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Rationale: mRNA technology has begun to play a significant role in the areas of therapeutic intervention and vaccine development. However, optimizing the mRNA sequence that influences protein expression levels is a resource-intensive and time-consuming process. This study introduces a new method to accelerate the selection of sequences of mRNA for optimal protein expression., Methods: We designed the mRNA sequences in such a way that a unique peptide barcode, corresponding to each mRNA sequence, is attached to the expressed protein. These barcodes, cleaved off by a protease and simultaneously quantified by mass spectrometry, reflect the protein expression, enabling a parallel analysis. We validated this method using two mRNAs, each with different untranslated regions (UTRs) but encoding enhanced green fluorescence protein (eGFP), and investigated whether the peptide barcodes could analyze the differential eGFP expression levels., Results: The fluorescence intensity of eGFP, a marker of its expression level, has shown noticeable changes between the two UTR sequences in mRNA-transfected cells when measured using flow cytometry. This suggests alterations in the expression level of eGFP due to the influence of different UTR sequences. Furthermore, the quantified amount of peptide barcodes that were released from eGFP showed consistent patterns with these changes., Conclusions: The experimental findings suggest that peptide barcodes serve as a valuable tool for assessing protein expression levels. The process of mRNA sequence selection, aimed at maximizing protein expression, can be enhanced by the parallel analysis of peptide barcodes using mass spectrometry., (© 2024 John Wiley & Sons Ltd.)

Transcriptomics of dry age-related macular degeneration (AMD) patients with premature aging revealed the upregulated pathways involved in glycerolipid metabolism, tyrosine metabolism, and pentose and glucuronate interconversion. To investigate natural strategies for modulating these implicated pathways, we examined the impact and underlying mechanism of luteoloside on premature AMD using a stress-induced premature senescence (SIPS)-associated AMD animal model in middle-aged mice that mimicked the dysregulated pathways observed in dry AMD patients with premature aging. Luteoloside supplementation resulted in a significant reduction in serum levels of the pro-inflammatory cytokine IL-1β and lipofuscin, along with increased serum activity of the antioxidant enzyme superoxide dismutase (SOD) and elevated levels of pigment epithelium-derived factor (PEDF), and preserved retinal thickness and structure in AMD mice. Furthermore, luteoloside supplementation effectively reversed the abnormal serum levels of metabolites, particularly by reducing harmful lysophosphatidylcholine (LysoPC) and increasing beneficial 4-guanidinobutanoic acid. In addition to its impact on metabolites, luteoloside modulated the composition of gut microbiota, promoting the enrichment of beneficial bacterial populations, including Lactobacillus, while reducing the abundance of harmful bacterial populations, including Bacteroides. Overall, our findings highlight the potential of luteoloside supplementation in regulating the dysregulated intestinal microbiota and metabolites in premature AMD, thereby reducing ocular levels of senescence-associated secretory phenotype (SASP) factors through the suppression of the p53-p21-retinoblastoma protein 1 (Rb1) axis., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Objective: Bipolar disorder (BD) is a debilitating neuropsychiatric disorder, which is associated with genetic variation through "vast but mixed" Genome-Wide Association Studies (GWAS). Transcriptome-Wide Association Study (TWAS) is more effective in explaining genetic factors that influence complex diseases and can help identifying risk genes more reliably. So, this study aims to identify potential BD risk genes in pedigrees with TWAS., Methods: We conducted a TWAS analysis with expression quantitative trait loci (eQTL) analysis on extended BD pedigrees, and the BD genome-wide association study (GWAS) summary data acquired from the Psychiatric Genomics Consortium (PGC). Furthermore, the BD-associated genes identified by TWAS were validated by mRNA expression profiles from the Gene Expression Omnibus (GEO) Datasets (GSE23848 and GSE46416). Functional enrichment and annotation analysis were implemented by RStudio (version 4.2.0)., Results: TWAS identified 362 genes with P value < 0.05, and 18 genes remain significant after Bonferroni correction, such as SEMA3G (P TWAS =1.07 × 10 -11 ), ALOX5AP (P TWAS =3.12 × 10 -8 ), and PLEC (P TWAS =1.27 × 10 -7 ). Further 6 overlapped genes were detected in integrative analysis, such as UQCRB (P TWAS =0.0020, P mRNA =0.0000), TMPRSS9 (P TWAS =0.0405, P mRNA =0.0032), and SNX10 (P TWAS =0.0104, P mRNA =0.0015). Using genes identified by TWAS, Gene Ontology (GO) enrichment analysis identified 40 significant GO terms, such as mitochondrial ATP synthesis coupled electron transport, mitochondrial respiratory, aerobic electron transport chain, oxidative phosphorylation, mitochondrial membrane proteins, and ubiquinone activity. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis identified significant 15 pathways for BD, such as Oxidative phosphorylation, endocannabinoids signaling, neurodegeneration, and reactive oxide species., Conclusions: We found a set of BD-associated genes and pathways, validating the important role of neurodevelopmental abnormalities, inflammatory responses, and mitochondrial dysfunction in the pathology of BD, offering novel information for comprehending the genetic basis of BD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Rett syndrome (RTT) is a rare neurodevelopmental disorder that primarily affects females and is characterized by a period of normal development followed by severe cognitive, motor, and communication impairment. The syndrome is predominantly caused by mutations in the MECP2. This study aimed to use comprehensive multi-omic analysis to identify the molecular and metabolic alterations associated with Rett syndrome., Methods and Results: Transcriptomic and metabolomic profiling was performed using neuron-like cells derived from the fibroblasts of 3 Rett syndrome patients with different MECP2 mutations (R168X, P152R, and R133C) and 1 healthy control. Differential gene expression, alternative splicing events, and metabolite changes were analyzed to identify the key pathways and processes affected in patients with Rett syndrome. Transcriptomic analysis showed there was significant down-regulation of genes associated with the extracellular matrix (ECM) and cytoskeletal components, which was particularly notable in patient P3 (R133C mutation), who had non-random X chromosome inactivation. Additionally, significant changes in microtubule-related gene expression and alternative splicing events were observed, especially in patient P2 (P152R mutation). Metabolomic profiling showed that there were alterations in metabolic pathways, particularly up-regulation of ketone body synthesis and degradation pathways, in addition to an increase in free fatty acid levels. Integrated analysis highlighted the interplay between structural gene down-regulation and metabolic shifts, underscoring the adaptive responses to cellular stress in Rett neurons., Conclusion: The present findings provide valuable insights into the molecular and metabolic landscape of Rett syndrome, emphasizing the importance of combining omic data to enlighten the molecular pathophysiology of this syndrome., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It's worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed., (© 2024. The Author(s).)

