Cell Type-specific Gene Research Articles

Single-cell chromatin profiling reveals genetic programs activating proregenerative states in nonmyocyte cells.

Cardiac regeneration requires coordinated participation of multiple cell types whereby their communications result in transient activation of proregenerative cell states. Although the molecular characteristics and lineage origins of these activated cell states and their contribution to cardiac regeneration have been studied, the extracellular signaling and the intrinsic genetic program underlying the activation of the transient functional cell states remain largely unexplored. In this study, we delineated the chromatin landscapes of the noncardiomyocytes (nonCMs) of the regenerating heart at the single-cell level and inferred the cis-regulatory architectures and trans-acting factors that control cell type-specific gene expression programs. Moreover, further motif analysis and cell-specific genetic manipulations suggest that the macrophage-derived inflammatory signal tumor necrosis factor-α, acting via its downstream transcription factor complex activator protein-1, functions cooperatively with discrete transcription regulators to activate respective nonCM cell types critical for cardiac regeneration. Thus, our study defines the regulatory architectures and intercellular communication principles in zebrafish heart regeneration.

Cell-type-specific cis-regulatory divergence in gene expression and chromatin accessibility revealed by human-chimpanzee hybrid cells.

Although gene expression divergence has long been postulated to be the primary driver of human evolution, identifying the genes and genetic variants underlying uniquely human traits has proven to be quite challenging. Theory suggests that cell-type-specific cis-regulatory variants may fuel evolutionary adaptation due to the specificity of their effects. These variants can precisely tune the expression of a single gene in a single cell-type, avoiding the potentially deleterious consequences of trans-acting changes and non-cell type-specific changes that can impact many genes and cell types, respectively. It has recently become possible to quantify human-specific cis-acting regulatory divergence by measuring allele-specific expression in human-chimpanzee hybrid cells-the product of fusing induced pluripotent stem (iPS) cells of each species in vitro. However, these cis-regulatory changes have only been explored in a limited number of cell types. Here, we quantify human-chimpanzee cis-regulatory divergence in gene expression and chromatin accessibility across six cell types, enabling the identification of highly cell-type-specific cis-regulatory changes. We find that cell-type-specific genes and regulatory elements evolve faster than those shared across cell types, suggesting an important role for genes with cell-type-specific expression in human evolution. Furthermore, we identify several instances of lineage-specific natural selection that may have played key roles in specific cell types, such as coordinated changes in the cis-regulation of dozens of genes involved in neuronal firing in motor neurons. Finally, using novel metrics and a machine learning model, we identify genetic variants that likely alter chromatin accessibility and transcription factor binding, leading to neuron-specific changes in the expression of the neurodevelopmentally important genes FABP7 and GAD1. Overall, our results demonstrate that integrative analysis of cis-regulatory divergence in chromatin accessibility and gene expression across cell types is a promising approach to identify the specific genes and genetic variants that make us human.

A human stomach cell type transcriptome atlas

BackgroundThe identification of cell type-specific genes and their modification under different conditions is central to our understanding of human health and disease. The stomach, a hollow organ in the upper gastrointestinal tract, provides an acidic environment that contributes to microbial defence and facilitates the activity of secreted digestive enzymes to process food and nutrients into chyme. In contrast to other sections of the gastrointestinal tract, detailed descriptions of cell type gene enrichment profiles in the stomach are absent from the major single-cell sequencing-based atlases.ResultsHere, we use an integrative correlation analysis method to predict human stomach cell type transcriptome signatures using unfractionated stomach RNAseq data from 359 individuals. We profile parietal, chief, gastric mucous, gastric enteroendocrine, mitotic, endothelial, fibroblast, macrophage, neutrophil, T-cell, and plasma cells, identifying over 1600 cell type-enriched genes.ConclusionsWe uncover the cell type expression profile of several non-coding genes strongly associated with the progression of gastric cancer and, using a sex-based subset analysis, uncover a panel of male-only chief cell-enriched genes. This study provides a roadmap to further understand human stomach biology.

Lineage-determining transcription factor-driven promoters regulate cell type-specific macrophage gene expression.

