Cell Type-specific Expression Patterns Research Articles

Brain single-cell transcriptomics highlights comorbidity-related cell type-specific changes of Parkinson’s disease with major depressive disorder after paraquat exposure

Paraquat (PQ), a commonly used herbicide, is a potent environmental neurotoxin associated with Parkinson’s disease (PD) and major depressive disorder (MDD). While the involvement of various brain cell types in the etiology of each disorder is well recognized, the specific cell subtypes implicated in the comorbidity of PD and MDD, especially under PQ neurotoxicity, remain poorly understood. In this study, we used single-cell RNA sequencing (scRNA-seq) to analyze brain tissues from mice with PQ-induced PD with MDD. By integrating genomic data with scRNA-seq profiles, we identified differences in cellular heterogeneity related to the pathogenesis of PD and MDD under PQ exposure. Our analysis of risk enrichment in genes with cell type-specific expression patterns revealed that astrocytes are predominantly linked to the comorbidity of PQ-induced PD and MDD. Furthermore, we identified a specific astrocyte subtype that plays a major role in the comorbidity-related changes observed in PQ-induced PD and MDD. This subtype appears to interact with and potentially transform into MDD-specific and PD-specific subtypes. Additionally, pathways related to chemical synaptic function and neuro-projection development were involved in all key stages of PD and MDD co-occurrence. We also identified RNF7 and MTCH2 as shared diagnostic hub genes for PD and MDD, which changed significantly in astrocytes following PQ exposure. These genes may serve as potential markers for astrocyte-specific prognostic diagnosis of PQ-induced PD with MDD. In summary, this study provides the first scRNA-seq profile of comorbidity in a PQ-exposed model. It highlights the heterogeneity of astrocytes in comorbidity and elucidates potential mechanisms underlying the co-occurrence of PD and MDD. These findings emphasize the need for further research into the pathogenesis of PD comorbid with MDD and offer novel insights into PQ neurotoxicity.

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets.

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

Prefrontal cortex astrocytes in major depressive disorder: exploring pathogenic mechanisms and potential therapeutic targets.

Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets. Functional enrichment and protein-protein interaction analyses emphasize the role of astrocytes in MDD progression. Machine learning is employed to develop a predictive model for MDD risk assessment. Single-cell and spatial transcriptomic analyses provide insights into cell type-specific expression patterns, particularly regarding astrocytes. We have identified significant genomic alterations and co-expression modules associated with MDD in the DLPFC. Key genes involved in neuroactive ligand-receptor interaction pathways, notably in astrocytes, have been highlighted. Additionally, we developed a predictive model for MDD risk assessment based on selected key genes. Single-cell and spatial transcriptomic analyses underscored the role of astrocytes in MDD. Virtual screening of compounds targeting GPR37L1, KCNJ10, and PPP1R3C proteins has identified potential therapeutic candidates. In summary, our comprehensive approach enhances the understanding of MDD's molecular underpinnings and offers promising opportunities for advancing therapeutic interventions, ultimately aiming to alleviate the burden of this debilitating mental health condition. KEY MESSAGES: Our investigation furnishes insightful revelations concerning the dysregulation of astrocyte-associated processes in MDD. We have pinpointed specific genes, namely KCNJ10, PPP1R3C, and GPR37L1, as potential candidates warranting further exploration and therapeutic intervention. We incorporate a virtual screening of small molecule compounds targeting KCNJ10, PPP1R3C, and GPR37L1, presenting a promising trajectory for drug discovery in MDD.

ScRNA-seq reveals the spatiotemporal distribution of camptothecin pathway and transposon activity in Camptotheca acuminata shoot apexes and leaves.

