs) were conducted using the \u0026quot;VizDimLoadings\u0026quot;, \u0026quot;DimPlot\u0026quot; and \u0026quot;DimHeatmap\u0026quot; functions to represent gene expression. subsequently with the parameter \u0026quot;resolution\u0026quot; set at 0.5, cellular clustering was detected utilizing the \u0026quot;JackStraw, num.replicate = 100\u0026quot;, \u0026quot;ScoreJackStraw, dims = 1:20\u0026quot;, \u0026quot;JackStrawPlot\u0026quot;, \u0026quot;ElbowPlot\u0026quot;, \u0026quot;FindNeighbors, dims = 1:20\u0026quot;, and \u0026quot;FindClusters\u0026quot; functions in the Seurat (34) package. Furthermore, the \u0026quot;RunUMAP\u0026quot; function in the uniform manifold approximation and projection (UMAP) method was utilized to find cell clusters and reduce dimension. To identify the genes that showed differential expression (DEGs) in each cluster, the \u0026quot;FindAllMarkers\u0026quot; function was utilized to evaluate the false discovery rates (Adj.Pvalue) and log2foldchange. For all clusters, DEGs with logfc. threshold = 1 and min.pct = 0.25 were regarded as the marker genes. The gene expression patterns of cell lines such as SW480, HT29 and HCT116 throughout several treatments with 5-fluorouracil and cisplatin 30M and 300M were also discovered by using the \u0026quot;FeaturePlot\u0026quot; function with reduction \u0026quot;umap\u0026quot; in the Seurat (34) package. Following that, the \u0026quot;HumanPrimaryCellAtlasData\u0026quot; function was used to computationally cluster and characterize various cell types using the SingleR (35), celldex (35) and SingleCellExperiment (36) R packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle Cell Pseudotime Trajectories Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the cell transitions in state, single-cell trajectory analysis was carried out using the R package Monocle2 (v2.30.0) (37). Cell types along with additional RDS data were imported into the R programming language. The parameters \u0026quot;expressionFamily = negbinomial.size\u0026quot; and \u0026quot;lowerDetectionLimit = 0.5\u0026quot; were respectively applied to generate a new object using the \u0026quot;newCellDataSet\u0026quot; function. The \u0026quot;reduceDimension\u0026quot; function was utilized to reduce the dimensionality using the parameters \u0026quot;reduction_method = DDRTree\u0026quot; and \u0026quot;max_components = 2\u0026quot;. Pseudotime and cell clustering were subsequently utilized to identify the cell lineage trajectories using the standard criteria of the Monocle2 (37) package in R programming language. Following that, the \u0026quot;plot_cell_trajectory\u0026quot; function was used to present the findings. Additionally, the \u0026quot;plot_genes_in_pseudotime\u0026quot; function was used to show dynamic modifications to pseudotime-dependent gene expression over pseudotime.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Gene Expression Profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial transcriptomics data (ST) was analyzed and revealed utilizing the Seurat (v5.0.1) (34) and SpacexR (38) packages in R programming language. We integrated single-cell RNA seq (scRNA-seq) data with spatial transcriptomics data to investigate gene expression localization within four colorectal cancer tissues such as: A121573-Rep1, A121573-Rep2, A938797Rep1 and A938797Rep2. The spatial transcriptomics dataset was loaded into R programming language using the \u0026quot;Load10X_Spatial\u0026quot; function, which reads 10X Genomics Visium output and initializes a Seurat object for spatial analysis. Subsequent preprocessing steps including: normalization (\u0026quot;NormalizeData\u0026quot;), identification of highly variable genes (\u0026quot;FindVariableFeatures\u0026quot;) and data scaling (\u0026quot;ScaleData\u0026quot;) functions. Dimensionality reduction is performed using Principal Component Analysis (PCA) via the \u0026quot;RunPCA\u0026quot; function, which captures the major sources of variation in the dataset. The resulting components are used to construct a shared nearest neighbor graph (\u0026quot;FindNeighbors\u0026quot;) and perform clustering (\u0026quot;FindClusters\u0026quot;). Uniform Manifold Approximation and Projection (UMAP) approaches were applied with \u0026quot;RunUMAP\u0026quot; function to visualize the spatial organization of clusters in two dimensions. To incorporate single-cell reference data, a SpatialRNA object were generated thought the count matrix and tissue coordinates from the spatial dataset using \u0026quot;SpatialRNA\u0026quot; function. A reference object is constructed from the scRNA-seq data using the \u0026quot;Reference\u0026quot; function, which includes cell type annotations and UMI counts. These objects are then used to initialize an RCTD (Robust Cell Type Decomposition) model with \u0026quot;create.RCTD\u0026quot; function. The \u0026quot;run.RCTD\u0026quot; function executes the deconvolution algorithm, estimating the contribution of each cell type at every spatial location. The results are then added to the Seurat metadata using \u0026quot;AddMetaData\u0026quot; function. Finally, the spatial gene expression of key marker such as (list genes and cell lines) is visualized using \u0026quot;SpatialFeaturePlot\u0026quot; function in R programming language. This integrated approach enables high-resolution mapping of gene expression across colorectal cancer tissues and provides valuable insights into the tissue architecture and gene regulatory programs underlying cancer progression like colon and rectum cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUBUNTU 22.04 LTS and R programming language (v4.3.2, R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org/) was utilized for statistical analysis and some packages including SRAToolkits, FASTQC, TRIMMOMATIC, HISAT2, HT-Seq, SVA, DESeq2, enrichR, Hmisc, Cytoscape, Seurat, celldex, SingleCellExperiment, SingleR, Monocle2 and SpacexR were utilized for data visualization. The findings of RNASeq-Read counts were analyzed using GraphPad Prism Software version 9.0 (GraphPad Software, San Diego, CA, USA). Next, Unpaired Student\u0026rsquo;s t-test was used to determine the statistical significance of the difference between normally distributed variables and a