Injuries to the spinal cord nervous system often result in permanent loss of sensory, motor, and autonomic functions. Accurately identifying the cellular state of spinal cord nerves is extremely important and could facilitate the development of new therapeutic and rehabilitative strategies. Existing experimental techniques for identifying the development of spinal cord nerves are both labor-intensive and costly. In this study, we developed a machine learning predictor, ScnML, for predicting subpopulations of spinal cord nerve cells as well as identifying marker genes. The prediction performance of ScnML was evaluated on the training dataset with an accuracy of 94.33%. Based on XGBoost, ScnML on the test dataset achieved 94.08% 94.24%, 94.26%, and 94.24% accuracies with precision, recall, and F1-measure scores, respectively. Importantly, ScnML identified new significant genes through model interpretation and biological landscape analysis. ScnML can be a powerful tool for predicting the status of spinal cord neuronal cells, revealing potential specific biomarkers quickly and efficiently, and providing crucial insights for precision medicine and rehabilitation recovery.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

The brain regulates multiple physiological processes in fish. Despite this, knowledge about the basic structure and function of distinct brain regions in non-model fish species remains limited due to their diversity and the scarcity of common biomarkers. In the present study, four major brain parts, the telencephalon, diencephalon, mesencephalon and rhombencephalon, were isolated in largemouth bass, Micropterus salmoides. Within these parts, nine brain regions and 74 nuclei were further identified through morphological and cytoarchitectonic analysis. Transcriptome analysis revealed a total of 7153 region-highly expressed genes and 176 region-specifically expressed genes. Genes related to growth, reproduction, emotion, learning, and memory were significantly overexpressed in the olfactory bulb and telencephalon (OBT). Feeding and stress-related genes were in the hypothalamus (Hy). Visual system-related genes were predominantly enriched in the optic tectum (OT), while vision and hearing-related genes were widely expressed in the cerebellum (Ce) region. Sensory input and motor output-related genes were in the medulla oblongata (Mo). Osmoregulation, stress response, sleep/wake cycles, and reproduction-related genes were highly expressed in the remaining brain (RB). Three candidate marker genes were further identified for each brain regions, such as neuropeptide FF (npff) for OBT, pro-melanin-concentrating hormone (pmch) for Hy, vesicular inhibitory amino acid transporter (viaat) for OT, excitatory amino acid transporter 1 (eaat1) for Ce, peripherin (prph) for Mo, and isotocin neurophysin (itnp) for RB. Additionally, the distribution of seven neurotransmitter-type neurons and five types of non-neuronal cells across different brain regions were analyzed by examining the expression of their marker genes. Notably, marker genes for glutamatergic and GABAergic neurons showed the highest expression levels across all brain regions. Similarly, the marker gene for radial astrocytes exhibited high expression compared to other markers, while those for microglia were the least expressed. Overall, our results provide a comprehensive overview of the structural and functional characteristics of distinct brain regions in the largemouth bass, which offers a valuable resource for understanding the role of central nervous system in regulating physiological processes in teleost.

BACKGROUND: With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta\'s role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types.
OBJECTIVE: We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases.
METHODS: We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types.
RESULTS: Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation.
CONCLUSIONS: This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.

UNASSIGNED: Spinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients\' quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society.
UNASSIGNED: In this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses.
UNASSIGNED: We identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments.
UNASSIGNED: In conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types, and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach-first by linking to expression of known marker genes, then using the NSForest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website, and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes within the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

Hepatocellular carcinoma (HCC) has been associated with hepatitis C viral (HCV) infection as a potential risk factor. Nonetheless, the precise genetic regulatory mechanisms triggered by the virus, leading to virus-induced hepatocarcinogenesis, remain unclear. We hypothesized that HCV proteins might modulate the activity of aberrantly methylated HCC genes through regulatory pathways. Virus-host regulatory pathways, interactions between proteins, gene expression, transport, and stability regulation, were reconstructed using the ANDSystem. Gene expression regulation was statistically significant. Gene network analysis identified four out of 70 HCC marker genes whose expression regulation by viral proteins may be associated with HCC: DNA-binding protein inhibitor ID - 1 (ID1), flap endonuclease 1 (FEN1), cyclin-dependent kinase inhibitor 2A (CDKN2A), and telomerase reverse transcriptase (TERT). It suggested the following viral protein effects in HCV/human protein heterocomplexes: HCV NS3(p70) protein activates human STAT3 and NOTC1; NS2-3(p23), NS5B(p68), NS1(E2), and core(p21) activate SETD2; NS5A inhibits SMYD3; and NS3 inhibits CCN2. Interestingly, NS3 and E1(gp32) activate c-Jun when it positively regulates CDKN2A and inhibit it when it represses TERT. The discovered regulatory mechanisms might be key areas of focus for creating medications and preventative therapies to decrease the likelihood of HCC development during HCV infection.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.
RESULTS: To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses.
CONCLUSIONS: Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.

BACKGROUND: The Pacific oyster, Crassostrea gigas, is an economically important shellfish around the world. Great efforts have been made to improve its growth rate through genetic breeding. However, the candidate marker genes, pathways, and potential lncRNAs involved in oyster growth regulation remain largely unknown. To identify genes, lncRNAs, and pathways involved in growth regulation, C. gigas spat was cultured at a low temperature (15 ℃) to yield a growth-inhibited model, which was used to conduct comparative transcriptome analysis with spat cultured at normal temperature (25 ℃).
RESULTS: In total, 8627 differentially expressed genes (DEGs) and 1072 differentially expressed lncRNAs (DELs) were identified between the normal-growth oysters (cultured at 25 ℃, hereinafter referred to as NG) and slow-growth oysters (cultured at 15 ℃, hereinafter referred to as SG). Functional enrichment analysis showed that these DEGs were mostly enriched in the AMPK signaling pathway, MAPK signaling pathway, insulin signaling pathway, autophagy, apoptosis, calcium signaling pathway, and endocytosis process. LncRNAs analysis identified 265 cis-acting pairs and 618 trans-acting pairs that might participate in oyster growth regulation. The expression levels of LNC_001270, LNC_003322, LNC_011563, LNC_006260, and LNC_012905 were inducible to the culture temperature and food abundance. These lncRNAs were located at the antisense, upstream, or downstream of the SREBP1/p62, CDC42, CaM, FAS, and PIK3CA genes, respectively. Furthermore, the expression of the trans-acting lncRNAs, including XR_9000022.2, LNC_008019, LNC_015817, LNC_000838, LNC_00839, LNC_011859, LNC_007294, LNC_006429, XR_002198885.1, and XR_902224.2 was also significantly associated with the expression of genes enriched in AMPK signaling pathway, insulin signaling pathway, autophagy, apoptosis, calcium signaling pathway, and endocytosis process.
CONCLUSIONS: In this study, we identified the critical growth-related genes and lncRNAs that could be utilized as candidate markers to illustrate the molecular mechanisms underlying the growth regulation of Pacific oysters.