Camptotheca acuminata Decne., a significant natural source of the anticancer drug camptothecin (CPT), synthesizes CPT through the monoterpene indole alkaloid (MIA) pathway. In this study, we used single-cell RNA sequencing (scRNA-seq) to generate datasets encompassing over 60,000 cells from C. acuminata shoot apexes and leaves. After cell clustering and annotation, we identified five major cell types in shoot apexes and four in leaves. Analysis of MIA pathway gene expression revealed that most of them exhibited heightened expression in proliferating cells (PCs) and vascular cells (VCs). In contrast to MIA biosynthesis in Catharanthus roseus, CPT biosynthesis in C. acuminata did not exhibit multicellular compartmentalization. Some putative genes encoding enzymes and transcription factors (TFs) related to the biosynthesis of CPT and its derivatives were identified through co-expression analysis. These include 19 cytochrome P450 genes, 8 O-methyltransferase (OMT) genes, and 62 TFs. Additionally, these pathway genes exhibited dynamic expression patterns during VC and EC development. Furthermore, by integrating gene and transposable element (TE) expression data, we constructed novel single-cell transcriptome atlases for C. acuminata. This approach significantly facilitated the identification of rare cell types, including peripheral zone cells (PZs). Some TE families displayed cell type specific, tissue specific, or developmental stage-specific expression patterns, suggesting crucial roles for these TEs in cell differentiation and development. Overall, this study not only provides novel insights into CPT biosynthesis and spatial-temporal TE expression characteristics in C. acuminata, but also serves as a valuable resource for further comprehensive investigations into the development and physiology of this species.

Establishment of single-cell transcriptional states during seed germination

Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary for establishment in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell type-specific gene expression and gene regulatory networks. We can better understand how the functions of different cell types are established and contribute to the embryo by determining how cell type-specific transcription begins and changes through germination. Here we describe a temporal analysis of the germinating Arabidopsis thaliana embryo at single-cell resolution. We define the highly dynamic cell type-specific patterns of gene expression and how these relate to changing cellular function as germination progresses. Underlying these are unique gene regulatory networks and transcription factor activity. We unexpectedly discover that most embryo cells transition through the same initial transcriptional state early in germination, even though cell identity has already been established during embryogenesis. Cells later transition to cell type-specific gene expression patterns. Furthermore, our analyses support previous findings that the earliest events leading to the induction of seed germination take place in the vasculature. Overall, our study constitutes a general framework with which to characterize Arabidopsis cell transcriptional states through seed germination, allowing investigation of different genotypes and other plant species whose seed strategies may differ.

Resolving the three-dimensional interactome of Human Accelerated Regions during human and chimpanzee neurodevelopment.

Human Accelerated Regions (HARs) are highly conserved across species but exhibit a significant excess of human-specific sequence changes, suggesting they may have gained novel functions in human evolution. HARs include transcriptional enhancers with human-specific activity and have been implicated in the evolution of the human brain. However, our understanding of how HARs contributed to uniquely human features of the brain is hindered by a lack of insight into the genes and pathways that HARs regulate. It is unclear whether HARs acted by altering the expression of gene targets conserved between HARs and their chimpanzee orthologs or by gaining new gene targets in human, a mechanism termed enhancer hijacking. We generated a high-resolution map of chromatin interactions for 1,590 HARs and their orthologs in human and chimpanzee neural stem cells (NSCs) to comprehensively identify gene targets in both species. HARs and their chimpanzee orthologs targeted a conserved set of 2,963 genes enriched for neurodevelopmental processes including neurogenesis and synaptic transmission. Changes in HAR enhancer activity were correlated with changes in conserved gene target expression. Conserved targets were enriched among genes differentially expressed between human and chimpanzee NSCs or between human and non-human primate developing and adult brain. Species-specific HAR gene targets did not converge on known biological functions and were not significantly enriched among differentially expressed genes, suggesting that HARs did not alter gene expression via enhancer hijacking. HAR gene targets, including differentially expressed targets, also showed cell type-specific expression patterns in the developing human brain, including outer radial glia, which are hypothesized to contribute to human cortical expansion. Our findings support that HARs influenced human brain evolution by altering the expression of conserved gene targets and provide the means to functionally link HARs with novel human brain features.

The microRNA expression in crypt-top and crypt-bottom colonic epithelial cell populations demonstrates cell-type specificity and correlates with endoscopic activity in ulcerative colitis.