p-value of 0.05 or less was considered as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e provides the overall statistics of transcriptome mapping for colorectal cancer cell lines, showing the sequencing quality and alignment efficiency. \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e summarizes the number of differentially expressed genes (DEGs) identified between various treatment doses for HCT116, HT29, and SW480 cell lines. \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e lists the DEGs detected between control and 5-Fluorouracil-treated HCT116 cells. \u003cb\u003eSupplementary Table S4\u003c/b\u003e shows the DEGs identified in HCT116 cells comparing control vs Cisplatin 30M treatment. \u003cb\u003eSupplementary Table S5\u003c/b\u003e presents the DEGs for HCT116 cells under control vs Cisplatin 300M treatment. \u003cb\u003eSupplementary Table S6\u003c/b\u003e includes the DEGs observed in HT29 cells when comparing control and Cisplatin 30M treatment. \u003cb\u003eSupplementary Table S7\u003c/b\u003e displays the DEGs for HT29 cells in control vs Cisplatin 300M conditions. \u003cb\u003eSupplementary Table S8\u003c/b\u003e provides the DEGs identified between control and 5-Fluorouracil-treated SW480 cells. \u003cb\u003eSupplementary Table S9\u003c/b\u003e shows the DEGs in SW480 cells under control vs Cisplatin 30M treatment. \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e0\u003c/b\u003e lists the DEGs obtained from SW480 cells comparing control with Cisplatin 300M treatment. \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e1\u003c/b\u003e presents the gene ontology (GO) enrichment analysis results for DEGs identified in the HCT116, HT29 and SW480 cell lines. \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e2\u003c/b\u003e contains the quality control assessment for both normal and tumor samples. All corresponding results text are included in the \u003cb\u003eSupplementary Results file\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCellular Landscape in Normal and Tumor Tissues\u003c/h2\u003e\u003cp\u003eThe umap plot indicates the number of cell clusters among normal and tumor tissues were analyzed through integrating the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, 22 clusters were generated by clustering cells utilizing the \"FindCluster\" function (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). By applying \"SingleR\", a computational annotation tool based on the Human Primary Cell Atlas database, we subsequently discovered 8 distinct cell types within these clusters. These cell types included: Epithelial cells (Cluster annotated: C0, C1, C2, C3, C5, C6, C7, C9, C11, C12, C13 and C19), Fibroblasts (Cluster annotated: C4, C10, C15 and C20), Tissue stem cells (Cluster annotated: C8), NK cell (Cluster annotated: C14), T cells (Cluster annotated: C16), Endothelial cells (Cluster annotated: C17), B cell (Cluster annotated: C18) and DC (Cluster annotated: C21) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cb\u003eSupplementary Table S13\u003c/b\u003e) and revealing that the most common cell types in samples of normal and tumor are Epithelial cell, Fibroblasts, Tissue stem cells and NK cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). After comparing the proportions of each population in the tumor and normal samples, it was found that in the immune cell population, the proportion of Fibroblasts (Normal vs. Tumor: 6.34% vs. 19.74%), Tissue stem cells (Normal vs. Tumor: 0.06% vs. 9.20%) and Endothelial cells (Normal vs. Tumor: 1.82% vs. 2.20%) in the tumor tissue samples were significantly increased, while the proportion of Epithelial cells (Normal vs. Tumor: 83.67% vs. 62.67%), NK cell (Normal vs. Tumor: 3.04% vs. 2.99%), T cells (Normal vs. Tumor: 2.90% vs. 1.72%), B cell (Normal vs. Tumor: 1.58% vs. 1.37%) and DC (Normal vs. Tumor: 0.55% vs. 0.075%) was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cb\u003eSupplementary Table S14\u003c/b\u003e). Also, we revealed that the proportions of 8 cell types for all samples using umap plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In order to find gene expression profiles, we applied the umap technique for unsupervised clustering and we found several significant genes, such as: (B cell: 97), (DC: 242), (Endothelial cells: 192), (Epithelial cells: 406), (Fibroblasts: 822), (NK cell: 174), (T cells: 93), (Tissue stem cells: 117). The full list of genes for the 8 cell types were reported in \u003cb\u003eSupplementary Table S15.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMapping Treatment Responsive Gene Expression into Single-Cell Landscapes of Colorectal Tumors\u003c/h2\u003e\u003cp\u003eTo further explore the relevance of treatment-induced gene expression changes in colorectal cancer cell lines, we selected differentially expressed genes from the HCT116 cell, HT29 and SW480 cell lines treated with 5-fluorouracil (5-FU) and cisplatin (30 M and 300 M) onto single-cell RNA-seq data derived from colorectal tumor tissues. This allowed us to assess the cell-type specificity of gene expression patterns in a more physiologically relevant context. In the HCT116-5FU group, genes such as KIF1A, IFIT3, OASL and HOXD13 were predominantly expressed in Epithelial cells, while SPARC showed enriched expression in Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For the HCT116-30 M cisplatin group, AKR1B10 and EGFLAM were associated with Epithelial cells and Fibroblasts, respectively, whereas IGHG1 exhibited specific expression in B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In the HCT116-300 M cisplatin group, all selected genes including: KMT2E-AS1, LINC01465 and RPL36AL displayed specific expression in Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In the HT29-cisplatin 30 M treatment group, most of the selected genes including: FBXO44, PDZD3, FGF17 and HERC6, showed high expression in Epithelial cells, whereas PHYHIP was predominantly expressed in Fibroblasts. Notably, TSPAN32 exhibited specific expression in T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In the SW480 5-fluorouracil (5-FU) group, the majority of responsive genes, such as: KITLG, RTL8A and NUDT6 were expressed in Epithelial cells, while VCAN was specifically expressed in Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In the SW480 cisplatin 30 M group, all examined genes (HCG15, LINC01465, TAT-AS1, KMT2E-AS1 and GGT6) demonstrated expression to Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). By contrast, in the SW480 cisplatin 300 M group, gene expression patterns were more heterogeneous. Genes such as FAT4 and SGCD were high expressed in Fibroblasts and several genes such as CACNA1E, LINC01465, OTOG, HCG15 and ADAM7) showed moderate expression in Epithelial cells and DCC gene were moderate expressed in B cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCell-Type Expression Patterns of key Hub Genes in Colorectal Tumor Tissues\u003c/h2\u003e\u003cp\u003eWe also evaluated a group of hub genes and treatment-common genes and their expression in the colorectal single-cell transcriptomic data. Within the hub genes group, the majority of genes including: ISG15, IFIT2, MYH9, BRCA2, FGFR2, HSPA4 and MED1 were predominantly expressed in Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Meanwhile, PDGFRA and COL12A1 exhibited enriched expression in Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the HT29 common group, which includes genes commonly regulated across multiple conditions in HT29 cells, all identified genes (TBCB, STK16, MRPL38 and SMPD4) showed expression in Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). For the SW480 common group, SUSD2 was expressed in Epithelial cells, while OLFML2A was enriched in Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Altogether, the integration of bulk RNA-seq derived treatment signatures from colorectal cancer cell lines with single-cell transcriptomic data from colorectal tumors provides a high-resolution view of how distinct chemotherapeutic agents and doses may selectively effects in epithelial, stromal and immune cell populations within the tumor microenvironment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eDevelopment of Pseudotime Trajectories Between Normal-Tumor Tissues\u003c/h2\u003e\u003cp\u003ePseudotime trajectory evaluation was conducted for every cell type to obtain insight into differentiation developments. After that, individual cells were classified using the clusters of normal-tumor, pseudotime and class in order to generate the tree-like structure of the entire lineage progression trajectory using the Monocle2 package in the R programming language (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Epithelial cells and Fibroblasts were detected mainly during the initial point of the pseudotime trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Around the final part of trajectories branch 1, Fibroblasts were most prevalent and the majority of the Fibroblasts and Tissue stem cells were located at the end of trajectory branch 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Our findings indicates that Epithelial cells and Fibroblasts may be the initial stages of tumor cells that spread (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In the HCT116 5-fluorouracil (5-FU) group, the pseudotime trajectories showed a very low expression of KIF1A in Epithelial cells, while SPARC exhibited high expression in Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Moderate expression levels were observed for IFIT3, OASL and HOXD13 in Epithelial cells along the pseudotime trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In the HCT116 cisplatin 30 M group, EGFLAM and AKR1B10 showed very low expression in Fibroblasts and Epithelial cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In contrast, IGHG1 exhibited moderate expression in B cells during pseudotime progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In the HCT116 cisplatin 300 M group, KMT2E-AS1 showed moderate expression in Epithelial cells, while both LINC01465 and RPL36AL displayed very low expression in the Epithelial cells throughout the pseudotime trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In the HT29 cisplatin 30 M group, FBXO44 and HERC6 showed very low expression in Epithelial cells along the pseudotime trajectory, whereas PDZD3 exhibited high expression in the same cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). FGF17 was moderately expressed in Epithelial cells and PHYHIP showed high expression in Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Additionally, TSPAN32 displayed high expression in T cells during pseudotime development (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In the SW480 5-fluorouracil (5-FU) group, VCAN exhibited high expression in Fibroblasts, while both KITLG and RTL8A showed moderate expression in Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Notably, NUDT6 showed no detectable expression along the pseudotime trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). In the SW480 cisplatin 30 M group, HCG15 displayed high expression in Epithelial cells, whereas LINC01465 and TAT-AS1 had very low expression in the same cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). KMT2E-AS1 and GGT6 exhibited moderate expression in Epithelial cells throughout the trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). In the SW480 cisplatin 300 M group, SGCD showed high expression in Fibroblasts and HCG15 also revealed highly expressed in Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Moderate expression levels were observed for FAT4 in fibroblasts and DCC in B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). In contrast, CACNA1E was not expressed and LINC01465, OTOG and ADAM7 showed very low expression in Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eSpatial Gene Expression Landscape in Colorectal Tumors\u003c/h2\u003e\u003cp\u003eSpatial transcriptomic analysis was performed on four colorectal tumor tissue samples. These included two replicates from a rectal adenocarcinoma patient with confirmed lymph node involvement and liver metastasis (S5-A121573) and two replicates from a second rectal adenocarcinoma patient without evidence of lymphatic or hepatic spread (S6-A938797). Comparative assessment of gene expression patterns between tumor tissues based on distinct metastatic characteristics was made possible by this sample combination. In HCT116-5FU group, across all four tissue sections, a consistent spatial