Mammalian promoters consist of multifarious elements, which make them unique and support the selection of the proper transcript variants required under diverse conditions in distinct cell types. However, their direct DNA-transcription factor (TF) interactions are mostly unidentified. Murine bone marrow-derived macrophages (BMDMs) are a widely used model for studying gene expression regulation. Thus, this model serves as a rich source of various next-generation sequencing data sets, including a large number of TF cistromes. By processing and integrating the available cistromic, epigenomicand transcriptomic data from BMDMs, we characterized the macrophage-specific direct DNA-TF interactions, with a particular emphasis on those specific for promoters. Whilst active promoters are enriched for certain types of typically methylatable elements, more than half of them contain non-methylatable and prototypically promoter-distal elements. In addition, circa 14% of promoters-including that of Csf1r-are composed exclusively of 'distal' elements that provide cell type-specific gene regulation by specialized TFs. Similar to CG-rich promoters, these also contain methylatable CG sites that are demethylated in a significant portion and show high polymerase activity. We conclude that this unusual class of promoters regulates cell type-specific gene expression in macrophages, and such a mechanism might exist in other cell types too.

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.

Intestinal epithelial adaptations to vertical sleeve gastrectomy defined at single-cell resolution

The gut plays a key role in regulating metabolic health. Dietary factors disrupt intestinal physiology and contribute to obesity and diabetes, whereas bariatric procedures such as vertical sleeve gastrectomy (VSG) cause gut adaptations that induce robust metabolic improvements. However, our understanding of these adaptations at the cellular and molecular levels remains limited. In a validated murine model, we leverage single-cell transcriptomics to determine how VSG impacts different cell lineages of the small intestinal epithelium. We define cell type-specific genes and pathways that VSG rescues from high-fat diet perturbation and characterize additional rescue-independent changes brought about by VSG. We show that Paneth cells have increased expression of the gut peptide Reg3g after VSG. We also find that VSG restores pathways pertaining to mitochondrial respiration and cellular metabolism, especially within crypt-based cells. Overall, our study provides unprecedented molecular resolution of VSG's therapeutic effects on the gut epithelium.

Multiomic Analysis of Neuroinflammation and Occult Infection in Sudden Infant Death Syndrome

Antemortem infection is a risk factor for sudden infant death syndrome (SIDS)-the leading postneonatal cause of infant mortality in the developed world. Manifestations of infection and inflammation are not always apparent in clinical settings or by standard autopsy; thus, enhanced resolution approaches are needed. To ascertain whether a subset of SIDS cases is associated with neuroinflammation and occult infection. In this case-control study, postmortem fluids from SIDS cases and controls collected between July 2011 and November 2018 were screened for elevated inflammatory markers, specifically cerebrospinal fluid (CSF) neopterin and CSF and serum cytokines. CSF, liver, and brain tissue from SIDS cases with elevated CSF neopterin were subjected to metagenomic next-generation sequencing (mNGS) to probe for infectious pathogens. Brainstem tissue from a subset of these cases was analyzed by single-nucleus RNA sequencing (snRNAseq) to measure cell type-specific gene expression associated with neuroinflammation and infection. All tissue and fluid analyses were performed from April 2019 to January 2023 in a pathology research laboratory. Included was autopsy material from infants dying of SIDS and age-matched controls dying of known causes. There were no interventions or exposures. CSF neopterin levels were measured by high-performance liquid chromatography. Cytokines were measured by multiplex fluorometric assay. mNGS was performed on liver, CSF, brain, and brainstem tissue. snRNAseq was performed on brainstem tissue. A cohort of 71 SIDS cases (mean [SD] age, 55.2 [11.4] postconceptional weeks; 42 male [59.2%]) and 20 controls (mean [SD] age, 63.2 [16.9] postconceptional weeks; 11 male [55.0%]) had CSF and/or serum available. CSF neopterin was screened in 64 SIDS cases and 15 controls, with no exclusions. Tissues from 6 SIDS cases were further analyzed. For CSF neopterin measures, SIDS samples were from infants with mean (SD) age of 54.5 (11.3) postconceptional weeks (38 male [59.4%]) and control samples were from infants with mean (SD) age of 61.5 (17.4) postconceptional weeks (7 male [46.7%]). A total of 6 SIDS cases (9.3%) with high CSF neopterin were identified, suggestive of neuroinflammation. mNGS detected human parechovirus 3 (HPeV3) in tissue and CSF from 1 of these 6 cases. snRNAseq of HPeV3-positive brainstem tissue (medulla) revealed dramatic enrichment of transcripts for genes with predominately inflammatory functions compared with 3 age-matched SIDS cases with normal CSF neopterin levels. Next-generation molecular tools in autopsy tissue provide novel insight into pathogens that go unrecognized by normal autopsy methodology, including in infants dying suddenly and unexpectedly.

