A new normalization for Nanostring nCounter gene expression data

Challenges and opportunities in processing NanoString nCounter data

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Ketogenic diet modifies ribosomal protein dysregulation in KMT2D Kabuki syndrome

BACKGROUND

Kabuki syndrome (KS) is a genetic disorder caused by DNA mutations in KMT2D, a lysine methyltransferase that methylates histones and other proteins, and therefore modifies chromatin structure and subsequent gene expression. Ketones, derived from the ketogenic diet, are histone deacetylase inhibitors that can 'open' chromatin and encourage gene expression. Preclinical studies have shown that the ketogenic diet rescues hippocampal memory neurogenesis in mice with KS via the epigenetic effects of ketones.

METHODS

Single-cell RNA sequencing and mass spectrometry-based proteomics were used to explore molecular mechanisms of disease in individuals with KS (n = 4) versus controls (n = 4).

FINDINGS

Pathway enrichment analysis indicated that loss of function mutations in KMT2D are associated with ribosomal protein dysregulation at an RNA and protein level in individuals with KS (FDR <0.05). Cellular proteomics also identified immune dysregulation and increased abundance of other lysine modification and histone binding proteins, representing a potential compensatory mechanism. A 12-year-old boy with KS, suffering from recurrent episodes of cognitive decline, exhibited improved cognitive function and neuropsychological assessment performance after 12 months on the ketogenic diet, with concomitant improvement in transcriptomic ribosomal protein dysregulation.

INTERPRETATION

Our data reveals that lysine methyltransferase deficiency is associated with ribosomal protein dysfunction, with secondary immune dysregulation. Diet and the production of bioactive molecules such as ketone bodies serve as a significant environmental factor that can induce epigenetic changes and improve clinical outcomes. Integrating transcriptomic, proteomic, and clinical data can define mechanisms of disease and treatment effects in individuals with neurodevelopmental disorders.

FUNDING

This study was supported by the Dale NHMRC Investigator Grant (APP1193648) (R.D), Petre Foundation (R.D), and The Sydney Children's Hospital Foundation/Kids Research Early and Mid-Career Researcher Grant (E.T).

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Digital spatial profiling identifies molecular changes involved in development of colitis-associated colorectal cancer

Objective

Chronic colonic inflammation seen in inflammatory bowel disease (IBD) is a risk factor for colorectal cancer (CRC). Colitis-associated cancers (CAC) are molecularly different from sporadic CRC. This study aimed to evaluate spatially defined molecular changes associated with neoplastic progression to identify mechanisms of action and potential biomarkers for prognostication.

Design

IBD patients who had undergone colectomy for treatment of their IBD or dysplasia were identified from an institutional database. Formalin-fixed paraffin embedded samples from areas of normal, inflamed, dysplastic and adenocarcinoma tissue were identified for digital spatial profiling using the Nanostring GeoMx™ Cancer Transcriptome Atlas. RNA expression and quantification of 1812 genes was measured and analysed in a spatial context to compare differences in gene expression.

Results

Sixteen patients were included, nine patients had CAC, two had dysplasia only and five had colitis only. Significant, step-wise differences in gene expression were seen between tissue types, mainly involving progressive over-expression of collagen genes associated with stromal remodelling. Similarly, MYC over-expression was associated with neoplastic progression. Comparison of normal and inflamed tissue from patients who progressed to those who did not also showed significant differences in immune-related genes, including under-expression of thte chemokines CCL18, CCL25 and IL-R7, as well as CD3, CD6 and lysozyme. The known oncogene CD24 was significantly overexpressed.

Conclusion

Both tissue types and patient groups are molecularly distinguishable on the basis of their gene expression patterns. Further prospective work is necessary to confirm these differences and establish their clinical significance and potential utility as biomarkers.

nf-core/nanostring: a pipeline for reproducible NanoString nCounter analysis

MOTIVATION

The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures.

RESULTS

Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS.

AVAILABILITY AND IMPLEMENTATION

The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.

Effects of Mycobacterium vaccae NCTC 11659 and Lipopolysaccharide Challenge on Polarization of Murine BV-2 Microglial Cells

Previous studies have shown that the in vivo administration of soil-derived bacteria with anti-inflammatory and immunoregulatory properties, such as Mycobacterium vaccae NCTC 11659, can prevent a stress-induced shift toward an inflammatory M1 microglial immunophenotype and microglial priming in the central nervous system (CNS). It remains unclear whether M. vaccae NCTC 11659 can act directly on microglia to mediate these effects. This study was designed to determine the effects of M. vaccae NCTC 11659 on the polarization of naïve BV-2 cells, a murine microglial cell line, and BV-2 cells subsequently challenged with lipopolysaccharide (LPS). Briefly, murine BV-2 cells were exposed to 100 µg/mL whole-cell, heat-killed M. vaccae NCTC 11659 or sterile borate-buffered saline (BBS) vehicle, followed, 24 h later, by exposure to 0.250 µg/mL LPS (Escherichia coli 0111: B4; n = 3) in cell culture media vehicle (CMV) or a CMV control condition. Twenty-four hours after the LPS or CMV challenge, cells were harvested to isolate total RNA. An analysis using the NanoString platform revealed that, by itself, M. vaccae NCTC 11659 had an "adjuvant-like" effect, while exposure to LPS increased the expression of mRNAs encoding proinflammatory cytokines, chemokine ligands, the C3 component of complement, and components of inflammasome signaling such as Nlrp3. Among LPS-challenged cells, M. vaccae NCTC 11659 had limited effects on differential gene expression using a threshold of 1.5-fold change. A subset of genes was assessed using real-time reverse transcription polymerase chain reaction (real-time RT-PCR), including Arg1, Ccl2, Il1b, Il6, Nlrp3, and Tnf. Based on the analysis using real-time RT-PCR, M. vaccae NCTC 11659 by itself again induced "adjuvant-like" effects, increasing the expression of Il1b, Il6, and Tnf while decreasing the expression of Arg1. LPS by itself increased the expression of Ccl2, Il1b, Il6, Nlrp3, and Tnf while decreasing the expression of Arg1. Among LPS-challenged cells, M. vaccae NCTC 11659 enhanced LPS-induced increases in the expression of Nlrp3 and Tnf, consistent with microglial priming. In contrast, among LPS-challenged cells, although M. vaccae NCTC 11659 did not fully prevent the effects of LPS relative to vehicle-treated control conditions, it increased Arg1 mRNA expression, suggesting that M. vaccae NCTC 11659 induces an atypical microglial phenotype. Thus, M. vaccae NCTC 11659 acutely (within 48 h) induced immune-activating and microglial-priming effects when applied directly to murine BV-2 microglial cells, in contrast to its long-term anti-inflammatory and immunoregulatory effects observed on the CNS when whole-cell, heat-killed preparations of M. vaccae NCTC 11659 were given peripherally in vivo.

Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells

Skin-resident CD8+ T cells include distinct interferon-γ-producing [tissue-resident memory T type 1 (TRM1)] and interleukin-17 (IL-17)-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations use common mechanisms to establish tissue residence is unknown. In this work, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in the skin. TRM1 cells depend on a T-bet-Hobit-IL-15 axis, whereas TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, by targeting this pathway, skin TRM17 cells can be ablated without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.

Distinct neuroinflammatory signatures exist across genetic and sporadic amyotrophic lateral sclerosis cohorts

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of upper and lower motor neurons. ALS is on a pathogenetic disease spectrum with frontotemporal dementia, referred to as ALS-frontotemporal spectrum disorder (ALS-FTSD). For mutations associated with ALS-FTSD, such as the C9orf72 hexanucleotide repeat expansion, the molecular factors associated with heterogeneity along this spectrum require further characterization. Here, using a targeted NanoString molecular barcoding approach, we interrogate neuroinflammatory dysregulation and heterogeneity at the level of gene expression in post-mortem motor cortex tissue from a cohort of clinically heterogeneous C9-ALS-FTSD cases. We identified 20 dysregulated genes in C9-ALS-FTSD, with enrichment of microglial and inflammatory response gene sets. Two genes with significant correlations to available clinical metrics were selected for validation: FKBP5, a correlate of cognitive function, and brain-derived neurotrophic factor (BDNF), a correlate of disease duration. FKBP5 and its signalling partner, NF-κB, appeared to have a cell type-specific staining distribution, with activated (i.e. nuclear) NF-κB immunoreactivity in C9-ALS-FTSD. Expression of BDNF, a correlate of disease duration, was confirmed to be higher in individuals with long compared to short disease duration using BaseScope™ in situ hybridization. Our analyses also revealed two distinct neuroinflammatory panel signatures (NPS), NPS1 and NPS2, delineated by the direction of expression of proinflammatory, axonal transport and synaptic signalling pathways. We compared NPS between C9-ALS-FTSD cases and those from sporadic ALS and SOD1-ALS cohorts and identified NPS1 and NPS2 across all cohorts. Moreover, a subset of NPS was also able to separate publicly available RNA sequencing data from independent C9-ALS and sporadic ALS cohorts into two inflammatory subgroups. Importantly, NPS subgroups did not clearly segregate with available demographic, genetic, clinical or pathological features, highlighting the value of molecular stratification in clinical trials for inflammatory subgroup identification. Our findings thus underscore the importance of tailoring therapeutic approaches based on distinct molecular signatures that exist between and within ALS-FTSD cohorts.

Associations Between Epigenetic Age Acceleration and microRNA Expression Among U.S. Firefighters

Epigenetic changes may be biomarkers of health. Epigenetic age acceleration (EAA), the discrepancy between epigenetic age measured via epigenetic clocks and chronological age, is associated with morbidity and mortality. However, the intersection of epigenetic clocks with microRNAs (miRNAs) and corresponding miRNA-based health implications have not been evaluated. We analyzed DNA methylation and miRNA profiles from blood sampled among 332 individuals enrolled across 2 U.S.-based firefighter occupational studies (2015-2018 and 2018-2020). We considered 7 measures of EAA in leukocytes (PhenoAge, GrimAge, Horvath, skin-blood, and Hannum epigenetic clocks, and extrinsic and intrinsic epigenetic age acceleration). We identified miRNAs associated with EAA using individual linear regression models, adjusted for sex, race/ethnicity, chronological age, and cell type estimates, and investigated downstream effects of associated miRNAs with miRNA enrichment analyses and genomic annotations. On average, participants were 38 years old, 88% male, and 75% non-Hispanic white. We identified 183 of 798 miRNAs associated with EAA (FDR q < 0.05); 126 with PhenoAge, 59 with GrimAge, 1 with Horvath, and 1 with the skin-blood clock. Among miRNAs associated with Horvath and GrimAge, there were 61 significantly enriched disease annotations including age-related metabolic and cardiovascular conditions and several cancers. Enriched pathways included those related to proteins and protein modification. We identified miRNAs associated with EAA of multiple epigenetic clocks. PhenoAge had more associations with individual miRNAs, but GrimAge and Horvath had greater implications for miRNA-associated pathways. Understanding the relationship between these epigenetic markers could contribute to our understanding of the molecular underpinnings of aging and aging-related diseases.

