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We presented a method to find potential cancer attractors using single-cell RNA sequencing (scRNA-seq) data. We tested our method in a Glioblastoma Multiforme (GBM) dataset, an aggressive brain tumor presenting high heterogeneity. Using the cancer attractor concept, we argued that the GBM's underlying dynamics could partially explain the observed heterogeneity, with the dataset covering a representative region around the attractor. Exploratory data analysis revealed promising GBM's cellular clusters within a 3-dimensional marker space. We approximated the clusters' centroid as stable states and each cluster covariance matrix as defining confidence regions. To investigate the presence of attractors inside the confidence regions, we constructed a GBM gene regulatory network, defined a model for the dynamics, and prepared a framework for parameter estimation. An exploration of hyperparameter space allowed us to sample time series intending to simulate myriad variations of the tumor microenvironment. We obtained different densities of stable states across gene expression space and parameters displaying multistability across different clusters. Although we used our methodological approach in studying GBM, we would like to highlight its generality to other types of cancer. Therefore, this report contributes to an advance in the simulation of cancer dynamics and opens avenues to investigate potential therapeutic targets., (© 2024. The Author(s).)

In this study, we introduce StructmRNA, a new BERT-based model that was designed for the detailed analysis of mRNA sequences and structures. The success of DNABERT in understanding the intricate language of non-coding DNA with bidirectional encoder representations is extended to mRNA with StructmRNA. This new model uses a special dual-level masking technique that covers both sequence and structure, along with conditional masking. This enables StructmRNA to adeptly generate meaningful embeddings for mRNA sequences, even in the absence of explicit structural data, by capitalizing on the intricate sequence-structure correlations learned during extensive pre-training on vast datasets. Compared to well-known models like those in the Stanford OpenVaccine project, StructmRNA performs better in important tasks such as predicting RNA degradation. Thus, StructmRNA can inform better RNA-based treatments by predicting the secondary structures and biological functions of unseen mRNA sequences. The proficiency of this model is further confirmed by rigorous evaluations, revealing its unprecedented ability to generalize across various organisms and conditions, thereby marking a significant advance in the predictive analysis of mRNA for therapeutic design. With this work, we aim to set a new standard for mRNA analysis, contributing to the broader field of genomics and therapeutic development., (© 2024. The Author(s).)

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia., (© 2024. The Author(s).)

Postnatal kidney growth is substantial and involves expansion in kidney tubules without growth of new nephrons, which are the functional units of the kidney. Proliferation and differentiation pathways underpinning nephron elongation are not well defined. To address this, we performed sequential characterization of mouse kidney transcriptomics at the single cell level. Single nuclear RNA sequencing (snRNA-seq) was performed on kidney tissue from male and female mice at 1, 2, 4 and 12 weeks of age using the 10x Chromium platform. Unbiased clustering was performed on 68,775 nuclei from 16 animals. 31 discrete cellular clusters were seen, which were identified through comparison of their gene expression profiles to canonical markers of kidney cell populations. High levels of proliferation were evident at early time points in some cell types, especially tubular cells, but not in other cell types, for example podocytes. Proliferation was especially evident in Proximal Tubular Cells (PTCs) which are the most abundant cell type in the adult kidney. Uniquely when compared to other kidney cell types, PTCs demonstrated sex-specific expression profiles at late, but not early, time points. Mapping of PTC differentiation pathways using techniques including trajectory and RNA Velocity analyses delineated increasing PTC specialization and sex-specific phenotype specification. Our single-cell transcriptomics data characterise cellular states observed during kidney growth. We have identified PTC differentiation pathways that lead to sex-specific tubular cell phenotypes. Tubular proliferative responses are of central importance in postnatal kidney growth and have also been linked to kidney recovery versus fibrosis following injury. Our unbiased and comprehensive dataset of tubular cell development can be used to identify candidate pathways for therapeutic targeting., (© 2024. The Author(s).)

Chemical modifications to mRNA respond dynamically to environmental cues and are important modulators of gene expression. Nanopore direct RNA sequencing has been applied for assessing the presence of pseudouridine (ψ) modifications through basecalling errors and signal analysis. These approaches strongly depend on the sequence context around the modification, and the occupancies derived from these measurements are not quantitative. In this work, we combine direct RNA sequencing of synthetic RNAs bearing site-specific modifications and supervised machine learning models (ModQuant) to achieve near-analytical, site-specific ψ quantification. Our models demonstrate that the ionic current signal features important for accurate ψ classification are sequence dependent and encompass information extending beyond n + 2 and n - 2 nucleotides from the ψ site. This is contradictory to current models, which assume that accurate ψ classification can be achieved with signal information confined to the 5-nucleotide k-mer window (n + 2 and n - 2 nucleotides from the ψ site). We applied our models to quantitatively profile ψ occupancy in five mRNA sites in datasets from seven human cell lines, demonstrating conserved and variable sites. Our study motivates a wider pipeline that uses ground-truth RNA control sets with site-specific modifications for quantitative profiling of RNA modifications. The ModQuant pipeline and guide are freely available at https://github.com/wanunulab/ModQuant ., (© 2024. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) is a key technology for investigating cell development and analysing cell diversity across various diseases. However, the high dimensionality and extreme sparsity of scRNA-seq data pose great challenges for accurate cell type annotation. To address this, we developed a new cell-type annotation model called scGAA (general gated axial-attention model for accurate cell-type annotation of scRNA-seq). Based on the transformer framework, the model decomposes the traditional self-attention mechanism into horizontal and vertical attention, considerably improving computational efficiency. This axial attention mechanism can process high-dimensional data more efficiently while maintaining reasonable model complexity. Additionally, the gated unit was integrated into the model to enhance the capture of relationships between genes, which is crucial for achieving an accurate cell type annotation. The results revealed that our improved transformer model is a promising tool for practical applications. This theoretical innovation increased the model performance and provided new insights into analytical tools for scRNA-seq data., (© 2024. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT ., (© 2024. The Author(s).)