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot, and to prospect the future directions of this field., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets., (© 2024. The Author(s).)

Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable., (© 2024. The Author(s).)

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification., (© 2024. The Author(s).)

Background: B chromosomes are extra non-essential elements present in several eukaryotes. Unlike A chromosomes which are essential and present in all individuals of a species, B chromosomes are not necessary for normal functioning of an organism. Formerly regarded as genetically inactive, B chromosomes have been discovered to not only express their own genes, but also to exert influence on gene expression in A chromosomes. Recent studies have shown that, in some Psalidodon (Characiformes, Characidae) species, B chromosomes might be associated with phenotypic effects, such as changes in the reproductive cycle and gene expression., Methods and Results: In this study, we aimed to establish stable reference genes for RT-qPCR experiments conducted on gonads of three fish species within Psalidodon genus, both in the presence and absence of B chromosomes. The stability of five selected reference genes was assessed using NormFinder, geNorm, BestKeeper, and RefFinder algorithms. We determined ppiaa and pgk1 as the most stable genes in P. fasciatus, whereas ppiaa and hmbsa showed the highest stability in P. bockmanni. For P. paranae, tbp and hprt1 were the most stable genes in females, and ppiaa and hprt1 were the most stable in males., Conclusions: We determined the most stable reference genes in gonads of three Psalidodon species considering the presence of B chromosomes. This is the first report of reference gene stability in the genus and provides valuable tools to better understand the effects of B chromosomes at gene expression level., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Performing joint analysis of gene expression datasets from different experiments can present challenges brought on by multiple factors-differences in equipment, protocols, climate etc. "Cross-study normalization" is a general term for transformations aimed at eliminating such effects, thus making datasets more comparable. However, joint analysis of datasets from different species is rarely done, and there are no dedicated normalization methods for such inter-species analysis. In order to test the usefulness of cross-studies normalization methods for inter-species analysis, we first applied three cross-study normalization methods, EB, DWD and XPN, to RNA sequencing datasets from different species. We then developed a new approach to evaluate the performance of cross-study normalization in eliminating experimental effects, while also maintaining the biologically significant differences between species and conditions. Our results indicate that all normalization methods performed relatively well in the cross-species setting. We found XPN to be better at reducing experimental differences, and found EB to be better at preserving biological differences. Still, according to our in-silico experiments, in all methods it is not possible to enforce the preservation of the biological differences in the normalization process. In addition to the study above, in this work we propose a new dedicated cross-studies and cross-species normalization method. Our aim is to address the shortcoming mentioned above: in the normalization process, we wish to reduce the experimental differences while preserving the biological differences. We term our method as CSN, and base it on the performance evaluation criteria mentioned above. Repeating the same experiments, the CSN method obtained a better and more balanced conservation of biological differences within the datasets compared to existing methods. To summarize, we demonstrate the usefulness of cross-study normalization methods in the inter-species settings, and suggest a dedicated cross-study cross-species normalization method that will hopefully open the way to the development of improved normalization methods for the inter-species settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Feldman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Immune checkpoint blockades are actively adopted in diverse cancer types including metastatic melanoma and lung cancer. Despite of durable response in 20-30% of patients, we still lack molecular markers that could predict the patient responses reliably before treatment. Here we present a composite model for predicting anti-PD-1 response based on tumor mutation burden (TMB) and transcriptome sequencing data of 85 lung adenocarcinoma (LUAD) patients who received anti-PD-(L)1 treatment. We found that TMB was a good predictor (AUC = 0.81) for PD-L1 negative patients (n = 20). For PD-L1 positive patients (n = 65), we built an ensemble model of 100 XGBoost learning machines where gene expression, gene set activities and cell type composition were used as input features. The transcriptome-based models showed excellent accuracy (AUC > 0.9) and highlighted the contribution of T cell activities. Importantly, nonresponder patients with high prediction score turned out to have high CTLA4 expression, which suggested that neoadjuvant CTLA4 combination therapy might be effective for these patients. Our data and analysis results provide valuable insights into developing biomarkers and strategies for treating LUAD patients using immune checkpoint inhibitors., (© 2024. The Author(s).)

