Quantitative monitoring of gene expression patterns with a complementary DNA microarray

smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods

Recently, advances in fluorescent in-situ hybridization techniques and in imaging technology have enabled visualization and counting of individual RNA molecules in single cells. This has greatly enhanced the resolution in our understanding of transcriptional processes. Here, we adapt a recently published smiFISH protocol (single-molecule inexpensive fluorescent in-situ hybridization) to whole embryos across a range of arthropod model species, and also to non-embryonic tissues. Using multiple fluorophores with distinct spectra and white light laser confocal imaging, we simultaneously detect and separate single RNAs from up to eight different genes in a whole embryo. We also combine smiFISH with cell membrane immunofluorescence, and present an imaging and analysis pipeline for 3D cell segmentation and single-cell RNA counting in whole blastoderm embryos. Finally, using whole embryo single-cell RNA count data, we propose two alternative single-cell variability measures to the commonly used Fano factor, and compare the capacity of these three measures to address different aspects of single-cell expression variability.

Here, the authors combine single-molecule inexpensive FISH (smiFISH) with cell membrane immunofluorescence and mathematical methods to enable whole embryo segmentation in 3D image stacks and mRNA quantification of multiple genes in each cell of the embryo.

General Method for Post-Synthetic Modification of Oligonucleotides Based on Oxidative Amination of 4-Thio-2'-deoxyuridine

Functionalized oligonucleotides (ONs) are widely applied as target binding molecules for biosensing and regulators for gene expression. Numerous efforts have been focused on developing facile methods for preparing these useful ONs carrying diverse modifications. Herein, we present a general method for postsynthetic modification of ONs via oxidative amination of 4-thio-2'-deoxyuridine (4SdU). 4SdU-containing ON can be derived by both alkyl and aromatic amines. Using this approach, ONs are successfully attached with alkyne/azide, biotin and dansylamide moieties, and these as-prepared ONs possess the expected biorthogonal reactivity, streptavidin affinity and fluorescent property, respectively. Furthermore, we also directly install fluorophores to the ON nucleobase based on oxidative amination of 4SdU, and these fluorophores exhibit distinct luminescence behaviors before and after conjugation. We believe our method will be a versatile strategy for constructing various functionalized ONs used in a wide range of nucleic acid applications.

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Biomarkers of cholestasis

Cholestasis is a major pathological manifestation, often resulting in detrimental liver conditions, which occurs in a variety of indications collectively termed cholestatic liver diseases. The frequent asymptomatic character and complexity of cholestasis, together with the lack of a straightforward biomarker, hampers early detection and treatment of the condition. The 'omics' era, however, has resulted in a plethora of cholestatic indicators, yet a single clinically applicable biomarker for a given cholestatic disease remains missing. The criteria to fulfil as an ideal biomarker as well as the challenging molecular pathways in cholestatic liver diseases advocate for a scenario in which multiple biomarkers, originating from different domains, will be assessed concomitantly. This review gives an overview of classical clinical and novel molecular biomarkers in cholestasis, focusing on their benefits and drawbacks.

On the Consistency between Gene Expression and the Gene Regulatory Network of Corynebacterium glutamicum

Background: Transcriptional regulation of gene expression is crucial for the adaptation and survival of bacteria. Regulatory interactions are commonly modeled as Gene Regulatory Networks (GRNs) derived from experiments such as RNA-seq, microarray and ChIP-seq. While the reconstruction of GRNs is fundamental to decipher cellular function, even GRNs of economically important bacteria such as Corynebacterium glutamicum are incomplete. Materials and Methods: Here, we analyzed the predictive power of GRNs if used as in silico models for gene expression and investigated the consistency of the C. glutamicum GRN with gene expression data from the GEO database. Results: We assessed the consistency of the C. glutamicum GRN using real, as well as simulated, expression data and showed that GRNs alone cannot explain the expression profiles well. Conclusion: Our results suggest that more sophisticated mechanisms such as a combination of transcriptional, post-transcriptional regulation and signaling should be taken into consideration when analyzing and constructing GRNs.

From the Digital Data Revolution toward a Digital Society: Pervasiveness of Artificial Intelligence

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and responsible usage of such data. In this paper, we discuss these developments and the fields that have been particularly effected by the digital revolution. Our discussion is AI-centered showing domain-specific prospects but also intricacies for the method development in artificial intelligence. For instance, we discuss recent breakthroughs in deep learning algorithms and artificial intelligence as well as advances in text mining and natural language processing, e.g., word-embedding methods that enable the processing of large amounts of text data from diverse sources such as governmental reports, blog entries in social media or clinical health records of patients. Furthermore, we discuss the necessity of further improving general artificial intelligence approaches and for utilizing advanced learning paradigms. This leads to arguments for the establishment of statistical artificial intelligence. Finally, we provide an outlook on important aspects of future challenges that are of crucial importance for the development of all fields, including ethical AI and the influence of bias on AI systems. As potential end-point of this development, we define digital society as the asymptotic limiting state of digital economy that emerges from fully connected information and communication technologies enabling the pervasiveness of AI. Overall, our discussion provides a perspective on the elaborate relatedness of digital data and AI systems.