Colonic epithelial barrier dysfunction is one of the early events in ulcerative colitis (UC) and microRNAs (miRNAs) participate in its regulation. However, cell type-specific miRNome during UC is still unknown. Thus, we aimed to explore miRNA expression patterns in colon tissue and epithelial cells at active and quiescent UC. Small RNA-sequencing in colon tissue, crypt-bottom (CD44+), and crypt-top (CD66a+) colonic epithelial cells from two cohorts of UC patients (n=74) and healthy individuals (n=50) was performed. Data analysis encompassed differential expression, weighted gene co-expression network, correlation, gene-set enrichment analyses. Differentially expressed colonic tissue miRNAs showed potential involvement in regulation of interleukin-4 and interleukin-13 signalling during UC. As this pathway plays role in intestinal barrier regulation, consecutive analysis of spatially distinct colonic epithelial cell populations was performed. Cell-type (crypt-top and crypt-bottom) specific miRNA expression patterns were identified in both active and quiescent UC. Target genes of differentially expressed epithelial miRNAs at different disease activity were overrepresented in epithelial cell migration and therefore intestinal barrier integrity regulation. The pro-inflammatory miRNA co-expression module M1 correlated with endoscopic disease activity and successfully distinguished active and quiescent UC not only in both epithelial cell populations, but also in the colon tissue. The anti-inflammatory module M2 was specific to crypt-bottom cells and significantly enriched in the quiescent UC patients. miRNA expression was specific to colonic epithelial cell populations and UC state, reflecting endoscopic disease activity. Irrespective of the UC state, deregulated epithelial miRNAs were associated with regulation of intestinal barrier integrity.

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6’s role in hematopoietic differentiation and leukemia

BackgroundLong non-coding RNAs (lncRNAs) are pivotal players in cellular processes, and their unique cell-type specific expression patterns render them attractive biomarkers and therapeutic targets. Yet, the functional roles of most lncRNAs remain enigmatic. To address the need to identify new druggable lncRNAs, we developed a comprehensive approach integrating transcription factor binding data with other genetic features to generate a machine learning model, which we have called INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources).MethodsINFLAMeR was trained on high-throughput CRISPR interference (CRISPRi) screens across seven cell lines, and the algorithm was based on 71 genetic features. To validate the predictions, we selected candidate lncRNAs in the human K562 leukemia cell line and determined the impact of their knockdown (KD) on cell proliferation and chemotherapeutic drug response. We further performed transcriptomic analysis for candidate genes. Based on these findings, we assessed the lncRNA small nucleolar RNA host gene 6 (SNHG6) for its role in myeloid differentiation. Finally, we established a mouse K562 leukemia xenograft model to determine whether SNHG6 KD attenuates tumor growth in vivo.ResultsThe INFLAMeR model successfully reconstituted CRISPRi screening data and predicted functional lncRNAs that were previously overlooked. Intensive cell-based and transcriptomic validation of nearly fifty genes in K562 revealed cell type-specific functionality for 85% of the predicted lncRNAs. In this respect, our cell-based and transcriptomic analyses predicted a role for SNHG6 in hematopoiesis and leukemia. Consistent with its predicted role in hematopoietic differentiation, SNHG6 transcription is regulated by hematopoiesis-associated transcription factors. SNHG6 KD reduced the proliferation of leukemia cells and sensitized them to differentiation. Treatment of K562 leukemic cells with hemin and PMA, respectively, demonstrated that SNHG6 inhibits red blood cell differentiation but strongly promotes megakaryocyte differentiation. Using a xenograft mouse model, we demonstrate that SNHG6 KD attenuated tumor growth in vivo.ConclusionsOur approach not only improved the identification and characterization of functional lncRNAs through genomic approaches in a cell type-specific manner, but also identified new lncRNAs with roles in hematopoiesis and leukemia. Such approaches can be readily applied to identify novel targets for precision medicine.

HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC.

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCC database (HCCDB v1.0) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

Decoding the universal human chromatin landscape through teratoma-based profiling.

Teratoma formation is key for evaluating differentiation of human pluripotent stem cells into embryonic germ layers and serves as a model for understanding stem cell differentiation and developmental processes. Its potential for insights into epigenome and transcriptome profiling is significant. This study integrates the analysis of the epigenome and transcriptome of hESC-generated teratomas, comparing transcriptomes between hESCs and teratomas. It employs cell type-specific expression patterns from single-cell data to deconvolve RNA-Seq data and identify cell types within teratomas. Our results provide a catalog of activating and repressive histone modifications, while also elucidating distinctive features of chromatin states. Construction of an epigenetic signature matrix enabled the quantification of diverse cell populations in teratomas and enhanced the ability to unravel the epigenetic landscape in heterogeneous tissue contexts. This study also includes a single cell multiome atlas of expression (scRNA-Seq) and chromatin accessibility (scATAC-Seq) of human teratomas, further revealing the complexity of these tissues. A histology-based digital staining tool further complemented the annotation of cell types in teratomas, enhancing our understanding of their cellular composition. This research is a valuable resource for examining teratoma epigenomic and transcriptomic landscapes and serves as a model for epigenetic data comparison.

Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization.

Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment's heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L, PRCD, SMAD9, C2orf91, and HSD17B13, and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.

Cell Type-Specific Expression of Molecular Players in Morphine Action

Hidden drug abuse has become a concerning issue in Hong Kong, despite an overall decrease in reported cases. The opioid epidemic, fueled by substances like heroin and fentanyl, continues to be a global problem. Understanding the molecular mechanisms of morphine addiction is crucial for developing effective treatments to combat this problem. This study investigates the gene expression patterns of key players in morphine addiction in the Nucleus Accumbens (NAc) region of the mesolimbic dopamine system, a key area in drug-associated reward. The RNA transcripts produced in mice NAc under acute morphine treatment can reveal transcriptional changes that alter the regulation of gene expression related to its molecular mechanisms. By analyzing publicly available single-cell RNA sequencing data, we identified gene expression patterns in specific cell types for genes like BDNF, ΔFosB, CREB, and Npy, all of which were previously studied to be relevant to morphine addiction. The study also highlights novel differentially expressed genes in response to morphine treatment. These findings provide insight into new and relevant genes as well as their cell type-specific expression patterns to glean the pathways that they influence. Understanding these molecular processes can inform personalized pharmaceutical approaches and aid in the development of new therapies.

Synaptopodin-2 Isoforms Have Specific Binding Partners and Display Distinct, Muscle Cell Type-Specific Expression Patterns.

Synaptopodin-2 (SYNPO2) is a protein associated with the Z-disc in striated muscle cells. It interacts with α-actinin and filamin C, playing a role in Z-disc maintenance under stress by chaperone-assisted selective autophagy (CASA). In smooth muscle cells, SYNPO2 is a component of dense bodies. Furthermore, it has been proposed to play a role in tumor cell proliferation and metastasis in many different kinds of cancers. Alternative transcription start sites and alternative splicing predict the expression of six putative SYNPO2 isoforms differing by extended amino- and/or carboxy-termini. Our analyses at mRNA and protein levels revealed differential expression of SYNPO2 isoforms in cardiac, skeletal and smooth muscle cells. We identified synemin, an intermediate filament protein, as a novel binding partner of the PDZ-domain in the amino-terminal extension of the isoforms mainly expressed in cardiac and smooth muscle cells, and demonstrated colocalization of SYNPO2 and synemin in both cell types. A carboxy-terminal extension, mainly expressed in smooth muscle cells, is sufficient for association with dense bodies and interacts with α-actinin. SYNPO2 therefore represents an additional and novel link between intermediate filaments and the Z-discs in cardiomyocytes and dense bodies in smooth muscle cells, respectively. In pathological skeletal muscle samples, we identified SYNPO2 in the central and intermediate zones of target fibers of patients with neurogenic muscular atrophy, and in nemaline bodies. Our findings help to understand distinct functions of individual SYNPO2 isoforms in different muscle tissues, but also in tumor pathology.

Dissection of an impact of VDR and RXRA on the genomic activity of 1,25(OH)2D3 in A431 squamous cell carcinoma