GeoMx Digital Spatial Profiling (Nanostring) is a commercial spatial transcriptomics method to selectively analyze regions of interest within intact tissue sections. We show that decalcification with ethylene-diamine-tetra-acetic (EDTA) variably attenuates probe counts, while probes that are more resistant to this effect consequently appear overexpressed after quantile normalization. By determining the undisclosed full-length target sequences of probes used in the human whole transcriptome panel, hereby updating target transcripts and genes, we find that the gene-promiscuity of probes is an important factor that determines sensitivity to EDTA incubation., (© 2024. The Author(s).)

Both silicon (Si) and zinc (Zn) ions are essential elements to bone health and their mechanisms for promoting osteogenesis have aroused the extensive attention of researchers. Thereinto, the mechanism by which dual ions promote osteogenic differentiation remains to be elucidated. Herein, the effects of Si and Zn ions on the cytological behaviors of mBMSCs were firstly studied. Then, the molecular mechanism of Si-Zn dual ions regulating the osteogenic differentiation of mBMSCs was investigated via transcriptome sequencing technology. In the single-ion system, Si ion at the concentration of 1.5 mM (Si-1.5) had better comprehensive effects of cell proliferation, ALP activity and osteogenesis-related gene expression levels (ALP, Runx2, OCN, Col-I and BSP); Zn ion at the concentration of 50 μM (Zn-50) demonstrated better combining effects of cell proliferation, ALP activity and same osteogenic genes expression levels. In the dual-ion system, the Si (1.5 mM)-Zn (50 μM) group (Si1.5-Zn50) synthetically enhanced ALP activity and osteogenesis genes compared with single-ion groups. Analysis of the transcriptome sequencing results showed that Si ion had a certain effect on promoting the osteogenic differentiation of mBMSCs; Zn ion had a stronger effect of contributing to a better osteogenic differentiation of mBMSCs than that of Si ion; the Si-Zn dual ions had a synergistic enhancement on conducting to the osteogenic differentiation of mBMSCs compared to single ion (Si or Zn). This study offers a blueprint for exploring the regulation mechanism of osteogenic differentiation by dual ions., (© 2024. The Author(s).)

Single-cell RNA sequencing (scRNA-seq) data, susceptible to noise arising from biological variability and technical errors, can distort gene expression analysis and impact cell similarity assessments, particularly in heterogeneous populations. Current methods, including deep learning approaches, often struggle to accurately characterize cell relationships due to this inherent noise. To address these challenges, we introduce scAMF (Single-cell Analysis via Manifold Fitting), a framework designed to enhance clustering accuracy and data visualization in scRNA-seq studies. At the heart of scAMF lies the manifold fitting module, which effectively denoises scRNA-seq data by unfolding their distribution in the ambient space. This unfolding aligns the gene expression vector of each cell more closely with its underlying structure, bringing it spatially closer to other cells of the same cell type. To comprehensively assess the impact of scAMF, we compile a collection of 25 publicly available scRNA-seq datasets spanning various sequencing platforms, species, and organ types, forming an extensive RNA data bank. In our comparative studies, benchmarking scAMF against existing scRNA-seq analysis algorithms in this data bank, we consistently observe that scAMF outperforms in terms of clustering efficiency and data visualization clarity. Further experimental analysis reveals that this enhanced performance stems from scAMF's ability to improve the spatial distribution of the data and capture class-consistent neighborhoods. These findings underscore the promising application potential of manifold fitting as a tool in scRNA-seq analysis, signaling a significant enhancement in the precision and reliability of data interpretation in this critical field of study., Competing Interests: Competing interests statement:The authors declare no competing interest.

