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Abstract Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.

Objective: Hypoxia is an important feature of pancreatic ductal adenocarcinoma (PDAC). Previously, we found that hypoxia promotes ENO1 expression and PDAC invasion. However, the underlying molecular mechanism was remains unclear. Methods: The relationship between ENO1 expression and clinicopathological characteristics was analyzed in 84 patients with PADC. The effects of CoCl2-induced hypoxia and ENO1 downregulation on the apoptosis, invasion, and proliferation of PDAC cells were evaluated in vitro and in vivo. Hypoxia- and ENO1-induced gene expression was analyzed by transcriptomic sequencing. Results: The prognosis of PDAC with high ENO1 expression was poor (P < 0.05). High ENO1 expression was closely associated with histological differentiation and tumor invasion in 84 PDAC cases (P < 0.05). Hypoxia increased ENO1 expression in PDAC and promoted its migration and invasion. Apoptotic cells and the apoptosis marker caspase-3 in the CoCl2-treated ENO1-sh group were significantly elevated (P < 0.05). Transcriptomic sequencing indicated that CoCl2-induced PDAC cells initiated MAPK signaling. Under hypoxic conditions, PDAC cells upregulated ENO1 expression, thereby accelerating ERK phosphorylation and inhibiting apoptosis (P < 0.05). Consistent results were also observed in a PDAC-bearing mouse hindlimb ischemia model. Conclusions: Hypoxia-induced ENO1 expression promotes ERK phosphorylation and inhibits apoptosis, thus leading to PDAC survival and invasion. These results suggest that ENO1 is a potential therapeutic target for PDAC.

Abstract Background Hepatocellular carcinoma (HCC) often presents with satellite nodules, rendering current curative treatments ineffective in many patients. The heterogeneity of HCC is a major challenge in personalized medicine. The emergence of spatial transcriptomics (ST) provides a powerful strategy for delineating the complex molecular landscapes of tumours. Methods In this study, the heterogeneity of tissue-wide gene expression in tumour and adjacent nonneoplastic tissues using ST technology were investigated. The transcriptomes of nearly 10,820 tissue regions and identified the main gene expression clusters and their specific marker genes (differentially expressed genes, DEGs) in patients were analysed. The DEGs were analysed from two perspectives. First, two distinct gene profiles were identified to be associated with satellite nodules and conducted a more comprehensive analysis of both gene profiles. Their clinical relevance in human HCC was validated with Kaplan–Meier (KM) Plotter. Second, DEGs were screened with The Cancer Genome Atlas (TCGA) database to divide the HCC cohort into high- and low-risk groups according to Cox analysis. HCC patients from the International Cancer Genome Consortium (ICGC) cohort were used for validation. KM analysis was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Results Novel markers for the prediction of satellite nodules were identified and a tumour clusters-specific marker gene signature model (6 genes) for HCC prognosis was constructed. Conclusion The establishment of marker gene profiles may be an important step towards an unbiased view of HCC, and the 6-gene signature can be used for prognostic prediction in HCC. This analysis will help us to clarify one of the possible sources of HCC heterogeneity and uncover pathogenic mechanisms and novel antitumour drug targets.

