Cell-type Specificity Research Articles

Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes.

Spatial transcriptomics ( ST ) data provide spatially-informed gene expression for studying complex diseases such as Alzheimer's disease ( AD ). Existing studies using ST data to identify genes with spatially-informed differential gene expression ( DGE ) of complex diseases have limited power due to small sample sizes. Conversely, single-nucleus RNA sequencing ( snRNA-seq ) data offer larger sample sizes for studying cell-type specific ( CTS ) DGE but lack spatial information. In this study, we integrated ST and snRNA-seq data to enhance the power of spatially-informed CTS DGE analysis of AD-related phenotypes. First, we utilized the recently developed deep learning tool CelEry to infer the spatial location of ∼1.5M cells from snRNA-seq data profiled from dorsolateral prefrontal cortex ( DLPFC ) tissue of 436 postmortem brains in the ROS/MAP cohorts. Spatial locations of six cortical layers that have distinct anatomical structures and biological functions were inferred. Second, we conducted cortical-layer specific ( CLS ) and CTS DGE analyses for three quantitative AD-related phenotypes -- β-amyloid, tangle density, and cognitive decline. CLS-CTS DGE analyses were conducted based on linear mixed regression models with pseudo-bulk scRNA-seq data and inferred cortical layer locations. We identified 450 potential CLS-CTS significant genes with nominal p-values<10 -4 , including 258 for β-amyloid, 122 for tangle density, and 127 for cognitive decline. Majority of these identified genes, including the ones having known associations with AD (e.g., APOE , KCNIP3 , and CTSD ), cannot be detected by traditional CTS DGE analyses without considering spatial information. We also identified 8 genes shared across all three phenotypes, 21 between β-amyloid and tangle density, 10 between cognitive decline and tangle density, and 10 between β-amyloid and cognitive density. Particularly, Gene Set Enrichment Analyses with the CLS-CTS DGE results of microglia in cortical layer-6 of β-amyloid identified 12 significant AD-related pathways. Incorporating spatial information with snRNA-seq data detected significant genes and pathways for AD-related phenotypes that would not be identified by traditional CTS DGE analyses. These identified CLS-CTS significant genes not only help illustrate the pathogenesis of AD, but also provide potential CLS-CTS targets for developing therapeutics of AD.

Identification of cell-type specificity, trans- and cis-acting functions of plant lincRNAs from single-cell transcriptomes.

Long noncoding RNAs, including intergenic lncRNAs (lincRNAs), play a key role in various biological processes throughout the plant life cycle, and the advent of single-cell RNA sequencing (scRNA-seq) technology has opened up a valuable avenue for scrutinizing the intricate roles of lincRNAs in cellular processes. Here, we identified a new batch of lincRNAs using scRNA-seq data from diverse tissues of plants (rice, Arabidopsis, tomato, and maize). Based on well-annotated single-cell transcriptome atlases, plant lincRNAs were found to possess the same level of cell-type specificity as mRNAs and to be involved in the differentiation of certain cell types based on pseudo-time analysis. Many lincRNAs were predicted to play a hub role in the cell-type-specific co-expression networks of lincRNAs and mRNAs, suggesting their trans-acting abilities. Besides, plant lincRNAs were revealed to have potential cis-acting properties based on their genomic distances and expression correlations with the neighboring mRNAs. Furthermore, an online platform, PscLncRNA (http://ibi.zju.edu.cn/psclncrna/), was constructed for searching and visualizing all identified plant lincRNAs with annotated potential functions. Our work provides new insights into plant lincRNAs at single-cell resolution and an important resource for understanding and further investigation of plant lincRNAs.

Experience-dependent, sexually dimorphic synaptic connectivity defined by sex-specific cadherin expression.

Early-life experience influences subsequent maturation and function of the adult brain, sometimes even in a sex-specific manner, but underlying molecular mechanisms are poorly understood. We describe here how juvenile experience defines sexually dimorphic synaptic connectivity in the adult Caenorhabditis elegans nervous system. Starvation of juvenile males disrupts serotonin-dependent activation of the CREB transcription factor in a nociceptive sensory neuron, PHB. CREB acts through a cascade of transcription factors to control expression of an atypical cadherin protein, FMI-1/Flamingo/CELSR. During postembryonic development, FMI-1 promotes and maintains synaptic connectivity of PHB to a command interneuron, AVA, in both sexes, but a serotonin-dependent transcriptional regulatory cassette antagonizes FMI-1 expression in males, thereby establishing sexually dimorphic connectivity between PHB and AVA. A critical regulatory node is the CREB-target LIN-29, a Zn finger transcription factor that integrates four layers of information: sexual specificity, past experience, time and cell-type specificity. Our findings provide the mechanistic details of how an early juvenile experience defines sexually dimorphic synaptic connectivity.