The study aims to accurately identify differentially expressed genes (DEGs) and biological pathways in mycobacterial infections through bioinformatics for deeper disease understanding. Differentially expressed genes (DEGs) was explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Unique DEGs were submitted on least absolute shrinkage and selection operator (LASSO) regression analysis. 1,057 DEGs from two GSE datasets were identified, which were closely connected with NTM/ latent TB infection (LTBI)/active TB disease (ATB). It was demonstrated that these DEGs are mainly associated with detoxification processes, and virus and bacterial infections. Moreover, the METTL7B gene was the most informative marker for distinguishing LTBI and ATB with an area under the curve (AUC) of 0.983 (95%CI: 0.964 to 1). The significantly upregulated HBA1/2 genes were the most informative marker for distinguishing between individuals of IGRA-HC/NTM and LTBI (P < 0.001). Moreover, the upregulated HBD gene was also differ between IGRA-HC/NTM and ATB (P < 0.001). We have identified gene signatures associated with Mycobacterium infection in whole blood, which could be significant for understanding the molecular mechanisms and diagnosis of NTM, LTBI, or ATB., (© 2024. The Author(s).)

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike., (© 2024. The Author(s).)

The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition of different cell types in a quick and affordable way. In this study, we developed a quantile regression and deep learning-based method called Neural Network Immune Contexture Estimator (NNICE) to estimate the cell type abundance and its uncertainty by automatically deconvolving bulk RNA-seq data. The proposed NNICE model was able to successfully recover ground-truth cell type fraction values given unseen bulk mixture gene expression profiles from the same dataset it was trained on. Compared with baseline methods, NNICE achieved better performance on deconvolve both pseudo-bulk gene expressions (Pearson correlation R = 0.9) and real bulk gene expression data (Pearson correlation R = 0.9) across all cell types. In conclusion, NNICE combines statistic inference with deep learning to provide accurate and interpretable cell type deconvolution from bulk gene expression., (© 2024. The Author(s).)

Dilated cardiomyopathy (DCM) is a common cause of heart failure, thromboembolism, arrhythmias, and sudden cardiac death. The quality of life and long-term survival rates of patients with dilated DCM have greatly improved in recent decades. Nevertheless, the clinical prognosis for DCM patients remains unfavorable. The primary driving factors underlying the pathogenesis of DCM remain incompletely understood. The present study aimed to identify driving factors underlying the pathogenesis of DCM from the perspective of gene regulatory networks. Single-cell RNA sequencing data and bulk RNA data were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis, single-cell genomics analysis, and functional enrichment analysis were conducted using R software. The construction of Gene Regulatory Networks was performed using Python. We used the pySCENIC method to analyze the single-cell data and identified 401 regulons. Through variance decomposition, we selected 19 regulons that showed significant responsiveness to DCM. Next, we employed the ssGSEA method to assess regulons in two bulk RNA datasets. Significant statistical differences were observed in 9 and 13 regulons in each dataset. By intersecting these differentiated regulons and identifying shared targets that appeared at least twice, we successfully pinpointed three differentially expressed targets across both datasets. In this study, we assessed and identified 19 gene regulatory networks that were responsive to the disease. Furthermore, we validated these networks using two bulk RNA datasets of DCM. The elucidation of dysregulated regulons and targets (CDKN1A, SAT1, ZFP36) enhances the molecular understanding of DCM, aiding in the development of tailored therapies for patients., (© 2024. The Author(s).)

To explore the hub comorbidity genes and potential pathogenic mechanisms of hypopharyngeal carcinoma with esophageal carcinoma, and evaluate their diagnostic value for hypopharyngeal carcinoma with co-morbid esophageal carcinoma. We performed gene sequencing on tumor tissues from 6 patients with hypopharyngeal squamous cell carcinoma with esophageal squamous cell carcinoma (hereafter referred to as "group A") and 6 patients with pure hypopharyngeal squamous cell carcinoma (hereafter referred to as "group B"). We analyzed the mechanism of hub genes in the development and progression of hypopharyngeal squamous cell carcinoma with esophageal squamous cell carcinoma through bioinformatics, and constructed an ROC curve and Nomogram prediction model to analyze the value of hub genes in clinical diagnosis and treatment. 44,876 genes were sequenced in 6 patients with group A and 6 patients with group B. Among them, 76 genes showed significant statistical differences between the group A and the group B.47 genes were expressed lower in the group A than in the group B, and 29 genes were expressed higher. The top five hub genes were GABRG2, CACNA1A, CNTNAP2, NOS1, and SCN4B. GABRG2, CNTNAP2, and SCN4B in the hub genes have high diagnostic value in determining whether hypopharyngeal carcinoma patients have combined esophageal carcinoma (AUC: 0.944, 0.944, 0.972). These genes could possibly be used as potential molecular markers for assessing the risk of co-morbidity of hypopharyngeal carcinoma combined with esophageal carcinoma., (© 2024. The Author(s).)

Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data, but existing clustering algorithms struggle with the inherent high dimensionality, noise, and sparsity of scRNA-seq data. To overcome these limitations, we propose a clustering algorithm: the Dual Correlation Reduction network-based Extreme Learning Machine (DCRELM). First, DCRELM obtains the low-dimensional and dense result features of scRNA-seq data in an extreme learning machine (ELM) random mapping space. Second, the ELM graph distortion module is employed to obtain a dual view of the resulting features, effectively enhancing their robustness. Third, the autoencoder fusion module is employed to learn the attributes and structural information of the resulting features, and merge these two types of information to generate consistent latent representations of these features. Fourth, the dual information reduction network is used to filter the redundant information and noise in the dual consistent latent representations. Last, a triplet self-supervised learning mechanism is utilized to further improve the clustering performance. Extensive experiments show that the DCRELM performs well in terms of clustering performance and robustness. The code is available at https://github.com/gaoqingyun-lucky/awesome-DCRELM ., (© 2024. The Author(s).)

The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development., (© 2024. The Author(s).)

Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value., (© 2024. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes., (© 2024. The Author(s).)

The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the complexities of intricate diseases. However, due to the inherently low signal-to-noise ratio and the presence of an excessive number of missing values, scRNA-seq data analysis encounters unique challenges. Here, we present cnnImpute, a novel convolutional neural network (CNN) based method designed to address the issue of missing data in scRNA-seq. Our approach starts by estimating missing probabilities, followed by constructing a CNN-based model to recover expression values with a high likelihood of being missing. Through comprehensive evaluations, cnnImpute demonstrates its effectiveness in accurately imputing missing values while preserving the integrity of cell clusters in scRNA-seq data analysis. It achieved superior performance in various benchmarking experiments. cnnImpute offers an accurate and scalable method for recovering missing values, providing a useful resource for scRNA-seq data analysis., (© 2024. The Author(s).)

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power., (© 2023. The Author(s).)

High throughput RNA sequencing experiments are widely conducted and analyzed to identify differentially expressed genes (DEGs). The statistical models calculated for this task are often not clear to practitioners, and analyses may not be optimally tailored to the research hypothesis. Often, interaction effects (IEs) are the mathematical equivalent of the biological research question but are not considered for different reasons. We fill this gap by explaining and presenting the potential benefit of IEs in the search for DEGs using RNA-Seq data of mice that receive different diets for different time periods. Using an IE model leads to a smaller, but likely more biologically informative set of DEGs compared to a common approach that avoids the calculation of IEs., (© 2023. The Author(s).)

Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile-quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues., (© 2023. Springer Nature Limited.)

Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC., (© 2023. Springer Nature Limited.)

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets., (© 2023. The Author(s).)

RNA editing is a post-transcriptional modification with a cell-specific manner and important biological implications. Although single-cell RNA-seq (scRNA-seq) is an effective method for studying cellular heterogeneity, it is difficult to detect and study RNA editing events from scRNA-seq data because of the low sequencing coverage. To overcome this, we develop a computational method to systematically identify RNA editing sites of cell types from scRNA-seq data. To demonstrate its effectiveness, we apply it to scRNA-seq data of human hematopoietic stem/progenitor cells (HSPCs) with an annotated lineage differentiation relationship according to previous research and study the impacts of RNA editing on hematopoiesis. The dynamic editing patterns reveal the relevance of RNA editing on different HSPCs. For example, four microRNA (miRNA) target sites on 3' UTR of EIF2AK2 are edited across all HSPC populations, which may abolish the miRNA-mediated inhibition of EIF2AK2. Elevated EIF2AK2 may thus activate the integrated stress response (ISR) pathway to initiate global translational attenuation as a protective mechanism to maintain cellular homeostasis during HSPCs' differentiation. Besides, our findings also indicate that RNA editing plays an essential role in the coordination of lineage commitment and self-renewal of hematopoietic stem cells (HSCs). Taken together, we demonstrate the capacity of scRNA-seq data to exploit RNA editing events of cell types, and find that RNA editing may exert multiple modules of regulation in hematopoietic processes., (© 2023. The Author(s).)

Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types., (© 2023. The Author(s).)

The diagnosis and classification of soft tissue sarcomas (STS) remain challenging because of the rarity and overlapping morphologic manifestations of diverse STS subtypes. Characteristic gene fusions are commonly detected in STS and represent useful diagnostic markers. This study established and validated a custom-designed RNA sequencing panel that identified 64 gene fusions in STS. The analytical performance validation yielded excellent accuracy, with 100% (95% CI, 94.40%-100%) sensitivity and 93.33% (95% CI, 68.05%-99.83%) specificity. Clinical performances were further confirmed with 145 clinical formalin-fixed and paraffin-embedded (FFPE) samples from STS patients. Fusions were detected in 40% of samples (58/145). The common fusions SS18-SSX family, EWSR1-related fusions, COL1A1-PDGFB, FOXO1-associated fusions, and FUS-associated fusions were identified in corresponding STS subtypes. The RNA panel detected specific fusions in several cases where no conclusive diagnosis can be made based on the morphology and immunohistochemistry results. Data collected in this study demonstrate that the RNA fusions panel can better classify STS subtypes and serve as a good supplement for histopathology, exhibiting a great potential for the STS precise diagnosis., (© 2023. The Author(s).)

