The incredible shrinking world of DNA microarrays

Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing

Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.

Single-Step Printable Hydrogel Microarray Integrating Long-Chain DNA for the Discriminative and Size-Specific Sensing of Nucleic Acids

A simple approach to fabricating hydrogel-based DNA microarrays is reported by physically entrapping the rolling circle amplification (RCA) product inside printable in situ gelling hydrazone cross-linked poly(oligoethylene glycol methacrylate) hydrogels. The hydrogel-printed RCA microarray facilitates improved RCA immobilization (>65% even after vigorous washing) and resistance to denaturation relative to RCA-only printed microarrays in addition to size-discriminative sensing of DNA probes (herein, 27 or fewer nucleotides) depending on the internal porosity of the hydrogel. Furthermore, the high number of sequence repeats in the concatemeric RCA product enables high-sensitivity detection of complementary DNA probes without the need for signal amplification, with signal/noise ratios of 10 or more achieved over a short 30 min assay time followed by minimal washing. The inherent antifouling properties of the hydrogel enable discriminative hybridization in complex biological samples, particularly for short (∼10 nt) oligonucleotides whose hybridization in other assays tends to be transient and of low affinity. The scalable manufacturability and efficient performance of these hydrogel-printed RCA microarrays thus offer potential for rapid, parallel, and inexpensive sensing of short DNA/RNA biomarkers and ligands, a critical current challenge in diagnostic and affinity screening assays.

Non-coding RNAs as cell wall regulators in Saccharomyces cerevisiae

The cell wall of Saccharomyces cerevisiae is an extracellular organelle crucial for preserving its cellular integrity and detecting environmental cues. The cell wall is composed of mannoproteins attached to a polysaccharide network and is continuously remodelled as cells undergo cell division, mating, gametogenesis or adapt to stressors. This makes yeast an excellent model to study the regulation of genes important for cell wall formation and maintenance. Given that certain yeast strains are pathogenic, a better understanding of their life cycle is of clinical relevance. This is why transcriptional regulatory mechanisms governing genes involved in cell wall biogenesis or maintenance have been the focus of numerous studies. However, little is known about the roles of long non-coding RNAs (lncRNAs), a class of transcripts that are thought to possess little or no protein coding potential, in controlling the expression of cell wall-related genes. This review outlines currently known mechanisms of lncRNA-mediated regulation of gene expression in S. cerevisiae and describes examples of lncRNA-regulated genes encoding cell wall proteins. We suggest that the association of currently annotated lncRNAs with the coding sequences and/or promoters of cell wall-related genes highlights a potential role for lncRNAs as important regulators of the yeast cell wall structure.

PNME - A gene-gene parallel network module extraction method

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.

It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.

SPP1 and AGER as potential prognostic biomarkers for lung adenocarcinoma

Overdue treatment and prognostic evaluation lead to low survival rates in patients with lung adenocarcinoma (LUAD). To date, effective biomarkers for prognosis are still required. The aim of the present study was to screen differentially expressed genes (DEGs) as biomarkers for prognostic evaluation of LUAD. DEGs in tumor and normal samples were identified and analyzed for Kyoto Encyclopedia of Genes and Genomes/Gene Ontology functional enrichments. The common genes that are up and downregulated were selected for prognostic analysis using RNAseq data in The Cancer Genome Atlas. Differential expression analysis was performed with 164 samples in GSE10072 and GSE7670 datasets. A total of 484 DEGs that were present in GSE10072 and GSE7670 datasets were screened, including secreted phosphoprotein 1 (SPP1) that was highly expressed and DEGs ficolin 3, advanced glycosylation end-product specific receptor (AGER), transmembrane protein 100 that were lowly expressed in tumor tissues. These four key genes were subsequently verified using an independent dataset, GSE19804. The gene expression model was consistent with GSE10072 and GSE7670 datasets. The dysregulation of highly expressed SPP1 and lowly expressed AGER significantly reduced the median survival time of patients with LUAD. These findings suggest that SPP1 and AGER are risk factors for LUAD, and these two genes may be utilized in the prognostic evaluation of patients with LUAD. Additionally, the key genes and functional enrichments may provide a reference for investigating the molecular expression mechanisms underlying LUAD.

RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling

The development of genome-wide gene expression profiling technologies over the past two decades has produced great opportunity for researchers to explore the transcriptome and to better understand biological systems and their perturbation. In this chapter we provide an overview of microarray and massively parallel sequencing technologies and their application to gene expression analysis. We discuss factors that impact expression data generation and analysis that which should be considered in the application of these technology platforms. We further present the results of a simple illustration study to highlight performance similarities and differences in expression profiling of protein-coding mRNAs with each platform. Based on technical and analytical differences between the two platforms, reports in the literature comparing arrays and RNA-Seq for gene expression, and our own example study and experience, we provide recommendations for platform selection for gene expression studies.