The gradual discovery of cell-type and context specificity of microRNAs

The discovery of microRNAs (miRNAs) has revolutionized our understanding of gene regulation, particularly through their cell-type and context-specific functions. This perspective explores the gradual realization of miRNA specificity, beginning with the identification of lin-4 in Caenorhabditis elegans and progressing to the discovery of tissue-specific miRNAs such as miR-122 in the liver and miR-1 in muscle. A central focus is miR-34a, one of the most studied miRNAs, which exemplifies the importance of cellular context in miRNA function. miR-34a’s role in tumor suppression via the p53 pathway, demonstrating that its ability to induce apoptosis and cell cycle arrest depends on the cellular environment. miR-34a overexpression can have diverse effects on cell proliferation, ranging from strong suppression in certain cell types to minimal or paradoxical responses in others. In situ analysis of rat tissues revealed tissue-specific expression of miR-34a, with high levels in cerebral neurons and Purkinje cells and faint expression in renal, hepatic, and myocardial tissues. These findings suggest that miR-34a plays distinct roles depending on the tissue, contributing to homeostasis in some contexts while exhibiting a lesser regulatory role in others. This review synthesizes key studies that underscore the critical role of miRNAs, such as miR-34a, in regulating gene expression in a tissue and context-specific manner, providing insights into both normal physiology and disease pathogenesis.

Abstract B063: DiMMER: A robust computational pipeline for differential methylation marker evaluation in R of cell-free DNA fragments

Abstract Background: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample offers the potential to provide insight into the dynamic, organism-wide changes reflective of health and disease, making cfDNA an ideal target for serial, minimally invasive monitoring of disease-related changes. To this end, cell-type specific DNA methylation patterns offer a promising target to facilitate tissue of origin analysis, yet limited methods exist to identify differentially methylated regions that distinguish cell-types. Methods: We develop a robust differentially methylated marker region identification pipeline, DiMMER, that leverages cfDNA fragment level information of neighboring, co-regulated CpG sites on methylome-wide sequencing reads. Compared to prior methods that utilize simple heuristics in one-vs-all average methylation rate comparisons, we implement an individual pairwise statistical test procedure across all cell-types or groups under consideration. We assess the cell-specific nature of identified differentially methylated marker regions by generating in-silico mixtures from known cell-type of origin at each region, and calculating the area under the receiver operating characteristic curve (AUROC). We utilize our differentially methylated marker finding pipeline to identify melanocyte specific methylation marker regions and assess their functional role through annotations. We show these cell-specific regions are conserved in melanoma cell-lines and are distinct from the aberrant changes in a cancer context. Results: We identified the most cell-type specific differentially methylated marker regions across 24 distinct cell-type groups using our pipeline. Compared to a simpler one-vs-all heuristic, we demonstrate improved cell-type specificity evidenced by a higher AUROC on average. Using our pipeline we identified melanocyte-specific marker regions, which were commonly found in intronic and promoter regions of genes related to melanocyte development and differentiation. When comparing methylation patterns between melanocytes and melanoma cell-lines, we demonstrate melanocyte specific methylation marks are conserved through the transformation to malignant melanoma. Conclusion: As methylome-wide sequencing methods continue to rapidly develop and more cell-specific methylation data is generated, there is unmet need for more statistically robust differentially methylated marker finding tools. Here we present one such tool, DiMMER, and demonstrate the potential utility of identifying such regions to be used to assess organ-specific load as a measure of residual disease, as opposed to traditional circulating tumor DNA quanitification. Citation Format: Arthur P McDeed, Sidharth S Jain, Megan E McNamara, Amber Alley, Anton Wellstein, Jaeil Ahn. DiMMER: A robust computational pipeline for differential methylation marker evaluation in R of cell-free DNA fragments [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B063.