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches., Competing Interests: Competing interests statement:The authors declare no competing interest.

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Citrus aurantium L., sometimes known as "sour orange," is an important Chinese herb with young, immature fruits, or "zhishi," that are high in synephrine. Synephrine is a commonly utilized natural chemical with promising applications in effectively increasing metabolism, heat expenditure, energy level, oxidative fat, and weight loss. However, little is known about the genes and pathways involved in synephrine production during the critical developmental stages of C. aurantium L., which limits the development of the industry. According to this study, the concentration of synephrine gradually decreased as the fruit developed. Transcriptome sequencing was used to examine the DEGs associated with synephrine connections and served as the foundation for creating synephrine-rich C. aurantium L. Comparisons conducted between different developmental stages to obtain DEGs, and the number of DEGs varied from 690 to 3,019. Tyrosine and tryptophan biosynthesis, glycolysis/gluconeogenesis, pentose phosphate pathway, phenylalanine, and tyrosine metabolism were the main KEGG pathways that were substantially enriched. The results showed that 25 genes among these KEGG pathways may be related to synephrine synthesis. The WGCNA and one-way ANOVA analysis adoption variance across the groups suggested that 11 genes might play a crucial role in synephrine synthesis and should therefore be further analyzed. We also selected six DEGs at random and analyzed their expression levels by RT-qPCR, and high repeatability and reliability were demonstrated by our finished RNA-seq study results. These results may be useful in selecting or modifying genes to increase the quantity of synephrine in sour oranges., Competing Interests: The authors declare there are no competing interests., (©2024 Zhong et al.)

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods., (© 2024. The Author(s).)

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The "Seurat" package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU&#95;lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2&#95;cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing 'StepCox[both] + RSF', which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions., (© 2024. The Author(s).)

Human tumors are intricate ecosystems composed of diverse genetic clones and malignant cell states that evolve in a complex tumor micro-environment. Single-cell RNA-sequencing (scRNA-seq) provides a compelling strategy to dissect this intricate biology and has enabled a revolution in our ability to understand tumor biology over the last ten years. Here we reflect on this first decade of scRNA-seq in human tumors and highlight some of the powerful insights gleaned from these studies. We first focus on computational approaches for robustly defining cancer cell states and their diversity and highlight some of the most common patterns of gene expression intra-tumor heterogeneity (eITH) observed across cancer types. We then discuss ambiguities in the field in defining and naming such eITH programs. Finally, we highlight critical developments that will facilitate future research and the broader implementation of these technologies in clinical settings., Competing Interests: Declaration of interests M.L.S. is equity holder, scientific co-founder and advisory board member of Immunitas Therapeutics. I.T. is an advisory board member of Immunitas Therapeutics., (Copyright © 2024. Published by Elsevier Inc.)

Congenital heart disease (CHD) is one of the most significant birth defects leading to infant mortality worldwide. Circulating microRNAs (miRNAs) are emerging as novel biomarkers for the detection of cardiovascular diseases. In this study, we aimed to investigate the role of maternal serum miRNAs expression as biomarkers in the diagnosis and prediction of children with CHD. High-throughput sequencing of peripheral blood from pregnant women with abnormal and normal fetal hearts identified 1939 differentially expressed miRNAs, the first 11 of which were selected as predictive biomarkers of CHD. The expression of miRNAs in more clinical samples was then quantitatively verified by reverse transcriptase polymerase chain reaction and the correlation between abnormal miRNAs and CHD was analyzed. Two miRNAs (hsa-miR-3195 and hsa-miR-122-5p) were found to be significantly down-regulated in pregnant women with fetal CHD. By further bioinformatics analysis, we predicted that hsa-miR-3195 and hsa-miR-122-5p could induce CHD by influencing biometabolic processes. hsa-miR-3195 and hsa-miR-122-5p may serve as novel non-invasive biomarkers for prenatal detection of fetal CHD.