Multi-Omics Characterization and Origin Assessment of Bilateral Lung Adenoid Cystic Carcinoma: A Case Report

Background

Primary adenoid cystic carcinoma (ACC) of the lung, which arises from the bronchial gland and is rare, accounting for only 0.04–0.2% of all primary lung tumors. The genetic profiling of bilateral ACC of unknown primary site and application in postoperative decision-making are less reported.

Case Presentation

A 57-year-old male with a smoking history of over 30 years and multiple nodules in both lungs was present to our department. After assessing the bilateral solid nodules in his Positron Emission Tomography–Computed Tomography (PET/CT) scan, malignant lesions at the left lower lung, right lower lung, and right middle lung are suspected. Sequential selective video-assisted thoracoscopic surgeries (VATS) were performed. A genetic alteration test of 425 cancer-related genes and global gene expression profile of the specimens revealed intrapulmonary metastasis existed. The patient was followed up for three years without recurrence and tissue mutations in liquid biopsy.

Conclusion

We present a way of omics-based multiple pulmonary lesions origin assessment, facilitating post-operative differential diagnosis and treatment decision for difficult cases.

Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks

The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.

CuBlock: a cross-platform normalization method for gene-expression microarrays

Motivation

Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.

Results

We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log₂ transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.

Availability and implementation

CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Transcriptional Regulatory Network of Corynebacterium pseudotuberculosis

Corynebacterium pseudotuberculosis is a Gram-positive, facultative intracellular, pathogenic bacterium that infects several different hosts, yielding serious economic losses in livestock farming. It causes several diseases including oedematous skin disease (OSD) in buffaloes, ulcerative lymphangitis (UL) in horses, and caseous lymphadenitis (CLA) in sheep, goats and humans. Despite its economic and medical-veterinary importance, our understanding concerning this organism’s transcriptional regulatory mechanisms is still limited. Here, we review the state of the art knowledge on transcriptional regulatory mechanisms of this pathogenic species, covering regulatory interactions mediated by two-component systems, transcription factors and sigma factors. Key transcriptional regulatory players involved in virulence and pathogenicity of C. pseudotuberculosis, such as the PhoPR system and DtxR, are in the focus of this review, as these regulators are promising targets for future vaccine design and drug development. We conclude that more experimental studies are needed to further understand the regulatory repertoire of this important zoonotic pathogen, and that regulators are promising targets for future vaccine design and drug development.

A comprehensive overview and critical evaluation of gene regulatory network inference technologies

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.

Temperature and Concentration Dependence of Human Whole Blood and Protein Drying Droplets

The drying of bio-colloidal droplets can be used in many medical and forensic applications. The whole human blood is the most complex bio-colloid system, whereas bovine serum albumin (BSA) is the simplest. This paper focuses on the drying characteristics and the final morphology of these two bio-colloids. The experiments were conducted by varying their initial concentrations, and the solutions were dried under various controlled substrate temperatures using optical and scanning electron microscopy. The droplet parameters (the contact angle, the fluid front, and the first-order image statistics) reveal the drying process's unique features. Interestingly, both BSA and blood drying droplets' contact angle measurements show evidence of a concentration-driven transition as the behavior changes from non-monotonic to monotonic decrease. This result indicates that this transition behavior is not limited to multi-component bio-colloid (blood) only, but may be a phenomenon of a bio-colloidal solution containing a large number of interacting components. The high dilution of blood behaves like the BSA solution. The ring-like deposition, the crack morphology, and the microstructures suggest that the components have enough time to segregate and deposit onto the substrate under ambient conditions. However, there is insufficient time for evaporative-driven segregation to occur at elevated temperatures, as expected.

Development and perspectives of rapid detection technology in food and environment

Food safety become a hot issue currently with globalization of food trade and food supply chains. Chemical pollution, microbial contamination and adulteration in food have attracted more attention worldwide. Contamination with antibiotics, estrogens and heavy metals in water environment and soil environment have also turn into an enormous threat to food safety. Traditional small-scale, long-term detection technologies have been unable to meet the current needs. In the monitoring process, rapid, convenient, accurate analysis and detection technologies have become the future development trend. We critically synthesizing the current knowledge of various rapid detection technology, and briefly touched upon the problem which still exist in research process. The review showed that the application of novel materials promotes the development of rapid detection technology, high-throughput and portability would be popular study directions in the future. Of course, the ultimate aim of the research is how to industrialization these technologies and apply to the market.