Differential expression of genes controlling lymphocyte differentiation and migration in two distinct endotypes of type 1 diabetes

AIMS

Morphological studies of pancreas samples obtained from young people with recent-onset type 1 diabetes have revealed distinct patterns of immune cell infiltration of the pancreatic islets suggestive of two age-associated type 1 diabetes endotypes that differ by inflammatory responses and rates of disease progression. The objective of this study was to investigate whether these proposed disease endotypes are associated with pathological differences in immune cell activation and cytokine secretion by applying multiplexed gene expression analysis to pancreatic tissue from recent-onset type 1 diabetes cases.

METHODS

RNA was extracted from samples of fixed, paraffin-embedded pancreas tissue from type 1 diabetes cases characterised by endotype and from controls without diabetes. Expression levels of 750 genes associated with autoimmune inflammation were determined by hybridisation to a panel of capture and reporter probes and these were counted as a measure of gene expression. Normalised counts were analysed for differences in expression between 29 type 1 diabetes cases and 7 controls without diabetes, and between the two type 1 diabetes endotypes.

RESULTS

Ten inflammation-associated genes, including INS, were significantly under-expressed in both endotypes and 48 genes were more highly expressed. A different set of 13 genes associated with the development, activation and migration of lymphocytes was uniquely overexpressed in the pancreas of people developing diabetes at younger age.

CONCLUSIONS

The results provide evidence that histologically defined type 1 diabetes endotypes differ in their immunopathology and identify inflammatory pathways specifically involved in disease developing at a young age, essential for a better understanding of disease heterogeneity.

malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.

Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Leucocyte-derived micro-RNAs as candidate biomarkers in Brugada syndrome

AIMS

Risk stratification for sudden cardiac death in patients with Brugada syndrome remains a major challenge. Contemporary risk prediction models have only modest predictive value. The aim of this study was to assess the role of micro-RNAs from peripheral blood as candidate biomarkers in Brugada syndrome.

METHODS AND RESULTS

In this prospective study, Brugada patients and unaffected control individuals were enrolled for analysis of leucocyte-derived microRNAs (miRNAs) levels. Expression levels of 798 different circulating miRNAs were analysed on the NanoString® nCounter platform. All results were cross-validated by using a quantitative polymerase chain reaction. Micro-RNA expression levels of Brugada patients were compared with clinical data. A total of 21 definite Brugada patients (38% with a history of ventricular arrhythmia or cardiac arrest) and 30 unaffected control individuals were included in the study. Micro-RNA analysis showed a distinct expression profile in Brugada patients with 42 differentially expressed markers (38 up-regulated, 4 down-regulated miRNAs). The symptom status of Brugada patients was associated with a distinct miRNA signature. Micro-RNAs 145-5p and 585-3p were significantly up-regulated in symptomatic Brugada patients (P = 0.04). Incorporating miRNAs 145-5p and 585-3p into a multivariable model demonstrated significantly increased symptom prediction (area under the curve = 0.96; 95% confidence interval: 0.88-1.00).

CONCLUSION

Brugada patients display a distinct miRNA expression profile compared with unaffected control individuals. There is also evidence that certain miRNAs (miR-145-5p and miR-585-3p) are associated with the symptom status of Brugada patients. The results suggest the principal utility of leucocyte-derived miRNAs as prognostic biomarkers for Brugada syndrome.

Mena regulates nesprin-2 to control actin-nuclear lamina associations, trans-nuclear membrane signalling and gene expression

Interactions between cells and the extracellular matrix, mediated by integrin adhesion complexes, play key roles in fundamental cellular processes, including the sensing and transduction of mechanical cues. Here, we investigate systems-level changes in the integrin adhesome in patient-derived cutaneous squamous cell carcinoma cells and identify the actin regulatory protein Mena as a key node in the adhesion complex network. Mena is connected within a subnetwork of actin-binding proteins to the LINC complex component nesprin-2, with which it interacts and co-localises at the nuclear envelope. Moreover, Mena potentiates the interactions of nesprin-2 with the actin cytoskeleton and the nuclear lamina. CRISPR-mediated Mena depletion causes altered nuclear morphology, reduces tyrosine phosphorylation of the nuclear membrane protein emerin and downregulates expression of the immunomodulatory gene PTX3 via the recruitment of its enhancer to the nuclear periphery. We uncover an unexpected role for Mena at the nuclear membrane, where it controls nuclear architecture, chromatin repositioning and gene expression. Our findings identify an adhesion protein that regulates gene transcription via direct signalling across the nuclear envelope.

Intratumoral CD8+ T cells with a tissue-resident memory phenotype mediate local immunity and immune checkpoint responses in breast cancer

CD8⁺ tumor-infiltrating lymphocytes with a tissue-resident memory T (T_RM) cell phenotype are associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, the relative contribution of CD8⁺ T_RM cells to anti-tumor immunity and immune checkpoint blockade efficacy in breast cancer remains unknown. Here, we show that intratumoral CD8⁺ T cells in murine mammary tumors transcriptionally resemble those from TNBC patients. Phenotypic and transcriptional studies established two intratumoral sub-populations: one more enriched in markers of terminal exhaustion (T_EX-like) and the other with a bona fide resident phenotype (T_RM-like). Treatment with anti-PD-1 and anti-CTLA-4 therapy resulted in expansion of these intratumoral populations, with the T_RM-like subset displaying significantly enhanced cytotoxic capacity. T_RM-like CD8⁺ T cells could also provide local immune protection against tumor rechallenge and a T_RM gene signature extracted from tumor-free tissue was significantly associated with improved clinical outcomes in TNBC patients treated with checkpoint inhibitors.