This article describes how the transcriptional alterations of the innate immune system divide dysplasias into aggressive forms that, despite the treatment, relapse quickly and more easily, and others where the progression is slow and more treatable. It elaborates on how the immune system can change the extracellular matrix, favoring neoplastic progression, and how infections can enhance disease progression by increasing epithelial damage due to the loss of surface immunoglobulin and amplifying the inflammatory response. We investigated whether these dysregulated genes were linked to disease progression, delay, or recovery. These transcriptional alterations were observed using the RNA-based next-generation sequencing (NGS) panel Oncomine Immune Response Research Assay (OIRRA) to measure the expression of genes associated with lymphocyte regulation, cytokine signaling, lymphocyte markers, and checkpoint pathways. During the analysis, it became apparent that certain alterations divide dysplasia into two categories: progressive or not. In the future, these biological alterations are the first step to provide new treatment modalities with different classes of drugs currently in use in a systemic or local approach, including classical chemotherapy drugs such as cisplatin and fluorouracile, older drugs like fenretinide, and new checkpoint inhibitor drugs such as nivolumab and pembrolizumab, as well as newer options like T cell therapy (CAR-T). Following these observed alterations, it is possible to differentiate which dysplasias progress or not or relapse quickly. This information could, in the future, be the basis for determining a close follow-up, minimizing surgical interventions, planning a correct and personalized treatment protocol for each patient and, after specific clinical trials, tailoring new drug treatments.

In samples of harmful algal blooms (HABs), seawater can contain a high abundance of microorganisms and elemental ions. Along with the hardness of the walls of key HAB dinoflagellates such as Prorocentrum triestinum , this makes RNA extraction very difficult. These components interfere with RNA isolation, causing its degradation, in addition to the complex seawater properties of HABs that could hinder RNA isolation for effective RNA sequencing and transcriptome profiling. In this study, an RNA isolation technique was established through the modification of the Trizol method by applying the Micropestle System on cell pellets of P. triestinum frozen at -20 °C, obtained from 400 mL of culture with a total of 10 7 cells/mL. The results of the modified Trizol protocol generated quality RNA samples for transcriptomics sequencing, as determined by their measurement in Analyzer Agilent 4150.

Colorectal adenocarcinoma (COAD) has a poor prognosis. Cyclin-dependent kinase inhibitor 2A (CDKN2A) significantly affects the development and progression of various human tumors. However, the significance and pathological mechanisms of CDKN2A in COAD remain to be elucidated. We assessed expression levels, clinical significance, biological function, co-expressed genes, and enrichment of related pathways of CDKN2A in COAD using various databases, including The University of Alabama at Birmingham Cancer Data Analysis Portal, Gene Expression Profiling Interactive Analysis, Tumor Immune Estimation Resource, Human Protein Atlas, STRING, GeneMANIA, cBioPortal, and Linked Omics. Our investigation showed that CDKN2A was highly expressed in colon adenocarcinomas (P < .001). It is weakly expressed or not expressed in normal tissues. The survival time of patients with colon adenocarcinoma with high CDKN2A expression is significantly shorter than that of patients with low expression levels (P = .011). There was a significant positive correlation between the expression level of CDKN2A in colon adenocarcinoma tissues and the infiltration of CD4+ T cells, macrophages, and neutrophils. Moreover, there was a significant negative association between the expression level of CDKN2A in colon adenocarcinoma tissues and B cell infiltration. The ten hub genes included tumor protein 53, V-myc Avian Myelocytomatosis Viral Oncogene Homolog, AKT serine/threonine kinase 1, cyclin-dependent kinase 2, phosphatase and tensin homolog deleted on chromosome ten, cyclin D1, cyclin dependent kinase 4, cyclin dependent kinase inhibitor 1A, catenin beta 1, and B-Raf proto-oncogene, serine/threonine kinase. Mutations in the CDKN2A genome in colon adenocarcinoma reduce survival. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the differentially expressed genes were enriched in apoptotic signaling pathways and multiple pathways related to metabolic progression. Our results indicate that CDKN2A can be used as a marker of poor prognosis in patients with colon adenocarcinoma. CDKN2A may regulate the occurrence and development of colon adenocarcinomas by influencing immune cell infiltration and metabolic pathways., Competing Interests: The author have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