The forkhead transcription factor Foxj1 controls vertebrate olfactory cilia biogenesis and sensory neuron differentiation.

In vertebrates, olfactory receptors localize on multiple cilia elaborated on dendritic knobs of olfactory sensory neurons (OSNs). Although olfactory cilia dysfunction can cause anosmia, how their differentiation is programmed at the transcriptional level has remained largely unexplored. We discovered in zebrafish and mice that Foxj1, a forkhead domain-containing transcription factor traditionally linked with motile cilia biogenesis, is expressed in OSNs and required for olfactory epithelium (OE) formation. In keeping with the immotile nature of olfactory cilia, we observed that ciliary motility genes are repressed in zebrafish, mouse, and human OSNs. Strikingly, we also found that besides ciliogenesis, Foxj1 controls the differentiation of the OSNs themselves by regulating their cell type-specific gene expression, such as that of olfactory marker protein (omp) involved in odor-evoked signal transduction. In line with this, response to bile acids, odors detected by OMP-positive OSNs, was significantly diminished in foxj1 mutant zebrafish. Taken together, our findings establish how the canonical Foxj1-mediated motile ciliogenic transcriptional program has been repurposed for the biogenesis of immotile olfactory cilia, as well as for the development of the OSNs.

Unravelling cell type-specific responses to Parkinson’s Disease at single cell resolution

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder. The pathological hallmark of PD is loss of dopaminergic neurons and the presence of aggregated α-synuclein, primarily in the substantia nigra pars compacta (SNpc) of the midbrain. However, the molecular mechanisms that underlie the pathology in different cell types is not currently understood. Here, we present a single nucleus transcriptome analysis of human post-mortem SNpc obtained from 15 sporadic Parkinson’s Disease (PD) cases and 14 Controls. Our dataset comprises ∼84K nuclei, representing all major cell types of the brain, allowing us to obtain a transcriptome-level characterization of these cell types. Importantly, we identify multiple subpopulations for each cell type and describe specific gene sets that provide insights into the differing roles of these subpopulations. Our findings reveal a significant decrease in neuronal cells in PD samples, accompanied by an increase in glial cells and T cells. Subpopulation analyses demonstrate a significant depletion of tyrosine hydroxylase (TH) enriched astrocyte, microglia and oligodendrocyte populations in PD samples, as well as TH enriched neurons, which are also depleted. Moreover, marker gene analysis of the depleted subpopulations identified 28 overlapping genes, including those associated with dopamine metabolism (e.g., ALDH1A1, SLC6A3 & SLC18A2). Overall, our study provides a valuable resource for understanding the molecular mechanisms involved in dopaminergic neuron degeneration and glial responses in PD, highlighting the existence of novel subpopulations and cell type-specific gene sets.

Chorioamnionitis accelerates granule cell and oligodendrocyte maturation in the cerebellum of preterm nonhuman primates

BackgroundPreterm birth is often associated with chorioamnionitis and leads to increased risk of neurodevelopmental disorders, such as autism. Preterm birth can lead to cerebellar underdevelopment, but the mechanisms of disrupted cerebellar development in preterm infants are not well understood. The cerebellum is consistently affected in people with autism spectrum disorders, showing reduction of Purkinje cells, decreased cerebellar grey matter, and altered connectivity.MethodsPreterm rhesus macaque fetuses were exposed to intra-amniotic LPS (1 mg, E. coli O55:B5) at 127 days (80%) gestation and delivered by c-section 5 days after injections. Maternal and fetal plasma were sampled for cytokine measurements. Chorio-decidua was analyzed for immune cell populations by flow cytometry. Fetal cerebellum was sampled for histology and molecular analysis by single-nuclei RNA-sequencing (snRNA-seq) on a 10× chromium platform. snRNA-seq data were analyzed for differences in cell populations, cell-type specific gene expression, and inferred cellular communications.Results We leveraged snRNA-seq of the cerebellum in a clinically relevant rhesus macaque model of chorioamnionitis and preterm birth, to show that chorioamnionitis leads to Purkinje cell loss and disrupted maturation of granule cells and oligodendrocytes in the fetal cerebellum at late gestation. Purkinje cell loss is accompanied by decreased sonic hedgehog signaling from Purkinje cells to granule cells, which show an accelerated maturation, and to oligodendrocytes, which show accelerated maturation from pre-oligodendrocytes into myelinating oligodendrocytes.ConclusionThese findings suggest a role of chorioamnionitis on disrupted cerebellar maturation associated with preterm birth and on the pathogenesis of neurodevelopmental disorders among preterm infants.