From Transcriptomics to Treatment in Inherited Optic Neuropathies

Inherited optic neuropathies, including Leber Hereditary Optic Neuropathy (LHON) and Dominant Optic Atrophy (DOA), are monogenetic diseases with a final common pathway of mitochondrial dysfunction leading to retinal ganglion cell (RGC) death and ultimately loss of vision. They are, therefore, excellent models with which to investigate this ubiquitous disease process-implicated in both common polygenetic ocular diseases (e.g., Glaucoma) and late-onset central nervous system neurodegenerative diseases (e.g., Parkinson disease). In recent years, cellular and animal models of LHON and DOA have matured in parallel with techniques (such as RNA-seq) to determine and analyze the transcriptomes of affected cells. This confluence leaves us at a particularly exciting time with the potential for the identification of novel pathogenic players and therapeutic targets. Here, we present a discussion of the importance of inherited optic neuropathies and how transcriptomic techniques can be exploited in the development of novel mutation-independent, neuroprotective therapies.

Discovering pathways in benign prostate hyperplasia: A functional genomics pilot study

Benign prostate hyperplasia (BPH) is one of the well-known urological neoplasms common in males with an increasing number of associated deaths in aging males. It causes uncomfortable urinary symptoms, including urine flow blockage, and may cause bladder, urinary tract or kidney problems. The histopathological and clinical knowledge regarding BPH is limited. In the present study, an in silico approach was applied that uses genome-scale microarray expression data to discover a wide range of protein-protein interactions in addition to focusing on specific genes responsible for BPH to develop prognostic biomarkers. Various genes that were differentially expressed in BPH were identified. Gene and functional annotation clusters were determined and an interaction analysis with disease phenotypes of BPH was performed, as well as an RNA tissue specificity analysis. Furthermore, a molecular docking study of certain short-listed gene biomarkers, namely anterior gradient 2 (AGR2; PDB ID: 2LNT), steroid 5α-reductase 2 (PDB ID: 6OQX), zinc finger protein 3 (PDB ID: 5T00) and collagen type XII α1 chain (PDB ID: 1U5M), was performed in order to identify alternative Chinese herbal agents for the treatment of BPH. Data from the present study revealed that AGR2 receptor (PDB ID: 2LNT) and berberine (Huang Bo) form the most stable complex and therefore may be assessed in further pharmacological studies for the treatment of BPH.

The study of single cells in diabetic kidney disease

In the past few years there has been a rapid expansion of interest in the study of single cells, especially through the new techniques that involve single-cell RNA sequencing (scRNA-seq). Recently, these techniques have provided new insights into kidney health and disease, including insights into diabetic kidney disease (DKD). However, despite the interest and the technological advances, the study of individual cells in DKD is not a new concept. Many clinicians and researchers who work within the DKD space may be familiar with experimental techniques that actually involve the study of individual cells, but may be unfamiliar with newer scRNA-seq technology. Here, with the goal of improving accessibility to the single-cell field, we provide a primer on single-cell studies with a focus on DKD. We situate the technology in its historical context and provide a brief explanation of the common aspects of the different technologies available. Then we review some of the most important recent studies of kidney (patho)biology that have taken advantage of scRNA-seq techniques, before emphasizing the new insights into the molecular pathogenesis of DKD gleaned with these techniques. Finally, we highlight common pitfalls and limitations of scRNA-seq methods and we look toward the future to how single-cell experiments may be incorporated into the study of DKD and how to interpret the findings of these experiments.

Adversarial generation of gene expression data

Motivation

High-throughput gene expression can be used to address a wide range of fundamental biological problems, but datasets of an appropriate size are often unavailable. Moreover, existing transcriptomics simulators have been criticized because they fail to emulate key properties of gene expression data. In this article, we develop a method based on a conditional generative adversarial network to generate realistic transcriptomics data for Escherichia coli and humans. We assess the performance of our approach across several tissues and cancer-types.

Results

We show that our model preserves several gene expression properties significantly better than widely used simulators, such as SynTReN or GeneNetWeaver. The synthetic data preserve tissue- and cancer-specific properties of transcriptomics data. Moreover, it exhibits real gene clusters and ontologies both at local and global scales, suggesting that the model learns to approximate the gene expression manifold in a biologically meaningful way.

Availability and implementation

Code is available at: https://github.com/rvinas/adversarial-gene-expression.

Supplementary information

Supplementary data are available at Bioinformatics online.