Mycobacterium vaccae NCTC 11659, a Soil-Derived Bacterium with Stress Resilience Properties, Modulates the Proinflammatory Effects of LPS in Macrophages

Inflammatory conditions, including allergic asthma and conditions in which chronic low-grade inflammation is a risk factor, such as stress-related psychiatric disorders, are prevalent and are a significant cause of disability worldwide. Novel approaches for the prevention and treatment of these disorders are needed. One approach is the use of immunoregulatory microorganisms, such as Mycobacterium vaccae NCTC 11659, which have anti-inflammatory, immunoregulatory, and stress-resilience properties. However, little is known about how M. vaccae NCTC 11659 affects specific immune cell targets, including monocytes, which can traffic to peripheral organs and the central nervous system and differentiate into monocyte-derived macrophages that, in turn, can drive inflammation and neuroinflammation. In this study, we investigated the effects of M. vaccae NCTC 11659 and subsequent lipopolysaccharide (LPS) challenge on gene expression in human monocyte-derived macrophages. THP-1 monocytes were differentiated into macrophages, exposed to M. vaccae NCTC 11659 (0, 10, 30, 100, 300 µg/mL), then, 24 h later, challenged with LPS (0, 0.5, 2.5, 250 ng/mL), and assessed for gene expression 24 h following challenge with LPS. Exposure to M. vaccae NCTC 11659 prior to challenge with higher concentrations of LPS (250 ng/mL) polarized human monocyte-derived macrophages with decreased IL12A, IL12B, and IL23A expression relative to IL10 and TGFB1 mRNA expression. These data identify human monocyte-derived macrophages as a direct target of M. vaccae NCTC 11659 and support the development of M. vaccae NCTC 11659 as a potential intervention to prevent stress-induced inflammation and neuroinflammation implicated in the etiology and pathophysiology of inflammatory conditions and stress-related psychiatric disorders.

Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Minimal expression of dysferlin prevents development of dysferlinopathy in dysferlin exon 40a knockout mice

Dysferlin is a Ca²⁺-activated lipid binding protein implicated in muscle membrane repair. Recessive variants in DYSF result in dysferlinopathy, a progressive muscular dystrophy. We showed previously that calpain cleavage within a motif encoded by alternatively spliced exon 40a releases a 72 kDa C-terminal minidysferlin recruited to injured sarcolemma. Herein we use CRISPR/Cas9 gene editing to knock out murine Dysf exon 40a, to specifically assess its role in membrane repair and development of dysferlinopathy. We created three Dysf exon 40a knockout (40aKO) mouse lines that each express different levels of dysferlin protein ranging from ~ 90%, ~ 50% and ~ 10-20% levels of wild-type. Histopathological analysis of skeletal muscles from all 12-month-old 40aKO lines showed virtual absence of dystrophic features and normal membrane repair capacity for all three 40aKO lines, as compared with dysferlin-null BLAJ mice. Further, lipidomic and proteomic analyses on 18wk old quadriceps show all three 40aKO lines are spared the profound lipidomic/proteomic imbalance that characterises dysferlin-deficient BLAJ muscles. Collective results indicate that membrane repair does not depend upon calpain cleavage within exon 40a and that ~ 10-20% of WT dysferlin protein expression is sufficient to maintain the muscle lipidome, proteome and membrane repair capacity to crucially prevent development of dysferlinopathy.

Removing unwanted variation from large-scale RNA sequencing data with PRPS

Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.

Easy NanoString nCounter data analysis with the NanoTube

SUMMARY

The NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise.

AVAILABILITY AND IMPLEMENTATION

The NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessing and removing the effect of unwanted technical variations in microbiome data

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how in-silico approaches can minimize their impact. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. Using the novel Removing Unwanted Variations-III-Negative Binomial (RUV-III-NB), we identified several known experimental factors, specifically storage conditions and freeze-thaw cycles, as likely major sources of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performances of different correction methods, including ComBat, ComBat-seq, RUVg, RUVs, and RUV-III-NB. While RUV-III-NB performed consistently robust across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally. Our analyses suggest that a careful consideration of possible technical confounders is critical during experimental design of microbiome studies, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.

Checkpoint inhibitor immunotherapy diminishes oocyte number and quality in mice

Loss of fertility is a major concern for female reproductive-age cancer survivors, since a common side-effect of conventional cytotoxic cancer therapies is permanent damage to the ovary. While immunotherapies are increasingly becoming a standard of care for many cancers-including in the curative setting-their impacts on ovarian function and fertility are unknown. We evaluated the effect of immune checkpoint inhibitors blocking programmed cell death protein ligand 1 and cytotoxic T lymphocyte-associated antigen 4 on the ovary using tumor-bearing and tumor-free mouse models. We find that immune checkpoint inhibition increases immune cell infiltration and tumor necrosis factor-α expression within the ovary, diminishes the ovarian follicular reserve and impairs the ability of oocytes to mature and ovulate. These data demonstrate that immune checkpoint inhibitors have the potential to impair both immediate and future fertility, and studies in women should be prioritized. Additionally, fertility preservation should be strongly considered for women receiving these immunotherapies, and preventative strategies should be investigated in future studies.

AC-PCoA: Adjustment for confounding factors using principal coordinate analysis

Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data.

In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification.

With today’s unprecedented amount of data, researchers are challenged by the need to enhance meaningful signals without the interference of unwanted confounders hidden inside the data. Data visualization is an important step toward exploring and explaining data in order to intuitively identify the dominant patterns. Principal coordinate analysis (PCoA), as a visualization tool, allows flexible ways to define pairwise distances and project the samples into lower dimensions without changing the distances. However, when visualizing large-scale biological datasets, the true patterns are often hindered by unwanted confounding variations, either biologically or technically in origin. To eliminate these confounding factors and recover underlying signals, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, and showed that it significantly outperforms existing methods in visualization through three simulation studies and five real datasets. We further showed that the low-dimensional representations given by AC-PCoA provide promising results in statistical testing, clustering, and classification as well.

Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies

Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.

RUV-III-NB: normalization of single cell RNA-seq data

Normalization of single cell RNA-seq data remains a challenging task. The performance of different methods can vary greatly between datasets when unwanted factors and biology are associated. Most normalization methods also only remove the effects of unwanted variation for the cell embedding but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. We propose RUV-III-NB, a method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. Using pseudo-replicates, RUV-III-NB explicitly takes into account potential association with biology when removing unwanted variation. The method can be used for both UMI or read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using published datasets with different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve DE analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent and is not sensitive to the number of factors assumed to contribute to the unwanted variation.

Dissecting Tissue Compartment-Specific Protein Signatures in Primary and Metastatic Oropharyngeal Squamous Cell Carcinomas

Head and neck squamous cell carcinoma (HNSCC) often presents with locoregional or distant disease, despite multimodal therapeutic approaches, which include surgical resection, chemoradiotherapy, and more recently, immunotherapy for metastatic or recurrent HNSCC. Therapies often target the primary and nodal regional HNSCC sites, and their efficacy at controlling occult distant sites remains poor. While our understanding of the tumor microenvironment conducive to effective therapies is increasing, the biology underpinning locoregional sites remains unclear. Here, we applied targeted spatial proteomic approaches to primary and lymph node metastasis from an oropharyngeal SCC (OPSCC) cohort to understand the expression of proteins within tumors, and stromal compartments of the respective sites in samples of both matched and unmatched patients. In unmatched analyses of n = 43 primary and 11 nodal metastases, our data indicated that tumor cells in nodal metastases had higher levels of Ki-67, PARP, BAD, and cleaved caspase 9, suggesting a role for increased proliferation, DNA repair, and apoptosis within these metastatic cells. Conversely, in matched analyses (n = 7), pro-apoptotic markers BIM and BAD were enriched in the stroma of primary tumors. Univariate, overall survival (OS) analysis indicated CD25 in tumor regions of primary tumors to be associated with reduced survival (HR = 3.3, p = 0.003), while progesterone receptor (PR) was associated with an improved OS (HR = 0.33, p = 0.015). This study highlights the utility of spatial proteomics for delineating the tumor and stromal compartment composition, and utility toward understanding these properties in locoregional metastasis. These findings indicate unique biological properties of lymph node metastases that may elucidate further understanding of distant metastatic in OPSCC.

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Spatial Profiling Identifies Prognostic Features of Response to Adjuvant Therapy in Triple Negative Breast Cancer (TNBC)

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that has few effective treatment options due to its lack of targetable hormone receptors. Whilst the degree of tumour infiltrating lymphocytes (TILs) has been shown to associate with therapy response and prognosis, deeper characterization of the molecular diversity that may mediate chemotherapeutic response is lacking. Here we applied targeted proteomic analysis of both chemotherapy sensitive and resistant TNBC tissue samples by the Nanostring GeoMx Digital Spatial Platform (DSP). By quantifying 68 targets in the tumour and tumour microenvironment (TME) compartments and performing differential expression analysis between responsive and non-responsive tumours, we show that increased ER-alpha expression and decreased 4-1BB and MART1 within the stromal compartments is associated with adjuvant chemotherapy response. Similarly, higher expression of GZMA, STING and fibronectin and lower levels of CD80 were associated with response within tumour compartments. Univariate overall-survival (OS) analysis of stromal proteins supported these findings, with ER-alpha expression (HR=0.19, p=0.0012) associated with better OS while MART1 expression (HR=2.3, p=0.035) was indicative of poorer OS. Proteins within tumour compartments consistent with longer OS included PD-L1 (HR=0.53, p=0.023), FOXP3 (HR=0.5, p=0.026), GITR (HR=0.51, p=0.036), SMA (HR=0.59, p=0.043), while EPCAM (HR=1.7, p=0.045), and CD95 (HR=4.9, p=0.046) expression were associated with shorter OS. Our data provides early insights into the levels of these markers in the TNBC tumour microenvironment, and their association with chemotherapeutic response and patient survival.

Integrated mRNA-miRNA transcriptome analysis of bladder biopsies from patients with bladder pain syndrome identifies signaling alterations contributing to the disease pathogenesis

Background

Interstitial cystitis, or bladder pain syndrome (IC/BPS), is a chronic bladder disorder characterized by lower abdominal pain associated with the urinary bladder and accompanied by urinary frequency and urgency in the absence of identifiable causes. IC/PBS can be separated into the classic Hunner’s ulcerative type and the more prevalent non-ulcerative disease. Our aim was to unravel the biological processes and dysregulated cell signaling pathways leading to the bladder remodeling in non-ulcerative bladder pain syndrome (BPS) by studying the gene expression changes in the patients’ biopsies.

Methods

We performed paired microRNA (miRNA) and mRNA expression profiling in the bladder biopsies of BPS patients with non-Hunner interstitial cystitis phenotype, using comprehensive Next-generation sequencing (NGS) and studied the activated pathways and altered biological processes based on the global gene expression changes. Paired mRNA-miRNA transcriptome analysis delineated the regulatory role of the dysregulated miRNAs by identifying their targets in the disease-induced pathways.

