Landscape of cell heterogeneity and evolutionary trajectory in ulcerative colitis-associated colon cancer revealed by single-cell RNA sequencing

Dipeptidase-1-knockout mice develop invasive tumors with features of microsatellite-unstable colorectal cancer

Dipeptidase-1 (DPEP1) is highly upregulated in colorectal cancer (CRC), with its enzymatic function linked to invasion and metastasis. More recently, DPEP1 was found to serve as a receptor for neutrophils when expressed by activated endothelial cells. It is unknown whether neutrophils bind to DPEP1-expressing CRC cells and whether this impacts features of CRC. Neutrophils have been shown to be tumor promoting in cancers including CRC, where they act to exclude CD8+ T cells. Herein, we show that neutrophils bind DPEP1-expressing CRC cells. In addition, DPEP1 is preferentially expressed in microsatellite-stable (MSS) CRCs, in which there are a paucity of CD8+ T cells, whereas DPEP1 is negatively correlated with microsatellite-unstable (MSI-H) CRCs, which are T cell rich and are more responsive to immunotherapy. Remarkably, carcinogen-treated Dpep1-null mice develop multiple, large, plaque-like, locally invasive adenocarcinomas and squamous cell cancers in the distal colon. These adenocarcinomas exhibit a marked reduction in neutrophils and an influx CD8+ T cells, along with reduced expression of mismatch repair proteins, consistent with features of MSI-H CRC. These results establish DPEP1's importance in maintaining MSS CRC and its ability to shape the tumor microenvironment.

An Integrative analysis of single-cell RNA-seq, transcriptome and Mendelian randomization for the Identification and validation of NAD+ Metabolism-Related biomarkers in ulcerative colitis

Ulcerative colitis (UC) is a chronic and refractory inflammatory disease of the colon and rectum. This study utilized bioinformatics methods to explore the potential of Nicotinamide adenine dinucleotide (NAD+) metabolism-related genes (NMRGs) as key genes in UC. Using the GSE87466 dataset, differentially expressed NMRGs were identified through differential expression analysis, weighted gene co-expression network analysis (WGCNA), and NMRG scoring. These NMRGs were used as exposure factors, with UC as the outcome, to identify causal candidate genes through Mendelian randomization (MR) analysis. Key genes were further validated as biomarkers using machine learning and expression validation in external datasets (GSE75214, GSE224758). A nomogram based on the expression levels of these biomarkers was constructed to predict UC risk, and the biomarkers' expression was validated through real-time quantitative polymerase chain reaction (RT-qPCR). Subsequently, signaling pathway analysis, enrichment analysis, immune infiltration analysis, and drug prediction were conducted to comprehensively understand the biological roles of the key genes in the human body. Single-cell (GSE116222) and spatial transcriptomic analyses (GSE189184) revealed the expression patterns of these key genes in specific cell types. NCF2, IL1B, S100A8, and SLC26A2 were identified as biomarkers, with NCF2 and IL1B serving as protective factors and S100A8 and SLC26A2 as risk factors for UC. The nomogram based on these biomarkers demonstrated strong predictive value. Functional analysis revealed significant IL1B, NCF2, and S100A8 enrichment in pathways such as IL-4 and IL-13 signaling, while SLC26A2 was strongly associated with respiratory electron transport. Significant differences in immune cells, such as macrophages and neutrophils, were also observed. Single-cell analysis showed high expression of NCF2, IL1B, and S100A8 in monocytes, while SLC26A2 was primarily expressed in epithelial cells, intestinal epithelial cells, and mast cells. Overall, these findings reveal the roles of NMRGs, providing valuable insights into the diagnosis and treatment of UC patients.