Current RNA-seq methodology reporting limits reproducibility

Ribonucleic acid sequencing (RNA-seq) identifies and quantifies RNA molecules from a biological sample. Transformation from raw sequencing data to meaningful gene or isoform counts requires an in silico bioinformatics pipeline. Such pipelines are modular in nature, built using selected software and biological references. Software is usually chosen and parameterized according to the sequencing protocol and biological question. However, while biological and technical noise is alleviated through replicates, biases due to the pipeline and choice of biological references are often overlooked. Here, we show that the current standard practice prevents reproducibility in RNA-seq studies by failing to specify required methodological information. Peer-reviewed articles are intended to apply currently accepted scientific and methodological standards. Inasmuch as the bias-less and optimal RNA-seq pipeline is not perfectly defined, methodological information holds a meaningful role in defining the results. This work illustrates the need for a standardized and explicit display of methodological information in RNA-seq experiments.

Omics in Systems Biology: Current Progress and Future Outlook

Biological research has undergone tremendous changes over the past three decades. Research used to almost exclusively focus on a single aspect of a single molecule per experiment. Modern technologies have enabled thousands of molecules to be simultaneously analyzed and the way that these molecules influence each other to be discerned. The change is so dramatic that it has given rise to a whole new descriptive suffix (i.e., omics) to describe these fields of study. While genomics was arguably the initial driver of this new trend, it quickly spread to other biological entities resulting in the creation of transcriptomics, proteomics, metabolomics, etc. The development of these "big four omics" created a wave of other omic fields, such as epigenomics, glycomics, lipidomics, microbiomics, and even foodomics; all with the purpose of comprehensively studying all the molecular entities or processes within their respective domain. The large number of omic fields that are invented even led to the term "panomics" as a way to classify them all under one category. Ultimately, all of these omic fields are setting the foundation for developing systems biology; in which the focus will be on determining the complex interactions that occur within biological systems.

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

The rapid and enthusiastic adoption of single-cell RNA sequencing (scRNA-seq) has demonstrated that this technology is far more than just another way to perform transcriptome analysis. It is not an exaggeration to say that the advent of scRNA-seq is revolutionizing the details of whole-transcriptome snapshots from a tissue to a cell. With this disruptive technology, it is now possible to mine heterogeneity between tissue types and within cells like never before. This enables more rapid identification of rare and novel cell types, simultaneous characterization of multiple different cell types and states, more accurate and integrated understanding of their roles in life processes, and more. However, we are only at the beginning of unlocking the full potential of scRNA-seq applications. This is particularly true for plant sciences, where single-cell transcriptome profiling is in its early stage and has many exciting challenges to overcome. In this review, we compare and evaluate recent pioneering studies using the Arabidopsis root model, which has established new paradigms for scRNA-seq studies in plants. We also explore several new and promising single-cell analysis tools that are available to those wishing to study plant development and physiology at unprecedented resolution and scale. In addition, we propose some future directions on the use of scRNA-seq technology to tackle some of the critical challenges in plant research and breeding.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

Processing and Analysis of RNA-seq Data from Public Resources

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

MicroRNAs Regulating Autophagy in Neurodegeneration

Social and economic impacts of neurodegenerative diseases (NDs) become more prominent in our constantly aging population. Currently, due to the lack of knowledge about the aetiology of most NDs, only symptomatic treatment is available for patients. Hence, researchers and clinicians are in need of solid studies on pathological mechanisms of NDs. Autophagy promotes degradation of pathogenic proteins in NDs, while microRNAs post-transcriptionally regulate multiple signalling networks including autophagy. This chapter will critically discuss current research advancements in the area of microRNAs regulating autophagy in NDs. Moreover, we will introduce basic strategies and techniques used in microRNA research. Delineation of the mechanisms contributing to NDs will result in development of better approaches for their early diagnosis and effective treatment.

Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

This chapter deals with the topic of bioinformatics, computational, mathematics, and statistics tools applied to biology, essential for the analysis and characterization of biological molecules, in particular proteins, which play an important role in all cellular and evolutionary processes of the organisms. In recent decades, with the next generation sequencing technologies and bioinformatics, it has facilitated the collection and analysis of a large amount of genomic, transcriptomic, proteomic, and metabolomic data from different organisms that have allowed predictions on the regulation of expression, transcription, translation, structure, and mechanisms of action of proteins as well as homology, mutations, and evolutionary processes that generate structural and functional changes over time. Although the information in the databases is greater every day, all bioinformatics tools continue to be constantly modified to improve performance that leads to more accurate predictions regarding protein functionality, which is why bioinformatics research remains a great challenge.

