Translating RNA sequencing into clinical diagnostics: opportunities and challenges

Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays

Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays-Oncotype DX, Prosigna, and EndoPredict-using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives

The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.

Whole transcriptome RNA sequencing provides novel insights into the molecular dynamics of ovarian development in mud crab, Scylla paramamosain after mating

Ovarian development in animals is a complicated biological process, requiring the simultaneous coordination among various genes and pathways. To understand the dynamic changes and molecular regulatory mechanisms of ovarian development in mud crab (Scylla paramamosain), both histological observation and whole transcriptome sequencing of ovarian tissues at different mating stages were implemented in this study. The histological results revealed that ovarian development was delayed in unmated females (60 days after courtship behavior but not mating), who exhibited an oocyte diameter of 56.38 ± 15.17 μm. Conversely, mated females exhibited accelerated the ovarian maturation process, with females reaching ovarian stage III (proliferative stage) 23 days after mating and attained an average oocyte diameter of 132.19 ± 15.07 μm. Thus, mating process is essential in promoting the rapid ovarian development in mud crab. Based on the whole transcriptome sequencing analysis, a total of 518 mRNAs, 1502 lncRNAs, 18 circRNAs and 151 miRNAs were identified to be differentially expressed between ovarian tissues at different mating stages. Notably, six differentially expressed genes (DEGs) associated with ovarian development were identified, including ovary development-related protein, red pigment concentrating hormone receptor, G2/mitotic-specific cyclin-B3-like, lutropin-chorio gonadotropic hormone receptor, renin receptor, and SoxB2. More importantly, both DEGs and targets of differentially expressed non-coding RNAs (DEncRNAs) were enriched in renin-angiotensin system, TGF-β signaling, cell adhesion molecules, MAPK signaling pathway, and ECM-receptor interaction, suggesting that these pathways may play significant roles in the ovarian development of mud crabs. Moreover, competition endogenous RNA (ceRNA) networks were constructed while mRNAs were differentially expressed between mating stages were involved in Gene Ontology (GO) biological processes such as developmental process, reproduction, and growth. These findings could provide solid foundations for the future development of female mud crab maturation enhancement strategy, and improve the understanding of the ovarian maturation process in crustaceans.

Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response

Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as "Prol," was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.

Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma

Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosarcoma metastasis. In the first step, we used RNA-seq and online microarray data from osteosarcoma tissues and cell lines to identify differentially expressed miRNAs. Then, using seven feature selection algorithms for ranking, the first-ranked miRNAs were selected as input for five machine learning systems. Using network analysis and machine learning algorithms, we developed new diagnostic models that successfully differentiated metastatic osteosarcoma from non-metastatic samples based on newly discovered miRNA signatures. The results showed that miR-34c-3p and miR-154-3p act as the most promising models in the diagnosis of metastatic osteosarcoma. Validation for this model by RT-qPCR in benign tissue and osteosarcoma biopsies confirmed the lower expression of miR-34c-3p and miR-154-3p in OS samples. In addition, a direct correlation between miR-34c-3p expression, miR-154-3p expression and tumor grade was discovered. The combined values of miR-34c-3p and miR-154-3p showed 90 % diagnostic power (AUC = 0.90) for osteosarcoma samples and 85 % (AUC = 0.85) for metastatic osteosarcoma. Adhesion junction and focal adhesion pathways, as well as epithelial-to-mesenchymal transition (EMT) GO terms, were identified as the most significant KEGG and GO terms for the top miRNAs. The findings of this study highlight the potential use of novel miRNA expression signatures for early detection of metastatic osteosarcoma. These findings may help in determining therapeutic approaches with a quantitative and faster method of metastasis detection and also be used in the development of targeted molecular therapy for this aggressive cancer. Further research is needed to confirm the clinical utility of miR-34c-3p and miR-154-3p as diagnostic biomarkers for metastatic osteosarcoma.

Precision oncology: current and future platforms for treatment selection

Genomic profiling of hundreds of cancer-associated genes is now a component of routine cancer care. DNA sequencing can identify mutations, mutational signatures, and structural alterations predictive of therapy response and assess for heritable cancer risk, but it has been less useful for identifying predictive biomarkers of sensitivity to cytotoxic chemotherapies, antibody drug conjugates, and immunotherapies. The clinical adoption of molecular profiling platforms such as RNA sequencing better suited to identifying those patients most likely to respond to immunotherapies and drug combinations will be critical to expanding the benefits of precision oncology. This review discusses the potential advantages of innovative molecular and functional profiling platforms designed to replace or complement targeted DNA sequencing and the major hurdles to their clinical adoption.

Direct Zeptomole Detection of RNA Biomarkers by Ultrabright Fluorescent Nanoparticles on Magnetic Beads

Nucleic acids are important biomarkers in cancer and viral diseases. However, their ultralow concentration in biological/clinical samples makes direct target detection challenging, because it leads to slow hybridization kinetics with the probe and its insufficient signal-to-noise ratio. Therefore, RNA target detection is done by molecular (target) amplification, notably by RT-PCR, which is a tedious multistep method that includes nucleic acid extraction and reverse transcription. Here, a direct method based on ultrabright dye-loaded polymeric nanoparticles in a sandwich-like hybridization assay with magnetic beads is reported. The ultrabright DNA-functionalized nanoparticle, equivalent to ≈10 000 strongly emissive rhodamine dyes, is hybridized with the magnetic bead to the RNA target, providing the signal amplification for the detection. This concept (magneto-fluorescent sandwich) enables high-throughput detection of DNA and RNA sequences of varied lengths from 48 to 1362 nt with the limit of detection down to 0.3 fm using a plate reader (15 zeptomoles), among the best reported for optical sandwich assays. Moreover, it allows semi-quantitative detection of SARS-CoV-2 viral RNA directly in clinical samples without a dedicated RNA extraction step. The developed technology, combining ultrabright nanoparticles with magnetic beads, addresses fundamental challenges in RNA detection; it is expected to accelerate molecular diagnostics of diseases.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

PN, Gimenes N, Reis MX, Matsas S, Ferreira CG. Next-generation sequencing impact on cancer care: applications, challenges, and future directions