BackgroundHuman skin is the natural source, place of metabolism, and target for vitamin D3. The classical active form of vitamin D3, 1,25(OH)2D3, expresses pluripotent properties and is intensively studied in cancer prevention and therapy. To define the specific role of vitamin D3 receptor (VDR) and its co-receptor retinoid X receptor alpha (RXRA) in genomic regulation, VDR or RXRA genes were silenced in the squamous cell carcinoma cell line A431 and treated with 1,25(OH)2D3 at long incubation time points 24 h/72 h. Extending the incubation time of A431 WT (wild-type) cells with 1,25(OH)2D3 resulted in a two-fold increase in DEGs (differentially expressed genes) and a change in the amount of downregulated from 37% to 53%. VDR knockout led to a complete loss of 1,25(OH)2D3-induced genome-wide gene regulation at 24 h time point, but after 72 h, 20 DEGs were found, of which 75% were downregulated, and most of them belonged to the gene ontology group “immune response”. This may indicate the existence of an alternative, secondary response to 1,25(OH)2D3. In contrast, treatment of A431 ΔRXRA cells with 1,25(OH)2D3 for 24 h only partially affected DEGs, suggesting RXRA-independent regulation. Interestingly, overexpression of classic 1,25(OH)2D3 targets, like CYP24A1 (family 24 of subfamily A of cytochrome P450 member 1) or CAMP (cathelicidin antimicrobial peptide) was found to be RXRA-independent. Also, immunofluorescence staining of A431 WT cells revealed partial VDR/RXRA colocalization after 24 h and 72 h 1,25(OH)2D3 treatment.Comparison of transcriptome changes induced by 1,25(OH)2D3 in normal keratinocytes vs. cancer cells showed high cell type specific expression pattern with only a few genes commonly regulated by 1,25(OH)2D3. Activation of the genomic pathway at least partially reversed the expression of cancer-related genes, forming a basis for anti-cancer activates of 1,25(OH)2D3. In summary, VDR or RXRA independent genomic activities of 1,25(OH)2D3 suggest the involvement of alternative factors, opening new challenges in this field.

Single-cell epigenomics and spatiotemporal transcriptomics reveal human cerebellar development

Human cerebellar development is orchestrated by molecular regulatory networks to achieve cytoarchitecture and coordinate motor and cognitive functions. Here, we combined single-cell transcriptomics, spatial transcriptomics and single cell chromatin accessibility states to systematically depict an integrative spatiotemporal landscape of human fetal cerebellar development. We revealed that combinations of transcription factors and cis-regulatory elements (CREs) play roles in governing progenitor differentiation and cell fate determination along trajectories in a hierarchical manner, providing a gene expression regulatory map of cell fate and spatial information for these cells. We also illustrated that granule cells located in different regions of the cerebellar cortex showed distinct molecular signatures regulated by different signals during development. Finally, we mapped single-nucleotide polymorphisms (SNPs) of disorders related to cerebellar dysfunction and discovered that several disorder-associated genes showed spatiotemporal and cell type-specific expression patterns only in humans, indicating the cellular basis and possible mechanisms of the pathogenesis of neuropsychiatric disorders.

Single-Cell RNA-Seq Reveals Changes in Cell Subsets in the Cortical Microenvironment during Acute Phase of Ischemic Stroke Rats.

Ischemic stroke, the most common stroke type, has threatened human life and health. Currently, intravenous thrombolysis and endovascular thrombectomy are the mainstream treatment methods, but they may cause cerebral ischemia-reperfusion injury (CIRI), which aggravates brain injury. Consequently, it is worthwhile to start with a study of CIRI mechanism to identify better prevention and treatment methods. Applying single-cell RNA sequencing (scRNA-seq) technology to further understand the biological functions of various cell types in CIRI will facilitate the intervention of CIRI. This study aimed to establish a rat middle cerebral artery occlusion (MCAO) model to simulate cerebral ischemia-reperfusion, perform enzymatic hydrolysis, and suspend cerebral cortex tissue edema. Single-cell transcriptome sequencing was used, combined with cluster analysis, t-distributed stochastic neighbor embedding (t-SNE) visualization, and other bioinformatics methods to distinguish cell subgroups while using gene ontology (GO) function enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment to reveal the biological function of each cell subgroup. We identified 21 brain clusters with cell type-specific gene expression patterns and cell subpopulations, as well as 42 marker genes representing different cell subpopulations. The number of cells in clusters 0-3 increased significantly in MCAO group compared to that in the sham group, and nine-cell subpopulations exhibited remarkable differences in the number of genes. Subsequently, GO and KEGG analyses were performed on the top 40 differentially expressed genes (DEGs) in the six cell subpopulations with significant differences. These results indicate that biological processes and signaling pathways are involved in different cell subpopulations. ScRNA-seq revealed the diversity of cell differentiation and the unique information of cell subpopulations in the cortex of rats with acute ischemic stroke, providing novel insight into the pathological process and drug discovery in stroke.

