Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma

Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications

Hepatocellular Carcinoma (HCC) is one of the most common types of primary liver cancer. Current treatment options have limited efficacy against this malignancy, primarily owing to difficulties in early detection and the inherent resistance to existing drugs. Tumor heterogeneity is a pivotal factor contributing significantly to treatment resistance and recurrent manifestations of HCC. Intratumoral heterogeneity is an important aspect of the spectrum of complex tumor heterogeneity and contributes to late diagnosis and treatment failure. Therefore, it is crucial to thoroughly understand the molecular mechanisms of how tumor heterogeneity develops. This review aims to summarize the possible molecular dimensions of tumor heterogeneity with an emphasis on intratumoral heterogeneity, evaluate its profound impact on the diagnosis and therapeutic strategies for HCC, and explore the suitability of appropriate pre-clinical models that can be used to best study tumor heterogeneity; thus, opening new avenues for cancer treatment.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

Hepatic ChREBP orchestrates intrahepatic carbohydrate metabolism to limit hepatic glucose 6-phosphate and glycogen accumulation in a mouse model for acute Glycogen Storage Disease type Ib

OBJECTIVE

Carbohydrate Response Element Binding Protein (ChREBP) is a glucose 6-phosphate (G6P)-sensitive transcription factor that acts as a metabolic switch to maintain intracellular glucose and phosphate homeostasis. Hepatic ChREBP is well-known for its regulatory role in glycolysis, the pentose phosphate pathway, and de novo lipogenesis. The physiological role of ChREBP in hepatic glycogen metabolism and blood glucose regulation has not been assessed in detail, and ChREBP's contribution to carbohydrate flux adaptations in hepatic Glycogen Storage Disease type 1 (GSD I) requires further investigation.

METHODS

The current study aimed to investigate the role of ChREBP as a regulator of glycogen metabolism in response to hepatic G6P accumulation, using a model for acute hepatic GSD type Ib. The immediate biochemical and regulatory responses to hepatic G6P accumulation were evaluated upon G6P transporter inhibition by the chlorogenic acid S4048 in mice that were either treated with a short hairpin RNA (shRNA) directed against ChREBP (shChREBP) or a scrambled shRNA (shSCR). Complementary stable isotope experiments were performed to quantify hepatic carbohydrate fluxes in vivo.

RESULTS

ShChREBP treatment normalized the S4048-mediated induction of hepatic ChREBP target genes to levels observed in vehicle- and shSCR-treated controls. In parallel, hepatic shChREBP treatment in S4048-infused mice resulted in a more pronounced accumulation of hepatic glycogen and further reduction of blood glucose levels compared to shSCR treatment. Hepatic ChREBP knockdown modestly increased glucokinase (GCK) flux in S4048-treated mice while it enhanced UDP-glucose turnover as well as glycogen synthase and phosphorylase fluxes. Hepatic GCK mRNA and protein levels were induced by shChREBP treatment in both vehicle- and S4048-treated mice, while glycogen synthase 2 (GYS2) and glycogen phosphorylase (PYGL) mRNA and protein levels were reduced. Finally, knockdown of hepatic ChREBP expression reduced starch domain binding protein 1 (STBD1) mRNA and protein levels while it inhibited acid alpha-glucosidase (GAA) activity, suggesting reduced capacity for lysosomal glycogen breakdown.

CONCLUSIONS

Our data show that ChREBP activation controls hepatic glycogen and blood glucose levels in acute hepatic GSD Ib through concomitant regulation of glucose phosphorylation, glycogenesis, and glycogenolysis. ChREBP-mediated control of GCK enzyme levels aligns with corresponding adaptations in GCK flux. In contrast, ChREBP activation in response to acute hepatic GSD Ib exerts opposite effects on GYS2/PYGL enzyme levels and their corresponding fluxes, indicating that GYS2/PYGL expression levels are not limiting to their respective fluxes under these conditions.

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.

