HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC.

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCC database (HCCDB v1.0) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma.

BACKGROUND: Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy. METHODS: We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP). RESULTS: Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region. CONCLUSIONS: These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Heterogeneous murine peribiliary glands orchestrate compartmentalized epithelial renewal.

The extrahepatic branches of the biliary tree have glands that connect to the surface epithelium through narrow pits. The duct epithelia undergo homeostatic renewal, yet the identity and multiplicity of cells that maintain this tissue is unknown. Using marker-free and targeted clonal fate mapping in mice, we provide evidence that the extrahepatic bile duct is compartmentalized. Pit cholangiocytes of extramural glands renewed the surface epithelium, whereas basally oriented cholangiocytes maintained the gland itself. In contrast, basally positioned cholangiocytes replenished the surface epithelium in mural glands. Single-cell sequencing identified genes enriched in the base and surface epithelial populations, with trajectory analysis showing graded gene expression between these compartments. Epithelia were plastic, changing cellular identity upon fasting and refeeding. Gain of canonical Wnt signaling caused basal cell expansion, gastric chief cell marker expression, and a decrease in surface epithelial markers. Our results identify the cellular hierarchy governing extrahepatic biliary epithelial renewal.

The microbiota regulates hematopoietic stem and progenitor cell development by mediating inflammatory signals in the niche.

The commensal microbiota regulates the self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs) in bone marrow. Whether and how the microbiota influences HSPC development during embryogenesis is unclear. Using gnotobiotic zebrafish, we show that the microbiota is necessary for HSPC development and differentiation. Individual bacterial strains differentially affect HSPC formation, independent of their effects on myeloid cells. Early-life dysbiosis in chd8-/- zebrafish impairs HSPC development. Wild-type microbiota promote HSPC development by controlling basal inflammatory cytokine expression in kidney niche, and chd8-/- commensals elicit elevated inflammatory cytokines that reduce HSPCs and enhance myeloid differentiation. We identify an Aeromonas veronii strain with immuno-modulatory activities that fails to induce HSPC development in wild-type fish but selectively inhibits kidney cytokine expression and rebalances HSPC development in chd8-/- zebrafish. Our studies highlight the important roles of a balanced microbiome during early HSPC development that ensure proper establishment of lineal precursor for adult hematopoietic system.

Deficiency of Carbamoyl Phosphate Synthetase 1 Engenders Radioresistance in Hepatocellular Carcinoma via Deubiquitinating c-Myc.

PURPOSE: Tumor radiation resistance is the main obstacle to effective radiation therapy for patients with hepatocellular carcinoma (HCC). We identified the role of urea cycle key enzyme carbamoyl phosphate synthetase 1 (CPS1) in radioresistance of HCC and explored its mechanism, aiming to provide a novel radiosensitization strategy for the CPS1-deficiency HCC subtype. METHODS AND MATERIALS: The expression of CPS1 was measured by western blot and immunohistochemistry. Cell growth assay, EdU assay, cell apoptosis assay, cell cycle assay, clone formation assay, and subcutaneous tumor assay were performed to explore the relationship between CPS1 and radioresistance of HCC cells. Lipid metabonomic analysis was used for investigating the effects of CPS1 on lipid synthesis of HCC cells. RNA sequencing and coimmunoprecipitation assay were carried out to reveal the mechanism of CPS1 participating in the regulation of HCC radiation therapy resistance. Furthermore, 10074-G5, the specific inhibitor of c-Myc, was administered to HCC cells to investigate the role of c-Myc in CPS1-deficiency HCC cells. RESULTS: We found that urea cycle key enzyme CPS1 was frequently lower in human HCC samples and positively associated with the patient's prognosis. Functionally, the present study proved that CPS1 depletion could accelerate the development of HCC and induce radiation resistance of HCC in vitro and in vivo, and deficiency of CPS1 promoted the synthesis of some lipid molecules. Regarding the mechanism, we uncovered that inhibition of CPS1 upregulated CyclinA2 and CyclinD1 by stabilizing oncoprotein c-Myc at the posttranscriptional level and generated radioresistance of HCC cells. Moreover, inactivation of c-Myc using 10074-G5, a specific c-Myc inhibitor, could partially attenuate the proliferation and radioresistance induced by depletion of CPS1. CONCLUSIONS: Our results recapitulated that silencing CPS1 could promote HCC progression and radioresistance via c-Myc stability mediated by the ubiquitin-proteasome system, suggesting that targeting c-Myc in CPS1-deficiency HCC subtype may be a valuable radiosensitization strategy in the treatment of HCC.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics.

Sequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis is still complicated by high technical noises and low resolution. In addition to the transcriptomic data, matched histopathological images are usually generated for the same tissue sample along the ST experiment. The matched high-resolution histopathological images provide complementary cellular phenotypical information, providing an opportunity to mitigate the noises in ST data. We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST (TIST), which enables the identification of spatial clusters (SCs) and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images. TIST devises a histopathological feature extraction method based on Markov random field (MRF) to learn the cellular features from histopathological images, and integrates them with the transcriptomic data and location information as a network, termed TIST-net. Based on TIST-net, SCs are identified by a random walk-based strategy, and gene expression patterns are enhanced by neighborhood smoothing. We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods. Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios. TIST is available at http://lifeome.net/software/tist/ and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

Comprehensive analysis of spatial architecture in primary liver cancer.