The timely and precise diagnosis of appendicitis was deemed essential. This study sought to examine the diagnostic significance of hub genes linked to appendicitis and to delve deeper into the pathophysiology of the condition. Differential gene expression analysis revealed distinct genes in the appendicitis group compared to other abdominal pain group, while weighted gene co-expression network analysis identified appendicitis-associated modules. Further analysis of common genes was conducted using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis. The diagnostic efficiency of hub genes was explored through the use of nomograms and receiver operator characteristic curves. Additionally, immunoinfiltration analysis was performed to investigate the immune cell infiltration in both groups. The causal relationship between hub genes and appendicitis, as well as gut microbiota and appendicitis, was ultimately examined through Mendelian randomization. By conducting differential expression analysis and weighted gene co-expression network analysis, a total of 757 common genes were identified. Subsequent Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses revealed that these common genes were primarily associated with positive regulation of cell adhesion, focal adhesion, protein serine kinase activity, and amyotrophic lateral sclerosis. Utilizing Cytoscape software, the top 10 genes with the highest degree of interaction were identified as RPS3A, RPSA, RPL5, RPL37A, RPS27L, FLT3LG, ARL6IP1, RPL32, MRPL3, and GSPT1. Evaluation using nomograms and receiver operator characteristic curves demonstrated the diagnostic value of these hub genes. Ultimately, a causal relationship between hub genes and appendicitis was not identified in our study. Nevertheless, our findings indicate that appendicitis is correlated with 9 gut microbiota. This study identified 5 hub genes, specifically HSP90AA1, RPL5, MYC, CD44, and RPS3A, which exhibit diagnostic significance of appendicitis. Furthermore, the elucidation of these hub genes aids in enhancing our comprehension of the molecular pathways implicated in the development of appendicitis., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable 'gears' between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries., (© 2024. The Author(s).)

Breast cancer (BC) is a malignant neoplasm which is classified into various types defined by underlying molecular factors such as estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor positive (HER2+) and triple negative (TNBC). Early detection of ER+ and TNBC is crucial in the choice of diagnosis and appropriate treatment strategy. Here we report the key genes associated to ER+ and TNBC using RNA-Seq analysis and machine learning models. Three ER+ and TNBC RNA seq datasets comprising 164 patients in-toto were selected for standard NGS hierarchical data processing and data analyses protocols. Enrichment pathway analysis and network analysis was done and finally top hub genes were identified. To come with a reliable classifier which could distinguish the distinct transcriptome patterns associated to ER+ and TNBC, ML models were built employing Naïve Bayes, SVM and kNN. 1730 common DEG's exhibiting significant logFC values with 0.05 p-value threshold were identified. A list of top ten hub genes were screened on the basis of maximal clique centrality (MCC) which included CDC20, CDK1, BUB1, AURKA, CDCA8, RRM2, TTK, CENPF, CEP55 and NDC80.These genes were found to be involved in crucial cell cycle pathways. k-Nearest Neighbor (kNN) model was observed to be best classifier with accuracy 84%, specificity 66% and sensitivity 95% to differentiate between ER+ and TNBC RNA-Seq transcriptomes. Our screened list of 10 hub genes can thus help unearth novel molecular signatures implicated in ER+ and TNBC onset, prognosis and design of novel protocols for breast cancer diagnostics and therapeutics., (© 2024. The Author(s).)

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions., (© 2024. The Author(s).)

The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4 + T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αβ T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses., (© 2024. The Author(s).)

Cholangiocarcinoma (CCA) is a heterogeneous and aggressive malignancy with limited therapeutic options and poor prognosis. The identification of reliable prognostic biomarkers and a deeper understanding of the molecular subtypes are critical for the development of targeted therapies and improvement of patient outcomes. This study aims to uncover oxidative stress-related genes (ORGs) in CCA and develop a prognostic risk model using comprehensive transcriptomic analysis from The Cancer Genome Atlas (TCGA). Through LASSO regression analysis, we identified prognosis-related ORGs and constructed a prognostic signature consisting of six ORGs. This signature demonstrated strong predictive performance in survival analysis and ROC curve assessment. Functional enrichment and GSEA analyses revealed significant enrichment of immune-related pathways among different risk groups. GSVA analysis indicated reduced activity in inflammation and oxidative stress pathways in the high-risk subgroup, and xCell results showed lower immune cell infiltration levels in this group. Additionally, immune checkpoint genes and immune-related pathways were downregulated in the high-risk subgroup. Our research has developed a unique prognostic model focusing on oxidative stress, enabling accurate forecasting of patient outcomes and providing crucial insights and recommendations for the prognosis of individuals with CCA. Future studies should aim to validate these findings in clinical settings and further explore therapeutic targets within oxidative stress pathways., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Radiation-induced lung injury (RILI) frequently occurs as a complication following radiotherapy for chest tumors like lung and breast cancers. However, the precise underlying mechanisms of RILI remain unclear. In this study, we generated RILI models in rats treated with a single dose of 20 Gy and examined lung tissues by single-cell RNA sequencing (scRNA-seq) 2 weeks post-radiation. Analysis of lung tissues revealed 18 major cell populations, indicating an increase in cell-cell communication following radiation exposure. Neutrophils, macrophages, and monocytes displayed distinct subpopulations and uncovered potential for pro-inflammatory effects. Additionally, endothelial cells exhibited a highly inflammatory profile and the potential for reactive oxygen species (ROS) production. Furthermore, smooth muscle cells (SMC) showed a high propensity for extracellular matrix (ECM) deposition. Our findings broaden the current understanding of RILI and highlight potential avenues for further investigation and clinical applications.