The dramatic increase in the number of single-cell RNA-sequence (scRNA-seq) investigations is indeed an endorsement of the new-fangled proficiencies of next generation sequencing technologies that facilitate the accurate measurement of tens of thousands of RNA expression levels at the cellular resolution. Nevertheless, missing values of RNA amplification persist and remain as a significant computational challenge, as these data omission induce further noise in their respective cellular data and ultimately impede downstream functional analysis of scRNA-seq data. Consequently, it turns imperative to develop robust and efficient scRNA-seq data imputation methods for improved downstream functional analysis outcomes. To overcome this adversity, we have designed an imputation framework namely deep generative autoencoder network [DGAN]. In essence, DGAN is an evolved variational autoencoder designed to robustly impute data dropouts in scRNA-seq data manifested as a sparse gene expression matrix. DGAN principally reckons count distribution, besides data sparsity utilizing a gaussian model whereby, cell dependencies are capitalized to detect and exclude outlier cells via imputation. When tested on five publicly available scRNA-seq data, DGAN outperformed every single baseline method paralleled, with respect to downstream functional analysis including cell data visualization, clustering, classification and differential expression analysis. DGAN is executed in Python and is accessible at https://github.com/dikshap11/DGAN ., (© 2023. The Author(s).)

Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore, it is often coupled with log-transformation in scRNAseq applications, which can distort the data and obscure meaningful variation. We describe correspondence analysis (CA), a count-based alternative to PCA. CA is based on decomposition of a chi-squared residual matrix, avoiding distortive log-transformation. To address overdispersion and high sparsity in scRNAseq data, we propose five adaptations of CA, which are fast, scalable, and outperform standard CA and glmPCA, to compute cell embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. In particular, we find that CA with Freeman-Tukey residuals performs especially well across diverse datasets. Other advantages of the CA framework include visualization of associations between genes and cell populations in a "CA biplot," and extension to multi-table analysis; we introduce corralm for integrative multi-table dimension reduction of scRNAseq data. We implement CA for scRNAseq data in corral, an R/Bioconductor package which interfaces directly with single cell classes in Bioconductor. Switching from PCA to CA is achieved through a simple pipeline substitution and improves dimension reduction of scRNAseq datasets., (© 2023. The Author(s).)

Challenges related to high-quality RNA extraction from post-mortem tissue have limited RNA-sequencing (RNA-seq) application in certain skeletal muscle groups, including the intrinsic laryngeal muscles (ILMs). The present study identified critical factors contributing to substandard RNA extraction from the ILMs and established a suitable method that permitted high-throughput analysis. Here, standard techniques for tissue processing were adapted, and an effective means to control confounding effects during specimen preparation was determined. The experimental procedure consistently provided sufficient intact total RNA (N = 68) and RIN ranging between 7.0 and 8.6, which was unprecedented using standard RNA purification protocols. This study confirmed the reproducibility of the workflow through repeated trials at different postnatal time points and across the distinctive ILMs. High-throughput diagnostics from 90 RNA samples indicated no sequencing alignment scores below 70%, validating the extraction strategy. Significant differences between the standard and experimental conditions suggest circumvented challenges and broad applicability to other skeletal muscles. This investigation remains ongoing given the prospect of therapeutic insights to voice, swallowing, and airway disorders. The present methodology supports pioneering global transcriptome investigations in the larynx previously unfounded in literature., (© 2022. The Author(s).)

Spatial transcriptome analysis of formalin-fixed paraffin-embedded (FFPE) tissues using RNA-sequencing (RNA-seq) provides interactive information on morphology and gene expression, which is useful for clinical applications. However, despite the advantages of long-term storage at room temperature, FFPE tissues may be severely damaged by methylene crosslinking and provide less gene information than fresh-frozen tissues. In this study, we proposed a sensitive FFPE micro-tissue RNA-seq method that combines the punching of tissue sections (diameter: 100 μm) and the direct construction of RNA-seq libraries. We evaluated a method using mouse liver tissues at two years after fixation and embedding and detected approximately 7000 genes in micro-punched tissue-spots (thickness: 10 μm), similar to that detected with purified total RNA (2.5 ng) equivalent to the several dozen cells in the spot. We applied this method to clinical FFPE specimens of lung cancer that had been fixed and embedded 6 years prior, and found that it was possible to determine characteristic gene expression in the microenvironment containing tumor and non-tumor cells of different morphologies. This result indicates that spatial gene expression analysis of the tumor microenvironment is feasible using FFPE tissue sections stored for extensive periods in medical facilities., (© 2022. The Author(s).)

When biologically interpretation of the data obtained from the single-cell RNA sequencing (scRNA-seq) analysis is attempted, additional information on the location of the single cells, behavior of the surrounding cells, and the microenvironment they generate, would be very important. We developed an inexpensive, high throughput application while preserving spatial organization, named "semibulk RNA-seq" (sbRNA-seq). We utilized a microfluidic device specifically designed for the experiments to encapsulate both a barcoded bead and a cell aggregate (a semibulk) into a single droplet. Using sbRNA-seq, we firstly analyzed mouse kidney specimens. In the mouse model, we could associate the pathological information with the gene expression information. We validated the results using spatial transcriptome analysis and found them highly consistent. When we applied the sbRNA-seq analysis to the human breast cancer specimens, we identified spatial interactions between a particular population of immune cells and that of cancer-associated fibroblast cells, which were not precisely represented solely by the single-cell analysis. Semibulk analysis may provide a convenient and versatile method, compared to a standard spatial transcriptome sequencing platform, to associate spatial information with transcriptome information., (© 2022. The Author(s).)

Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout screening in addition to the regular gene expression. A full characterization of the transcriptional landscape of different groups of cells or tissues holds enormous potential for both basic science as well as clinical applications. Although many methods have been developed in the realm of differential gene expression analysis, they all geared towards a particular type of sequencing data and failed to perform well when applied in different types of transcriptomic data. To fill this gap, we offer a negative beta binomial t-test (NBBt-test). NBBt-test provides multiple functions to perform differential analyses of alternative splicing, polyadenylation, CRISPR knockout screening, and gene expression datasets. Both real and large-scale simulation data show superior performance of NBBt-test with higher efficiency, and lower type I error rate and FDR to identify differential isoforms and differentially expressed genes and differential CRISPR knockout screening genes with different sample sizes when compared against the current very popular statistical methods. An R-package implementing NBBt-test is available for downloading from CRAN ( https://CRAN.R-project.org/package=NBBttest )., (© 2022. The Author(s).)

This paper proposes a KC Score to measure feature importance in clustering analysis of high-dimensional data. The KC Score evaluates the contribution of features based on the correlation between the original features and the reconstructed features in the low dimensional latent space. A KC Score-based feature selection strategy is further developed for clustering analysis. We investigate the performance of the proposed strategy by conducting a study of four single-cell RNA sequencing (scRNA-seq) datasets. The results show that our strategy effectively selects important features for clustering. In particular, in three datasets, our proposed strategy selected less than 5% of the features and achieved the same or better clustering performance than when using all of the features., (© 2022. The Author(s).)

The full-length double-strand cDNA sequencing, one of the RNA-Seq methods, is a powerful method used to investigate the transcriptome status of a gene of interest, such as its transcription level and alternative splicing variants. Furthermore, full-length double-strand cDNA sequencing has the advantage that it can create a library from a small amount of sample and the library can be applied to long-read sequencers in addition to short-read sequencers. Nevertheless, one of our previous studies indicated that the full-length double-strand cDNA sequencing yields non-specific genomic DNA amplification, affecting transcriptome analysis, such as transcript quantification and alternative splicing analysis. In this study, it was confirmed that it is possible to produce the RNA-Seq library from only genomic DNA and that the full-length double-strand cDNA sequencing of genomic DNA yielded non-specific genomic DNA amplification. To avoid non-specific genomic DNA amplification, two methods were examined, which are the DNase I-treated full-length double-strand cDNA sequencing and poly(A) capture full-length double-strand cDNA sequencing. Contrary to expectations, the non-specific genomic DNA amplification was increased and the number of the detected expressing genes was reduced in DNase I-treated full-length double-strand cDNA sequencing. On the other hand, in the poly(A) capture full-length double-strand cDNA sequencing, the non-specific genomic DNA amplification was significantly reduced, accordingly the accuracy and the number of detected expressing genes and splicing events were increased. The expression pattern and percentage spliced in index of splicing events were highly correlated. Our results indicate that the poly(A) capture full-length double-strand cDNA sequencing improves transcript quantification accuracy and the detection ability of alternative splicing events. It is also expected to contribute to the determination of the significance of DNA variants to splicing events., (© 2022. The Author(s).)

Reference cell atlases powered by single cell and spatial transcriptomics technologies are becoming available to study healthy and diseased tissue at single cell resolution. One important use of these data resources is to compare cell types from new dataset with cell types in the reference atlases to evaluate their phenotypic similarities and differences, for example, for identifying novel cell types under disease conditions. For this purpose, rigorously-validated computational algorithms are needed to perform these cell type matching tasks that can compare datasets from different experiment platforms and sample types. Here, we present significant enhancements to FR-Match (v2.0)-a multivariate nonparametric statistical testing approach for matching cell types in query datasets to reference atlases. FR-Match v2.0 includes a normalization procedure to facilitate cross-platform cluster-level comparisons (e.g., plate-based SMART-seq and droplet-based 10X Chromium single cell and single nucleus RNA-seq and spatial transcriptomics) and extends the pipeline to also allow cell-level matching. In the use cases evaluated, FR-Match showed robust and accurate performance for identifying common and novel cell types across tissue regions, for discovering sub-optimally clustered cell types, and for cross-platform and cross-sample cell type matching., (© 2022. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of the cellular landscape of organs. Most single-cell protocols require fresh material, which limits sample size per experiment, and consequently, introduces batch effects. This is especially true for samples acquired through complex medical procedures, such as intestinal mucosal biopsies. Moreover, the tissue dissociation procedure required for obtaining single cells is a major source of noise; different dissociation procedures applied to different compartments of the tissue induce artificial gene expression differences between cell subsets. To overcome these challenges, we have developed a one-step dissociation protocol and demonstrated its use on cryopreserved gut mucosal biopsies. Using flow cytometry and scRNA-seq analysis, we compared this one-step dissociation protocol with the current gold standard, two-step collagenase digestion, and an adaptation of a recently published alternative, three-step cold-active Bacillus licheniformus protease digestion. Both cell viability and cell type composition were comparable between the one-step and two-step collagenase dissociation, with the former being more time-efficient. The cold protease digestion resulted in equal cell viability, but better preserves the epithelial cell types. Consequently, to analyze the rarer cell types, such as glial cells, larger total biopsy cell numbers are required as input material. The multi-step protocols affected cell types spanning multiple compartments differently. In summary, we show that cryopreserved gut mucosal biopsies can be used to overcome the logistical challenges and batch effects in large scRNA-seq studies. Furthermore, we demonstrate that using cryopreserved biopsies digested using a one-step collagenase protocol enables large-scale scRNA-seq, FACS, organoid generation and intraepithelial lymphocyte expansion., (© 2022. The Author(s).)