Single-Cell Transcriptome Reveals Cell Type–Specific Molecular Pathology in a 2VO Cerebral Ischemic Mouse Model

Post-ischemia memory impairment is a major sequela in cerebral ischemia patients. However, cell type–specific molecular pathology in the hippocampus after ischemia is poorly understood. In this study, we adopted a mouse two-vessel occlusion ischemia model (2VO model) to mimic cerebral ischemia–induced memory impairment and investigated the single-cell transcriptome in the hippocampi in 2VO mice. A total of 27,069 cells were corresponding 14 cell types with neuronal, glial, and vascular lineages. We next analyzed cell-specific gene alterations in 2VO mice and the function of these cell-specific genes. Differential expression analysis identified cell type–specific genes with altered expression in neurons, astrocytes, microglia, and oligodendrocytes in 2VO mice. Notably, four subtypes of oligodendrocyte precursor cells with distinct differentiation pathways were suggested. Taken together, this is the first single-cell transcriptome analysis of gene expression in a 2VO model. Furthermore, we suggested new types of oligodendrocyte precursor cells with angiogenesis and neuroprotective potential, which might offer opportunities to identify new avenues of research and novel targets for ischemia treatment.

Genome-wide binding analysis unveils critical implication of B-Myb-mediated transactivation in cancers.

B-Myb, also known as MYB proto-oncogene like 2 (MYBL2), is an important transcription factor implicated in transcription regulation, cell cycle and tumorigenesis. However, the molecular mechanism underlying B-Myb-controlled transactivation in different cell contexts as well as its functional implication in cancers remains elusive. In this study, we have conducted a comprehensive genome-wide analysis of B-Myb binding sites in multiple immortalized or cancer cell lines and identified its critical target genes. The results revealed that B-Myb regulates a common set of core cell cycle genes and cell type-specific genes through collaboration with other important transcription factors (e.g. NFY and MuvB complex) and binding to cell type-invariant promoters and cell type-specific enhancers and super-enhancers. KIF2C, UBE2C and MYC were further validated as B-Myb target genes. Loss-of-function analysis demonstrated that KIF2C knockdown inhibited tumor cell growth both in vitro and in vivo, suppressed cell motility and cell cycle progression, accompanied with defects in microtubule organization and mitosis, strongly suggesting that KIF2C is a critical regulator of cancer cell growth and mitosis, and maintains high cancer cell motility ability and microtubule dynamics. Pan-cancer transcriptomic analysis revealed that the overexpression of both B-Myb and KIF2C presents as independent prognostic markers in various types of cancer. Notably, B-Myb associates with NFYB, binds to target gene promoters, enhancers and super-enhancers, and provokes a cascade of oncogenic gene expression profiles in cancers. Overall, our results highly suggest the critical implication of B-Myb-mediated gene regulation in cancers, and the promising therapeutic and prognostic potentials of B-Myb and KIF2C for cancer diagnosis and treatment.

A budding yeast CAGE dataset comprising two cell types.

The budding yeast Saccharomyces cerevisiae is an excellent model organism for studying chromatin regulation with high-resolution genome-wide analyses. Since newly generated genome-wide data are often compared with publicly available datasets, expanding our dataset repertoire will be beneficial for the field. Information on transcription start sites (TSSs) determined at base pair resolution is essential for elucidating mechanisms of transcription and related chromatin regulation, yet no datasets that cover two different cell types are available. Here, we present a CAGE (cap analysis of gene expression) dataset for a-cells and α-cells grown in defined and rich media. Cell type-specific genes were differentially expressed as expected, ensuring the reliability of the data. Some of the differentially expressed TSSs were medium-specific or detected due to unrecognized chromosome rearrangement. By comparing the CAGE data with a high-resolution nucleosome map, major TSSs were primarily found in +1 nucleosomes, with a peak approximately 30 bp from the promoter-proximal end of the nucleosome. The dataset is available at DDBJ/GEA.

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.