The Statistics and Mathematics of High Dimension Low Sample Size Asymptotics

The aim of this paper is to establish several deep theoretical properties of principal component analysis for multiple-component spike covariance models. Our new results reveal an asymptotic conical structure in critical sample eigendirections under the spike models with distinguishable (or indistinguishable) eigenvalues, when the sample size and/or the number of variables (or dimension) tend to infinity. The consistency of the sample eigenvectors relative to their population counterparts is determined by the ratio between the dimension and the product of the sample size with the spike size. When this ratio converges to a nonzero constant, the sample eigenvector converges to a cone, with a certain angle to its corresponding population eigenvector. In the High Dimension, Low Sample Size case, the angle between the sample eigenvector and its population counterpart converges to a limiting distribution. Several generalizations of the multi-spike covariance models are also explored, and additional theoretical results are presented.

Review on porcine endogenous retrovirus detection assays--impact on quality and safety of xenotransplants

Xenotransplantation of porcine organs, tissues, and cells inherits a risk for xenozoonotic infections. Viable tissues and cells intended for transplantation have to be considered as potentially contaminated non-sterile products. The demands on microbial testing, based on the regulatory requirements, are often challenging due to a restricted shelf life or the complexity of the product itself. In Europe, the regulatory framework for xenogeneic cell therapy is based on the advanced therapy medicinal products (ATMP) regulation (2007), the EMA CHMP Guideline on xenogeneic cell-based medicinal products (2009), as well as the WHO and Council of Europe recommendations. In the USA, FDA guidance for industry (2003) regulates the use of xenotransplants. To comply with the regulations, validated test methods need to be established that reveal the microbial status of a transplant within its given shelf life, complemented by strictly defined action alert limits and supported by breeding in specific pathogen-free (SPF) facilities. In this review, we focus on assays for the detection of the porcine endogenous retroviruses PERV-A/-B/-C, which exhibit highly polymorphic proviral loci in pig genomes. PERVs are transmitted vertically and cannot be completely eliminated by breeding or gene knock out technology. PERVs entail a public health concern that will persist even if no evidence of PERV infection of xenotransplant recipients in vivo has been revealed yet. Nevertheless, infectious risks must be minimized by full assessment of pigs as donors by combining different molecular screening assays for sensitive and specific detection as well as a functional analysis of the infectivity of PERV including an adequate monitoring of recipients.

Drop drying on surfaces determines chemical reactivity - the specific case of immobilization of oligonucleotides on microarrays

BACKGROUND

Drop drying is a key factor in a wide range of technical applications, including spotted microarrays. The applied nL liquid volume provides specific reaction conditions for the immobilization of probe molecules to a chemically modified surface.

RESULTS

We investigated the influence of nL and μL liquid drop volumes on the process of probe immobilization and compare the results obtained to the situation in liquid solution. In our data, we observe a strong relationship between drop drying effects on immobilization and surface chemistry. In this work, we present results on the immobilization of dye labeled 20mer oligonucleotides with and without an activating 5'-aminoheptyl linker onto a 2D epoxysilane and a 3D NHS activated hydrogel surface.

CONCLUSIONS

Our experiments identified two basic processes determining immobilization. First, the rate of drop drying that depends on the drop volume and the ambient relative humidity. Oligonucleotides in a dried spot react unspecifically with the surface and long reaction times are needed. 3D hydrogel surfaces allow for immobilization in a liquid environment under diffusive conditions. Here, oligonucleotide immobilization is much faster and a specific reaction with the reactive linker group is observed. Second, the effect of increasing probe concentration as a result of drop drying. On a 3D hydrogel, the increasing concentration of probe molecules in nL spotting volumes accelerates immobilization dramatically. In case of μL volumes, immobilization depends on whether the drop is allowed to dry completely. At non-drying conditions, very limited immobilization is observed due to the low oligonucleotide concentration used in microarray spotting solutions. The results of our study provide a general guideline for microarray assay development. They allow for the initial definition and further optimization of reaction conditions for the immobilization of oligonucleotides and other probe molecule classes to different surfaces in dependence of the applied spotting and reaction volume.