[Evaluation of the toxicity of perfluorooctanoic acid toward human colorectal cancer cells using multi-dimensional approaches]

While human exposure to perfluorooctanoic acid (PFOA) can lead to ulcerative colitis, the molecular mechanisms responsible for PFOA-induced intestinal toxicity are unclear. Herein, we examined the toxicity of PFOA toward human colorectal cancer cells (HCT116) from three dimensions: the cytotoxic phenotype, cell respiration, and transcription levels of metabolism-related genes. Formazan was used to assess how PFOA exposure affects HCT116-cell relative viability, after which the mitochondrial respiratory activities of these cells were determined by analyzing extracellular flux. The quantitative real-time polymerase chain reaction (qPCR) method was used to detect metabolism-related gene expression levels. The cytotoxicity assay revealed that the HCT116 showed significantly inhibited relative activities compared to those of the control when exposed to 300 μmol/L PFOA for 48 h (p<0.01), with most cells retained at the G0/G1 stage. In contrast, the mitochondrial respiratory activities of the HCT116 were promoted by concentrations of PFOA as low as 50 μmol/L. Two genes related to cellular metabolism (dipeptidase 1 (DPEP1) and sphingosine kinase 1 (SPHK1)) were found to be related to the PFOA-promoted formation of ulcerative colitis using our self-developed Metabolic Gene and Pathway Query software and Comparative Toxicogenomics Database (CTD). The qPCR studies revealed that DPEP1 and SPHK1 expression levels were enhanced by 8-10 times in HCT116 exposed to 300 μmol/L PFOA relative to the control, whereas this trend was not observed for HCT116 exposed to 50 μmol/L PFOA. Collectively, these results suggest that the respiratory activity of cellular mitochondria may serve as an index for determining the interference effects associated with PFOA and that metabolic pathways mediated by DPEP1 and SPHK1 may be involved in the development of PFOA-induced ulcerative colitis. Future studies should investigate the relationships between changes in metabolism-related genes (DPEP1 and SPHK1) and the mitochondrial respiratory activities of intestinal cells, and verify the roles played by the DPEP1 and SPHK1 genes in PFOA-induced intestinal inflammation using in-vivo models.

Insight into dipeptidase 1: structure, function, and mechanism in gastrointestinal cancer diseases

Dipeptidase 1 (DPEP1), initially identified as a renal membrane enzyme in mature human kidneys, plays a pivotal role in various cellular processes. It facilitates the exchange of materials and signal transduction across cell membranes, contributing significantly to dipeptide hydrolysis, glucose and lipid metabolism, immune inflammation, and ferroptosis, among other cellular functions. Extensive research has delineated the complex role of DPEP1 in oncogenesis and tumor progression, with its influence being context dependent. DPEP1 has been observed to promote oncogenic activities in hepatocellular carcinoma, non-small cell lung cancer, colorectal cancer, and lymphoblastic malignancies and is hypothesized to participate in multiple biological processes, including tumor cell invasion, metastatic spread, cellular signaling pathways, cell-matrix interactions, and evasion of immune surveillance. Conversely, DPEP1 has been identified as a tumor suppressor in pancreatic adenocarcinoma, lobular breast carcinoma, and Wilms tumor. Moreover, the role of DPEP1 in colorectal cancer has been increasingly recognized in recent research. Emerging evidence suggests that DPEP1 substantially augments the metastatic and invasive potential of colorectal cancer cells, facilitates immune evasion, and confers resistance to chemotherapeutic agents. Despite these findings, the precise molecular mechanisms remain to be fully characterized. This systematic review endeavors to elucidate the structural and functional attributes of the DPEP1 protein, with the aim to clarify its regulatory mechanisms and assess its clinical relevance in oncology. Gaining a thorough understanding of the physiological role and molecular underpinnings of DPEP1 is critical to informing the diagnostic, therapeutic, and prognostic paradigms of related pathologies. It is anticipated that these insights will facilitate the discovery of novel therapeutic targets and generate new investigative trajectories, particularly in the clinical management of colorectal cancer.