Fundamentally precision oncology illustrates the path in which molecular profiling of tumors can illuminate their biological behavior, diversity, and likely outcomes by identifying distinct genetic mutations, protein levels, and other biomarkers that underpin cancer progression. Next-generation sequencing became an indispensable diagnostic tool for diagnosis and treatment guidance in current clinical practice. Nowadays, tissue analysis benefits from further support through methods like comprehensive genomic profiling and liquid biopsies. However, precision medicine in the field of oncology presents specific hurdles, such as the cost-benefit balance and widespread accessibility, particularly in countries with low- and middle-income. A key issue is how to effectively extend next-generation sequencing to all cancer patients, thus empowering treatment decision-making. Concerns also extend to the quality and preservation of tissue samples, as well as the evaluation of health technologies. Moreover, as technology advances, novel next-generation sequencing assessments are being developed, including the study of Fragmentomics. Therefore, our objective was to delineate the primary uses of next-generation sequencing, discussing its' applications, limitations, and prospective paths forward in Oncology.

Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review

Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.

Exploring single-cell RNA sequencing as a decision-making tool in the clinical management of Fuchs' endothelial corneal dystrophy

Single-cell RNA sequencing (scRNA-seq) has enabled the identification of novel gene signatures and cell heterogeneity in numerous tissues and diseases. Here we review the use of this technology for Fuchs' Endothelial Corneal Dystrophy (FECD). FECD is the most common indication for corneal endothelial transplantation worldwide. FECD is challenging to manage because it is genetically heterogenous, can be autosomal dominant or sporadic, and progress at different rates. Single-cell RNA sequencing has enabled the discovery of several FECD subtypes, each with associated gene signatures, and cell heterogeneity. Current FECD treatments are mainly surgical, with various Rho kinase (ROCK) inhibitors used to promote endothelial cell metabolism and proliferation following surgery. A range of emerging therapies for FECD including cell therapies, gene therapies, tissue engineered scaffolds, and pharmaceuticals are in preclinical and clinical trials. Unlike conventional disease management methods based on clinical presentations and family history, targeting FECD using scRNA-seq based precision-medicine has the potential to pinpoint the disease subtypes, mechanisms, stages, severities, and help clinicians in making the best decision for surgeries and the applications of therapeutics. In this review, we first discuss the feasibility and potential of using scRNA-seq in clinical diagnostics for FECD, highlight advances from the latest clinical treatments and emerging therapies for FECD, integrate scRNA-seq results and clinical notes from our FECD patients and discuss the potential of applying alternative therapies to manage these cases clinically.

An optimized workflow for transcriptomic analysis from archival paraformaldehyde-fixed retinal tissues collected by laser capture microdissection

RNA sequencing (RNA-seq) coupled with laser capture microdissection (LCM) is a powerful tool for transcriptomic analysis in unfixed fresh-frozen tissues. Fixation of ocular tissues for immunohistochemistry commonly involves the use of paraformaldehyde (PFA) followed by embedding in Optimal Cutting Temperature (OCT) medium for long-term cryopreservation. However, the quality of RNA derived from such archival PFA-fixed/OCT-embedded samples is often compromised, limiting its suitability for transcriptomic studies. In this study, we aimed to develop a methodology to extract high-quality RNA from PFA-fixed canine eyes by utilizing LCM to isolate retinal tissue. We demonstrate the efficacy of an optimized LCM and RNA purification protocol for transcriptomic profiling of PFA-fixed retinal specimens. We compared four pairs of canine retinal tissues, where one eye was subjected to PFA-fixation prior to OCT embedding, while the contralateral eye was embedded fresh frozen (FF) in OCT without fixation. Since the RNA obtained from PFA-fixed retinas were contaminated with genomic DNA, we employed two rounds of DNase I treatment to obtain RNA suitable for RNA-seq. Notably, the quality of sequencing reads and gene sets identified from both PFA-fixed and FF tissues were nearly identical. In summary, our study introduces an optimized workflow for transcriptomic profiling from PFA-fixed archival retina. This refined methodology paves the way for improved transcriptomic analysis of preserved ocular tissue, bridging the gap between optimal sample preservation and high-quality RNA data acquisition.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Fusion Challenges in Solid Tumors: Shaping the Landscape of Cancer Care in Precision Medicine

Targeting actionable fusions has emerged as a promising approach to cancer treatment. Next-generation sequencing (NGS)-based techniques have unveiled the landscape of actionable fusions in cancer. However, these approaches remain insufficient to provide optimal treatment options for patients with cancer. This article provides a comprehensive overview of the actionability and clinical development of targeted agents aimed at driver fusions. It also highlights the challenges associated with fusion testing, including the evaluation of patients with cancer who could potentially benefit from testing and devising an effective strategy. The implementation of DNA NGS for all tumor types, combined with RNA sequencing, has the potential to maximize detection while considering cost effectiveness. Herein, we also present a fusion testing strategy aimed at improving outcomes in patients with cancer.

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

Transcriptional pathobiology and multi-omics predictors for Parkinson's disease

Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinson's disease (PD) are urgently needed. In this study, we leverage the large-scale whole-blood total RNA-seq dataset from the Accelerating Medicine Partnership in Parkinson's Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). There were 1,111 significant marker RNAs, including 491 genes, 599 eRNAs, and 21 circRNAs, that were first discovered in the PPMI cohort (FDR < 0.05) and confirmed in the PDBP/BioFIND cohorts (nominal p < 0.05). Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-alpha signaling pathway. We further compare the PD-associated genes in blood with those in post-mortem brain dopamine neurons in our BRAINcode cohort. 44 genes show significant changes with the same direction in both PD brain neurons and PD blood, including neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Finally, we built a novel multi-omics machine learning model to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous studies and might aid the decision-making for PD diagnosis in clinical practice. In summary, this study delineates a wide spectrum of the known and novel RNAs linked to PD and are detectable in circulating blood cells in a harmonized, large-scale dataset. It provides a generally useful computational framework for further biomarker development and early disease prediction.