Identification of key genes and the pathophysiology associated with allergen-specific immunotherapy for allergic rhinitis

BackgroundAllergen-specific immunotherapy (AIT) is a causative treatment in allergic rhinitis (AR), comprising long-term allergen administration and over three years of treatment. This study is carried out for revealing the mechanisms and key genes of AIT in AR.MethodsThe present study utilized online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE37157 and GSE29521 to analyze the hub genes changes related to AIT in AR. Based on limma package, differential expression analysis for the two groups (samples of allergic patients prior to AIT and samples of allergic patients undergoing AIT) was performed to obtain differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were conducted using DAVID database. A Protein-Protein Interaction network (PPI) was built and a significant network module was acquired by using Cytoscape software (Cytoscape, 3.7.2). Utilizing the miRWalk database, we identified potential gene biomarkers, constructed interaction networks of target genes and microRNAs (miRNAs) using Cytoscape software, and explore the cell type-specific expression patterns of these genes in peripheral blood using publicly available single-cell RNA sequencing data (GSE200107). Finally, we are using PCR to detect changes in the hub genes that are screened using the above method in peripheral blood before and after AIT treatment.ResultsGSE37157 and GSE29521 included 28 and 13 samples, respectively. A total of 119 significantly co-upregulated DEGs and 33 co-downregulated DEGs were obtained from two datasets. The GO and KEGG analyses demonstrated that protein transport, positive regulation of apoptotic process, Natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, TNF signaling pathway, B cell receptor signaling pathway and Apoptosis may be potential candidate therapeutic targets for AIT of AR. From the PPI network, 20 hub genes were obtained. Among them, the PPI sub-networks of CASP3, FOXO3, PIK3R1, PIK3R3, ATF4, and POLD3 screened out from our study have been identified as reliable predictors of AIT in AR, especially the PIK3R1.ConclusionOur analysis has identified novel gene signatures, thereby contributing to a more comprehensive understanding of the molecular mechanisms underlying AIT in the treatment of AR.

69-LB: Sex-Specific Dynamics of GLP-1R and GIPR Expression in the Hypothalamus and the Amygdala of Mice

GIPR:GLP-1R co-agonism improves obesity and cardio-metabolic disease with superior efficacy in both male and female mice compared to mono-agonism. We detected sex-specific differences in metabolic phenotypes, which may point to differing sensitivity for GLP-1R and/or GIPR in areas of the hypothalamus regulating energy balance (ARC, VMH, DMH) and in areas of the amygdala regulating cardiovascular functions. To optimize future therapeutic benefits and minimize side effects from a perspective of sex differences, we investigated whether GLP1R and GIPR expression levels in specific cell populations of the hypothalamus and the amygdala regions are co-determined by sex and diet. Female and male C57Bl6 mice were fed normal chow or 58% high fat diet (HFD) for 21 days (n = 4, total mouse number 16). Spatial transcriptomics by in-situ hybridization method RNAscope™ was combined with immunofluorescence staining for neurons (NeuN), astrocytes (GFAP) and microglia (iba1) to identify cell type specific gene expression patterns of GLP1R and GIPR in female and male brains. In the hypothalamus, both sexes, gene expression of GLP-1R was higher compared to GIPR (ARC, VMH and DMH). In our studies GLP-1R and GIPR were mostly expressed in neurons, marginally in microglia and not observed in astrocytes in both sexes. HFD exposure reduced GLP-1R and GIPR gene expression in males only. In the Amygdala, neuronal GIPR gene expression levels were higher compared to GLP-1R in both sexes. HFD reduced GIPR and GLP-1R gene expression in males only. In females, HFD induced a slight increase of GIPR gene expression specifically in the central and basolateral amygdala. Our results reveal sex-specific dynamics in GLP-1R and GIPR expression patterns in metabolically relevant areas of the hypothalamus and the amygdala induced by HFD exposure. This may imply sex-specific dosing regimens of GIP:GLP-1R co-agonists for future treatments of cardio-metabolic disease Disclosure S. M. Hofmann: None. S. Zhang: None. R. Tom: None. T. D. Müller: Research Support; Novo Nordisk A/S, Speaker's Bureau; Eli Lilly and Company, AstraZeneca, Mercodia AB, Stock/Shareholder; Novo Nordisk A/S, Eli Lilly and Company. M. H. Tschöp: Consultant; Boehringer Ingelheim Pharma GmbH&Co. KG.

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.