Single nuclei RNA sequencing (snRNA-seq) has evolved as a powerful tool to study complex human diseases. Single cell resolution enables the study of novel cell types, biological processes, cell trajectories, and cell-cell signaling pathways. snRNA-seq largely relies on the dissociation of intact nuclei from human tissues. However, the study of complex tissues using small core biopsies presents many technical challenges. Here, an optimized protocol for single nuclei isolation is presented for frozen and RNAlater preserved human kidney biopsies. The described protocol is fast, low cost, and time effective due to the elimination of cell sorting and ultra-centrifugation. Samples can be processed in 90 min or less. This method is effective for obtaining normal nuclei morphology without signs of structural damage. Using snRNA-seq, 16 distinct kidney cell clusters were recovered from normal and peri-transplant acute kidney injury allograft samples, including immune cell clusters. Quality control measurements demonstrated that these optimizations eliminated cellular debris and allowed for a high yield of high-quality nuclei and RNA for library preparation and sequencing. Cellular disassociation did not induce cellular stress responses, which recapitulated transcriptional patterns associated with standardized methods of nuclei isolation. Future applications of this protocol will allow for thorough investigations of small biobank biopsies, identifying cell-specific injury pathways and driving the discovery of novel diagnostics and therapeutic targets., (© 2022. The Author(s).)

Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing., (© 2022. The Author(s).)

Current methods of high-throughput RNA sequencing of prokaryotes, including transcriptome analysis or ribosomal profiling, need deep sequencing to achieve sufficient numbers of effective reads (e.g., mapping to mRNA) in order to also find weakly expressed genetic elements. The fraction of high-quality reads mapping to coding RNAs (i.e., mRNA) is mainly influenced by the large content of rRNA and, to a lesser extent, tRNA in total RNA. Thus, depletion of rRNA increases coverage and thus sequencing costs. RiboZero, a depletion kit based on probe hybridisation and rRNA-removal was found to be most efficient in the past, but it was discontinued in 2018. To facilitate comparability with previous experiments and to help choose adequate replacements, we compare three commercially available rRNA depletion kits also based on hybridization and magnetic beads, i.e., riboPOOLs, RiboMinus and MICROBExpress, with the former RiboZero. Additionally, we constructed biotinylated probes for magnetic bead capture and rRNA depletion in this study. Based on E. coli, we found similar efficiencies in rRNA depletion for riboPOOLs and the self-made depletion method; both comparable to the former RiboZero, followed by RiboMinus, succeeded by MICROBExpress. Further, our in-house protocol allows customized species-specific rRNA or even tRNA depletion or depletion of other RNA targets. Both, the self-made biotinylated probes and riboPOOLs, were most successful in reducing the rRNA content and thereby increasing sequencing depth concerning mRNA reads. Additionally, the number of reads matching to weakly expressed genes are increased. In conclusion, the self-made specific biotinylated probes and riboPOOLs are an adequate replacement for the former RiboZero. Both are very efficient in depleting rRNAs, increasing mRNA reads and thus sequencing efficiency., (© 2022. The Author(s).)

Protein synthesis is dysregulated in many diseases, but we lack a systems-level picture of how signaling molecules and RNA binding proteins interact with the translational machinery, largely due to technological limitations. Here we present riboPLATE-seq, a scalable method for generating paired libraries of ribosome-associated and total mRNA. As an extension of the PLATE-seq protocol, riboPLATE-seq utilizes barcoded primers for pooled library preparation, but additionally leverages anti-rRNA ribosome immunoprecipitation on whole polysomes to measure ribosome association (RA). We compare RA to its analogue in ribosome profiling and RNA sequencing, translation efficiency, and demonstrate both the performance of riboPLATE-seq and its utility in detecting translational alterations induced by specific inhibitors of protein kinases., (© 2022. The Author(s).)