Results

EIF2 Signaling and Regulation of eIF4 and p70S6K Signaling, activated in response to cellular stress, were among the most significantly regulated processes during BPS. Leukotriene Biosynthesis nociceptive pathway, important in inflammatory diseases and neuropathic pain, was also significantly activated. The biological processes identified using Gene Ontology over-representation analysis were clustered into six main functional groups: cell cycle regulation, chemotaxis of immune cells, muscle development, muscle contraction, remodeling of extracellular matrix and peripheral nervous system organization and development. Compared to the Hunner’s ulcerative type IC, activation of the immune pathways was modest in non-ulcerative BPS, limited to neutrophil chemotaxis and IFN-γ-mediated signaling. We identified 62 miRNAs, regulated and abundant in BPS and show that they target the mRNAs implicated in eIF2 signalling pathway.

Conclusions

The bladders of non-ulcerative BPS patients recruited in this study had alterations consistent with a strong cell proliferative response and an up-regulation of smooth muscle contractility, while the contribution of inflammatory processes was modest. Pathway analysis of the integrated mRNA-miRNA NGS dataset pinpointed important regulatory miRNAs whose dysregulation might contribute to the pathogenesis. Observed molecular changes in the peripheral nervous system organization and development indicate the potential role of local bladder innervation in the pain perceived in this type of BPS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-021-00934-0.

Glycoprotein Pathways Altered in Frontotemporal Dementia With Autoimmune Disease

Behavioral variant frontotemporal dementia (bvFTD) is a younger onset form of neurodegeneration initiated in the frontal and/or temporal lobes with a slow clinical onset but rapid progression. bvFTD is highly complex biologically with different pathological signatures and genetic variants that can exhibit a spectrum of overlapping clinical manifestations. Although the role of innate immunity has been extensively investigated in bvFTD, the involvement of adaptive immunity in bvFTD pathogenesis is poorly understood. We analyzed blood serum proteomics to identify proteins that are associated with autoimmune disease in bvFTD. Eleven proteins (increased: ATP5B, CALML5, COLEC11, FCGBP, PLEK, PLXND1; decreased: APOB, ATP8B1, FAM20C, LOXL3, TIMD4) were significantly altered in bvFTD with autoimmune disease compared to those without autoimmune disease. The majority of these proteins were enriched for glycoprotein-associated proteins and pathways, suggesting that the glycome is targeted in bvFTD with autoimmune disease.

Longitudinal evaluation of whole blood miRNA expression in firefighters

BACKGROUND

Dysregulated microRNA (miRNA) expression could provide a mechanism linking firefighter exposure to increased cancer risk.

OBJECTIVE

To determine if changes in longitudinal miRNA expression in firefighters are associated with occupational exposures.

METHODS

Whole blood MiRNA was evaluated in 52 new recruits prior to live-fire training and 20-37 months later. Linear mixed effects models adjusted for age, ethnicity, BMI, and batch effects were used to determine associations separately for all fires and structure fires only between employment duration, cumulative fire-hours and fire-runs, and time since most recent fire with (1) nine a priori and (2) the full array of 799 miRNAs.

RESULTS

For multivariable models including all fires, two a priori miRNAs were associated with employment duration and four with time since most recent fire. For multivariable models restricted to structure fires, three a priori miRNAs were associated with employment duration and one with fire-runs. Additional miRNAs from the full array were associated with employment duration for all fires and/or structure fires. In general, tumor suppressive miRNAs decreased and oncogenic miRNAs increased with exposure.

SIGNIFICANCE

Changes in miRNAs may serve as biomarkers of exposure effects and a mechanism for increased cancer risk in firefighters.

The Ratio of Exhausted to Resident Infiltrating Lymphocytes Is Prognostic for Colorectal Cancer Patient Outcome

Immunotherapy success in colorectal cancer is mainly limited to patients whose tumors exhibit high microsatellite instability (MSI). However, there is variability in treatment outcomes within this group, which is in part driven by the frequency and characteristics of tumor-infiltrating immune cells. Indeed, the presence of specific infiltrating immune-cell subsets has been shown to correlate with immunotherapy response and is in many cases prognostic of treatment outcome. Tumor-infiltrating lymphocytes (TIL) can undergo distinct differentiation programs, acquiring features of tissue-residency or exhaustion, a process during which T cells upregulate inhibitory receptors, such as PD-1, and lose functionality. Although residency and exhaustion programs of CD8⁺ T cells are relatively well studied, these programs have only recently been appreciated in CD4⁺ T cells and remain largely unknown in tumor-infiltrating natural killer (NK) cells. In this study, we used single-cell RNA sequencing (RNA-seq) data to identify signatures of residency and exhaustion in colorectal cancer-infiltrating lymphocytes, including CD8⁺, CD4⁺, and NK cells. We then tested these signatures in independent single-cell data from tumor and normal tissue-infiltrating immune cells. Furthermore, we used versions of these signatures designed for bulk RNA-seq data to explore tumor-intrinsic mutations associated with residency and exhaustion from TCGA data. Finally, using two independent transcriptomic datasets from patients with colon adenocarcinoma, we showed that combinations of these signatures, in particular combinations of NK-cell activity signatures, together with tumor-associated signatures, such as TGFβ signaling, were associated with distinct survival outcomes in patients with colon adenocarcinoma.

A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.

Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.