Transcript profiling in Arabidopsis with genome tiling microarrays

Microarray technology is at present a standardized workflow for genome-wide expression analysis. Whole-genome tiling microarrays have emerged as an important platform for flexible and comprehensive expression profiling. In this chapter we describe a detailed standardized workflow for experiments assessing the transcriptome of Arabidopsis using tiling arrays and provide useful hints for critical steps from experimental design to data analysis. Although the protocol is optimized for AGRONOMICS1 arrays, it can readily be adapted to other tiling arrays. AGRONOMICS1 is the first platform that enables strand-specific expression analysis of the Arabidopsis genome with a single array. Moreover, it includes all perfect match probes from the original ATH1 array, allowing readily integration with the large existing ATH1 knowledge base. This workflow is designed for the analysis of raw data for any number of samples and it does not pose any particular hardware requirements.

Network-based gene expression biomarkers for cold and heat patterns of rheumatoid arthritis in traditional chinese medicine

In Traditional Chinese Medicine (TCM), patients with Rheumatoid Arthritis (RA) can be classified into two main patterns: cold-pattern and heat-pattern. This paper identified the network-based gene expression biomarkers for both cold- and heat-patterns of RA. Gene expression profilings of CD4+ T cells from cold-pattern RA patients, heat-pattern RA patients, and healthy volunteers were obtained using microarray. The differentially expressed genes and related networks were explored using DAVID, GeneSpring software, and the protein-protein interactions (PPI) method. EIF4A2, CCNT1, and IL7R, which were related to the up-regulation of cell proliferation and the Jak-STAT cascade, were significant gene biomarkers of the TCM cold pattern of RA. PRKAA1, HSPA8, and LSM6, which were related to fatty acid metabolism and the I-κB kinase/NF-κB cascade, were significant biomarkers of the TCM heat-pattern of RA. The network-based gene expression biomarkers for the TCM cold- and heat-patterns may be helpful for the further stratification of RA patients when deciding on interventions or clinical trials.

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis

Background

The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals.

Methodology/Principal Findings

Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients.

Conclusions

In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

Integrated bio-inorganic hybrid systems for nano-forensics

This tutorial review describes a new class of data processing system that applies information theory at the molecular level. We also summarize the recent multidisciplinary advances in biotechnology and nanotechnology that have facilitated the development of reliable nano-level code systems. After a brief introduction of information theory, we present possible ways to adapt this concept to the molecular world. This review explains the requirements and solutions for each step necessary to apply a nano code system to real products. Finally, we introduce a designed nano code system for agricultural products as one example of the many possible applications for nano codes.

Statistical expression deconvolution from mixed tissue samples

MOTIVATION

Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied.

RESULTS

We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental datasets found in the National Center for Biotechnology Information Gene Expression Omnibus repository.

AVAILABILITY

R code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml.

Collection of epithelial cells from rodent mammary gland via laser capture microdissection yielding high-quality RNA suitable for microarray analysis

Laser capture microdissection (LCM) enables collection of cell populations highly enriched for specific cell types that have the potential of yielding critical information about physiological and pathophysiological processes. One use of cells collected by LCM is for gene expression profiling. Samples intended for transcript analyses should be of the highest quality possible. RNA degradation is an ever-present concern in molecular biological assays, and LCM is no exception. This paper identifies issues related to preparation, collection, and processing in a lipid-rich tissue, rodent mammary gland, in which the epithelial to stromal cell ratio is low and the stromal component is primarily adipocytes, a situation that presents numerous technical challenges for high-quality RNA isolation. Our goal was to improve the procedure so that a greater probe set present call rate would be obtained when isolated RNA was evaluated using Affymetrix microarrays. The results showed that the quality of RNA isolated from epithelial cells of both mammary gland and mammary adenocarcinomas was high with a probe set present call rate of 65% and a high signal-to-noise ratio.

Methylation analysis by DNA immunoprecipitation

DNA methylation regulates gene expression primarily through modification of chromatin structure. Global methylation studies have revealed biologically relevant patterns of DNA methylation in the human genome affecting sequences such as gene promoters, gene bodies, and repetitive elements. Disruption of normal methylation patterns and subsequent gene expression changes have been observed in several diseases especially in human cancers. Immunoprecipitation (IP)-based methods to evaluate methylation status of DNA have been instrumental in such genome-wide methylation studies. This review describes techniques commonly used to identify and quantify methylated DNA with emphasis on IP based platforms. In an effort to consolidate the wealth of information and highlight critical aspects of methylated DNA analysis, sample considerations, experimental and bioinformatic approaches for analyzing genome-wide methylation profiles, and the benefit of integrating DNA methylation data with complementary dimensions of genomic data are discussed.