Liver transcriptome analysis reveals PSC-attributed gene set associated with fibrosis progression

BackgroundPrimary Sclerosing Cholangitis (PSC) is a chronic heterogenous cholangiopathy with unknown etiology where chronic inflammation of the bile ducts leads to multifocal biliary strictures and biliary fibrosis with consecutive cirrhosis development. We here aimed to identify a PSC-specific gene signature associated with biliary fibrosis development. MethodsWe performed RNA-sequencing of 47 liver biopsies from people with PSC (n = 16), primary biliary cholangitis (PBC, n = 15), and metabolic dysfunction-associated steatotic liver disease (MASLD, n = 16) with different fibrosis stages to identify a PSC-specific gene signature associated with biliary fibrosis progression. For validation, we compared an external transcriptome data set of liver biopsies from people with PSC (n = 73) with different fibrosis stages (DOI: 10.1002/hep.31488; baseline samples from NCT01672853). ResultsDifferential gene expression analysis of the liver transcriptome from PSC patients with advanced versus early fibrosis revealed 431 genes associated with fibrosis development. Of those, 367 were identified as PSC-associated when compared to PBC or MASLD. Validation against an external data set of 73 liver biopsies from PSC patients with different fibrosis stages led to a condensed set of 150 (out of 367) DEGs. Cell type specificity assignment of those genes by using published single cell RNA-seq data revealed genetic disease drivers expressed by cholangiocytes (e.g. CXCL1, SPP1), fibroblasts, innate and adaptive immune cells while deconvolution along fibrosis progression of the PSC, PBC, MASLD samples highlighted an early involvement of macrophage- and neutrophil-associated genes in PSC fibrosis. ConclusionWe reveal a PSC-attributed gene signature associated with biliary fibrosis development that may enable the identification of potential new biomarkers and therapeutic targets in PSC-related fibrogenesis. IMPACT AND IMPLICATIONSPrimary sclerosing cholangitis (PSC) is an inflammatory liver disease that is characterized by multifocal inflammation of bile ducts and subsequent biliary fibrosis. Herein, we identify a PSC-specific gene set of biliary fibrosis progression attributing to a uniquely complex milieu of different cell types, including innate and adaptive immune cells while neutrophils and macrophages showed an earlier involvement in fibrosis initiation in PSC in contrast to PBC and MASLD. Thus, our unbiased approach lays an important groundwork for further mechanistic studies for research into PSC-specific fibrosis.

Cell-type-aware regulatory landscapes governing monoterpene indole alkaloid biosynthesis in the medicinal plant Catharanthus roseus.

In plants, the biosynthetic pathways of some specialized metabolites are partitioned into specialized or rare cell types, as exemplified by the monoterpenoid indole alkaloid (MIA) pathway of Catharanthus roseus (Madagascar Periwinkle), the source of the anticancer compounds vinblastine and vincristine. In the leaf, the C. roseus MIA biosynthetic pathway is partitioned into three cell types with the final known steps of the pathway expressed in the rare cell type termed idioblast. How cell-type specificity of MIA biosynthesis is achieved is poorly understood. We generated single-cell multi-omics data from C. roseus leaves. Integrating gene expression and chromatin accessibility profiles across single cells, as well as transcription factor (TF)-binding site profiles, we constructed a cell-type-aware gene regulatory network for MIA biosynthesis. We showcased cell-type-specific TFs as well as cell-type-specific cis-regulatory elements. Using motif enrichment analysis, co-expression across cell types, and functional validation approaches, we discovered a novel idioblast-specific TF (Idioblast MYB1, CrIDM1) that activates expression of late-stage MIA biosynthetic genes in the idioblast. These analyses not only led to the discovery of the first documented cell-type-specific TF that regulates the expression of two idioblast-specific biosynthetic genes within an idioblast metabolic regulon but also provides insights into cell-type-specific metabolic regulation.

Machine-guided design of cell-type-targeting cis-regulatory elements.

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing and stimulus responses, which collectively define the thousands of unique cell types in the body1-3. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for these intended purposes has arisen naturally. Here we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell-type specificity. We take advantage of innovations in deep neural network modelling of CRE activity across three cell types, efficient in silico optimization and massively parallel reporter assays to design and empirically test thousands of CREs4-8. Through large-scale in vitro validation, we show that synthetic sequences are more effective at driving cell-type-specific expression in three cell lines compared with natural sequences from the human genome and achieve specificity in analogous tissues when tested in vivo. Synthetic sequences exhibit distinct motif vocabulary associated with activity in the on-target cell type and a simultaneous reduction in the activity of off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs from massively parallel reporter assay models and demonstrate the required literacy to write fit-for-purpose regulatory code.

Deep phenotyping reveals CRH and FKBP51-dependent behavioral profiles following chronic social stress exposure in male mice.