Predictive role of oxidative stress-related genes in colon cancer: a retrospective cohort study based on The Cancer Genome Atlas

PURPOSE

This study aimed to elucidate the predictive role of an oxidative stress-related genes (OSRGs) model in colon cancer.

MATERIALS AND METHODS

First, OSRGs that were differentially expressed between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA)-(Colorectal Adenocarcinoma) COAD dataset. Then, Lasso COX regression was performed to develop an optimal prognostic model patients were stratified into high- and low-risk groups based on the expression patterns of these genes. The model's validity was confirmed through Kaplan-Meier survival curves and receiver operating characteristic curve (ROC) analysis. Additionally, enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to uncover underlying mechanisms.

RESULTS

A totally of 115 differentially expressed OSRGs were identified within the TCGA cohort, with 17 significantly linked to overall survival. These 17 genes were used to formulate a prognostic model that differentiated patients into distinct risk groups, with the high-risk group demonstrating a notably inferior overall survival rate. The risk score, when integrated with clinical and pathological data, emerged as an independent prognostic indicator of colon cancer. Further analyses revealed that the disparity in prognostic outcomes between risk groups could be attributed to the reactive oxygen species pathway and the p53 signaling pathway.

CONCLUSION

A new prediction model was established based on OSRGs. CYP19A1, NOL3 and UCN were found to be highly expressed in tumor tissues and substantial clinical predictive significance. These findings offer new insights into the role of oxidative stress in colon cancer.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Comprehensive analysis reveals key genes and environmental toxin exposures underlying treatment response in ulcerative colitis based on in-silico analysis and Mendelian randomization

BACKGROUND

UC is increasingly prevalent worldwide and represents a significant global disease burden. Although medical therapeutics are employed, they often fall short of being optimal, leaving patients struggling with treatment non-responsiveness and many related complications.

MATERIALS AND METHODS

The study utilized gene microarray data and clinical information from GEO. Gene enrichment and differential expression analyses were conducted using Metascape and Limma, respectively. Lasso Regression Algorithm was constructed using glmnet and heat maps were generated using pheatmap. ROC curves were used to assess diagnostic parameter capability, while XSum was employed to screen for small-molecule drugs exacerbating UC. Molecular docking was carried out using Autodock Vina. The study also performed Mendelian randomization analysis based on TwoSampleMR and used CTD to investigate the relationship between exposure to environmental chemical toxicants and UC therapy responsiveness.

RESULTS

Six genes (ELL2, DAPP1, SAMD9L, CD38, IGSF6, and LYN) were found to be significantly overexpressed in UC patient samples that did not respond to multiple therapies. Lasso analysis identified ELL2 and DAPP1 as key genes influencing UC treatment response. Both genes accurately predicted intestinal inflammation in UC and impacted the immunological infiltration status. Clofibrate showed therapeutic potential for UC by binding to ELL2 and DAPP1 proteins. The study also reviews environmental toxins and drug exposures that could impact UC progression.

CONCLUSIONS

We used microarray technology to identify DAPP1 and ELL2 as key genes that impact UC treatment response and inflammatory progression. Clofibrate was identified as a promising UC treatment. Our review also highlights the impact of environmental toxins on UC treatment response, providing valuable insights for personalized clinical management.

Circular RNA: A promising new star of vaccine

Circular RNAs (circRNAs) are a class of single-stranded RNAs with covalently closed structures. Owing to their not having 3' or 5' ends, circRNAs are highly durable and insusceptible to exonuclease-mediated degradation. Moreover, some circRNAs with certain structures are translatable, making them novel vaccines. Vaccines are efficient tools for immunotherapy, such as for the prevention of infectious diseases and cancer treatment. The immune system is activated during immunotherapy to fight against abnormal allies or invaders. CircRNA vaccines represent a potential new avenue in the vaccine era. Recently, several circRNA vaccines have been synthesized and tested in vitro and in vivo. Our review briefly introduces the current understanding of the biology and function of translatable circRNAs, molecular biology, synthetic methods, delivery of circRNA, and current circRNA vaccines. We also discussed the challenges and future directions in the field by summarizing the developments in circRNA vaccines in the past few years.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Colitis-associated carcinogenesis: crosstalk between tumors, immune cells and gut microbiota