IntroductionHypoxia is an important characteristic of solid tumors. However, spatial transcriptomics (ST) of hypoxia-associated heterogeneity is not clear.MethodsThis study integrated Spatial Transcriptomics (ST) with immunofluorescence to demonstrate their spatial distribution in human claudin-low breast cancer MDA-MB-231 engraft. ST spots were clustered with differentially expression genes. The data were combined with hypoxia-specific marker and angiogenesis marker-labeled serial sections to indicate the spatial distribution of hypoxia and hypoxia-inducted transcriptional profile. Moreover, marker genes, cluster-specific hypoxia genes, and their co-essential relationship were identified and mapped in every clusters. The clinicopathological association of marker genes of hypoxia-dependent spatial clusters was explored in 1904 breast cancers from METABRIC database.ResultsThe tumor from center to periphery were enriched into five hypoxia-dependent subgroups with differentially expressed genes, which were matched to necrosis, necrosis periphery, hypoxic tumor, adaptive survival tumor, and invasive tumor, respectively. Different subgroups demonstrated distinct hypoxia condition and spatial heterogeneity in biological behavior and signaling pathways. Cox regression analysis showed that the invasive tumor (cluster 0) and hypoxic tumor (cluster 6) score could be served as independent prognostic factors in claudin-low patients. KM analysis indicated that high invasive tumor (cluster 0) and hypoxic tumor (cluster 6) score was associated with poor prognoses of claudin-low patients. Further analysis showed that hypoxia-induced immune checkpoints, such as CD276 and NRP1, upregulation in invasive tumor to block infiltration and activation of B cells and CD8+ T cells to change tumor immune microenvironment. DiscussionThis study reveals hypoxia-dependent spatial heterogeneity in claudin-low breast cancer and highlights its potential value as a predictive biomarker of clinical outcomes and immunotherapy response. The molecules found in this study also provided potential molecular mechanisms and therapeutic targets for subsequent studies.

Abstract Background The expression and activation of eukaryotic translation initiation factor 4E (eIF4E) is associated with cell transformation and tumor initiation, but the functional role and the mechanism whereby it drives immune cell infiltration in breast cancer (BRCA) remain uncertain. Methods Oncomine, Timer and UALCAN were used to analyze the expression of eIF4E in various cancers. PrognoScan, Kaplan–Meier plotter, and GEPIA were utilized to analyze the prognostic value of eIF4E in select cancers. In vitro cell experiments were used to verify the role of eIF4E in promoting the progression of BRCA. ImmuCellAI and TIMER database were used to explore the relationship between eIF4E and tumor infiltrating immune cells. The expression of a macrophage marker (CD68+) and an M2-type marker (CD163+) was evaluated using immunohistochemistry in 50 invasive BRCA samples on tissue microarrays. The Human Protein Atlas (HPA) database was used to show the expression of eIF4E and related immune markers. LinkedOmics and NetworkAnalyst were used to build the signaling network. Results Through multiple dataset mining, we found that the expression of eIF4E in BRCA was higher than that in normal tissues, and patients with increased eIF4E expression had poorer survival and a higher cumulative recurrence rate in BRCA. At the cellular level, BRCA cell migration and invasion were significantly inhibited after eIF4E expression was inhibited by siRNA. Immune infiltration analysis showed that the eIF4E expression level was significantly associated with the tumor purity and immune infiltration levels of different immune cells in BRCA. The results from immunohistochemical (IHC) staining further proved that the expression of CD68+ and CD163+ were significantly increased and correlated with poor prognosis in BRCA patients (P

Abstract Background Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied. Results Here, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis. Conclusions In summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

Abstract A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.

Objective: Hypoxia is a significant feature of solid tumors, including pancreatic ductal adenocarcinoma (PDAC). It is associated with tumor invasion, metastasis, and drug resistance. However, the spatial distribution of hypoxia-related heterogeneity in PDAC remains unclear. Methods: Spatial transcriptomics (STs), a new technique, was used to investigate the ST features of engrafted human PDAC in the ischemic hind limbs of nude mice. Transcriptomes from ST spots in the hypoxic tumor and the control were clustered using differentially-expressed genes. These data were compared to determine the spatial organization of hypoxia-induced heterogeneity in PDAC. Clinical relevance was validated using the Tumor Cancer Genome Atlas and KM-plotter databases. The CMAP website was used to identify molecules that may serve as therapeutic targets for PDAC. Results: ST showed that the tumor cell subgroups decreased to 7 subgroups in the hypoxia group, compared to 9 subgroups in the control group. Different subgroups showed positional characteristics and different gene signatures. Subgroup 6 located at the invasive front showed a higher proliferative ability under hypoxia. Subgroup 6 had active functions including cell proliferation, invasion, and response to stress. Expressions of hypoxia-related genes, LDHA, TPI1, and ENO1, induced changes. CMAP analysis indicated that ADZ-6482, a PI3K inhibitor, was targeted by the invasive subgroup in hypoxic tumors. Conclusions: This study is the first to describe hypoxic microenvironment-induced spatial transcriptome changes in PDAC, and to identify potential treatment targets for PDAC. These data will provide the basis for further investigations of the prognoses and treatments of hypoxic tumors.

