To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.

Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.

Advances in mass spectrometry enabled high throughput profiling of lipids but differential analysis and biological interpretation of lipidomics datasets remains challenging. To overcome this barrier, we present LipidSuite, an end-to-end differential lipidomics data analysis server. LipidSuite offers a step-by-step workflow for preprocessing, exploration, differential analysis and enrichment analysis of untargeted and targeted lipidomics. Three lipidomics data formats are accepted for upload: mwTab file from Metabolomics Workbench, Skyline CSV Export, and a numerical matrix. Experimental variables to be used in analysis are uploaded in a separate file. Conventional lipid names are automatically parsed to enable lipid class and chain length analyses. Users can interactively explore data, choose subsets based on sample types or lipid classes or characteristics, and conduct univariate, multivariate and unsupervised analyses. For complex experimental designs and clinical cohorts, LipidSuite offers confounding variables adjustment. Finally, data tables and plots can be both interactively viewed or downloaded for publication or reports. Overall, we anticipate this free, user-friendly webserver to facilitate differential lipidomics data analysis and re-analysis, and fully harness biological interpretation from lipidomics datasets. LipidSuite is freely available at http://suite.lipidr.org.

Actinic keratosis (AK) is a premalignant lesion, common on severely photodamaged skin, that can progress over time to cutaneous squamous cell carcinoma (SCC). A high bacterial load of Staphylococcus aureus is associated with AK and SCC, but it is unknown whether this has a direct impact on skin cancer development. To determine whether S. aureus can have cancer-promoting effects on skin cells, we performed RNA sequencing and shotgun proteomics on primary human keratinocytes after challenge with sterile culture supernatant (‘secretome’) from four S. aureus clinical strains isolated from AK and SCC. Secretomes of two of the S. aureus strains induced keratinocytes to overexpress biomarkers associated with skin carcinogenesis and upregulated the expression of enzymes linked to reduced skin barrier function. Further, these strains induced oxidative stress markers and all secretomes downregulated DNA repair mechanisms. Subsequent experiments on an expanded set of lesion-associated S. aureus strains confirmed that exposure to their secretomes led to increased oxidative stress and DNA damage in primary human keratinocytes. A significant correlation between the concentration of S. aureus phenol soluble modulin toxins in secretome and the secretome-induced level of oxidative stress and genotoxicity in keratinocytes was observed. Taken together, these data demonstrate that secreted compounds from lesion-associated clinical isolates of S. aureus can have cancer-promoting effects in keratinocytes that may be relevant to skin oncogenesis.

Cholangiocarcinoma (CCA) is a primary malignant tumor of the epithelial lining of biliary track associated with endemic Opisthorchis viverrini (Ov) infection in northeastern Thailand. Ov-associated periductal fibrosis (PDF) is the precancerous lesion for CCA, and can be detected by ultrasonography (US) to facilitate early detection. However, US cannot be used to distinguish PDF from cancer. Therefore, the objective of this study was to discover and qualify potential urine biomarkers for CCA detection in at-risk population. Biomarker discovery was conducted on pooled urine samples, 42 patients per group, with PDF or normal bile duct confirmed by ultrasound. After depletion of high abundance proteins, 338 urinary proteins were identified from the 3 samples (normal-US, PDF-US, CCA). Based on fold change and literature review, 70 candidate proteins were selected for qualification by multiple reaction monitoring mass spectrometry (MRM-MS) in 90 individual urine samples, 30 per group. An orthogonal signal correction projection to latent structures discriminant analysis (O-PLS-DA) multivariate model constructed from the 70 candidate biomarkers significantly discriminated CCA from normal and PDF groups (P = 0.003). As an independent validation, the expression of 3 candidate proteins was confirmed by immunohistochemistry in CCA tissues: Lysosome associated membrane glycoprotein 1 (LAMP1), lysosome associated membrane glycoprotein 2 (LAMP2) and cadherin-related family member 2 (CDHR2). Further evaluation of these candidate biomarkers in a larger cohort is needed to support their applicability in a clinical setting for screening and monitoring early CCA and for CCA surveillance.