distribution of gene expression was observed. SPARC exhibited high expression and was enriched in central and upper-right and left regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and indicating localized fibroblast proliferation and corresponding with stromal-dense regions. In contrast, Epithelial-associated genes displayed varying levels of expression in central tumor regions: IFIT3 and HOXD13 showed moderate expression, OASL exhibited low expression and KIF1A demonstrated very low expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In the HCT116 30 M group, spatial transcriptomic profiling revealed differential gene expression concentrated primarily in the central regions of tumor tissue. The fibroblast-associated gene EGFLAM showed low expression, while the epithelial gene AKR1B10 exhibited moderate expression within the same regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, IGHG1, a B-cell marker, demonstrated high expression in both central and upper-right tumor areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In the HCT116\u0026ndash;300 M group, gene expression patterns remained largely confined to central and upper tumor regions. The epithelial gene KMT2E-AS1 showed moderate expression and RPL36AL exhibited high expression in these areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Conversely, LINC01465 was very weakly expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In the HT29 30 M group, a heterogeneous spatial pattern of epithelial gene expression was observed. Genes such as FBXO44, PDZD3 and HERC6 exhibited moderate expression and enriched mainly in central and upper regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). FGF17 and TSPAN32, associated with epithelial cells and T-cells respectively, showed very low expression in the right side of the tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). PHYHIP, related to the fibroblasts, also showed very low expression in central regions of the tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the SW480 5FU group, gene expression was concentrated in epithelial and fibroblast compartments. VCAN (fibroblast marker) and KITLG (epithelial marker) showed high and moderate expression in the central and upper-left regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Notably, RTL8A (epithelial marker) demonstrated moderate expression in central and upper-right tissues, while NUDT6 was weakly expressed in central regions of the tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). In the SW480 30 M group, most genes showed weak to moderate expression in epithelial regions. KMT2E-AS1 and GGT6 exhibited moderate to high expression in central and upper areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). In contrast, LINC01465 and TAT-AS1 showed very low expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). In the SW480 300 M group, spatial gene expression was generally low. Fibroblast-related genes such as FAT4 and SGCD showed low expression in central regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Epithelial genes including CACNA1E, LINC01465 and OTOG displayed very low expression and no expression, while DCC (B-cell marker) also exhibited very low expression in central regions of the tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). In the hub-genes panel, several genes demonstrated notable expression patterns. MYH9 (epithelial marker) showed high expression and HSPA4 (epithelial marker), ISG15 (epithelial marker) and PDGFRA (fibroblast marker) were moderately expressed and enriched mainly in central and upper tumor regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). COL12A1 (fibroblast marker), showed high expression in the central and upper regions of the tissue and underscoring significant stromal involvement in these samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Meanwhile, FGFR2 (epithelial marker), BRCA2 (epithelial marker), MED1 (epithelial marker) and IFIT2 (epithelial marker) had low to very low expression in the central regions of the tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In the HT29 common genes, epithelial genes such as MRPL38 and SMPD4 exhibited high to moderate expression, particularly in central and upper-left regions of the tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). TBCB and STK16 were moderately expressed and enriched in the central regions of the tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). In the SW480 common genes, both SUSD2 (epithelial marker) and OLFML2A (fibroblast marker) showed moderate to high expression in central and upper tissue regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Our spatial transcriptomic analysis across multiple colorectal tumor tissues revealed consistent spatial localization. Notably, epithelial-related genes predominantly exhibited moderate to high expression in the central regions of the tumor, while stromal and immune-related genes demonstrated variable expression in peripheral or upper regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSuccessful chemotherapy of colorectal cancer (CRC) is severely restricted by the complexities of tumor heterogeneity and numerous microenvironmental communications, which continue to rank it amongst the worldwide top causes of cancer-related morbidity and mortality (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Comprehensive molecular examination of tumor dynamics and treatment responses is crucial, as demonstrated by the prevalence of therapeutic resistance and tumor growth in spite of advancements in chemotherapy, drugs including cisplatin and 5-fluorouracil (5-FU) (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In this study, we analyzed the complicated cellular and molecular landscape of colorectal cancer by utilizing a multi-omics approach such as: bulk-RNASeq, single-cell RNA seq (scRNA-seq) and spatial transcriptomics. Utilizing publicly accessible bulk-RNASeq datasets of three studied colorectal cancer cell lines (HCT116, HT29, and SW480) treated with various chemotherapeutic regimens, our goal was to investigate the spatial and temporal evolution of gene expression patterns that underlying tumor development as well as pharmaceutical response. Crucial changes in the composition of cells