Abstract Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

PurposeAngiotropism is the process by which cancer cells attach to and migrate along blood vessels to acquire vasculature, disseminate, and metastasize. However, the molecular basis for such vessel–tumor interactions has not been fully elucidated, partly due to limited experimental models. In this study, we aimed to observe and explore the molecular mechanism underlying angiotropism in melanoma.MethodsTo monitor the interactions of human melanoma cells with the vasculature in vivo, a murine coxenograft model was employed by co-injecting highly and poorly invasive melanoma cells subcutaneously. To identify key pathways and genes involved in the angiotropic phenotype of melanoma, analysis of differentially expressed genes (DEGs) and gene set enrichment analysis (GSEA) were performed. The role of tissue factor pathway inhibitor 2 (TFPI2) in angiotropism was evaluated by immunostaining, adhesion assay, shRNA, and in vivo tumorigenicity. Angiotropism and TFPI2 expression were examined in surgical specimens of melanoma by immunohistochemical staining. Data from The Cancer Genome Atlas (TCGA) were analyzed to explore the expression and prognostic implications of TFPI2 in uveal and cutaneous melanoma.ResultsHighly invasive melanoma cells spread along the branches of intratumor blood vessels to the leading edge of invasion in the coxenograft model, resembling angiotropic migration. Mechanisms underlying angiotropism were primarily associated with molecular function regulators, regulation of cell population proliferation, developmental processes, cell differentiation, responses to cytokines and cell motility/locomotion. TFPI2 downregulation weakened the perivascular migration of highly invasive melanoma cells. High levels of TFPI2 were correlated with worse and better survival in uveal and cutaneous melanoma, respectively.ConclusionThese results provide a straightforward in vivo model for the observation of angiotropism and suggest that TFPI2 could inhibit the angiotropic phenotype of melanoma.

Lung adenocarcinomas frequently harbour KRAS mutations, of which a subset are characterized by co-mutation of KEAP1. Here the authors show, in mice, that Kras G12D mutant tumours are metabolically distinct, with a bronchiolar cell-of-origin.

Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma-style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.

An outbreak of harmful algal blooms (HABs) often leads to the death of fish and other marine organisms and causes serious losses to human economic activities. Modified clay (MC) technology is an effective way to control HABs. Although the material and preparation process are based on the premise of green and safety, the potential eco-environmental impacts of MC on non-target organisms should still have to receive attention before field applications can occur. Therefore, the effects of one commonly used modified clay, polyaluminum chloride-modified clay (PAC-MC), on the survival, growth, and oxidative stress of the marine medaka (Oryzias melastigma) were studied. The toxicity test results showed that the 96-h median lethal concentration (96-h LC50) of PAC-MC for newly hatched medaka larvae was 5.204 g/L, which was much higher than the concentration used on site (4–10 t/km2). Within the concentration range of PAC-MC used in this experiment (≤2 g/L), the morphology, heart rate, growth, and aluminum content of larvae did not change with the increase in the modified clay concentration. Low concentrations of PAC-MC (≤0.5 g/L) did not significantly affect catalase (CAT) activity, superoxide dismutase (SOD) activity, peroxidase (POD) activity, and the content of malondialdehyde (MDA), but higher concentrations of PAC-MC (such as 2 g/L) caused oxidative damage to the larvae and increased the antioxidant enzyme activity of the larvae. The present study revealed that under an effective dosage for treating harmful algal blooms on site, PAC-MC had no adverse effects on the survival, growth, oxidative stress, and aluminum content of the newly hatched marine medaka, which provides a scientific basis for the field application of modified clay.