Detecting harmful algal blooms with nucleic acid amplification-based biotechnological tools

Harmful algal blooms (HABs) represent a growing threat to aquatic ecosystems and humans. Effective HAB management and mitigation efforts strongly rely on the availability of timely and in-situ tools for the detection of microalgae. In this sense, nucleic acid-based (molecular) methods are being considered for the unequivocal identification of microalgae as an attractive alternative to the currently used time-consuming and laboratory-based light microscopy techniques. This review provides an overview of the progress made on new molecular biotechnological tools for microalgal detection, particularly focusing on those that combine a nucleic acid (DNA or RNA) amplification step with detection. Different types of amplification processes (thermal and isothermal) and detection formats (e.g. microarrays, biosensors, lateral flows) are presented, and a comprehensive overview of their advantages and limitations is provided Although isothermal techniques are an attractive alternative to thermal amplification to reach in-situ analysis, further development is still required. Finally, current challenges, critical steps and future directions of the whole analysis process (from sample procurement to in-situ implementation) are described.

On the intersection of molecular bioelectronics and biosensors: 20 Years of C3B

Formed in 2000 at Virginia Commonwealth University, the Center for Bioelectronics, Biosensors and Biochips (C3B®) has subsequently been located at Clemson University and at Texas A&M University. Established as an industry-university collaborative center of excellence, the C3B has contributed new knowledge and technology in the areas of i) molecular bioelectronics, ii) responsive polymers, iii) multiplexed biosensor systems, and iv) bioelectronic biosensors. Noteworthy contributions in these areas include i) being the first to report direct electron transfer of oxidoreductase enzymes enabled by single walled carbon nanotubes and colloidal clays, ii) the molecular level integration of inherently conductive polymers with bioactive hydrogels using bi-functional monomers such as poly(pyrrole-co-3-pyrrolylbutyrate-conj-aminoethylmethacrylate) [PyBA-conj-AEMA] and 3-(1-ethyl methacryloylate)aniline to yield hetero-ladder electroconductive hydrogels, iii) the development of a multi-analyte physiological status monitoring biochip, and iv) the development of a bioanalytical Wien-bridge oscillator for the fused measurement to lactate and glucose. The present review takes a critical look of these contributions over the past 20 years and offers some perspective on the future of bioelectronics-based biosensors and systems. Particular attention is given to multiplexed biosensor systems and data fusion for rapid decision making.

A duplex probe-directed recombinase amplification assay for detection of single nucleotide polymorphisms on 8q24 associated with prostate cancer

Single nucleotide polymorphisms (SNPs) have important application value in the research of population genetics, hereditary diseases, tumors, and drug development. Conventional methods for detecting SNPs are typically based on PCR or DNA sequencing, which is time-consuming, costly, and requires complex instrumentation. In this study, we present a duplex probe-directed recombinase amplification (duplex-PDRA) assay that can perform real-time detection of two SNPs (rs6983267 and rs1447295) in four reactions in two tubes at 39°C within 30 min. The sensitivity of duplex-PDRA was 2×10³-10⁴ copies per reaction and no cross-reactivity was observed. A total of 382 clinical samples (179 prostate cancer patients and 203 controls) from northern China were collected and tested by duplex-PDRA assay and direct sequencing. The genotyping results were completely identical. In addition, the association analysis of two SNPs with prostate cancer risk and bone metastasis was conducted. We found that the TT genotype of rs6983267 (OR: 0.42; 95%CI: 0.23-0.78; P=0.005) decreased the risk of prostate cancer, while the CA genotype of rs1447295 (OR: 1.89; 95%CI: 1.20-2.96; P=0.005) increased the risk of prostate cancer. However, no association between the two SNPs (rs6983267 and rs1447295) and bone metastasis in prostate cancer was found in this study (P>0.05). In conclusion, the duplex-PDRA assay is an effective method for the simultaneous detection of two SNPs and shows great potential for widespread use in research and clinical settings.

Defining and Managing High-Risk Multiple Myeloma: Current Concepts

Multiple myeloma is a very heterogeneous disease. Despite advances in diagnostics and therapeutics, a subset of patients still experiences abbreviated responses to therapy, frequent relapses, and short survival and is considered to have high-risk multiple myeloma (HRMM). Stage III diagnosis according to the International Staging System; the presence of del(17p), t(4;14), or t(14;16) by fluorescence in situ hybridization; certain gene expression patterns; high serum lactic dehydrogenase level; and the presence of extramedullary disease at diagnosis are all considered indicators of HRMM. More recent evidence shows that patients who experience response to therapy but with a high burden of measurable residual disease or persistence of abnormal FDG uptake on PET/CT scan after initial therapy also have unfavorable outcomes, shaping the concept of dynamic risk assessment. Triplet therapy with proteasome inhibitors, immunomodulatory agents, and corticosteroids and autologous hematopoietic cell transplantation remain the pillars of HRMM therapy. Recent evidence indicates a benefit of immunotherapy with anti-CD38 monoclonal antibodies in HRMM. Future trials will inform the impact of novel immunotherapeutic approaches, including T-cell engagers, CAR T cells, and nonimmunotherapeutic approaches in HRMM. Those agents are likely to be deployed early in the disease course in the setting of risk- and response-adapted trials.