in the tumor microenvironment have been demonstrated using single-cell transcriptome profiling of colorectal tumors and adjacent normal tissues. By integrating all samples and employing unsupervised clustering, we detected 22 distinct clusters representing 8 major cell types, including epithelial cells, fibroblasts, tissue stem cells, endothelial cells, NK cells, T cells, B cells, and dendritic cells (DCs). Significant changes in the number of different cell types were found when the cellular landscape of tumor and normal tissues was compared. These findings provided insight into the biological mechanisms underlying the development and spread of colorectal cancer (CRC). One significant observation was the major increase in fibroblasts identified in tumor tissues. The accumulation of cancer-associated fibroblasts (CAFs), which are important regulators of tumor growth, correlates with this enrichment (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). By influencing the extracellular matrix, circulating pro-tumorigenic cytokines (such as TGF-β and IL-6), and facilitating immune defense, CAFs promote tumor growth and metastasis (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Similarly, the significant increase in tissue stem cells suggests the activation of stem-like programs within the tumor. These cells are frequently associated with characteristics of cancer stem cells (CSCs), including the capacity for self-replication, resistance to treatment, and metastasis (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Tumor tissues demonstrated a substantial decrease in epithelial cells, which might be attributed to epithelial adaptability, including EMT, in which epithelial cells develop mesenchymal characteristics and invasive capacities, resulting in facilitating local invasion and metastasis (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In tumor samples, there was a notable decrease in a number of immune cell types. The number of B cells, T cells, NK cells, and particularly dendritic cells decreased. DCs are critical for activating adaptive immune responses by antigen presentation (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), whereas NK cells and cytotoxic T cells are crucial for suppressing tumors by directly eradicating cancer cells (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This modified cellular architecture not only encourages tumor growth and metastatic dissemination but also constructs challenges to efficient immune-mediated elimination and therapeutic response and understanding these modifications is critical for the discovery of specific therapies. Pseudo-temporal trajectory demonstrated that the dynamic evolution of cell states from early tumorigenic fibroblast and epithelial aggregates to more differentiation and therapy-adapted phenotypic. Additionally, epithelial cells and fibroblasts were identified as key participants in the early phases of tumor lineage progression, which is accordance with their demonstrated roles in stromal remodeling and tumor formation (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In HCT116-5FU group, low KIF1A expression in epithelial cells and high SPARC levels in fibroblasts along the pseudotime trajectories were detected and demonstrating stromal activation and possible drug resistance pathways (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In contrast, groups treated with cisplatin showed altered expression of genes such EGFLAM, AKR1B10, and IGHG1 and suggesting that immunological and epithelial components are regulated differentially. These findings emphasize the critical need for considering tumor heterogeneity for developing therapeutics approaches (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Among the identified hub genes, FGFR2 and PDGFRA were prominently expressed in epithelial cells and fibroblasts, respectively, during the pseudotime trajectories. These receptor tyrosine kinases have been reported for stabilizing stromal connections, survival of cells, and development and changes expression of FGFR2 during pseudotime trajectory can contribute to the recurrence of colorectal cancer (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The crucial role of innate immune signaling pathways in regulating tumor cell activity and reaction to chemotherapy treatment can be further demonstrated by the reasonable expression of interferon-stimulated genes (ISGs) such IFIT2, IFIT3, and MYH9 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Spatial transcriptomic profiling provided complementary insights by localizing gene expression within tumor microenvironments. Epithelial-associated genes such as KMT2E-AS1, AKR1B10, MRPL38, and SMPD4 were found to be more abundant in central tumor regions. This is consistent with earlier findings that tumor cores contained cells that are highly proliferative and active in metabolic processes (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Conversely, stromal markers such FAT4, VCAN, COL12A1, and SPARC were mainly detected in higher or peripheral regions, which is indicative of fibroblast infiltration and active extracellular matrix transformation, among other findings which have been related to the encouragement of aggression and metastasis (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Based to our pseudotime and spatial analyses, SPARC (Secreted Protein Acidic and Rich in Cysteine) is significantly expressed in fibroblasts and has a detectable localization in stromal-rich regions of the tumor tissue. This spatial distribution is consistent with its widely recognized roles in fibroblast proliferation and extracellular matrix remodeling, which help establish a microenvironment that promotes tumor growth and chemoresistance (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Consequently, Toshikatsu Naito and et al (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), revealed that the expression level of SPARC was substantially increased in CRC tissues comparable to normal tissues, which is consistent with our findings. Furthermore, immune cell markers such as TSPAN32 and IGHG1 demonstrated different temporal expression distinctions and spatial distributions, underscoring the crucial role of immune cell activation status and recruitment