Colorectal cancer (CRC) is the third most common cancer worldwide. One of the main causes of colorectal cancer is inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD). Intestinal epithelial cells (IECs), intestinal mesenchymal cells (IMCs), immune cells, and gut microbiota construct the main body of the colon and maintain colon homeostasis. In the development of colitis and colitis-associated carcinogenesis, the damage, disorder or excessive recruitment of different cells such as IECs, IMCs, immune cells and intestinal microbiota play different roles during these processes. This review aims to discuss the various roles of different cells and the crosstalk of these cells in transforming intestinal inflammation to cancer, which provides new therapeutic methods for chemotherapy, targeted therapy, immunotherapy and microbial therapy.

Prognostic Biomarkers of Cell Proliferation in Colorectal Cancer (CRC): From Immunohistochemistry to Molecular Biology Techniques

Colorectal cancer (CRC) is one of the most common and severe malignancies worldwide. Recent advances in diagnostic methods allow for more accurate identification and detection of several molecular biomarkers associated with this cancer. Nonetheless, non-invasive and effective prognostic and predictive testing in CRC patients remains challenging. Classical prognostic genetic markers comprise mutations in several genes (e.g., APC, KRAS/BRAF, TGF-β, and TP53). Furthermore, CIN and MSI serve as chromosomal markers, while epigenetic markers include CIMP and many other candidates such as SERP, p14, p16, LINE-1, and RASSF1A. The number of proliferation-related long non-coding RNAs (e.g., SNHG1, SNHG6, MALAT-1, CRNDE) and microRNAs (e.g., miR-20a, miR-21, miR-143, miR-145, miR-181a/b) that could serve as potential CRC markers has also steadily increased in recent years. Among the immunohistochemical (IHC) proliferative markers, the prognostic value regarding the patients' overall survival (OS) or disease-free survival (DFS) has been confirmed for thymidylate synthase (TS), cyclin B1, cyclin D1, proliferating cell nuclear antigen (PCNA), and Ki-67. In most cases, the overexpression of these markers in tissues was related to worse OS and DFS. However, slowly proliferating cells should also be considered in CRC therapy (especially radiotherapy) as they could represent a reservoir from which cells are recruited to replenish the rapidly proliferating population in response to cell-damaging factors. Considering the above, the aim of this article is to review the most common proliferative markers assessed using various methods including IHC and selected molecular biology techniques (e.g., qRT-PCR, in situ hybridization, RNA/DNA sequencing, next-generation sequencing) as prognostic and predictive markers in CRC.

Identification of hepatocellular carcinoma-related subtypes and development of a prognostic model: a study based on ferritinophagy-related genes

BACKGROUND

Hepatocellular carcinoma still has a high incidence and mortality rate worldwide, and further research is needed to investigate its occurrence and development mechanisms in depth in order to identify new therapeutic targets. Ferritinophagy is a type of autophagy and a key factor in ferroptosis that could influence tumor onset and progression. Although, the potential role of ferritinophagy-related genes (FRGs) in liver hepatocellular carcinoma (LIHC) is unknown.

METHODS

Single-cell RNA sequencing (scRNA-seq) data of LIHC were obtained from the Gene Expression Omnibus (GEO) dataset. In addition, transcriptome and clinical follow-up outcome data of individuals with LIHC were extracted from the The Cancer Genome Atlas (TCGA) dataset. FRGs were collected through the GeneCards database. Differential cell subpopulations were distinguished, and differentially expressed FRGs (DEFRGs) were obtained. Differential expression of FRGs and prognosis were observed according to the TCGA database. An FRG-related risk model was constructed to predict patient prognosis by absolute shrinkage and selection operator (LASSO) and COX regression analyses, and its prognosis predictive power was validated. Ultimately, the association between risk score and tumor microenvironment (TME), immune cell infiltration, immune checkpoints, drug sensitivity, and tumor mutation burden (TMB) was analyzed. We also used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the expression of key genes in normal liver cells and liver cancer cells.