Abstract High mobility group protein A2 (HMGA2) is a transcription factor that plays an important role in the invasion and metastasis of gastric carcinoma (GC). The term vasculogenic mimicry (VM) refers to the unique ability of aggressive tumour cells to mimic the pattern of embryonic vasculogenic networks. However, the relationship between HMGA2 and VM formation remains unclear. In the present study, we examined concomitant HMGA2 expression and VM in 228 human GC samples and 4 GC cell lines. Our data indicate that HMGA2 is not only significantly associated with VM formation but also influences the prognosis of patients with gastric carcinoma. Overexpression of HMGA2 significantly increased cell motility, invasiveness, and VM formation both in vitro and in vivo. A luciferase reporter assay, Co-IP and ChIP demonstrated that HMGA2 induced the expression of Twist1 and VE-cadherin by binding to the Twist1 promoter. Moreover, we observed a decrease in VE-cadherin following Twist1 knockdown in cells overexpressing HMGA2. This study indicates that HMGA2 promotes VM in GC via Twist1-VE-cadherin signalling and influences the prognosis of patients with GC.

Abstract Background The incidence and mortality rates of hepatocellular carcinoma (HCC) have steadily increased in recent years. A hypoxic microenvironment is one of the most important characteristics of solid tumors which has been shown to promote tumor metastasis, epithelial-mesenchymal transition and angiogenesis. Epithelial-mesenchymal transition and vasculogenic mimicry have been regarded as crucial contributing factors to cancer progression. HIF-1α functions as a master transcriptional regulator in the adaptive response to hypoxia. Lysyl oxidases like 2 (LOXL2) is a member of the lysyl oxidase family, which main function is to catalyze the covalent cross-linkages of collagen and elastin in the extracellular matrix. Recent work has demonstrated that HIF-1α promotes the expression of LOXL2, which is believed to amplify tumor aggressiveness. LOXL2 has shown to promote metastasis and is correlated with poor prognosis in hepatocellular carcinoma. The purpose of our study is to explore the role of HIF-1α in progression and metastasis of hepatocellular carcinoma by promoting the expression of LOXL2 as well as the potential regulatory mechanism. Methods HIF-1α, LOXL2 expression and CD31/periodic acid-Schiff double staining in HCC patient samples were examined by immunohistochemical staining. shRNA plasmids against HIF-1α was used to determine whether LOXL2 been increased by HIF-1α. We monitored a series of rescue assays to demonstrate our hypothesis that LOXL2 is required and sufficient for HIF-1α induced EMT and VM formation, which mediates cellular transformation and takes effect in cellular invasion. Then we performed GeneChip® Human Transcriptome Array (HTA) 2.0 in HepG2 cells, HepG2 cells overexpressed LOXL2 and HepG2 cells treated with CoCl2. Results In clinical HCC tissues, it confirmed a positive relationship between HIF-1α and LOXL2 protein. Importantly, HIF-1α and LOXL2 high expression and the presence of vasculogenic mimicry were correlated to poor prognosis. HIF-1α was found to induce EMT, HCC cell migration, invasion and VM formation by regulating LOXL2. The results of microarray assays were analyzed. Conclusion HIF-1α plays an important role in the development of HCC by promoting HCC metastasis, EMT and VM through up-regulating LOXL2. This study highlights the potential therapeutic value of targeting LOXL2 for suppression of HCC metastasis and progression.

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

The author wishes to make the following corrections to this paper [...]