Germline PTCH1: c.361_362insAlu alteration identified by comprehensive exome and RNA sequencing in a patient with Gorlin syndrome

Gorlin syndrome can be caused by pathogenic/likely pathogenic (P/LP) variants in the tumor suppressor gene PTCH1 (9q22.1-q31), which encodes the receptor for the sonic hedgehog (SHH) ligand. We present a 12-month-old boy clinically diagnosed with Gorlin syndrome who was found to have significantly delayed development, palmar pitting, palmar and plantar keratosis, short hands, frontal bossing, coarse face, hypertelorism, a bifid rib, misaligned and missing teeth, and SHH-activated medulloblastoma. Genetic testing, including a pediatric cancer panel and genome sequencing with peripheral blood, failed to identify any P/LP variants in PTCH1. Paired tumor/normal exome sequencing was performed, which identified a germline NM_000264.5 (PTCH1): c.361_362ins? alteration through manual review of sequencing reads. Clinical RNA sequencing further demonstrated an Alu insertion at this region (PTCH1: c.361_362insAlu), providing molecular confirmation of Gorlin syndrome. This finding exemplifies a unique mechanism for PTCH1 disruption in the germline and highlights the importance of comprehensive analysis, including manual review of DNA sequencing reads and the utility of RNA analysis to detect variant types which may not be identified by routine genetic screening techniques.

Comparison of RNA-Sequencing Methods for Degraded RNA

RNA sequencing (RNA-Seq) is a powerful technique and is increasingly being used in clinical research and drug development. Currently, several RNA-Seq methods have been developed. However, the relative advantage of each method for degraded RNA and low-input RNA, such as RNA samples collected in the field of clinical setting, has remained unknown. The Standard method of RNA-Seq captures mRNA by poly(A) capturing using Oligo dT beads, which is not suitable for degraded RNA. Here, we used three commercially available RNA-Seq library preparation kits (SMART-Seq, xGen Broad-range, and RamDA-Seq) using random primer instead of Oligo dT beads. To evaluate the performance of these methods, we compared the correlation, the number of detected expressing genes, and the expression levels with the Standard RNA-Seq method. Although the performance of RamDA-Seq was similar to that of Standard RNA-Seq, the performance for low-input RNA and degraded RNA has decreased. The performance of SMART-Seq was better than xGen and RamDA-Seq in low-input RNA and degraded RNA. Furthermore, the depletion of ribosomal RNA (rRNA) improved the performance of SMART-Seq and xGen due to increased expression levels. SMART-Seq with rRNA depletion has relative advantages for RNA-Seq using low-input and degraded RNA.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

Detection and identification of single ribonucleotide monophosphates using a dual in-plane nanopore sensor made in a thermoplastic via replication

We report the generation of ∼8 nm dual in-plane pores fabricated in a thermoplastic via nanoimprint lithography (NIL). These pores were connected in series with nanochannels, one of which served as a flight tube to allow the identification of single molecules based on their molecular-dependent apparent mobilities (i.e., dual in-plane nanopore sensor). Two different thermoplastics were investigated including poly(methyl methacrylate), PMMA, and cyclic olefin polymer, COP, as the substrate for the sensor both of which were sealed using a low glass transition cover plate (cyclic olefin co-polymer, COC) that could be thermally fusion bonded to the PMMA or COP substrate at a temperature minimizing nanostructure deformation. Unique to these dual in-plane nanopore sensors was two pores flanking each side of the nanometer flight tube (50 × 50 nm, width × depth) that was 10 μm in length. The utility of this dual in-plane nanopore sensor was evaluated to not only detect, but also identify single ribonucleotide monophosphates (rNMPs) by using the travel time (time-of-flight, ToF), the resistive pulse event amplitude, and the dwell time. In spite of the relatively large size of these in-plane pores (∼8 nm effective diameter), we could detect via resistive pulse sensing (RPS) single rNMP molecules at a mass load of 3.9 fg, which was ascribed to the unique structural features of the nanofluidic network and the use of a thermoplastic with low relative dielectric constants, which resulted in a low RMS noise level in the open pore current. Our data indicated that the identification accuracy of individual rNMPs was high, which was ascribed to an improved chromatographic contribution to the nano-electrophoresis apparent mobility. With the ToF data only, the identification accuracy was 98.3%. However, when incorporating the resistive pulse sensing event amplitude and dwell time in conjunction with the ToF and analyzed via principal component analysis (PCA), the identification accuracy reached 100%. These findings pave the way for the realization of a novel chip-based single-molecule RNA sequencing technology.

Covalent Organic Frameworks as Nano-Reservoir for Room Temperature RNA Storage

The emerging role of Ribonucleic acids (RNAs) as therapeutics is alluring. However, RNAs are extremely labile under ambient conditions and typically need to be stored in cryogenic conditions (-20 °C to -80 °C). Hence, storage, stabilization, and transportation of RNA under ambient conditions have been an arduous task and remain an unsolved problem. In this work, a guanidinium-based ionic covalent organic framework (COF), TTGCl with nanotubular morphology, was synthesized and used as nano-reservoirs for room-temperature storage of RNA. To understand the role of the nanotubular morphology and chemical nature of TTGCl in stabilizing the RNA structure and for comparison purposes, a neutral COF, TMT-TT, is synthesized and studied. Further, density functional theory (DFT) studies confirmed non-covalent interaction between the COFs and the RNA nucleobases, facilitating reversible storage of RNA. RNA loaded in COFs was found to be resistant to enzymatic degradation when treated with RNase. Gel electrophoresis and sequencing confirmed the structural integrity of the recovered RNAs and their further processibility.

Next-Generation Sequencing and Emerging Technologies

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

DNA Quantity and Quality Comparisons between Cryopreserved and FFPE Tumors from Matched Pan-Cancer Samples

Personalized cancer care requires molecular characterization of neoplasms. While the research community accepts frozen tissues as the gold standard analyte for molecular assays, the source of tissue for testing in clinical cancer care comes almost universally from formalin-fixed, paraffin-embedded tissue (FFPE). As newer technologies emerge for DNA characterization that requires higher molecular weight DNA, it was necessary to compare the quality of DNA in terms of DNA length between FFPE and cryopreserved samples. We hypothesized that cryopreserved samples would yield higher quantity and superior quality DNA compared to FFPE samples. We analyzed DNA metrics by performing a head-to-head comparison between FFPE and cryopreserved samples from 38 human tumors representing various cancer types. DNA quantity and purity were measured by UV spectrophotometry, and DNA from cryopreserved tissue demonstrated a 4.2-fold increase in DNA yield per mg of tissue (p-value < 0.001). DNA quality was measured on a fragment microelectrophoresis analyzer, and again, DNA from cryopreserved tissue demonstrated a 223% increase in the DNA quality number and a 9-fold increase in DNA fragments > 40,000 bp (p-value < 0.0001). DNA from the cryopreserved tissues was superior to the DNA from FFPE samples in terms of DNA yield and quality.