Maize is a heavy consumer of fertilizer nitrogen (N) which not only results in the high cost of cultivation but may also lead to environmental pollution. Therefore, there is a need to develop N-use efficient genotypes, a prerequisite for which is a greater understanding of N-deficiency stress adaptation. In this study, comparative transcriptome analysis was performed using leaf and root tissues from contrasting inbred lines, viz., DMI 56 (tolerant to N stress) and DMI 81 (susceptible to N stress) to delineate the differentially expressed genes (DEGs) under low-N stress. The contrasting lines were grown hydroponically in modified Hoagland solution having either sufficient- or deficient-N, followed by high-throughput RNA-sequencing. A total of 8 sequencing libraries were prepared and 88-97% of the sequenced raw reads were mapped to the reference B73 maize genome. Genes with a p value ≤ 0.05 and fold change of ≥ 2.0 or ≤ - 2 were considered as DEGs in various combinations performed between susceptible and tolerant genotypes. DEGs were further classified into different functional categories and pathways according to their putative functions. Gene Ontology based annotation of these DEGs identified three different functional categories: biological processes, molecular function, and cellular component. The KEGG and Mapman based analysis revealed that most of the DEGs fall into various metabolic pathways, biosynthesis of secondary metabolites, signal transduction, amino acid metabolism, N-assimilation and metabolism, and starch metabolism. Some of the key genes involved in N uptake (high-affinity nitrate transporter 2.2 and 2.5), N assimilation and metabolism (glutamine synthetase, asparagine synthetase), redox homeostasis (SOD, POX), and transcription factors (MYB36, AP2-EREBP) were found to be highly expressed in the tolerant genotype compared to susceptible one. The candidate genes identified in the present study might be playing a pivotal role in low-N stress adaptation in maize and hence could be useful in augmenting further research on N metabolism and development of N-deficiency tolerant maize cultivars., (© 2022. The Author(s).)

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/ ., (© 2022. The Author(s).)

Characterization of the microbial community is essential for understanding the symbiotic relationships between microbes and host insects. Chrysomya megacephala is a vital resource, a forensic insect, a pollinator, and a vector for enteric bacteria, protozoa, helminths, and viruses. However, research on its microbial community is incomprehensive, particularly at the pupal stage, which comprises approximately half of the entire larval development stage and is important entomological evidence in forensic medicine. For the first time, this study investigated the bacterial communities of C. megacephala pupae at different ages using third-generation sequencing technology. The results showed that C. megacephala has a diverse and dynamic bacterial community. Cluster analysis at ≥ 97% similarity produced 154 operational taxonomic units (OTUs) that belonged to 10 different phyla and were distributed into 15 classes, 28 orders, 50 families, 88 genera, and 130 species. Overall, the number of bacterial OTUs increased with the development of pupae, and the relative abundance of Wolbachia in the Day5 group was significantly lower than that in the other groups. Within the pupal stage, Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla of bacteria. At the genus level, Wolbachia and Ignatzschineria coexisted, a rarely known feature. In addition, we found Erysipelothrix rhusiopathiae, the etiological agent of swine erysipelas, which is rarely identified in insects. This study enriches the understanding of the microbial community of C. megacephala and provides a reference for better utilization and control of C. megacephala., (© 2022. The Author(s).)

Despite the recent precipitous decline in the cost of genome sequencing, library preparation for RNA-seq is still laborious and expensive for applications such as high throughput screening. Limited availability of RNA generated by some experimental workflows poses an additional challenge and increases the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies-Swift RNA and Swift Rapid RNA, presently offered by Integrated DNA Technologies (IDT) -alongside the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark gene expression in these kits, at input quantities ranging between 10 to 500 ng. We found normalized read counts between all treatment groups to be in high agreement. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits offer shorter workflow times enabled by their patented Adaptase technology. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways directly attributable to input mRNA amount., (© 2022. The Author(s).)

In this study, two long-read sequencing (LRS) techniques, MinION from Oxford Nanopore Technologies and Sequel from the Pacific Biosciences, were used for the transcriptional characterization of a prototype baculovirus, Autographa californica multiple nucleopolyhedrovirus. LRS is able to read full-length RNA molecules, and thereby distinguish between transcript isoforms, mono- and polycistronic RNAs, and overlapping transcripts. Altogether, we detected 875 transcript species, of which 759 were novel and 116 were annotated previously. These RNA molecules include 41 novel putative protein coding transcripts [each containing 5'-truncated in-frame open reading frames (ORFs), 14 monocistronic transcripts, 99 polygenic RNAs, 101 non-coding RNAs, and 504 untranslated region isoforms. This work also identified novel replication origin-associated transcripts, upstream ORFs, cis-regulatory sequences and poly(A) sites. We also detected RNA methylation in 99 viral genes and RNA hyper-editing in the longer 5'-UTR transcript isoform of the canonical ORF 19 transcript., (© 2022. The Author(s).)

Members of the genus Bougainvillea are rich sources of natural dyes, pigments, and traditional medicines. They are also commonly used as ornamentals in roadside landscape construction. However, the horticultural development of Bougainvillea flowers with extended growth periods and coloration is not always feasible. One reason is limited molecular knowledge and no genomic information for Bougainvillea. Here, we compiled an integrative transcriptome of all expressed transcripts for Bougainvillea × buttiana Miss Manila by integrating 20 Illumina-sequencing RNA transcriptomes. The integrative transcriptome consisted of 97,623 distinct transcripts. Of these, 47,006 were protein-coding, 31,109 were non-coding, and 19,508 were unannotated. In addition, we affirmed that the integrative transcriptome could serve as a surrogate reference to the genome in aiding accurate transcriptome assembly. For convenience, we curated the integrative transcriptome database for Bougainvillea, namely InTransBo, which can be freely accessed at http://www.bio-add.org/InTransBo/index.jsp . To the best of our knowledge, the present study is the most comprehensive genomic resource for Bougainvillea up-to-date. The integrative transcriptome helps fill the genomic gap and elucidate the transcriptional nature of Bougainvillea. It may also advance progress in the precise regulation of flowering in horticulture. The same strategy can be readily applied toward the systematic exploration of other plant species lacking complete genomic information., (© 2022. The Author(s).)