In the present work, silver nanoparticles (AgNPs) were prepared by a simple and green method using carrageenan as reducing and capping agent. The as-synthesized carrageenan-AgNPs was demonstrated as an effective duel colorimetric sensing for selective and sensitive recognition of Cu2+ and S2−, which could be used to detect these ions with naked eyes. In addition, the possible sensing mechanism was that Cu2+ ions caused serious aggregation of carrageenan-AgNPs, which led to the color change of carrageenan-AgNPs. AgNPs were etched by S2− forming Ag2S, which played an important role in the determination of S2− ions. Furthermore, it has been successfully applied to the determination of Cu2+ and S2− in tap water and lake water, showing its great potential for the analysis of environmental water samples.

Single-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorporate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses. To this end, we developed scPipe, an R/Bioconductor package that integrates barcode demultiplexing, read alignment, UMI-aware gene-level quantification and quality control of raw sequencing data generated by multiple protocols that include CEL-seq, MARS-seq, Chromium 10X, Drop-seq and Smart-seq. scPipe produces a count matrix that is essential for downstream analysis along with an HTML report that summarises data quality. These results can be used as input for downstream analyses including normalization, visualization and statistical testing. scPipe performs this processing in a few simple R commands, promoting reproducible analysis of single-cell data that is compatible with the emerging suite of open-source scRNA-seq analysis tools available in R/Bioconductor and beyond. The scPipe R package is available for download from https://www.bioconductor.org/packages/scPipe.

To evaluate the prognostic value of OCT4 expression and vasculogenic mimicry (VM) in human breast cancer, we examined OCT4 expression and VM formation using immunohistochemistry and CD31/PAS (periodic acid-schiff) double staining on 90 breast cancer specimens. All patients were followed up for five–149 months following surgery. Survival curves were generated using Kaplan-Meier method. Multivariate analysis was performed using Cox regression model to assess the prognostic values. Results showed positive correlation between OCT4 expression and VM formation (p < 0.05). Both OCT4 expression and VM were also positively correlated with lymph node metastasis, higher histological grade, and Nottingham prognostic index (p < 0.05). Patients with OCT4 expression or VM formation exhibited poorer overall survival (OS) and disease-free survival (DFS) than OCT4-negative or VM-negative patients (p < 0.05). OCT4-positive/VM-positive patients also had the worst OS and DFS (p < 0.05). In multivariate survival analysis, VM, Nottingham prognostic index (NPI), and Her2 were independent prognostic factors related to OS and OCT4-positive/VM-positive patients, whereas NPI and Her2 were independent predictors of DFS. These results suggest that a combined OCT4 expression/VM could improve the prognostic judgment for breast cancer patients.

The transcription factor Runx2 has been reported to promote epithelial-mesenchymal transition (EMT) in many tumors. Vasculogenic mimicry (VM) is described as the mimicry of endothelial cells by tumor cells to form microvascular tubes in aggressive tumors. Galectin-3 has been reported to regulate cell invasion, migration, and VM formation; it could be regulated by Runx2. However, the relationship between Runx2, Galectin-3, EMT, and VM has not been studied in hepatocellular carcinoma (HCC). We examined Runx2 expression in 89 human HCC samples and found Runx2 expression was associated with VM. Clinical-pathological data analysis revealed that Runx2 expression was associated with a shorter survival period. Overexpression of Runx2 promoted EMT and enhanced cell migration, invasion, and VM formation in HepG2 cells. Conversely, the downregulation of Runx2 inhibited EMT and reduced cell invasion, migration, and VM formation in SMMC7721. Galectin-3 expression declined following the downregulation of Runx2 in HepG2 cells, and increased in SMMC7721 cells after Runx2 knockdown. We consistently demonstrated that the downregulation of LGALS3 in HepG2-Runx2 cells reduced cell migration; invasion and VM formation; while upregulation of LGALS3 in SMMC7721-shRunx2 cells enhanced cell migration, invasion, and VM formation. The results indicate that Runx2 could promote EMT and VM formation in HCC and Galectin-3 might have some function in this process.