Host Immune Response Modulation in Avian Coronavirus Infection: Tracheal Transcriptome Profiling In Vitro and In Vivo

Infectious bronchitis virus (IBV) is a highly contagious Gammacoronavirus causing moderate to severe respiratory infection in chickens. Understanding the initial antiviral response in the respiratory mucosa is crucial for controlling viral spread. We aimed to characterize the impact of IBV Delmarva (DMV)/1639 and IBV Massachusetts (Mass) 41 at the primary site of infection, namely, in chicken tracheal epithelial cells (cTECs) in vitro and the trachea in vivo. We hypothesized that some elements of the induced antiviral responses are distinct in both infection models. We inoculated cTECs and infected young specific pathogen-free (SPF) chickens with IBV DMV/1639 or IBV Mass41, along with mock-inoculated controls, and studied the transcriptome using RNA-sequencing (RNA-seq) at 3 and 18 h post-infection (hpi) for cTECs and at 4 and 11 days post-infection (dpi) in the trachea. We showed that IBV DMV/1639 and IBV Mass41 replicate in cTECs in vitro and the trachea in vivo, inducing host mRNA expression profiles that are strain- and time-dependent. We demonstrated the different gene expression patterns between in vitro and in vivo tracheal IBV infection. Ultimately, characterizing host-pathogen interactions with various IBV strains reveals potential mechanisms for inducing and modulating the immune response during IBV infection in the chicken trachea.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Comprehensive transcriptomic profiling of liver cancer identifies that histone and PTEN are major regulators of SCU‑induced antitumor activity

Worldwide, liver cancer is the most frequent fatal malignancy. Liver cancer prognosis is poor because patients frequently receive advanced-stage diagnoses. The current study aimed to establish the potential pharmacological targets and the biological networks of scutellarein (SCU) in liver cancer, a natural product known to have low toxicity and side effects. To identify the differentially expressed genes between SCU-treated and SCU-untreated HepG2 cells, RNA sequencing (RNA-seq) was carried out. A total of 463 genes were revealed to have differential expression, of which 288 were upregulated and 175 were downregulated in the group that had received SCU treatment compared with a control group. Gene Ontology (GO) enrichment analysis of associated biological process terms revealed they were mostly involved in the regulation of protein heterodimerization activity and nucleosomes. Interaction of protein-protein network analysis using Search Tool for the Retrieval of Interacting Genes/Proteins resulted in two crucial interacting hub targets; namely, histone H1-4 and protein tyrosine phosphatase receptor type C. Additionally, the crucial targets were validated using western blotting. Overall, the present study demonstrated that the use of RNA-seq data, with bioinformatics tools, can provide a valuable resource to identify the pharmacological targets that could have important biological roles in liver cancer.

Transcriptomic changes associated with oral immunotherapy for food allergy

This review summarizes recent advances in characterizing the transcriptional pathways associated with outcomes following Oral Immunotherapy. Recent technological advances including single-cell sequencing are transforming the ways in which the transcriptional landscape is understood. The application of these technologies is still in its infancy in food allergy but here we summarize current understanding of gene expression changes following oral immunotherapy for food allergy and specific signatures underpinning the different clinical outcomes of desensitization and remission (sustained unresponsiveness). T helper 2A cells have been identified as a cell type which correlates with disease activity and is modified by treatment. Molecular features at study entry may differentiate individuals who achieve more positive outcomes during OIT. Recent findings point to T cell anergy and Type 1 interferon pathways as potential mechanisms supporting redirection of the allergen-specific immune response away from allergy towards remission. Despite these developments in our understanding of immune mechanisms following OIT, there are still significant gaps. Additional studies examining immune signatures associated with long term and well-defined clinical outcomes are required to gain a more complete understanding of the pathways leading to remission of allergy, in order to optimize treatments and gain improved outcomes for patients.

Glycan-specific molecularly imprinted polymers towards cancer diagnostics: merits, applications, and future perspectives

Aberrant glycans are a hallmark of cancer states. Notably, emerging evidence has demonstrated that the diagnosis of cancers with tumour-specific glycan patterns holds great potential to address unmet medical needs, especially in improving diagnostic sensitivity and selectivity. However, despite vast glycans having been identified as potent markers, glycan-based diagnostic methods remain largely limited in clinical practice. There are several reasons that prevent them from reaching the market, and the lack of anti-glycan antibodies is one of the most challenging hurdles. With the increasing need for accelerating the translational process, numerous efforts have been made to find antibody alternatives, such as lectins, boronic acids and aptamers. However, issues concerning affinity, selectivity, stability and versatility are yet to be fully addressed. Molecularly imprinted polymers (MIPs), synthetic antibody mimics with tailored cavities for target molecules, hold the potential to revolutionize this dismal progress. MIPs can bind a wide range of glycan markers, even those without specific antibodies. This capacity effectively broadens the clinical applicability of glycan-based diagnostics. Additionally, glycoform-resolved diagnosis can also be achieved through customization of MIPs, allowing for more precise diagnostic applications. In this review, we intent to introduce the current status of glycans as potential biomarkers and critically evaluate the challenges that hinder the development of in vitro diagnostic assays, with a particular focus on glycan-specific recognition entities. Moreover, we highlight the key role of MIPs in this area and provide examples of their successful use. Finally, we conclude the review with the remaining challenges, future outlook, and emerging opportunities.