in the colorectal cancer microenvironment (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). IGHG1 (Immunoglobulin Heavy Constant Gamma 1) was predominantly expressed in B cell populations and localized spatially to immune cell-rich niches within the tumor tissue. These particular niches are frequently associated with additional lymphoid structures or peritumoral lymphoid aggregates, which are evidence of an active immunological microenvironment (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The presence of IGHG1 indicates its function in humoral immune responses and may help influence tumor-immune interactions (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). In accordance with our results, Guangjian Yang and colleagues (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) indicated that the expression level of IGHG1 was significantly higher in CRC tissues compared to normal tissues. RPL36AL (Ribosomal Protein L36a Like) was spatially concentrated in core tumor regions and demonstrated considerable expression mostly in epithelial components in pseudotime trajectories. This region might be indicative of spots within tumor cores with a strong need for synthesis of protein and proliferation activity, which is consistent with the function of ribosomal protein synthesis to facilitate the proliferation of cancer cells (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Moderate expression of PDZD3 (PDZ domain containing 3) in epithelial cells and its geographical detection adjacent to tumor-stromal interfaces indicate that it might be involved in cell binding and polarization activities that affect the epithelial mesenchymal transition (EMT) and possibilities for metastasis (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). PDZD3 expression was significantly elevated in irritable bowel syndrome compared to normal tissues, according to Michael Camilleri and colleagues (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). HERC6 (HECT And RLD Domain Containing E3 Ubiquitin Protein Ligase Family Member 6) showed low to moderate expression in epithelial populations, with spatial expression confined mostly to central tumor regions and maybe indicating their roles in intracellular immune signaling networks that response to challenges associated with tumors (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). VCAN (Versican) was abundantly expressed in fibroblasts and spatially localized to stromal compartments surrounding tumor nests. The crucial role of this proteoglycan in encouraging the spread of colorectal cancer is supported by its spatial restriction to tumor margins and its well-established role in supporting cell migration and invasion (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Consequently, Shun Chida and colleagues (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) found that in stage II\u0026ndash;III colon cancer, a higher expression level of the Stromal VCAN protein was related to a lower relapse-free survival (RFS). Hosseini and colleagues (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) subsequently demonstrated that colorectal tumor tissues had much higher VCAN expression levels than normal tissues. RTL8A (Retrotransposon Gag Like 8A) was detected low expression and mostly found in epithelial cells. spatial data showed that it was distributed differently within tumor cores and demonstrating to a variety of expression patterns that could be related to regulatory functions or genomic instability (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). GGT6 (Gamma-Glutamyltransferase 6) expression was spatially enriched in the core regions of tumors and was moderate in epithelial cells. It might play an influence in oxidative stress adaptability, which is crucial for tumor survival during chemotherapy and provided its involvement in glutathione metabolism (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Accordingly, Sristi Anupam and et al (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), demonstrate that increased GGT levels in CRC patients are associated to worse outcomes, including lymph node invasion, advanced tumor stages, and a lower overall survival rate. FAT4 (FAT Atypical Cadherin 4) demonstrated expressed mainly in fibroblasts and spatial enrichment at the invasive front and margins of tumor. In colorectal cancer, invasive characteristics may be influenced by reduced FAT4 expression or its spatial restriction and suggesting its tumor suppressor role in modulating cell division and development (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). FAT4 expression was lower in CRC tissues compared to nonmalignant tissues, according to Ran Wei and et al (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). According to research by Qianyuan Li et al (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), FAT4 gene expression level were increased when 5-FU concentrations increased in colorectal tumor cells. Additionally, 5-FU treatment decreased angiogenesis, cell migration, invasion, proliferation, and the EMT process while elevating FAT4 expression (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). SGCD (Sarcoglycan Delta) was spatially localized in the stroma and expressed at low levels in fibroblasts, especially around tumor borders and indicating that it may play a role in preserving the cellular structure of the stromal (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Jianling Liu and colleagues (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) found that the expression of SGCD was lower in CRC tissues compared to normal tissues. The spatial distribution of these genes inside distinct tumor regions demonstrates the interaction of immune components, stromal cells, and tumor cells in constructing the tumor microenvironment for colorectal cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research integrates bulk and single-cell RNA-seq with pseudotime and spatial transcriptomics to uncover the cellular complexity and gene expression patterns of colorectal cancer. By utilizing spatial transcriptomics approach, critical gene expression distributions amongst immune cells, fibroblasts, and epithelial cells were identified in each spot and these genes may contribute to tumor