RESULTS

We ultimately identified 8 cell types, and 7 differentially expressed FRGs genes (ZFP36, NCOA4, FTH1, FTL, TNF, PCBP1, CYB561A3) were found among immune cells, and we found that Monocytes and Macrophages were closely related to FRGs genes. Subsequently, COX regression analysis showed that patients with high expression of FTH1, FTL, and PCBP1 had significantly worse prognosis than those with low expression, and our survival prediction model, constructed based on age, stage, and risk score, showed better prognostic prediction ability. Our risk model based on 3 FRGs genes ultimately revealed significant differences between high-risk and low-risk groups in terms of immune infiltration and immune checkpoint correlation, drug sensitivity, and somatic mutation risk. Finally, we validated the key prognostic genes FTH1, FTL, using qRT-PCR, and found that the expression of FTH1 and FTL was significantly higher in various liver cancer cells than in normal liver cells. At the same time, immunohistochemistry showed that the expression of FTH1, FTL in tumor tissues was significantly higher than that in para-tumor tissues.

CONCLUSION

This study identifies a considerable impact of FRGs on immunity and prognosis in individuals with LIHC. The collective findings of this research provide new ideas for personalized treatment of LIHC and a more targeted therapy approach for individuals with LIHC to improve their prognosis.

Single-cell analysis reveals cellular reprogramming in advanced colon cancer following FOLFOX-bevacizumab treatment

Introduction

The combination of FOLFOX and bevacizumab (FOLFOX-Bev) is a promising treatment for advanced colorectal cancer (CRC). However, the response of the tumor microenvironment to FOLFOX-Bev is still largely unexplored.

Methods

We conducted single-cell transcriptomic analysis of CRC samples derived from a patient before and after treatment to gain insights into the cellular changes associated with FOLFOX-Bev treatment.

Results

We found that cancer cells with high proliferative, metastatic, and pro-angiogenic properties respond better to FOLFOX-Bev treatment. Moreover, FOLFOX-Bev enhances CD8+ T cell cytotoxicity, thereby boosting the anti-tumor immune response. Conversely, FOLFOX-Bev impairs the functionality of tumor-associated macrophages, plasma cells, and cancer-associated fibroblasts, leading to a decrease in VEGFB-mediated angiogenesis. Furthermore, FOLFOX-Bev treatment reset intercellular communication, which could potentially affect the function of non-cancer cells.

Discussion

Our findings provide valuable insights into the molecular mechanisms underlying the response of advanced CRC to FOLFOX-Bev treatment and highlight potential targets for improving the efficacy of this treatment strategy.

A novel enterocyte-related 4-gene signature for predicting prognosis in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a fatal disease, and its cases are constantly increasing worldwide. Further, the therapeutic and management strategies for patients with COAD are still unsatisfactory due to the lack of accurate patient classification and prognostic models. Therefore, our study aims to identify prognostic markers in patients with COAD and construct a cell subtype-specific prognostic model with high accuracy and robustness.

Methods

Single-cell transcriptomic data of six samples were retrieved from the Gene expression omnibus (GEO) database. The cluster annotation and cell-cell communication analysis identified enterocytes as a key player mediating signal communication networks. A four-gene signature prognostic model was constructed based on the enterocyte-related differentially expressed genes (ERDEGs) in patients with COAD of the Cancer Genome Atlas cohort. The prognostic model was validated on three external validation cohorts from the GEO database. The correlation between immune cell infiltration, immunotherapy response, drug sensitivity, and the four-gene signature prognostic model was investigated. Finally, immunohistochemistry (IHC) was performed to determine the expression of the four genes.