Characterization of non-invasive oropharyngeal samples and nucleic acid isolation for molecular diagnostics

Molecular diagnostics is an increasingly important clinical tool, especially in routine sampling. We evaluated two non-invasive methods (oral swabs and mouthwashes) for sampling nucleic acids from the oral/pharyngeal area. We created a workflow from sample collection (n = 59) to RT-qPCR based analysis. The samples were further characterized in terms of their cellular composition as well as the purity, degradation and microbial content of the derived DNA/RNA. We determined the optimal housekeeping genes applicable for these types of samples. The cellular composition indicated that mouthwashes contained more immune cells and bacteria. Even though the protocol was not specifically optimized to extract bacterial RNA it was possible to derive microbial RNA, from both sampling methods. Optimizing the protocol allowed us to generate stable quantities of DNA/RNA. DNA/RNA purity parameters were not significantly different between the two sampling methods. Even though integrity analysis demonstrated a high level of degradation of RNA, corresponding parameters confirmed their sequencing potential. RT-qPCR analysis determined TATA-Box Binding Protein as the most favorable housekeeping gene. In summary, we have developed a robust method suitable for multiple downstream diagnostic techniques. This protocol can be used as a foundation for further research endeavors focusing on developing molecular diagnostics for the oropharyngeal cavity.

Application of Advanced Molecular Methods to Study Early-Onset Neonatal Sepsis

Early-onset sepsis (EOS) is a global health issue, considered one of the primary causes of neonatal mortality. Diagnosis of EOS is challenging because its clinical signs are nonspecific, and blood culture, which is the current gold-standard diagnostic tool, has low sensitivity. Commonly used biomarkers for sepsis diagnosis, including C-reactive protein, procalcitonin, and interleukin-6, lack specificity for infection. Due to the disadvantages of blood culture and other common biomarkers, ongoing efforts are directed towards identifying innovative molecular approaches to diagnose neonates at risk of sepsis. This review aims to gather knowledge and recent research on these emerging molecular methods. PCR-based techniques and unrestricted techniques based on 16S rRNA sequencing and 16S-23S rRNA gene interspace region sequencing offer several advantages. Despite their potential, these approaches are not able to replace blood cultures due to several limitations; however, they may prove valuable as complementary tests in neonatal sepsis diagnosis. Several microRNAs have been evaluated and have been proposed as diagnostic biomarkers in EOS. T2 magnetic resonance and bioinformatic analysis have proposed potential biomarkers of neonatal sepsis, though further studies are essential to validate these findings.

New Breakthroughs in the Diagnosis of Leptomeningeal Carcinomatosis: A Review of Liquid Biopsies of Cerebrospinal Fluid

Leptomeningeal carcinomatosis represents a terminal stage and is a devastating complication of cancer. Despite its high incidence, current diagnostic methods fail to accurately detect this condition in a timely manner. This failure to diagnose leads to the refusal of treatment and the absence of clinical trials, hampering the development of new therapy strategies. The use of liquid biopsy is revolutionizing the field of diagnostic oncology. The dynamic and non-invasive detection of tumor markers has enormous potential in cancer diagnostics and treatment. Leptomeningeal carcinomatosis is a condition where invasive tissue biopsy is not part of the routine diagnostic analysis, making liquid biopsy an essential diagnostic tool. Several elements in cerebrospinal fluid (CSF) have been investigated as potential targets of liquid biopsy, including free circulating tumor cells, free circulating nucleic acids, proteins, exosomes, and even non-tumor cells as part of the dynamic tumor microenvironment. This review aims to summarize current breakthroughs in the research on liquid biopsy, including the latest breakthroughs in the identification of tumor cells and nucleic acids, and give an overview of future directions in the diagnosis of leptomeningeal carcinomatosis.

An Exome Capture-Based RNA-Sequencing Assay for Genome-Wide Identification and Prioritization of Clinically Important Fusions in Pediatric Tumors

This study reports the development of an exome capture-based RNA-sequencing assay to detect recurring and novel fusions in hematologic, solid, and central nervous system tumors. The assay used Twist Comprehensive Exome capture with either fresh or formalin-fixed samples and a bioinformatic platform that provides fusion detection, prioritization, and downstream curation. A minimum of 50 million uniquely mapped reads, a consensus read alignment/fusion calling approach using four callers (Arriba, FusionCatcher, STAR-Fusion, and Dragen), and custom software were used to integrate, annotate, and rank the candidate fusion calls. In an evaluation of 50 samples, the number of calls varied substantially by caller, from a mean of 24.8 with STAR-Fusion to 259.6 with FusionCatcher; only 1.1% of calls were made by all four callers. Therefore a filtering and ranking algorithm was developed based on multiple criteria, including number of supporting reads, calling consensus, genes involved, and cross-reference against databases of known cancer-associated or likely false-positive fusions. This approach was highly effective in pinpointing known clinically relevant fusions, ranking them first in 47 of 50 samples (94%). Detection of pathogenic gene fusions in three diagnostically challenging cases highlights the importance of a genome-wide and nontargeted method for fusion detection in pediatric cancer.

Whole Blood Transcriptome Analysis in Patients with Trigeminal Neuralgia: a Prospective Clinical Study

Trigeminal neuralgia (TN) brings a huge burden to patients, without long-term effective treatment. This study aimed to explore the differentially expressed genes (DEGs) and related enrichment pathways in patients with TN. This was a study of transcriptome sequencing and bioinformatics analysis of human samples. Whole blood samples were collected from the TN patients and pain-free controls. RNA was extracted to conduct the RNA-sequencing and the subsequent bioinformatics analysis. DEGs between the two groups were derived. Kyoto encyclopedia of genes and genomes (KEGG) and Gene ontology (GO) was used to find the enrichment pathways of DEGs. Protein protein interaction (PPI) network was used to depict the interaction between DEGs and find the most important gene, hub gene. Compared with the control group, there were 117 up-regulated DEGs and 103 down-regulated DEGs in the whole blood of patients in the TN group. Pathway enrichment analysis showed that DEGs were mainly enriched in the neuroimmune and metabolic pathways. The PPI network demonstrated that colony stimulating factor 2 (CSF2) was the most important hub gene in the whole blood of TN patients. This study shows the expression of the transcriptome in the whole blood samples of TN patients. The neuroimmune responses and key hub gene CSF2 in the whole blood cells play a vital role in the occurrence of TN. Our research provides a theoretical basis for the diagnosis and treatments of TN. This study was registered at clinicaltrials.gov in June 2021 (No. NCT04923399).