Targeted Single-cell Isolation of Spontaneously Escaping Live Melanoma Cells for Comparative Transcriptomics

Solid cancer cells escape the primary tumor mass by transitioning from an epithelial-like state to an invasive migratory state. As they escape, metastatic cancer cells employ interchangeable modes of invasion, transitioning between fibroblast-like mesenchymal movement to amoeboid migration, where cells display a rounded morphology and navigate the extracellular matrix in a protease-independent manner. However, the gene transcripts that orchestrate the switch between epithelial, mesenchymal, and amoeboid states remain incompletely mapped, mainly due to a lack of methodologies that allow the direct comparison of the transcriptomes of spontaneously invasive cancer cells in distinct migratory states. Here, we report a novel single-cell isolation technique that provides detailed three-dimensional data on melanoma growth and invasion, and enables the isolation of live, spontaneously invasive cancer cells with distinct morphologies and invasion parameters. Via the expression of a photoconvertible fluorescent protein, compact epithelial-like cells at the periphery of a melanoma mass, elongated cells in the process of leaving the mass, and rounded amoeboid cells invading away from the mass were tagged, isolated, and subjected to single-cell RNA sequencing. A total of 462 differentially expressed genes were identified, from which two candidate proteins were selected for further pharmacologic perturbation, yielding striking effects on tumor escape and invasion, in line with the predictions from the transcriptomics data. This work describes a novel, adaptable, and readily implementable method for the analysis of the earliest phases of tumor escape and metastasis, and its application to the identification of genes underpinning the invasiveness of malignant melanoma.

Significance

This work describes a readily implementable method that allows for the isolation of individual live tumor cells of interest for downstream analyses, and provides the single-cell transcriptomes of melanoma cells at distinct invasive states, both of which open avenues for in-depth investigations into the transcriptional regulation of the earliest phases of metastasis.

Normalization of hepatic ChREBP activity does not protect against liver disease progression in a mouse model for Glycogen Storage Disease type Ia

BACKGROUND

Glycogen storage disease type 1a (GSD Ia) is an inborn error of metabolism caused by a defect in glucose-6-phosphatase (G6PC1) activity, which induces severe hepatomegaly and increases the risk for liver cancer. Hepatic GSD Ia is characterized by constitutive activation of Carbohydrate Response Element Binding Protein (ChREBP), a glucose-sensitive transcription factor. Previously, we showed that ChREBP activation limits non-alcoholic fatty liver disease (NAFLD) in hepatic GSD Ia. As ChREBP has been proposed as a pro-oncogenic molecular switch that supports tumour progression, we hypothesized that ChREBP normalization protects against liver disease progression in hepatic GSD Ia.

METHODS

Hepatocyte-specific G6pc knockout (L-G6pc^-/-) mice were treated with AAV-shChREBP to normalize hepatic ChREBP activity.

RESULTS

Hepatic ChREBP normalization in GSD Ia mice induced dysplastic liver growth, massively increased hepatocyte size, and was associated with increased hepatic inflammation. Furthermore, nuclear levels of the oncoprotein Yes Associated Protein (YAP) were increased and its transcriptional targets were induced in ChREBP-normalized GSD Ia mice. Hepatic ChREBP normalization furthermore induced DNA damage and mitotic activity in GSD Ia mice, while gene signatures of chromosomal instability, the cytosolic DNA-sensing cGAS-STING pathway, senescence, and hepatocyte dedifferentiation emerged.

CONCLUSIONS

In conclusion, our findings indicate that ChREBP activity limits hepatomegaly while decelerating liver disease progression and protecting against chromosomal instability in hepatic GSD Ia. These results disqualify ChREBP as a therapeutic target for treatment of liver disease in GSD Ia. In addition, they underline the importance of establishing the context-specific roles of hepatic ChREBP to define its therapeutic potential to prevent or treat advanced liver disease.

Unraveling the heterogeneity of cholangiocarcinoma and identifying biomarkers and therapeutic strategies with single-cell sequencing technology

Cholangiocarcinoma (CCA) is a common malignant tumor of the biliary tract that carries a high burden of morbidity and a poor prognosis. Due to the lack of precise diagnostic methods, many patients are often diagnosed at advanced stages of the disease. The current treatment options available are of varying efficacy, underscoring the urgency for the discovery of more effective biomarkers for early diagnosis and improved treatment. Recently, single-cell sequencing (SCS) technology has gained popularity in cancer research. This technology has the ability to analyze tumor tissues at the single-cell level, thus providing insights into the genomics and epigenetics of tumor cells. It also serves as a practical approach to study the mechanisms of cancer progression and to explore therapeutic strategies. In this review, we aim to assess the heterogeneity of CCA using single-cell sequencing technology, with the ultimate goal of identifying possible biomarkers and potential treatment targets.