CRISPR-based enrichment strategies for targeted sequencing

The ability to easily produce or procure sequencing data has expanded to be within the reach of most clinics and research laboratories, but the complexity of sequence analysis remains a hurdle for many scientists, and a decline in sequencing cost means that the generation of gratuitous information in a given experiment is a challenge that is more and more often being encountered. To address this issue, methods have been present, some dating to the advent of nucleic acid sequencing, for capturing, targeting, or otherwise enriching specific nucleic acids in order to obtain greater depth of reads from a small portion of sequences within a complex sample. However, many of these methods have been complicated and laborious, relying on the design of hundreds to thousands of oligonucleotide probes, fabrication of microarray chips, and long hybridization times. Here, we review these methods, their benefits and uses, and catalog and discuss the implications of a recent development that has enabled a more efficient and expanded set of tools for enriching nucleic acids - the application of CRISPR technology. This introduction and analysis of the capabilities of new CRISPR-based enrichment strategies shows that it has the potential to expand the scope of enrichment to new possibilities, including the coupling of DNA and RNA targeting with long-read, portable sequencing platforms. Moreover, there are several areas where CRISPR-enrichment is a logical next step to more powerful and simplified sequencing for applications such as diagnostics and environmental monitoring.

Pheno-RNA, a method to associate genes with a specific phenotype, identifies genes linked to cellular transformation

Cellular transformation is associated with dramatic changes in gene expression, but it is difficult to determine which regulated genes are oncogenically relevant. Here we describe Pheno-RNA, a general approach to identifying candidate genes associated with a specific phenotype. Specifically, we generate a "phenotypic series" by treating a nontransformed breast cell line with a wide variety of molecules that induce cellular transformation to various extents. By performing transcriptional profiling across this phenotypic series, the expression profile of every gene can be correlated with the strength of the transformed phenotype. We identify ∼200 genes whose expression profiles are very highly correlated with the transformation phenotype, strongly suggesting their importance in transformation. Within biological categories linked to cancer, some genes show high correlations with the transformed phenotype, but others do not. Many genes whose expression profiles are highly correlated with transformation have never been associated with cancer, suggesting the involvement of heretofore unknown genes in cancer.

Physiological Roles of ERM Proteins and Transcriptional Regulators in Supporting Membrane Expression of Efflux Transporters as Factors of Drug Resistance in Cancer

One factor contributing to the malignancy of cancer cells is the acquisition of drug resistance during chemotherapy via increased expression of efflux transporters, such as P-glycoprotein (P-gp), multidrug resistance-associated proteins (MRPs), and breast cancer resistance protein (BCRP). These transporters operate at the cell membrane, and are anchored in place by the scaffold proteins ezrin (Ezr), radixin (Rdx), and moesin (Msn) (ERM proteins), which regulate their functional activity. The identity of the regulatory scaffold protein(s) differs depending upon the transporter, and also upon the tissue in which it is expressed, even for the same transporter. Another factor contributing to malignancy is metastatic ability. Epithelial-mesenchymal transition (EMT) is the first step in the conversion of primary epithelial cells into mesenchymal cells that can be transported to other organs via the blood. The SNAI family of transcriptional regulators triggers EMT, and SNAI expression is used is an indicator of malignancy. Furthermore, EMT has been suggested to be involved in drug resistance, since drug excretion from cancer cells is promoted during EMT. We showed recently that ERM proteins are induced by a member of the SNAI family, Snail. Here, we first review recent progress in research on the relationship between efflux transporters and scaffold proteins, including the question of tissue specificity. In the second part, we review the relationship between ERM scaffold proteins and the transcriptional regulatory factors that induce their expression.

l-DNA Duplex Formation as a Bioorthogonal Information Channel in Nucleic Acid-Based Surface Patterning

Photolithographic in situ synthesis of nucleic acids enables extremely high oligonucleotide sequence density as well as complex surface patterning and combined spatial and molecular information encoding. No longer limited to DNA synthesis, the technique allows for total control of both chemical and Cartesian space organization on surfaces, suggesting that hybridization patterns can be used to encode, display or encrypt informative signals on multiple chemically orthogonal levels. Nevertheless, cross-hybridization reduces the available sequence space and limits information density. Here we introduce an additional, fully independent information channel in surface patterning with in situ l-DNA synthesis. The bioorthogonality of mirror-image DNA duplex formation prevents both cross-hybridization on chimeric l-/d-DNA microarrays and also results in enzymatic orthogonality, such as nuclease-proof DNA-based signatures on the surface. We show how chimeric l-/d-DNA hybridization can be used to create informative surface patterns including QR codes, highly counterfeiting resistant authenticity watermarks, and concealed messages within high-density d-DNA microarrays.