Comprehensive analysis of the gene expression profile of the male and female BTBR mice with diabetic nephropathy

Comprehensive insight into the gender-based gene expression-related omics data in a rodent model of diabetic nephropathy (DN) is scarce. In the present study, the gender-based genes regulating different pathways involved in the progression of DN were explored through an unbiased RNA sequence of kidneys from BTBR mice with DN. We identified 17,739 and 17,981 genes in male and female DN mice; 1121 and 655 genes were expressed differentially (DEGs, differentially expressed genes) in male and female DN mice; both genders displayed only 195 DEGs. In the male DN mice, the number of upregulated genes was nearly the same as that of the down-regulated genes. In contrast, the number of upregulated genes was lesser than that of the down-regulated genes in the female DN mice, manifesting a remarkable gender disparity during the progression of DN in this animal model. Gene Ontology (GO) and KEGG-enriched results showed that most of these DEGs were related to the critical biological processes, including metabolic pathways, natural oxidation, bile secretion, and PPAR signaling; all are highly associated with DN. Notably, the DEGs significantly enriched for steroid hormone biosynthesis pathway were identified in both genders; the number of DEGs increased was 22 in male DN mice and 14 in female DN mice. Specifically, the Ugt1a10, Akr1c12, and Akr1c14 were upregulated in both genders. Interestingly, the Hsd11b1 gene was upregulated in female DN mice but downregulated in male DN mice. These results suggest that a significant gender-based variance in the gene expression occurs during the progression of DN and may be playing a role in the advancement of DN in the BTBR mouse model.

Development and validation of a new diagnostic prediction model of ENHO and NOX4 for early diagnosis of systemic sclerosis

Objective

Systemic sclerosis (SSc) is a chronic autoimmune disease characterized by fibrosis. The challenge of early diagnosis, along with the lack of effective treatments for fibrosis, contribute to poor therapeutic outcomes and high mortality of SSc. Therefore, there is an urgent need to identify suitable biomarkers for early diagnosis of SSc.

Methods

Three skin gene expression datasets of SSc patients and healthy controls were downloaded from Gene Expression Omnibus (GEO) database (GSE130955, GSE58095, and GSE181549). GSE130955 (48 early diffuse cutaneous SSc and 33 controls) were utilized to screen differentially expressed genes (DEGs) between SSc and normal skin samples. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) were performed to identify diagnostic genes and construct a diagnostic prediction model. The results were further validated in GSE58095 (61 SSc and 36 controls) and GSE181549 (113 SSc and 44 controls) datasets. Receiver operating characteristic (ROC) curves were applied for assessing the level of diagnostic ability. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the diagnostic genes in skin tissues of out cohort (10 SSc and 5 controls). Immune infiltration analysis were performed using CIBERSORT algorithm.

Results

A total of 200 DEGs were identified between SSc and normal skin samples. Functional enrichment analysis revealed that these DEGs may be involved in the pathogenesis of SSc, such as extracellular matrix remodeling, cell-cell interactions, and metabolism. Subsequently, two critical genes (ENHO and NOX4) were identified by LASSO and SVM-RFE. ENHO was found down-regulated while NOX4 was up-regulated in skin of SSc patients and their expression levels were validated by above three datasets and our cohort. Notably, these differential expressions were more pronounced in patients with diffuse cutaneous SSc than in those with limited cutaneous SSc. Next, we developed a novel diagnostic model for SSc using ENHO and NOX4, which demonstrated strong predictive power in above three cohorts and in our own cohort. Furthermore, immune infiltration analysis revealed dysregulated levels of various immune cell subtypes within early SSc skin specimens, and a negative correlation was observed between the levels of ENHO and Macrophages M1 and M2, while a positive correlation was observed between the levels of NOX4 and Macrophages M1 and M2.

Conclusion

This study identified ENHO and NOX4 as novel biomarkers that can be serve as a diagnostic prediction model for early detection of SSc and play a potential role in the pathogenesis of the disease.

Tree shrews as a new animal model for systemic sclerosis research

Objective

Systemic sclerosis (SSc) is a chronic systemic disease characterized by immune dysregulation and fibrosis for which there is no effective treatment. Animal models are crucial for advancing SSc research. Tree shrews are genetically, anatomically, and immunologically closer to humans than rodents. Thus, the tree shrew model provides a unique opportunity for translational research in SSc.

Methods

In this study, a SSc tree shrew model was constructed by subcutaneous injection of different doses of bleomycin (BLM) for 21 days. We assessed the degree of inflammation and fibrosis in the skin and internal organs, and antibodies in serum. Furthermore, RNA sequencing and a series of bioinformatics analyses were performed to analyze the transcriptome changes, hub genes and immune infiltration in the skin tissues of BLM induced SSc tree shrew models. Multiple sequence alignment was utilized to analyze the conservation of selected target genes across multiple species.

Results

Subcutaneous injection of BLM successfully induced a SSc model in tree shrew. This model exhibited inflammation and fibrosis in skin and lung, and some developed esophageal fibrosis and secrum autoantibodies including antinuclear antibodies and anti-scleroderma-70 antibody. Using RNA sequencing, we compiled skin transcriptome profiles in SSc tree shrew models. 90 differentially expressed genes (DEGs) were identified, which were mainly enriched in the PPAR signaling pathway, tyrosine metabolic pathway, p53 signaling pathway, ECM receptor interaction and glutathione metabolism, all of which are closely associated with SSc. Immune infiltration analysis identified 20 different types of immune cells infiltrating the skin of the BLM-induced SSc tree shrew models and correlations between those immune cells. By constructing a protein-protein interaction (PPI) network, we identified 10 hub genes that were significantly highly expressed in the skin of the SSc models compared to controls. Furthermore, these genes were confirmed to be highly conserved in tree shrews, humans and mice.

Conclusion

This study for the first time comfirmed that tree shrew model of SSc can be used as a novel and promising experimental animal model to study the pathogenesis and translational research in SSc.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness.