The fast-advancing single cell RNA sequencing (scRNA-seq) technology enables researchers to study the transcriptome of heterogeneous tissues at a single cell level. The initial important step of analyzing scRNA-seq data is usually to accurately annotate cells. The traditional approach of annotating cell types based on unsupervised clustering and marker genes is time-consuming and laborious. Taking advantage of the numerous existing scRNA-seq databases, many supervised label assignment methods have been developed. One feature that many label assignment methods shares is to label cells with low confidence as "unassigned." These unassigned cells can be the result of assignment difficulties due to highly similar cell types or caused by the presence of unknown cell types. However, when unknown cell types are not expected, existing methods still label a considerable number of cells as unassigned, which is not desirable. In this work, we develop a neural network-based cell annotation method called NeuCA (Neural network-based Cell Annotation) for scRNA-seq data obtained from well-studied tissues. NeuCA can utilize the hierarchical structure information of the cell types to improve the annotation accuracy, which is especially helpful when data contain closely correlated cell types. We show that NeuCA can achieve more accurate cell annotation results compared with existing methods. Additionally, the applications on eight real datasets show that NeuCA has stable performance for intra- and inter-study annotation, as well as cross-condition annotation. NeuCA is freely available as an R/Bioconductor package at https://bioconductor.org/packages/NeuCA ., (© 2022. The Author(s).)

Leaf rust, caused by Puccinia triticina Eriks., is the most common rust disease of wheat (Triticum aestivum L.) worldwide. Owing to the rapid evolution of virulent pathotypes, new and effective leaf rust resistance sources must be found. Aegilops tauschii, an excellent source of resistance genes to a wide range of diseases and pests, may provide novel routes for resistance to this disease. In this study, we aimed to elucidate the transcriptome of leaf rust resistance in two contrasting resistant and susceptible Ae. tauschii accessions using RNA-sequencing. Gene ontology, analysis of pathway enrichment and transcription factors provided an apprehensible review of differentially expressed genes and highlighted biological mechanisms behind the Aegilops-P. triticina interaction. The results showed the resistant accession could uniquely recognize pathogen invasion and respond precisely via reducing galactosyltransferase and overexpressing chromatin remodeling, signaling pathways, cellular homeostasis regulation, alkaloid biosynthesis pathway and alpha-linolenic acid metabolism. However, the suppression of photosynthetic pathway and external stimulus responses were observed upon rust infection in the susceptible genotype. In particular, this first report of comparative transcriptome analysis offers an insight into the strength and weakness of Aegilops against leaf rust and exhibits a pipeline for future wheat breeding programs., (© 2022. The Author(s).)

RNA sequencing offers unprecedented access to the transcriptome. Key to this is the identification and quantification of many different species of RNA from the same sample at the same time. In this study we describe a novel protocol for simultaneous detection of coding and non-coding transcripts using modifications to the Ion Total RNA-Seq kit v2 protocol, with integration of QIASeq FastSelect rRNA removal kit. We report highly consistent sequencing libraries can be produced from both frozen high integrity mouse hippocampal tissue and the more challenging post-mortem human tissue. Removal of rRNA using FastSelect was extremely efficient, resulting in less than 1.5% rRNA content in the final library. We identified > 30,000 unique transcripts from all samples, including protein-coding genes and many species of non-coding RNA, in biologically-relevant proportions. Furthermore, the normalized sequencing read count for select genes significantly negatively correlated with Ct values from qRT-PCR analysis from the same samples. These results indicate that this protocol accurately and consistently identifies and quantifies a wide variety of transcripts simultaneously. The highly efficient rRNA depletion, coupled with minimized sample handling and without complicated and high-loss size selection protocols, makes this protocol useful to researchers wishing to investigate whole transcriptomes., (© 2022. The Author(s).)

The microbiota of the nasopharyngeal tract (NT) play a role in host immunity against respiratory infectious diseases. However, scant information is available on interactions of SARS-CoV-2 with the nasopharyngeal microbiome. This study characterizes the effects of SARS-CoV-2 infection on human nasopharyngeal microbiomes and their relevant metabolic functions. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 patients = 8, recovered humans = 7, and healthy people = 7) were collected, and underwent to RNAseq-based metagenomic investigation. Our RNAseq data mapped to 2281 bacterial species (including 1477, 919 and 676 in healthy, COVID-19 and recovered metagenomes, respectively) indicating a distinct microbiome dysbiosis. The COVID-19 and recovered samples included 67% and 77% opportunistic bacterial species, respectively compared to healthy controls. Notably, 79% commensal bacterial species found in healthy controls were not detected in COVID-19 and recovered people. Similar dysbiosis was also found in viral and archaeal fraction of the nasopharyngeal microbiomes. We also detected several altered metabolic pathways and functional genes in the progression and pathophysiology of COVID-19. The nasopharyngeal microbiome dysbiosis and their genomic features determined by our RNAseq analyses shed light on early interactions of SARS-CoV-2 with the nasopharyngeal resident microbiota that might be helpful for developing microbiome-based diagnostics and therapeutics for this novel pandemic disease., (© 2021. The Author(s).)