TF-EPI: an interpretable enhancer-promoter interaction detection method based on Transformer.

The detection of enhancer-promoter interactions (EPIs) is crucial for understanding gene expression regulation, disease mechanisms, and more. In this study, we developed TF-EPI, a deep learning model based on Transformer designed to detect these interactions solely from DNA sequences. The performance of TF-EPI surpassed that of other state-of-the-art methods on multiple benchmark datasets. Importantly, by utilizing the attention mechanism of the Transformer, we identified distinct cell type-specific motifs and sequences in enhancers and promoters, which were validated against databases such as JASPAR and UniBind, highlighting the potential of our method in discovering new biological insights. Moreover, our analysis of the transcription factors (TFs) corresponding to these motifs and short sequence pairs revealed the heterogeneity and commonality of gene regulatory mechanisms and demonstrated the ability to identify TFs relevant to the source information of the cell line. Finally, the introduction of transfer learning can mitigate the challenges posed by cell type-specific gene regulation, yielding enhanced accuracy in cross-cell line EPI detection. Overall, our work unveils important sequence information for the investigation of enhancer-promoter pairs based on the attention mechanism of the Transformer, providing an important milestone in the investigation of cis-regulatory grammar.

Transcription Factor TAL1 in Erythropoiesis.

Lineage-specific transcription factors (TFs) regulate differentiation of hematopoietic stem cells (HSCs). They are decisive for the establishment and maintenance of lineage-specific gene expression programs during hematopoiesis. For this they create a regulatory network between TFs, epigenetic cofactors, and microRNAs. They activate cell-type specific genes and repress competing gene expression programs. Disturbance of this process leads to impaired lineage fidelity and diseases of the blood system. The TF T-cell acute leukemia 1 (TAL1) is central for erythroid differentiation and contributes to the formation of distinct gene regulatory complexes in progenitor cells and erythroid cells. A TAL1/E47 heterodimer binds to DNA with the TFs GATA-binding factor 1 and 2 (GATA1/2), the cofactors LIM domain only 1 and 2 (LMO1/2), and LIM domain-binding protein 1 (LDB1) to form a core TAL1 complex. Furthermore, cell-type-dependent interactions of TAL1 with other TFs such as with runt-related transcription factor 1 (RUNX1) and Kruppel-like factor 1 (KLF1) are established. Moreover, TAL1 activity is regulated by the formation of TAL1 isoforms, posttranslational modifications (PTMs), and microRNAs. Here, we describe the function of TAL1 in normal hematopoiesis with a focus on erythropoiesis.

Single-cell analysis reveals the transcriptional alterations in the submandibular glands of aged mice

ObjectivesAging-related salivary gland changes, such as lymphocyte infiltration and acinar cell loss decrease saliva secretion, thereby affecting quality of life. The precise molecular mechanisms underlying these changes remain unclear. MethodsWe here performed single-cell RNA sequencing to clarify gene expression changes in each cell type comprising the submandibular glands (SMGs) of adult and aged mice. ResultsThe proportion of acinar cells decreased in various epithelial clusters annotated with cell type-specific marker genes. Expression levels of the cellular senescence markers, Cdkn2a/p16 and Cdkn1a/p21, were increased in the basal and striated ducts of aged SMGs relative to their levels in those of adult SMGs. In contrast, senescence-associated secretory phenotype-related genes, except transforming growth factor-β, exhibited little change in expression in aged SMGs relative to adult SMGs. Conclusions: Gene Ontology analysis revealed increased expression levels of genes encoding major histocompatibility complex (MHC) class I components in the ductal component cells of aged SMGs. MHC class I expression may thus be associated with salivary gland aging.