Clinical Utility and Benefits of Comprehensive Genomic Profiling in Cancer

BACKGROUND

Comprehensive genomic profiling (CGP) with next-generation sequencing detects genetic alterations of hundreds of genes simultaneously and multiple molecular biomarkers with one test. In the personalized medicine era, CGP is increasingly used for cancer diagnosis, treatment selection, and prognosis prediction.

CONTENT

In this review, we summarize the benefits of CGP, clinical utility of CGP, and challenges of setting up CGP in the clinical laboratories. Besides the genetic alterations identified in the cancer-related genes, other biomarkers such as tumor mutational burden, microsatellite instability, and homologous recombination deficiency are critical for initiating targeted therapy. Compared with conventional tests, CGP uses less specimen and shortens the turnaround time if multiple biomarkers need to be tested. RNA fusion assay and liquid biopsy are helpful additions to DNA-based CGP by detecting fusions/splicing variants and complementing tissue-based CGP findings, respectively.

SUMMARY

Many previous hurdles for implementing CGP in the clinical laboratories have been gradually alleviated such as the decrease in sequencing cost, availability of both open-source and commercial bioinformatics tools, and improved reimbursement. These changes have helped to make CGP available to a greater population of cancer patients for improving characterization of their tumors and expanding their eligibility for clinical trials. Additionally, sequencing results of the hundreds of genes on CGP panels could be further analyzed to better understand the biology of various cancers and identify new biomarkers.

Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification

Cell-free RNAs (cfRNAs) offer an opportunity to detect diseases from a transcriptomic perspective, however, existing techniques have fallen short in generating a comprehensive cell-free transcriptome profile. We develop a sensitive library preparation method that is robust down to 100 µl input plasma to analyze cfRNAs independent of their 5'-end modifications. We show that it outperforms adapter ligation-based method in detecting a greater number of cfRNA species. We perform transcriptome-wide characterizations in 165 lung cancer, 30 breast cancer, 37 colorectal cancer, 55 gastric cancer, 15 liver cancer, and 133 cancer-free participants and demonstrate its ability to identify transcriptomic changes occurring in early-stage tumors. We also leverage machine learning analyses on the differentially expressed cfRNA signatures and reveal their robust performance in cancer detection and classification. Our work sets the stage for in-depth study of the cfRNA repertoire and highlights the value of cfRNAs as cancer biomarkers in clinical applications.

Challenges and opportunities to bridge translational to clinical research for personalized mitochondrial medicine

Mitochondrial disorders are a group of rare and heterogeneous genetic diseases characterized by dysfunctional mitochondria leading to deficient adenosine triphosphate synthesis and chronic energy deficit in patients. The majority of these patients exhibit a wide range of phenotypic manifestations targeting several organ systems, making their clinical diagnosis and management challenging. Bridging translational to clinical research is crucial for improving the early diagnosis and prognosis of these intractable mitochondrial disorders and for discovering novel therapeutic drug candidates and modalities. This review provides the current state of clinical testing in mitochondrial disorders, discusses the challenges and opportunities for converting basic discoveries into clinical settings, explores the most suited patient-centric approaches to harness the extraordinary heterogeneity among patients affected by the same primary mitochondrial disorder, and describes the current outlook of clinical trials.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BACKGROUND

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

RESULTS

We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

CONCLUSION

SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

A comprehensive assessment of exome capture methods for RNA sequencing of formalin-fixed and paraffin-embedded samples

RNA-Seq analysis of Formalin-Fixed and Paraffin-Embedded (FFPE) samples has emerged as a highly effective approach and is increasingly being used in clinical research and drug development. However, the processing and storage of FFPE samples are known to cause extensive degradation of RNAs, which limits the discovery of gene expression or gene fusion-based biomarkers using RNA sequencing, particularly methods reliant on Poly(A) enrichment. Recently, researchers have developed an exome targeted RNA-Seq methodology that utilizes biotinylated oligonucleotide probes to enrich RNA transcripts of interest, which could overcome these limitations. Nevertheless, the standardization of this experimental framework, including probe designs, sample multiplexing, sequencing read length, and bioinformatic pipelines, remains an essential requirement. In this study, we conducted a comprehensive comparison of three main commercially available exome capture kits and evaluated key experimental parameters, to provide the overview of the advantages and limitations associated with the selection of library preparation protocols and sequencing platforms. The results provide valuable insights into the best practices for obtaining high-quality data from FFPE samples.

Epigenetic and transcriptional regulation of the human angiotensinogen gene by high salt

Hypertension is caused by a combination of genetic and environmental factors. Angiotensinogen (AGT) is a component of RAAS, that regulates blood pressure. The human angiotensinogen (hAGT) gene has -6A/-6G polymorphism and -6A variant is associated with human hypertension. In this study, we have investigated the epigenetic regulation of the hAGT. To understand transcriptional regulation of the hAGT, we have made transgenic animals containing -6A. We show that HS affects DNA methylation and modulates transcriptional regulation of this gene in liver and kidney. High salt (HS) increases hAGT gene expression in -6A TG mice. We have observed that the number of CpG sites in the hAGT promoter is decreased after HS treatment. In the liver, seven CpG sites are methylated whereas after HS treatment, only three CpG sites remain methylated. In the kidney, five CpG sites are methylated, whereas after HS treatment, only three CpG sites remain methylated. These results suggest that HS promotes DNA demethylation and increasing AGT gene expression. RT-PCR and immunoblot analysis show that hAGT gene expression is increased by HS. Chip assay has shown that transcription factors bind strongly after HS treatment. RNA-Seq identified differentially expressed genes, novel target genes associated with hypertension, top canonical pathways, upstream regulators. One of the plausible mechanisms for HS induced up-regulation of the hAGT gene is through IL-6/JAK/STAT3/AGT axis.