Background: Gene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate developing harmonized data handling and processing pipelines which in turn require considerable computational resources and in-depth bioinformatics expertise., Results: Here, we present Cortexa-a comprehensive web portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform provides SplicePCA-a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All standardized gene expression and alternative splicing datasets can be downloaded for further in-depth downstream analysis without the need for extensive preprocessing., Conclusions: Cortexa provides a robust and readily available resource for unraveling the complexity of gene expression and alternative splicing regulatory processes in the mouse brain. The data portal is available at https://cortexa-rna.com/., (© 2024. The Author(s).)

Background: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging., Results: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property., Conclusions: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM ., (© 2024. The Author(s).)

Objective: Sago palm (Metroxylon sagu Rottb.) is one of the most important economic crops abundantly found in Mukah, Sarawak, Malaysia. The robustness of the palm triggered the Sarawak government's selection as one of the state's commodity crops, with the opening of several sago palm plantations. However, stunted (non-trunking) palms were reported in several sago palm plantations despite attaining a maturity period of more than ten years after cultivation. Research targeting this problem has been conducted in various fields, yet information on molecular mechanisms is still scarce. This study aimed to determine the genes responsible for sago palm's normal phenotype (trunking) by attaining leaf transcriptomes from samples of all trunking sago palms from different sago palm plantations., Data Description: The conventional CTAB method was employed in the present investigation to extract total RNA from leaf tissues. Transcriptome sequencing was conducted on the Illumina NovaSeq 6000 platform. Differential expression analysis was performed using the DESeq2 package. A total of 6,119 differentially expressed genes, comprising 4,384 downregulated and 1,735 upregulated genes, were expressed in all three sago palm datasets. The datasets provide insights into the commonly expressed genes among trunking sago palms., (© 2024. The Author(s).)

Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Duan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome., (©2024 The Authors; Published by the American Association for Cancer Research.)

Clustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points. We show that DPMUnc out-performs existing methods on simulated data. We cluster immune-mediated diseases (IMD) using GWAS summary statistics, which have uncertainty linked with the sample size of the study. DPMUnc separates autoimmune from autoinflammatory diseases and isolates other subgroups such as adult-onset arthritis. We additionally consider how DPMUnc can be used to cluster gene expression datasets that have been summarised using gene signatures. We first introduce a novel procedure for generating a summary of a gene signature on a dataset different to the one where it was discovered, which incorporates a measure of the variability in expression across signature genes within each individual. We summarise three public gene expression datasets containing patients with a range of IMD, using three relevant gene signatures. We find association between disease and the clusters returned by DPMUnc, with clustering structure replicated across the datasets. The significance of this work is two-fold. Firstly, we demonstrate that when data has associated uncertainty, this uncertainty should be used to inform clustering and we present a method which does this, DPMUnc. Secondly, we present a procedure for using gene signatures in datasets other than where they were originally defined. We show the value of this procedure by summarising gene expression data from patients with immune-mediated diseases using relevant gene signatures, and clustering these patients using DPMUnc., Competing Interests: This work is supported by the Wellcome Trust (WT220788 to CW, WT220024 to KN) and the Medical Research Council (MC UU 00002/4 to CW, MC UU 00002/13 PDWK). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., (Copyright: © 2024 Nicholls et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here, we present a multiple imputation method that accommodates high dimensional gene expression data by incorporating principal component analysis of the transcriptome into the multiple imputation prediction models to avoid bias. Simulation studies using three datasets show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R Bioconductor package, RNAseqCovarImpute that integrates with the limma-voom pipeline for differential expression analysis., (© 2024. The Author(s).)