Single Cell RNASeq Analysis of AD and Control Post Mortem Brain Samples

Alzheimer’s disease (AD) is a devastative late life disease, molecularly characterized by biochemical lesions of amyloid β plaques and Tau tangles. the 2nd most frequent one worldwide with no early detection methods/cure. I have applied single cell sequencing and analysed post mortem brain samples (using a Chromium machine) from 2 AD and 2 control frontal cortex samples. All the samples had ethics approval, and the RNA was tested for quality. The samples were obtained from the Edenborough MRC brain bank. Very few genes have been linked to far to AD (mainly through GWAS studies [1]). Overall, I quantified about 24,000 nuclei. Using statistical, bioinformatics and computational analyses I have detected significant differential expression changes in astrocyte, neuronal and microglial marker genes. I also conducted network analyses. The percentage of reads passing filter was slightly low (68%). Overall output was a bit lower than optimal. There was still over 1.6Bn clusters generated though, which is within the specification for an S1 flow-cell, and base call quality (quality control) was excellent: 97% >Q30. The read counts mapping to genome occupancy rate was also fairy low (63-81%). The number of barcodes is as follows: sample 1 (AD, Braak I-II) - 6,223 sample 2 (control) 6,262 sample 3 (AD Braak I-II) 5,332 sample 4 (AD, Braak I-II) 5,332 as well. The data is available for download under the GEO (accession number GSE175814). to assess global and cell-type specific gene expression impacts in Alzheimer’s versus control brain samples, we performed a holistic comparison of matching cell populations in AD and controls with the software cell Harmony. To identify possible gene regulatory network underlying transcriptional regulation in these distinct populations, we further explored prior evidenced transcription factor targets relationships from cell Harmony. Importantly, we observe both common and cell-type specific core transcription factors regulators, with both enriched downstream targets and regulated transcription factors. Upregulated networks in Astrocytes were associated with broad regulation by HIF1A, SOX2, NRF1, and RB1. the proportion of endothelial cells was higher in the Alzheimer’s disease samples than the neurological control samples. results reveal that the cell type-specific transcriptomic changes in Alzheimer’s disease are associated with 4 molecular pathways: angiogenesis in endothelial cells, immune response in endothelial cells and microglia, myelination in oligodendrocytes, and synaptic signalling in astrocytes and neurons. I also compared the results with microarray data from large cohort studies that examined samples from the prefrontal cortex (Alzheimer’s disease: n = 310; Neurological controls: n = 157) or temporal cortex (Alzheimer’s disease: n = 106; Neurological controls: n = 135). Among the DEGs identified in our snRNA-seq analysis, 1,113 and 764 genes were significantly differentially expressed in the microarray data from the prefrontal cortex and temporal cortex, respectively. subcluster analysis of microglia identified 13 subpopulations; VGF signalling was also found as changed. In the future, imaging of post mortem Alzheimer’s patients may verify brain cellular morphology/cell volume of vascular changes in the Alzheimer’s compared to control samples. My study findings [2] may open new venues to our understanding of the disease underlying molecular processes and cellular signaling. Regarding the glial cell changes, our main conclusion is that the glial cells may be reactive in the disease and not causal.

Predicting cell-type specific disease genes of diabetes with the biological network

Type 2 diabetes (T2D) is a chronic condition that can lead to significant harm, such as heart disease, kidney disease, nerve damage, and blindness. Although T2D-related genes have been identified through Genome-wide association studies (GWAS) and various computational methods, the biological mechanism of T2D at the cell type level remains unclear. Exploring cell type-specific genes related to T2D is essential to understand the cellular mechanisms underlying the disease. To address this issue, we introduce DiGCellNet (predicting Disease Genes with Cell type specificity based on biological Networks), a model that integrates graph convolutional network (GCN) and multi-task learning (MTL) to predict T2D-associated cell type-specific genes based on the biological network. Our work represents the first attempt to predict cell type-specific disease genes using GCN and MTL. We evaluate our approach by predicting genes specific to four cell types and demonstrate that the proposed DiGCellNet outperforms other models that combine node embeddings with traditional machine learning algorithms. Moreover, DiGCellNet successfully identifies CALM1 as a gene specific to beta cell type in T2D cases, and this association is confirmed using an independent dataset. The code is available at https://github.com/23AIBox/23AIBox-DiGCellNet.

3D Enhancer-promoter networks provide predictive features for gene expression and coregulation in early embryonic lineages.

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages: the trophectoderm, the epiblast and the primitive endoderm. Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements through which transcriptional regulators enact these fates remain understudied. Here, we characterize, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observe extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although distinct groups of genes are irresponsive to topological changes. In each lineage, a high degree of connectivity, or 'hubness', positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a predictive model for transcriptional regulation (3D-HiChAT) that outperforms models using only 1D promoter or proximal variables to predict levels and cell-type specificity of gene expression. Using 3D-HiChAT, we identify, in silico, candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments, we validate several enhancers that control gene expression in their respective lineages. Our study identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to comprehensively understand lineage-specific transcriptional behaviors.