progression. Our findings demonstrate the advantages of spatially resolution evaluation in understanding the biology of colorectal cancer and the opportunity of uncovering novel therapeutic targets and biomarkers that can improve patient outcomes in colorectal cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRC, Colorectal Cancer; SPARC, Secreted Protein Acidic and Cysteine Rich; VCAN, Versican; KMT2E-AS1, KMT2E Antisense RNA 1; COL12A1, Collagen Type XII Alpha 1 Chain; IGHG1, Immunoglobulin Heavy Constant Gamma 1; IFIT3, Interferon Induced Protein With Tetratricopeptide Repeats 3; RPL36AL, Ribosomal Protein L36a-Like; GGT6, Gamma-Glutamyltransferase 6; MYH9, Myosin Heavy Chain 9; MRPL38, Mitochondrial Ribosomal Protein L38; SMPD4, Sphingomyelin Phosphodiesterase 4; FOBT, Fecal Occult Blood Test; scRNA-seq, Single-cell RNA Sequencing; ST, Spatial Transcriptomics; 5-FU, 5-Fluorouracil; GEO, Gene Expression Omnibus; SRA, Sequence Read Archive; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, Differentially Expressed Genes; PPIs, Protein\u0026ndash;Protein Interactions; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; QC, Quality Control; CCA, Canonical Correlation Analysis; PCA, Principal Component Analysis; UMAP, Uniform Manifold Approximation and Projection; UMI, Unique Molecular Identifier; RCTD, Robust Cell Type Decomposition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the researchers at Greenleaf Lab from Stanford University (USA); which made the single cell data available. We would like to thank the Visium HD Spatial Gene Expression datasets and the 10x Leadership Team and the members of the 10x Genomics microscopy, flow, sequencing and histo-pathology core facilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, S.T.H. and K.A.T.; Methodology, S.T.H. and K.A.T.; Software, S.T.H. and K.A.T.; Validation, S.T.H.; Formal analysis, S.T.H., K.A.T., R.B., R.A. and F.N.; Investigation, S.T.H.; Resources, S.T.H.; Data curation, S.T.H.; Writing\u0026mdash;original draft preparation, S.T.H. and K.A.T.; Writing\u0026mdash;review and editing, S.T.H., K.A.T., R.B., R.A. and F.N.; Visualization, S.T.H.; Supervision, F.N.; Project administration, S.T.H. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. These data can be found at SRP345690 (https://www.ncbi.nlm.nih.gov/Traces/study?acc=SRP345690), SRP360190 (https://www.ncbi.nlm.nih.gov/Traces/study?acc=SRP360190) and SRP351625 (https://www.ncbi.nlm.nih.gov/Traces/study?acc=SRP351625) from Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra), GSE201348 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201348) from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and spatial transcriptomics data were downloaded from https://zenodo.org/records/7760264. All other data supporting the findings of this study are available within the article and the supporting information or from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal code was developed for pre-processing of the bulk-RNASeq data in Linux environment, single-cell RNA-Seq and spatial transcriptomics data and all codes is available from the lead contact upon request. Other data analysis approaches, including algorithm and packages are described in methods section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA: a cancer journal for clinicians. 2023;73(3):233-54.\u003c/li\u003e\n\u003cli\u003eSawicki T, Ruszkowska M, Danielewicz A, Niedźwiedzka E, Arłukowicz T, Przybyłowicz KE. 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Nature genetics. 2020;52(6):594-603.\u003c/li\u003e\n\u003cli\u003eEble JA, Niland S. The extracellular matrix in tumor progression and metastasis. Clinical \u0026amp; experimental metastasis. 2019;36(3):171-98.\u003c/li\u003e\n\u003cli\u003eNaito T, Yuge R, Kitadai Y, Takigawa H, Higashi Y, Kuwai T, et al. Mesenchymal stem cells induce tumor stroma formation and epithelial-mesenchymal transition through SPARC expression in colorectal cancer. Oncology reports. 2021;45(6):104.\u003c/li\u003e\n\u003cli\u003eGe P, Wang W, Li L, Zhang G, Gao Z, Tang Z, et al. Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of colorectal cancer. Biomedicine \u0026amp; Pharmacotherapy. 2019;118:109228.\u003c/li\u003e\n\u003cli\u003eSchumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science. 2022;375(6576):eabf9419.\u003c/li\u003e\n\u003cli\u003eLi X, Ni R, Chen J, Liu Z, Xiao M, Jiang F, Lu C. The presence of IGHG1 in human pancreatic carcinomas is associated with immune evasion mechanisms. Pancreas. 2011;40(5):753-61.\u003c/li\u003e\n\u003cli\u003eYang G, Li G, Du X, Zhou W, Zou X, Liu Y, et al. Down-regulation of IGHG1 enhances Protoporphyrin IX accumulation and inhibits hemin biosynthesis in colorectal cancer by suppressing the MEK-FECH axis. Open Life Sciences. 2021;16(1):930-6.\u003c/li\u003e\n\u003cli\u003eSchmidt S, Denk S, Wiegering A. Targeting protein synthesis in colorectal cancer. Cancers. 2020;12(5):1298.\u003c/li\u003e\n\u003cli\u003eZhang N, Ng AS, Cai S, Li Q, Yang L, Kerr D. Novel therapeutic strategies: targeting epithelial\u0026ndash;mesenchymal transition in colorectal cancer. The Lancet Oncology. 2021;22(8):e358-e68.\u003c/li\u003e\n\u003cli\u003eCamilleri M, Carlson P, Acosta A, Busciglio I. Colonic mucosal gene expression and genotype in irritable bowel syndrome patients with normal or elevated fecal bile acid excretion. American Journal of Physiology-Gastrointestinal and Liver Physiology. 2015;309(1):G10-G20.\u003c/li\u003e\n\u003cli\u003eOsborn O, Olefsky JM. The cellular and signaling networks linking the immune system and metabolism in disease. Nature medicine. 2012;18(3):363-74.\u003c/li\u003e\n\u003cli\u003eChida S, Okayama H, Noda M, Saito K, Nakajima T, Aoto K, et al. Stromal VCAN expression as a potential prognostic biomarker for disease recurrence in stage II\u0026ndash;III colon cancer. Carcinogenesis. 2016;37(9):878-87.\u003c/li\u003e\n\u003cli\u003eHosseini SM, Jafary F, Hosseini M, Mahjoubi F. VCAN gene expression and its association with tumor stage and lymph node metastasis in colorectal cancer patients. Biomedical Research (India). 2018;29(6):1110-4.\u003c/li\u003e\n\u003cli\u003eComaills V, Kabeche L, Morris R, Buisson R, Yu M, Madden MW, et al. Genomic instability is induced by persistent proliferation of cells undergoing epithelial-to-mesenchymal transition. Cell reports. 