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Colorectal cancer (CRC) is a wide-spread gastrointestinal cancer that is associated with augmented morbidity and mortality, and we do not yet have a deep understanding of its epidemiology and carcinogenicity. The transcriptome can reveal the complexity and heterogeneity of tumors and uncover new biomarkers or treatment options. In this study, we identified messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), round RNAs (circRNAs), and microRNAs (miRNAs) using whole-transcriptome sequencing and generated competing endogenous RNA (ceRNA) modulatory axes. We conducted whole transcriptome sequencing on 10 CRC and para-cancer (CRCP) samples and discovered 2465 differentially expressed (DE) mRNAs (DEmRNAs), 77 DE miRNAs (DEmiRNAs). 2852 DE lncRNAs (DElncRNAs) and 1477 DE circRNAs (DEcircRNAs). In addition, utilizing co-DE analysis, we generated the ceRNA axis. Subsequently, we employed the ceRNA axis to identify essential genes and corresponding associations with lncRNAs, circRNAs, and miRNAs in CRC. ceRNA regulatory network including mRNA-miRNA-lncRNA and mRNA-miRNA-circRNA. These modulatory axes potentially modulate the positive regulation of smooth muscle contraction, melanosome, plasma membrane, integral plasma membrane component and so on. Finally, the results of RNA sequencing (RNA-SEQ) were combined with the TCGA and GEO databases, and the DEGs strongly correlated with the TCGA-COAD overall survival (OS) as estimated by univariate cox and logarithmic rank analyses were cross-analyzed, and the co-upregulated DEGs were screened. Among the many DEs, KPNA2 was chosen for additional analysis. Using invitro experimentations, western blot, CCK8, EdU and other experiments were performed to verify the results. We found siRNA-based KPNA2 depletion reduces bladder cancer cells' viability, migratory, and proliferative activities, which showed that the DEmRNA profiles were comparable to the sequencing information, confirming that the sequencing data were very reliable. These evidences highlight the ceRNA regulatory mechanisms in CRC and will aid future research into the molecular mechanisms behind colorectal cancer prevention and treatment., (© 2024. The Author(s).)

Motivation: Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose., Results: Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale., Availability and Implementation: SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image., (© The Author(s) 2024. Published by Oxford University Press.)

The northern bark beetle, Ips duplicatus , is an emerging economic pest, reportedly infesting various species of spruce ( Picea spp.), pine ( Pinus spp.), and larch ( Larix spp.) in Central Europe. Recent climate changes and inconsistent forest management practices have led to the rapid spread of this species, leaving the current monitoring strategies inefficient. As understanding the molecular components of pheromone detection is key to developing novel control strategies, we generated antennal transcriptomes from males and females of this species and annotated the chemosensory proteins. We identified putative candidates for 69 odorant receptors (ORs), 50 ionotropic receptors (IRs), 25 gustatory receptors (GRs), 27 odorant-binding proteins (OBPs), including a tetramer-OBP, 9 chemosensory proteins (CSPs), and 6 sensory neuron membrane proteins (SNMPs). However, no sex-specific chemosensory genes were detected. The phylogenetic analysis revealed conserved orthology in bark beetle chemosensory proteins, especially with a major forest pest and co-habitant, Ips typographus . Recent large-scale functional studies in I. typographus chemoreceptors add greater significance to the orthologous sequences reported here. Nevertheless, identifying chemosensory genes in I. duplicatus is valuable to understanding the chemosensory system and its evolution in bark beetles (Coleoptera) and, generally, insects.

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Motivation: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity., Results: Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest., Availability and Implementation: https://github.com/song0309/asGNN/., (© The Author(s) 2024. Published by Oxford University Press.)

Summary: Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes., Availability and Implementation: Graspot software is available at https://github.com/zhan009/Graspot., (© The Author(s) 2024. Published by Oxford University Press.)

Summary: Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path., Availability and Implementation: The data and code are publicly available at https://github.com/rsinghlab/scNODE., (© The Author(s) 2024. Published by Oxford University Press.)

The growing availability of single-cell and spatially resolved transcriptomics has led to the development of many approaches to infer cell-cell communication, each capturing only a partial view of the complex landscape of intercellular signalling. Here we present LIANA+, a scalable framework built around a rich knowledge base to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially resolved data. By extending and unifying established methodologies, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication via diverse molecular mediators, including those measured in multi-omics data. LIANA+ is accessible at https://github.com/saezlab/liana-py with extensive vignettes ( https://liana-py.readthedocs.io/ ) and provides an all-in-one solution to intercellular communication inference., (© 2024. The Author(s).)

Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The complex and dynamic cellular composition of the human endometrium remains poorly understood. Previous endometrial single-cell atlases profiled few donors and lacked consensus in defining cell types. We introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas (313,527 cells) combining published and new endometrial single-cell transcriptomics datasets of 63 women with and without endometriosis. HECA assigns consensus and identifies previously unreported cell types, mapped in situ using spatial transcriptomics and validated using a new independent single-nuclei dataset (312,246 nuclei, 63 donors). In the functionalis, we identify intricate stromal-epithelial cell coordination via transforming growth factor beta (TGFβ) signaling. In the basalis, we define signaling between fibroblasts and an epithelial population expressing progenitor markers. Integration of HECA with large-scale endometriosis genome-wide association study data pinpoints decidualized stromal cells and macrophages as most likely dysregulated in endometriosis. The HECA is a valuable resource for studying endometrial physiology and disorders, and for guiding microphysiological in vitro systems development., (© 2024. The Author(s).)