Nitric oxide-related gene and microRNA expression in peripheral blood in pregnancy vary by self-reported race

Adverse pregnancy outcomes disproportionately affect non-Hispanic (NH) Black patients in the United States. Structural racism has been associated with increased psychosocial distress and inflammation and may trigger oxidative stress. Thus, the nitric oxide (NO) pathway (involved in the regulation of inflammation and oxidative stress) may partly explain the underlying disparities in obstetric outcomes.Cohort study of 154 pregnant patients with high-risk obstetric histories; n = 212 mRNAs and n = 108 microRNAs (miRNAs) in the NO pathway were evaluated in circulating white blood cells. NO pathway mRNA and miRNA transcript counts were compared by self-reported race; NH Black patients were compared with women of other races/ethnicities. Finally, miRNA-mRNA expression levels were correlated.Twenty-two genes (q < 0.10) were differentially expressed in self-identified NH Black individuals. Superoxide dismutase 1 (SOD1), interleukin-8 (IL-8), dynein light chain LC8-type 1 (DYNLL1), glutathione peroxidase 4 (GPX4), and glutathione peroxidase 1 (GPX1) were the five most differentially expressed genes among NH Black patients compared to other patients. There were 63 significantly correlated miRNA-mRNA pairs (q < 0.10) demonstrating potential miRNA regulation of associated target mRNA expression. Ten miRNAs that were identified as members of significant miRNA-mRNA pairs were also differentially expressed among NH Black patients (q < 0.10).These findings support an association between NO pathway and inflammation and infection-related mRNA and miRNA expression in blood drawn during pregnancy and patient race/ethnicity. These findings may reflect key differences in the biology of inflammatory gene dysregulation that occurs in response to the stress of systemic racism and that underlies disparities in pregnancy outcomes.

Protocol for the processing and downstream analysis of phosphoproteomic data with PhosR

Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and functional analysis such as kinase activity inference and signalome construction. Together, PhosR facilitates interpretation and discovery from large-scale phosphoproteomic data sets.

For complete details on the use and execution of this protocol, please refer to Kim et al. (2021).

•

This protocol describes how to run and interpret the results of PhosR

•

PhosR performs filtering, imputation, and normalization of phosphoproteomic data

•

PhosR enables kinase-substrate predictions and signalome construction

•

The step-by-step protocol provides a comprehensive introduction to phospho-data analysis

An approach for normalization and quality control for NanoString RNA expression data

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString's commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

Urinary miRNA profiles discriminate between obstruction-induced bladder dysfunction and healthy controls

Urgency, frequency and incomplete emptying are the troublesome symptoms often shared between benign prostatic obstruction-induced (BLUTD) and neurogenic (NLUTD) lower urinary tract dysfunction. Previously, using bladder biopsies, we suggested a panel of miRNA biomarkers for different functional phenotypes of the bladder. Urine is a good source of circulating miRNAs, but sex- and age-matched controls are important for urinary metabolite comparison. In two groups of healthy subjects (average age 32 and 57 years old, respectively) the total protein and RNA content was very similar between age groups, but the number of secreted extracellular vesicles (uEVs) and expression of several miRNAs were higher in the young healthy male volunteers. Timing of urine collection was not important for these parameters. We also evaluated the suitability of urinary miRNAs for non-invasive diagnosis of bladder outlet obstruction (BOO). A three urinary miRNA signature (miR-10a-5p, miR-301b-3p and miR-363-3p) could discriminate between controls and patients with LUTD (BLUTD and NLUTD). This panel of representative miRNAs can be further explored to develop a non-invasive diagnostic test for BOO. The age-related discrepancy in the urinary miRNA content observed in this study points to the importance of selecting appropriate, age-matched controls.

Mid-pregnancy maternal blood nitric oxide-related gene and miRNA expression are associated with preterm birth

Aim: The nitric oxide (NO) pathway modulates inflammation and may influence birth timing. Patients & methods: Case-control analysis of 136 pregnant women with RNA obtained <28 weeks; n = 212 mRNAs and n = 108 miRNAs in the NO pathway were evaluated. NO-pathway mRNA and miRNA transcript counts in women delivering preterm versus at term were compared, miRNA-mRNA expression levels correlated and prediction models generated. Results: Fourteen genes were differentially expressed in women delivering <37 weeks; 13/14 were also differentially expressed in those delivering <34 weeks (q <0.10) versus term births. Multiple miRNA-mRNA pairs were correlated. Models with gene expression better predicted prematurity than models with only clinical or nongenomic predictors. Conclusion: Maternal blood NO pathway-related mRNA and miRNA expression is associated with prematurity.

Dual targeting of the epigenome via FACT complex and histone deacetylase is a potent treatment strategy for DIPG

Diffuse intrinsic pontine glioma (DIPG) is an aggressive and incurable childhood brain tumor for which new treatments are needed. CBL0137 is an anti-cancer compound developed from quinacrine that targets facilitates chromatin transcription (FACT), a chromatin remodeling complex involved in transcription, replication, and DNA repair. We show that CBL0137 displays profound cytotoxic activity against a panel of patient-derived DIPG cultures by restoring tumor suppressor TP53 and Rb activity. Moreover, in an orthotopic model of DIPG, treatment with CBL0137 significantly extends animal survival. The FACT subunit SPT16 is found to directly interact with H3.3K27M, and treatment with CBL0137 restores both histone H3 acetylation and trimethylation. Combined treatment of CBL0137 with the histone deacetylase inhibitor panobinostat leads to inhibition of the Rb/E2F1 pathway and induction of apoptosis. The combination of CBL0137 and panobinostat significantly prolongs the survival of mice bearing DIPG orthografts, suggesting a potential treatment strategy for DIPG.

PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.