2016;17(10):2632-47.\u003c/li\u003e\n\u003cli\u003eAnupam S, Goel S, Mehta DK, Das R. Comprehensing the role of serum GGT in colorectal carcinoma: cancer risk, prognostic and diagnostic significance. Clinical and Translational Oncology. 2024:1-8.\u003c/li\u003e\n\u003cli\u003eWei R, Xiao Y, Song Y, Yuan H, Luo J, Xu W. FAT4 regulates the EMT and autophagy in colorectal cancer cells in part via the PI3K-AKT signaling axis. Journal of Experimental \u0026amp; Clinical Cancer Research. 2019;38:1-14.\u003c/li\u003e\n\u003cli\u003eLi Q, Zhou X, Fang Z, Pan Z, Zhou H. Up-regulation of FAT4 enhances the chemosensitivity of colorectal cancer cells treated by 5-FU. Translational Cancer Research. 2020;9(1):309.\u003c/li\u003e\n\u003cli\u003eFeng Y, Ma W, Zang Y, Guo Y, Li Y, Zhang Y, et al. Spatially organized tumor-stroma boundary determines the efficacy of immunotherapy in colorectal cancer patients. Nature Communications. 2024;15(1):10259.\u003c/li\u003e\n\u003cli\u003eLiu J, Wang D, Zhang C, Zhang Z, Chen X, Lian J, et al. Identification of liver metastasis-associated genes in human colon carcinoma by mRNA profiling. Chinese Journal of Cancer Research. 2018;30(6):633.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 614px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Collected RNA-Seq Datasets for Transcriptomic Profiling in Colorectal Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eDataset Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eAccession ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eSamples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eBulk RNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eSRP345690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIllumina HiSeq 4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 217px;\"\u003e\n \u003cp\u003eColorectal cancer cell lines (HCT116, HT29, SW480) treated with Cisplatin (30M \u0026amp; 300M) and 5-Fluorouracil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eBulk RNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eSRP360190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eBulk RNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eSRP351625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eBulk RNA-seq (Total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eHCT116 = 7, HT29 = 6, SW480 = 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eSingle-cell RNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGSE201348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIllumina NovaSeq 6000 (10XGenomics)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10 tissues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e5 normal (23,105 genes - 15,941 cells)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5 tumors (23,362 genes - 17,713 cells)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eSpatial transcriptomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e10X Visium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eTumor slides:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA121573-Rep1, A121573-Rep2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA938797-Rep1, A938797-Rep2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spatial Transcriptomics, Colorectal Cancer, Tumor Landscape, Single Cell, Cisplatin, 5-Fluorouracil","lastPublishedDoi":"10.21203/rs.3.rs-8003617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8003617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) remains a leading cause of cancer-related mortality, mainly as outcomes of varying treatment responses and an increase of drug resistance. Although cisplatin and 5-fluorouracil (5-FU) are used in medical treatment widely, it remains unknown exactly molecular pathways explain numerous therapeutic responses. This study aimed to identify genes responsive to these two drugs and to characterize their expression patterns and associated cell populations using an integrative multi-omics approach. We first analyzed bulk RNA-seq datasets from CRC cell lines (HCT116, HT29, and SW480) treated with 5-FU and cisplatin to identify differentially expressed genes (DEGs) and pathways. Next, we assessed the expression levels and cell-type specificity of these DEGs in single-cell RNA-seq data from ten colorectal tissue samples (five tumors and five normal tissues). Finally, spatial transcriptomics from four CRC tumor slides were examined to map the localization of treatment-responsive genes within the tumor microenvironment. Our results revealed that epithelial and fibroblast populations exhibited distinct transcriptional adaptations to chemotherapy. Pseudotime trajectories showed fibroblast enrichment at later transition states and suggesting a role in remodeling during treatment adaptation. Spatial mapping demonstrated that fibroblast-associated genes (SPARC, COL12A1, VCAN) were localized to stromal-rich peripheral regions, while epithelial markers (IFIT3, MYH9, KMT2E-AS1) were concentrated in tumor cores, particularly under high-dose cisplatin. Collectively, these findings demonstrate that epithelial plasticity and fibroblast-mediated remodeling contribute to drug resistance, highlighting possible targets to enhance cancer therapy sensitivity because chemotherapy induces considerable cellular and spatial modifications in the landscape of colorectal tumors.\u003c/p\u003e","manuscriptTitle":"Comprehensive Multi-omics Integration of Bulk, Single cell and Spatial Transcriptomics Reveals Temporal and Spatial Gene Expression to Cisplatin and 5-Fluorouracil in Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 07:20:48","doi":"10.21203/rs.3.rs-8003617/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cbc87ba-cf54-4828-bbf3-9a0351cb0f5b","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57263233,"name":"Biological sciences/Cancer"},{"id":57263234,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":57263235,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-05T06:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 07:20:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8003617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8003617","identity":"rs-8003617","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}