Cell free ACE2 RNA: A potential biomarker of COVID-19 severity

Despite the downward trend of COVID-19 pandemic and increased immunity of the general population, COVID-19 is still an elusive disease with risks due to emerging variants. Fast and reliable diagnosis of COVID-19 disease would allow better therapeutic interventions for patients at risk to develop more severe outcomes. Cell-free RNAs (cfRNAs) have been proven to be an effective biomarker in cancer and infectious diseases. It has been reported that cfRNAs are amplified in the bloodstream of these patients and at earlier stages of the disease, reflecting tissue damage. Hence, we hypothesize that cfRNAs may serve as a potential indicator of COVID-19 disease severity. To our knowledge, this is the first report to display a significant link between COVID-19 severity and cfRNA of angiotensin converting enzyme-2 (ACE2), the receptor for SARS-CoV-2 virus. qRT-PCR analysis of liquid biopsies from COVID-19 patients (n = 82) displayed a significant increase in ACE2-cfRNA levels in patients with severe manifestations. This finding correlated with blood biomarkers (ANC, WBC, and Creatinine) that were also significantly increased in these patients. We previously showed that bronchial cells from obese subjects express higher ACE2 levels, hence, we further analysed the involvement of obesity as a main contributor to severe outcomes. We confirm a significant increase of ACE2-cfRNA in the plasma of obese/overweight (Ob/Ov) COVID-19 patients compared to lean subjects, with no observed significant change in blood biomarkers. These findings suggest that monitoring ACE2-cfRNAs, as a biomarker, during COVID-19 infection may allow for better disease management, specifically for severe-COVID-19 patients.

Pre-analytical effects on whole transcriptome and targeted RNA sequencing analysis in cytology: The effects of prolonged time in storage of effusion specimens prior to preservation

OBJECTIVES

To investigate the pre-analytics of the molecular testing of cytology specimens, we studied the effects of time in refrigerator storage (4°C) of malignant effusions on RNA sequencing (RNAseq) results.

METHODS

Ten effusion specimens were stored in a refrigerator (4°C) for different durations (day 0, 1, 4, and 7). All specimens were prepared as cytospins fixed in either Carnoy's solution or 95% ethanol (EtOH) and in an RNA preservative for a fresh frozen (FF) high-quality reference. Whole transcriptome (wt) and targeted (t)RNAseq of two multigene expression signatures were performed. We then compared transcript expression levels (including mutant allele fraction) according to pre-analytical variables using a concordance correlation coefficient (CCC) and a mixed effect model.

RESULTS

Sequencing results were mostly stable over increasing time in storage. Cytospins fixed in Carnoy's solution were more concordant with FF samples than cytospins fixed in 95% EtOH at all timepoints. This finding was consistent for both wtRNAseq (averages: day 0 CCC = 0.98 vs 0.91; day 7 CCC = 0.88 vs 0.78) and tRNAseq methods (averages: day 0 CCC = 0.98 vs 0.81; day 7 CCC = 0.98 vs 0.90). Cytospins fixed in Carnoy's solution did not show significant changes in expression over timepoints or between expression signatures, whereas 95% EtOH did.

CONCLUSION

RNAseq can be accurately performed on effusion specimens after prolonged refrigerator storage. RNA extracted from scraped cytospin slides fixed in Carnoy's solution was marginally superior to 95% EtOH fixation, but either method had comparable analytic performance to high-quality FF RNA samples.

The understudied global experiment of pollution's impacts on wildlife and human health: The ethical imperative for interdisciplinary research

The global impact of pollution on human and wildlife health is a growing concern. The health impacts of pollution are significant and far-reaching yet poorly understood as no one field of research has the practices and methodologies required to encapsulate the diversity of these consequences. This paper advocates that interdisciplinary research is essential to comprehend the full extent of the impact of pollution. Medical and ecological research play a key role in investigating the health consequences of the pollution crisis, yet the wildlife experience is often neglected. This paper outlines how applying advanced techniques and expertise adapted in medical research to wildlife exposed to pollutants offers a unique perspective to understanding the full diversity of impacts to health. The challenges that impede the progress of this research include the lack of support for interdisciplinary research among funding streams, limitations in field-specific techniques, and a lack of communication between researchers from different disciplines. Of awarded funding from major national research councils across Australia, Europe, and the United States of America, only 0.5% is dedicated to pollution focused research. This is inclusive of laboratory equipment, mitigation strategies, quantification of environmental samples and health consequences research. Of that, 0.03% of funding is awarded to explaining the wildlife experience and documenting the health consequences observed despite being model organisms to environmentally and biologically relevant models for pollution exposure. This calls for a coordinated effort to overcome these hurdles and to promote interdisciplinary research in order to fully comprehend the consequences of pollution exposure and protect the health of humans, wildlife, and the environment. An interdisciplinary approach to this problem is timely given the magnitude of negative health consequences associated with exposure, the number of pollutants already present within the environment and the continual development of new compounds.

RNA-seq RNAaccess identified as the preferred method for gene expression analysis of low quality FFPE samples

Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses.

Microfluidic Device-Based Virus Detection and Quantification in Future Diagnostic Research: Lessons from the COVID-19 Pandemic

The global economic and healthcare crises experienced over the past three years, as a result of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has significantly impacted the commonplace habits of humans around the world. SARS-CoV-2, the virus responsible for the coronavirus 2019 (COVID-19) phenomenon, has contributed to the deaths of millions of people around the world. The potential diagnostic applications of microfluidic devices have previously been demonstrated to effectively detect and quasi-quantify several different well-known viruses such as human immunodeficiency virus (HIV), influenza, and SARS-CoV-2. As a result, microfluidics has been further explored as a potential alternative to our currently available rapid tests for highly virulent diseases to better combat and manage future potential outbreaks. The outbreak management during COVID-19 was initially hindered, in part, by the lack of available quantitative rapid tests capable of confirming a person's active infectiousness status. Therefore, this review will explore the use of microfluidic technology, and more specifically RNA-based virus detection methods, as an integral part of improved diagnostic capabilities and will present methods for carrying the lessons learned from COVID-19 forward, toward improved diagnostic outcomes for future pandemic-level threats. This review will first explore the context of the COVID-19 pandemic and how diagnostic technology was shown to have required even greater advancements to keep pace with the transmission of such a highly infectious virus. Secondly, the historical significance of integrating microfluidic technology in diagnostics and how the different types of genetic-based detection methods may vary in their potential practical applications. Lastly, the review will summarize the past, present, and future potential of RNA-based virus detection/diagnosis and how it might be used to better prepare for a future pandemic.

Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.