Enhancement of Probe Density in DNA Sensing by Tuning the Exponential Growth Regime of Polyelectrolyte Multilayers

Surface-based biosensing devices benefit from a dedicated design of the probe layer present at the transducing interface. The layer architecture, its physicochemical properties, and the embedding of the receptor sites affect the probability of binding the analyte. Here, the enhancement of the probe density at the sensing interface by tuning the exponential growth regime of polyelectrolyte multilayers (PEMs) is presented. PEMs were made of poly-l-lysine (PLL), with appended clickable dibenzocyclooctyne (DBCO) groups and oligo(ethylene glycol) chains, and poly(styrene sulfonate) (PSS). The DNA probe loading and target hybridization efficiencies of the PEMs were evaluated as a function of the PLL layer number and the growth regime by a quartz crystal microbalance (QCM). An amplification factor of 25 in the target DNA detection was found for a 33-layer exponentially grown PEM compared to a monolayer. A Voigt-based model showed that DNA probe binding to the DBCO groups is more efficient in the open, exponentially grown films, while the hybridization efficiencies appeared to be high for all layer architectures. These results show the potential of such engineered gel-like structures to increase the detection of bio-relevant analytes in biosensing systems.

Expanding detection windows for discriminating single nucleotide variants using rationally designed DNA equalizer probes

Combining experimental and simulation strategies to facilitate the design and operation of nucleic acid hybridization probes are highly important to both fundamental DNA nanotechnology and diverse biological/biomedical applications. Herein, we introduce a DNA equalizer gate (DEG) approach, a class of simulation-guided nucleic acid hybridization probes that drastically expand detection windows for discriminating single nucleotide variants in double-stranded DNA (dsDNA) via the user-definable transformation of the quantitative relationship between the detection signal and target concentrations. A thermodynamic-driven theoretical model was also developed, which quantitatively simulates and predicts the performance of DEG. The effectiveness of DEG for expanding detection windows and improving sequence selectivity was demonstrated both in silico and experimentally. As DEG acts directly on dsDNA, it is readily adaptable to nucleic acid amplification techniques, such as polymerase chain reaction (PCR). The practical usefulness of DEG was demonstrated through the simultaneous detection of infections and the screening of drug-resistance in clinical parasitic worm samples collected from rural areas of Honduras.

ERgene: Python library for screening endogenous reference genes

In gene expression analysis, sample differences and experimental operation differences are common, but sometimes, these differences will cause serious errors to the results or even make the results meaningless. Finding suitable internal reference genes efficiently to eliminate errors is a challenge. Aside from the need for high efficiency, there is no package for screening endogenous genes available in Python. Here, we introduce ERgene, a Python library for screening endogenous reference genes. It has extremely high computational efficiency and simple operation steps. The principle is based on the inverse process of the internal reference method, and the robust matrix block operation makes the selection of internal reference genes faster than any other method.

A materials-science perspective on tackling COVID-19

The ongoing SARS-CoV-2 pandemic highlights the importance of materials science in providing tools and technologies for antiviral research and treatment development. In this Review, we discuss previous efforts in materials science in developing imaging systems and microfluidic devices for the in-depth and real-time investigation of viral structures and transmission, as well as material platforms for the detection of viruses and the delivery of antiviral drugs and vaccines. We highlight the contribution of materials science to the manufacturing of personal protective equipment and to the design of simple, accurate and low-cost virus-detection devices. We then investigate future possibilities of materials science in antiviral research and treatment development, examining the role of materials in antiviral-drug design, including the importance of synthetic material platforms for organoids and organs-on-a-chip, in drug delivery and vaccination, and for the production of medical equipment. Materials-science-based technologies not only contribute to the ongoing SARS-CoV-2 research efforts but can also provide platforms and tools for the understanding, protection, detection and treatment of future viral diseases.

Increased comparability between RNA-Seq and microarray data by utilization of gene sets

The field of transcriptomics uses and measures mRNA as a proxy of gene expression. There are currently two major platforms in use for quantifying mRNA, microarray and RNA-Seq. Many comparative studies have shown that their results are not always consistent. In this study we aim to find a robust method to increase comparability of both platforms enabling data analysis of merged data from both platforms. We transformed high dimensional transcriptomics data from two different platforms into a lower dimensional, and biologically relevant dataset by calculating enrichment scores based on gene set collections for all samples. We compared the similarity between data from both platforms based on the raw data and on the enrichment scores. We show that the performed data transforms the data in a biologically relevant way and filters out noise which leads to increased platform concordance. We validate the procedure using predictive models built with microarray based enrichment scores to predict subtypes of breast cancer using enrichment scores based on sequenced data. Although microarray and RNA-Seq expression levels might appear different, transforming them into biologically relevant gene set enrichment scores significantly increases their correlation, which is a step forward in data integration of the two platforms. The gene set collections were shown to contain biologically relevant gene sets. More in-depth investigation on the effect of the composition, size, and number of gene sets that are used for the transformation is suggested for future research.