Heterogeneity is the major challenge for cancer prevention and therapy. Here, we first constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from seven patients. The progressive comparison of spatial tumor microenvironment (TME) characteristics from nontumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity, and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100-µm-wide cluster-cluster boundary contribute to maintaining intratumor architecture and the PROM1+ and CD47+ cancer stem cell niches are related to TME remodeling and tumor metastasis. Last, we proposed a TLS-50 signature to accurately locate tertiary lymphoid structures (TLSs) spatially and unveiled that the distinct composition of TLSs is shaped by their distance to tumor cells. Our study provides previous unknown insights into the diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

Discovery of a Carbamoyl Phosphate Synthetase 1-Deficient HCC Subtype With Therapeutic Potential Through Integrative Genomic and Experimental Analysis.

BACKGROUND AND AIMS: Metabolic reprogramming plays an important role in tumorigenesis. However, the metabolic types of different tumors are diverse and lack in-depth study. Here, through analysis of big databases and clinical samples, we identified a carbamoyl phosphate synthetase 1 (CPS1)-deficient hepatocellular carcinoma (HCC) subtype, explored tumorigenesis mechanism of this HCC subtype, and aimed to investigate metabolic reprogramming as a target for HCC prevention. APPROACH AND RESULTS: A pan-cancer study involving differentially expressed metabolic genes of 7,764 tumor samples in 16 cancer types provided by The Cancer Genome Atlas (TCGA) demonstrated that urea cycle (UC) was liver-specific and was down-regulated in HCC. A large-scale gene expression data analysis including 2,596 HCC cases in 7 HCC cohorts from Database of HCC Expression Atlas and 17,444 HCC cases from in-house hepatectomy cohort identified a specific CPS1-deficent HCC subtype with poor clinical prognosis. In vitro and in vivo validation confirmed the crucial role of CPS1 in HCC. Liquid chromatography-mass spectrometry assay and Seahorse analysis revealed that UC disorder (UCD) led to the deceleration of the tricarboxylic acid cycle, whereas excess ammonia caused by CPS1 deficiency activated fatty acid oxidation (FAO) through phosphorylated adenosine monophosphate-activated protein kinase. Mechanistically, FAO provided sufficient ATP for cell proliferation and enhanced chemoresistance of HCC cells by activating forkhead box protein M1. Subcutaneous xenograft tumor models and patient-derived organoids were employed to identify that blocking FAO by etomoxir may provide therapeutic benefit to HCC patients with CPS1 deficiency. CONCLUSIONS: In conclusion, our results prove a direct link between UCD and cancer stemness in HCC, define a CPS1-deficient HCC subtype through big-data mining, and provide insights for therapeutics for this type of HCC through targeting FAO.

Modeling gene regulatory networks using neural network architectures.

Gene regulatory networks (GRNs) encode the complex molecular interactions that govern cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer GRNs and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data. In particular, we developed a neural network version of the structural equation model (SEM) to explicitly model the regulatory relationships among genes. Benchmark results show that DeepSEM achieves comparable or better performance on a variety of single-cell computational tasks, such as GRN inference, scRNA-seq data visualization, clustering and simulation, compared with the state-of-the-art methods. In addition, the gene regulations predicted by DeepSEM on cell-type marker genes in the mouse cortex can be validated by epigenetic data, which further demonstrates the accuracy and efficiency of our method. DeepSEM can provide a useful and powerful tool to analyze scRNA-seq data and infer a GRN.

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing.

Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

Artificial-cell-type aware cell-type classification in CITE-seq.

MOTIVATION: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. RESULTS: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. AVAILABILITY AND IMPLEMENTATION: http://github.com/QiuyuLian/CITE-sort. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Targeted sequencing reveals the mutational landscape responsible for sorafenib therapy in advanced hepatocellular carcinoma.