Results

We found that the proportion of epithelial cells was obviously large in COAD samples. Further analysis of epithelial cells showed that the activity of the enterocytes was highest in the cell-cell communication network. Based on enterocyte data, 30 ERDEGs were identified and a 4-gene prognostic model including CPM, CLCA4, ELOVL6, and ATP2A3 was developed and validated. The risk score derived from this model was considered as an independent variable factor to predict overall survival. The patients were divided into high- and low-risk groups based on the median riskscore value. The correlation between immune cell infiltration, immunotherapy response, immune status, clinical characteristics, drug sensitivity, and risk score was analyzed. IHC confirmed the expression of signature genes in tissues from normal individuals, patients with polyps, and COAD.

Conclusion

In this study, we constructed and validated a novel four-gene signature prognostic model, which could effectively predict the response to immunotherapy and overall survival in patients with COAD. More importantly, this model provides useful insight into the management of colon cancer patients and aids in designing personalized therapy.

Effect of Lactobacillus fermentum ZS40 on the NF-κB signaling pathway in an azomethane-dextran sulfate sodium-induced colon cancer mouse model

The occurrence of intestinal diseases such as colon cancer is closely related to the intestinal flora. Lactobacillus fermentum is a gut probiotic that plays an important role in chronic intestinal inflammation and colon cancer. In the current study, we investigated the effect of Lactobacillus fermentum ZS40 on NF-κB signaling pathway of azomethane-dextran sulfate sodium (AOM-DSS) -induced colon cancer in mice. Animals were divided into control group (NC), AOM-DSS-induced model group (CRC), AOM-DSS plus high-dose Lactobacillus fermentum ZS40 (ZS40-H), AOM-DSS plus low-dose Lactobacillus fermentum ZS40 (ZS40-L), AOM-DSS plus Lactobacillus bulgaricus (BLA), and AOM-DSS plus sulfasalazine (SD)-treated group. Observation of animal physiological activity (body weight and defecation), biochemical measurements, histopathological examination of colon tissue, qPCR to evaluate the expression of inflammation-related genes, immunohistochemical analysis of CD34 and CD117, and Western blot analysis of NF-κB signaling pathway were performed. Compared with the CRC group, the ZS40-H, ZS40-L, BLA, and SD groups had decreased levels of colon cancer marker proteins CD34 and CD117, and the number of abnormal colonic lesions observed by colon histology decreased, while the ZS40-H group showed excellent results. In addition, all probiotic interventions showed weight loss effects. The expression of inflammatory stimulators TNF-α and IL-1β in the probiotic treatment group decreased; the expression of key proteins IκBα and p65 in the NF-κB signaling pathway also decreased, resulting in a decrease in the expression of the target protein Cox-2. Therefore, administration of Lactobacillus fermentum ZS40 as a probiotic can alleviate intestinal inflammation and prevent colon cancer in mice.

A Detailed Overview About the Single-Cell Analyses of Solid Tumors Focusing on Colorectal Cancer

In recent years, the evolution of the molecular biological technical background led to the widespread application of single-cell sequencing, a versatile tool particularly useful in the investigation of tumor heterogeneity. Even 10 years ago the comprehensive characterization of colorectal cancers by The Cancer Genome Atlas was based on measurements of bulk samples. Nowadays, with single-cell approaches, tumor heterogeneity, the tumor microenvironment, and the interplay between tumor cells and their surroundings can be described in unprecedented detail. In this review article we aimed to emphasize the importance of single-cell analyses by presenting tumor heterogeneity and the limitations of conventional investigational approaches, followed by an overview of the whole single-cell analytic workflow from sample isolation to amplification, sequencing and bioinformatic analysis and a review of recent literature regarding the single-cell analysis of colorectal cancers.