The field of transcriptomics uses and measures mRNA as a proxy of gene expression. There are currently two major platforms in use for quantifying mRNA, microarray and RNA-Seq. Many comparative studies have shown that their results are not always consistent. In this study we aim to find a robust method to increase comparability of both platforms enabling data analysis of merged data from both platforms. We transformed the high dimensional transcriptomics data from the two different platforms into lower dimensional, and biologically relevant gene set scores. These gene sets were defined a-priori as specific combination of genes (e.g. up-regulated in a certain pathway). We observed that although microarray and RNA-Seq expression levels might appear different, using these gene sets to transform the data significantly increases their correlation. This is a step forward in data integration of the two platforms. More in-depth investigation on the effect of the composition, size, and number of gene sets that are used for the transformation is suggested for future research.

Ovarian cancer and the evolution of subtype classifications using transcriptional profiling†

Ovarian cancer is a complex disease with multiple subtypes, each having distinct histopathologies and variable responses to treatment. This review highlights the technological milestones and the studies that have applied them to change our definitions of ovarian cancer. Over the past 50 years, technologies such as microarrays and next-generation sequencing have led to the discovery of molecular alterations that define each of the ovarian cancer subtypes and has enabled further subclassification of the most common subtype, high-grade serous ovarian cancer (HGSOC). Improvements in mutational profiling have provided valuable insight, such as the ubiquity of TP53 mutations in HGSOC tumors. However, the information derived from these technological advances has also revealed the immense heterogeneity of this disease, from variation between patients to compositional differences within single masses. In looking forward, the emerging technologies for single-cell and spatially resolved transcriptomics will allow us to better understand the cellular composition and structure of tumors and how these contribute to the molecular subtypes. Attempts to incorporate the complexities ovarian cancer has resulted in increasing sophistication of model systems, and the increased precision in molecular profiling of ovarian cancers has already led to the introduction of inhibitors of poly (ADP-ribose) polymerases as a new class of treatments for ovarian cancer with DNA repair deficiencies. Future endeavors to define increasingly accurate classification strategies for ovarian cancer subtypes will allow for confident prediction of disease progression and provide important insight into potentially targetable molecular mechanisms specific to each subtype.

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable of quantitatively comparing many thousands of proteins obtained from cells and organisms.

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

Paired like homeodomain 1 and SAM and SH3 domain-containing 1 in the progression and prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy with high morbidity and mortality rates. In spite of numerous advancements have been made in therapeutic methods, the prognosis of HNSCC patients remains poor. Therefore, investigation of crucial genes during HNSCC tumorigenesis which could be exploited as biomarkers and therapeutic targets is greatly needed. In this study, original data of four independent datasets was downloaded from the Gene Expression Omnibus database and analyzed through R language to screen out differentially expressed genes. Paired like homeodomain 1 and SAM and SH3 domain-containing 1 were selected to be further explored through multiple online databases. Quantitative real-time polymerase chain reaction analysis and immunohistochemistry assay were adopted to validate the downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and statistical analysis indicated their close associations with patient prognosis. In vitro experiments demonstrated the inhibitory effect of paired like homeodomain 1 and SAM and SH3 domain-containing 1 on HNSCC progression. Overall, we identified the aberrant downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and suggested the potential of utilizing them as therapeutic targets or efficient biomarkers for diagnosis and prognosis evaluation. Our findings may provide novel evidences for the development of new strategies for HNSCC treatment.

Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing

Improved identification of structural variants (SVs) in cancer can lead to more targeted and effective treatment options as well as advance our basic understanding of the disease and its progression. We performed whole-genome sequencing of the SKBR3 breast cancer cell line and patient-derived tumor and normal organoids from two breast cancer patients using Illumina/10x Genomics, Pacific Biosciences (PacBio), and Oxford Nanopore Technologies (ONT) sequencing. We then inferred SVs and large-scale allele-specific copy number variants (CNVs) using an ensemble of methods. Our findings show that long-read sequencing allows for substantially more accurate and sensitive SV detection, with between 90% and 95% of variants supported by each long-read technology also supported by the other. We also report high accuracy for long reads even at relatively low coverage (25×–30×). Furthermore, we integrated SV and CNV data into a unifying karyotype-graph structure to present a more accurate representation of the mutated cancer genomes. We find hundreds of variants within known cancer-related genes detectable only through long-read sequencing. These findings highlight the need for long-read sequencing of cancer genomes for the precise analysis of their genetic instability.

A simple, scalable approach to building a cross-platform transcriptome atlas

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.

New Diagnostic Approaches for Undiagnosed Rare Genetic Diseases

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.