Rationale: The existence of primary and acquired drug resistance is the main obstacle for the effect of multi-kinase inhibitor sorafenib and regorafenib in advanced hepatocellular carcinoma (HCC). However, plenty of patients did not significantly benefit from sorafenib treatment and little is known about the mechanism of drug resistance. Methods: Laser capture microdissection was used to acquire matched normal liver and tumor tissues on formalin-fixed paraffin-embedded specimens collected before sorafenib therapy from the first surgery of 119 HCC patients. Ultra-deep sequencing (~1000×) targeting whole exons of 440 genes in microdissected specimens and siRNA screen in 7 cell lines were performed to find mutations associated with differential responses to sorafenib. Patient-derived xenograft models were employed to determine the role of TP53 in response to sorafenib. Lentiviruses harboring wild-type and c.G52C-mutant OCT4 were applied to explore the function of OCT4 in resistance to sorafenib. ChIP-PCR assay for analysis of OCT4 transcriptional activity was performed to explore the affinity with the KITLG promoter. Statistical analyses were used to associate levels of p53 and OCT4 with tumor features and patient outcomes. Results: Total 1,050 somatic mutations and 26 significant driver genes were identified. SiRNA screening in 7 HCC cell lines was further performed to identify mutations associated with differential responses to sorafenib. A recurrent nonsynonymous mutation c.G52C in OCT4 (OCT4mut) was strongly associated with good response to sorafenib, whereas the stop-gain mutation in TP53 showed the opposite outcome both in vitro and in vivo. OCT4wt-induced stem cell factor (encoded by KITLG gene, SCF) expression and cross-activation of c-KIT/FLT3-BRAF signals were identified indispensably for sorafenib resistance, which could be reversed by the combination of c-KIT tyrosine kinase inhibitors or neutralizing antibody against SCF. Mechanistically, an OCT4 binding site in upstream of KITLG promoter was identified with a higher affinity to wildtype of OCT4 rather than G52C-mutant form, which is indispensable for OCT4-induced expression of KITLG and sorafenib resistance. Conclusion: Our study reported a novel somatic mutation in OCT4 (c.G52C) responsible for the sorafenib effect, and also shed new light on the treatment of HCC through the combination of specific tyrosine kinase inhibitors according to individual genetic patterns.

Integrative molecular analysis of metastatic hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the major type of primary liver cancer. Intrahepatic metastasis, such as portal vein tumor thrombosis (PVTT), strongly indicates poor prognosis of HCC. But now, there are limited understandings of the molecular features and mechanisms of those metastatic HCCs. METHODS: To characterize the molecular alterations of the metastatic HCCs, we implemented an integrative analysis of the copy number variations (CNVs), DNA methylations and transcriptomes of matched adjacent normal, primary tumor and PVTT samples from 19 HCC patients. RESULTS: CNV analysis identified a frequently amplified focal region chr11q13.3 and a novel deletion peak chr19q13.41 containing three miRNAs. The integrative analysis with RNA-seq data suggests that CNVs and differential promoter methylations regulate distinct oncogenic processes. Then, we used individualized differential analysis to identify the differentially expressed genes between matched primary tumor and PVTT of each patient. Results show that 5 out of 19 studied patients acquire evidential progressive alterations of gene expressions (more than 1000 differentially expressed genes were identified in each patient). While, another subset of eight patients have nearly identical gene expressions between the corresponding matched primary tumor and PVTT. Twenty genes were found to be recurrently and progressively differentially expressed in multiple patients. These genes are mainly associated with focal adhesion, xenobiotics metabolism by cytochrome P450 and amino acid metabolism. For several differentially expressed genes in metabolic pathways, their expressions are significantly associated with overall survivals and vascular invasions of HCC patients. The following transwell assay experiments validate that they can regulate invasive phenotypes of HCC cells. CONCLUSIONS: The metastatic HCCs with PVTTs have significant molecular alterations comparing with adjacent normal tissues. The recurrent alteration patterns are similar to several previously published general HCC cohorts, but usually with higher severity. By an individualized differential analysis strategy, the progressively differentially expressed genes between the primary tumor and PVTT were identified for each patient. A few patients aquire evidential progressive alterations of gene expressions. And, experiments show that several recurrently differentially expressed genes can strongly regulate HCC cell invasions.

HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas.

Hepatocellular carcinoma (HCC) is highly heterogeneous in nature and has been one of the most common cancer types worldwide. To ensure repeatability of identified gene expression patterns and comprehensively annotate the transcriptomes of HCC, we carefully curated 15 public HCC expression datasets that cover around 4000 clinical samples and developed the database HCCDB to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces. The global differential gene expression landscape of HCC was established by analyzing the consistently differentially expressed genes across multiple datasets. Moreover, a 4D metric was proposed to fully characterize the expression pattern of each gene by integrating data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). To facilitate a comprehensive understanding of gene expression patterns in HCC, HCCDB also provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses, including differential expression analysis, tissue-specific and tumor-specific expression analysis, survival analysis, and co-expression analysis. HCCDB is freely accessible at http://lifeome.net/database/hccdb.

Recurrently deregulated lncRNAs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) cells often invade the portal venous system and subsequently develop into portal vein tumour thrombosis (PVTT). Long noncoding RNAs (lncRNAs) have been associated with HCC, but a comprehensive analysis of their specific association with HCC metastasis has not been conducted. Here, by analysing 60 clinical samples' RNA-seq data from 20 HCC patients, we have identified and characterized 8,603 candidate lncRNAs. The expression patterns of 917 recurrently deregulated lncRNAs are correlated with clinical data in a TCGA cohort and published liver cancer data. Matched array data from the 60 samples show that copy number variations (CNVs) and alterations in DNA methylation contribute to the observed recurrent deregulation of 235 lncRNAs. Many recurrently deregulated lncRNAs are enriched in co-expressed clusters of genes related to cell adhesion, immune response and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. Thus, we provide a valuable resource of functional lncRNAs and biomarkers associated with HCC tumorigenesis and metastasis.