MLXIPL promotes the migration, invasion, and glycolysis of hepatocellular carcinoma cells by phosphorylation of mTOR

BACKGROUND

Hepatocellular carcinoma (HCC) is associated with a high occurrence, mortality, and poor prognosis. MLX interacting protein like (MLXIPL) is an important regulator of glucolipid metabolism and is involved in tumor progression. We aimed to clarify the role of MLXIPL in HCC and its underlying mechanisms.

METHODS

The level of MLXIPL was predicted using bioinformatic analysis and verified using quantitative real-time PCR (qPCR), immunohistochemical analysis, and western blot. We assessed the effects of MLXIPL on biological behaviors using the cell counting kit-8, colony formation, and Transwell assay. Glycolysis was evaluated using the Seahorse method. The interaction between MLXIPL and mechanistic target of rapamycin kinase (mTOR) was confirmed using RNA immunoprecipitation and co-immunoprecipitation. mTOR expression was detected in HCC cells using qPCR, immunofluorescence analysis, and western blot.

RESULTS

The results showed that MLXIPL levels were elevated in both HCC tissues and HCC cell lines. Knockdown of MLXIPL impeded HCC cell growth, invasion, migration, and glycolysis. Moreover, MLXIPL combined with mTOR to induce phosphorylation of mTOR. Activated mTOR abrogated the effects on cellular processes induced by MLXIPL.

CONCLUSION

MLXIPL promoted the malignant progression of HCC by activating phosphorylation of mTOR, suggesting an important role of the combination of MLXIPL and mTOR in HCC.

The Value of Single-cell Technologies in Solid Organ Transplantation Studies

Single-cell technologies open up new opportunities to explore the behavior of cells at the individual level. For solid organ transplantation, single-cell technologies can provide in-depth insights into the underlying mechanisms of the immunological processes involved in alloimmune responses after transplantation by investigating the role of individual cells in tolerance and rejection. Here, we review the value of single-cell technologies, including cytometry by time-of-flight and single-cell RNA sequencing, in the context of solid organ transplantation research. Various applications of single-cell technologies are addressed, such as the characterization and identification of immune cell subsets involved in rejection or tolerance. In addition, we explore the opportunities for analyzing specific alloreactive T- or B-cell clones by linking phenotype data to T- or B-cell receptor data, and for distinguishing donor- from recipient-derived immune cells. Moreover, we discuss the use of single-cell technologies in biomarker identification and risk stratification, as well as the remaining challenges. Together, this review highlights that single-cell approaches contribute to a better understanding of underlying immunological mechanisms of rejection and tolerance, thereby potentially accelerating the development of new or improved therapies to avoid allograft rejection.

Advances in single-cell sequencing technology in the field of hepatocellular carcinoma

Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.

Deciphering the role of predicted miRNAs of polyomaviruses in carcinogenesis

Human polyomaviruses are relatively common in the general population. Polyomaviruses maintain a persistent infection after initial infection in childhood, acting as an opportunistic pathogen in immunocompromised populations and their association has been linked to carcinogenesis. A comprehensive understanding of the underlying molecular mechanisms of carcinogenesis in consequence of polyomavirus infection remains elusive. However, the critical role of viral miRNAs and their potential targets in modifying the transcriptome profile of the host remains largely unknown. Polyomavirus-derived miRNAs have the potential to play a substantial role in carcinogenesis. Employing computational approaches, putative viral miRNAs along with their target genes have been predicted and possible roles of the targeted genes in many significant biological processes have been obtained. Polyomaviruses have been observed to target intracellular signal transduction pathways through miRNA-mediated epigenetic regulation, which may contribute to cancer development. In addition, BKPyV-infected human renal cell microarray data was coupled with predicted target genes and analysis of the downregulated genes indicated that viruses target multiple signaling pathways (e.g. MAPK signaling pathway, PI3K-Akt signaling pathway, PPAR signaling pathway) in the host as well as turning off several tumor suppression genes (e.g. FGGY, EPHX2, CACNA2D3, CDH16) through miRNA-induced mechanisms, assuring cell transformation. This study provides a conceptual framework for the underlying molecular mechanisms involved in the course of carcinogenesis upon polyomavirus infection.

Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

The Role of Chronic Liver Diseases in the Emergence and Recurrence of Hepatocellular Carcinoma: An Omics Perspective

Hepatocellular carcinoma (HCC) typically develops from a background of cirrhosis resulting from chronic inflammation. This inflammation is frequently associated with chronic liver diseases (CLD). The advent of next generation sequencing has enabled extensive analyses of molecular aberrations in HCC. However, less attention has been directed to the chronically inflamed background of the liver, prior to HCC emergence and during recurrence following surgery. Hepatocytes within chronically inflamed liver tissues present highly activated inflammatory signaling pathways and accumulation of a complex mutational landscape. In this altered environment, cells may transform in a stepwise manner toward tumorigenesis. Similarly, the chronically inflamed environment which persists after resection may impact the timing of HCC recurrence. Advances in research are allowing an extensive epigenomic, transcriptomic and proteomic characterization of CLD which define the emergence of HCC or its recurrence. The amount of data generated will enable the understanding of oncogenic mechanisms in HCC from the CLD perspective and provide the possibility to identify robust biomarkers or novel therapeutic targets for the treatment of primary and recurrent HCC. Importantly, biomarkers defined by the analysis of CLD tissue may permit the early detection or prevention of HCC emergence and recurrence. In this review, we compile the current omics based evidence of the contribution of CLD tissues to the emergence and recurrence of HCC.

Single-Cell Transcriptomics of Liver Cancer: Hype or Insights?

Hepatocellular carcinoma (HCC) is characterized by its high degrees of both inter- and intratumoral heterogeneity. Its complex tumor microenvironment is also crucial in promoting tumor progression. Recent advances in single-cell RNA sequencing provide an important highway to characterize the underlying pathogenesis and heterogeneity of HCC in an unprecedented degree of resolution. This review discusses the up-to-date discoveries from the latest studies of HCC with respect to the strength of single-cell RNA sequencing. We discuss its use in the dissection of the landscape of the intricate HCC ecosystem and highlight the major features at cellular levels, including the malignant cells, different immune cell types, and the various cell-cell interactions, which are crucial for developing effective immunotherapies. Finally, its translational applications will be discussed. Altogether, these explorations may give us some hints at the tumor growth and progression and drug resistance and recurrence, particularly in this era of personalized medicine.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Understanding initiation and progression of hepatocellular carcinoma through single cell sequencing

Unsatisfied clinical outcome drives to better understand hepatic carcinogenesis, microenvironment and escape of immune surveillance in hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-Seq) has generated enormous data to pinpoint pathophysiologic alterations in tumor microenvironment (TME) or trace lineage development in cancer stem cells (CSCs), circulating tumor cells (CTCs), and subsets of immune cells, such as exhausting T cells, tumor-associated macrophages (TAMs), dendritic cells or other lineages. New insights have significantly advanced current understanding in progression, poor responses to molecular-targeted therapeutics or immune checkpoint inhibitors, metastasis in both basic research and clinical practice. The present review intends to cover a basic workflow of the scRNA-seq technology, existing limitations and improvement areas. Moreover, in-depth understanding in TME, exhausting T cells, CSCs, CTCs, tumor-associated macrophages, dendritic cells in HCC facilitates implementation of personalized and precise therapy in an era of availability with an array of systemic regimens.

An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis

Hepatocellular carcinoma is a primary liver cancer caused by the accumulation of genetic mutation patterns associated with epidemiological conditions. This lethal malignancy exhibits tumor heterogeneity, which is considered as one of the main reasons for drug resistance development and failure of clinical trials. Recently, single-cell technology (SCT), a new advanced sequencing technique that analyzes every single cell in a tumor tissue specimen, aids complete insight into the genetic heterogeneity of cancer. This helps in identifying and assessing rare cell populations by analyzing the difference in gene expression pattern between individual cells of single biopsy tissue which normally cannot be identified from pooled cell gene expression pattern (traditional sequencing technique). Thus, SCT improves the clinical diagnosis, treatment, and prognosis of hepatocellular carcinoma as the limitations of other techniques impede this cancer research progression. Application of SCT at the genomic, transcriptomic, and epigenomic levels to promote individualized hepatocellular carcinoma diagnosis and therapy. The current review has been divided into ten sections. Herein we deliberated on the SCT, hepatocellular carcinoma diagnosis, tumor microenvironment analysis, single-cell genomic sequencing, single-cell transcriptomics, single-cell omics sequencing for biomarker development, identification of hepatocellular carcinoma origination and evolution, limitations, challenges, conclusions, and future perspectives.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.