Stable gene expression for normalisation and single-sample scoring

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

Differential responses in placenta and fetal thymus at 12 days post infection elucidate mechanisms of viral level and fetal compromise following PRRSV2 infection

BACKGROUND

A pregnant gilt infected with porcine reproductive and respiratory syndrome virus (PRRSV) can transmit the virus to her fetuses across the maternal-fetal-interface resulting in varying disease outcomes. However, the mechanisms leading to variation in fetal outcome in response to PRRSV infection are not fully understood. Our objective was to assess targeted immune-related gene expression patterns and pathways in the placenta and fetal thymus to elucidate the molecular mechanisms involved in the resistance/tolerance and susceptibility of fetuses to PRRSV2 infection. Fetuses were grouped by preservation status and PRRS viral load (VL): mock infected control (CTRL), no virus detected (UNINF), virus detected in the placenta only with viable (PLCO-VIA) or meconium-stained fetus (PLCO-MEC), low VL with viable (LVL-VIA) or meconium-stained fetus (LVL-MEC), and high VL with viable (HVL-VIA) or meconium-stained fetus (HVL-MEC).

RESULTS

The host immune response was initiated only in fetuses with detectable levels of PRRSV. No differentially expressed genes (DEG) in either the placenta or thymus were identified in UNINF, PLCO-VIA, and PLCO-MEC when compared to CTRL fetuses. Upon fetal infection, a set of core responsive IFN-inducible genes (CXCL10, IFIH1, IFIT1, IFIT3, ISG15, and MX1) were strongly upregulated in both tissues. Gene expression in the thymus is a better differentiator of fetal VL; the strong downregulation of several innate and adaptive immune pathways (e.g., B Cell Development) are indicative of HVL. Gene expression in the placenta may be a better differentiator of fetal demise than the thymus, based-on principle component analysis clustering, gene expression patterns, and dysregulation of the Apoptosis and Ubiquitination pathways.

CONCLUSION

Our data supports the concept that fetal outcome in response to PRRSV2 infection is determined by fetal, and more significantly placental response, which is initiated only after fetal infection. This conceptual model represents a significant step forward in understanding the mechanisms underpinning fetal susceptibility to the virus.

Identifying the Transcriptional Response of Cancer and Inflammation-Related Genes in Lung Cells in Relation to Ambient Air Chemical Mixtures in Houston, Texas

Atmospheric pollution represents a complex mixture of air chemicals that continually interact and transform, making it difficult to accurately evaluate associated toxicity responses representative of real-world exposure. This study leveraged data from a previously published article and reevaluated lung cell transcriptional response induced by outdoor atmospheric pollution mixtures using field-based exposure conditions in the industrialized Houston Ship Channel. The tested hypothesis was that individual and co-occurring chemicals in the atmosphere relate to altered expression of critical genes involved in inflammation and cancer-related processes in lung cells. Human lung cells were exposed at an air-liquid interface to ambient air mixtures for 4 h, with experiments replicated across 5 days. Real-time monitoring of primary and secondary gas-phase pollutants, as well as other atmospheric conditions, was simultaneously conducted. Transcriptional analysis of exposed cells identified critical genes showing differential expression associated with both individual and chemical mixtures. The individual pollutant identified with the largest amount of associated transcriptional response was benzene. Tumor necrosis factor (TNF) and interferon regulatory factor 1 (IRFN1) were identified as key upstream transcription factor regulators of the cellular response to benzene. This study is among the first to measure lung cell transcriptional responses in relation to real-world, gas-phase air mixtures.

Digital Multiplexed Gene Expression Analysis of mRNA and miRNA from Routinely Processed and Stained Cytological Smears: A Proof-of-Principle Study

OBJECTIVE

Although transcriptomic assessments of small samples using high-throughput techniques are usually performed on fresh or frozen tissues, there is a growing demand for those performed on stained cellular specimens already used for diagnostic purposes.

STUDY DESIGN

The possibility of detecting mRNAs and microRNAs (miRNAs) from routinely processed cytological samples using nCounter® technology was explored. Fresh samples from pleural and peritoneal effusions were analyzed using 2 parallel methods: samples were smeared and routinely stained using the May-Grünwald-Giemsa or Diff-Quik® method and mounted using conventional methods, and they were also studied following a snap freezing method, in which samples were maintained at -80°C until use. mRNAs and miRNAs were assessed and compared after total RNA extraction from both routinely processed samples and their matched frozen controls.

RESULTS

A good concordance was found between the gene expression measured in routinely processed samples and their matched frozen controls for the majority of mRNAs and miRNAs tested. However, the standard deviation of low-expressed miRNA was high.

CONCLUSIONS

Although nCounter® technology is a robust method to measure and characterize both mRNAs and miRNAs from routinely processed cytological samples, caution is recommended for the interpretation of low-expressed miRNA.

Serum microRNA is a biomarker for post-operative monitoring in glioma

PURPOSE

A circulating biomarker has potential to provide more accurate information for glioma progression post treatment, however no such biomarker is currently available. We aimed to discover a microRNA serum biomarker for longitudinal monitoring of glioma patients.

METHODS

A prospectively collected cohort of 91 glioma patients and 17 healthy controls underwent pre and post-operative serum miRNA profiling using Nanostring®. Differentially expressed miRNAs were discovered using a machine learning random forest analysis. Candidate miRNAs were then assessed by droplet digital PCR in 11 patients with multiple follow up samples and compared to tumor volume based on magnetic resonance imaging.

RESULTS

A 9-gene miRNA signature was identified that could distinguish between glioma and healthy controls with 99.8% accuracy. Two miRNAs miR-223 and miR-320e, best demonstrated dynamic changes that correlated closely with tumor volume in LGG and GBM respectively. Importantly, miRNA levels did not increase in two cases of pseudo-progression, indicating the potential utility of this test in guiding treatment decisions.

CONCLUSIONS

We identified a highly accurate 9-miRNA signature associated with glioma serum. Additionally, we observed dynamic changes in specific miRNAs correlating with tumor volume over long-term follow up. These results support a large prospective validation study of serum miRNA biomarkers in glioma.

Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets

Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.