The co-chaperone FKBP51, encoded by FKBP5 gene, is recognized as a psychiatric risk factor for anxiety and depressive disorders due to its crucial role in the stress response. Another key modulator in stress response regulation is the corticotropin releasing hormone (CRH), which is co-expressed with FKBP51 in many stress-relevant brain-regions and cell-types. Together, they intricately influence the balance of the hypothalamic-pituitary-adrenal (HPA) axis, one of the primary stress response systems. Previous research underscores the potential moderating effects these genes have on the regulation of the stressful life events towards the vulnerability of major depressive disorder (MDD). However, the specific function of FKBP51 in CRH-expressing neurons remains largely unexplored. Here, through deep behavioral phenotyping, we reveal heightened stress effects in mice lacking FKBP51 in CRH co-expressing neurons (CRHFKBP5-/-), particularly evident in social contexts. Our findings highlight the importance of considering cell-type specificity and context in comprehending stress responses and advocate for the utilization of machine-learning-driven phenotyping of mouse models. By elucidating these intricacies, we lay down the groundwork for personalized interventions aimed at enhancing stress resilience and individual well-being.

Comparative Validation of Scintillator Materials for X-Ray-Mediated Neuronal Control in the Deep Brain.

When exposed to X-rays, scintillators emit visible luminescence. X-ray-mediated optogenetics employs scintillators for remotely activating light-sensitive proteins in biological tissue through X-ray irradiation. This approach offers advantages over traditional optogenetics, allowing for deeper tissue penetration and wireless control. Here, we assessed the short-term safety and efficacy of candidate scintillator materials for neuronal control. Our analyses revealed that lead-free halide scintillators, such as Cs3Cu2I5, exhibited significant cytotoxicity within 24 h and induced neuroinflammatory effects when injected into the mouse brain. In contrast, cerium-doped gadolinium aluminum gallium garnet (Ce:GAGG) nanoparticles showed no detectable cytotoxicity within the same period, and injection into the mouse brain did not lead to observable neuroinflammation over four weeks. Electrophysiological recordings in the cerebral cortex of awake mice showed that X-ray-induced radioluminescence from Ce:GAGG nanoparticles reliably activated 45% of the neuronal population surrounding the implanted particles, a significantly higher activation rate than europium-doped GAGG (Eu:GAGG) microparticles, which activated only 10% of neurons. Furthermore, we established the cell-type specificity of this technique by using Ce:GAGG nanoparticles to selectively stimulate midbrain dopamine neurons. This technique was applied to freely behaving mice, allowing for wireless modulation of place preference behavior mediated by midbrain dopamine neurons. These findings highlight the unique suitability of Ce:GAGG nanoparticles for X-ray-mediated optogenetics. The deep tissue penetration, short-term safety, wireless neuronal control, and cell-type specificity of this system offer exciting possibilities for diverse neuroscience applications and therapeutic interventions.

How Parkinson's Disease-Linked LRRK2 Mutations Affect Different CNS Cell Types.

LRRK2 is a relatively common genetic risk factor for Parkinson's disease (PD), with six coding variants known to cause familial PD. Non-coding variation at the same locus is also associated with sporadic PD. LRRK2 plays a role in many different intracellular signaling cascades including those involved in endolysosomal function, cytoskeletal dynamics, and Ca2+ homeostasis. PD-causing LRRK2 mutations cause hyperactive LRRK2 kinase activity, resulting in altered cellular signaling. Importantly, LRRK2 is lowly expressed in neurons and prominently expressed in non-neuronal cells in the brain. In this review, we will summarize recent and novel findings on the effects of PD-causing LRRK2 mutations in different nervous system cell types. This review will also provide novel insight into future areas of research at the intersection of LRRK2 cell biology, cell type specificity, and PD.

Identifying transcription factors with cell-type specific DNA binding signatures

BackgroundTranscription factors (TFs) bind to different parts of the genome in different types of cells, but it is usually assumed that the inherent DNA-binding preferences of a TF are invariant to cell type. Yet, there are several known examples of TFs that switch their DNA-binding preferences in different cell types, and yet more examples of other mechanisms, such as steric hindrance or cooperative binding, that may result in a “DNA signature” of differential binding.ResultsTo survey this phenomenon systematically, we developed a deep learning method we call SigTFB (Signatures of TF Binding) to detect and quantify cell-type specificity in a TF’s known genomic binding sites. We used ENCODE ChIP-seq data to conduct a wide scale investigation of 169 distinct TFs in up to 14 distinct cell types. SigTFB detected statistically significant DNA binding signatures in approximately two-thirds of TFs, far more than might have been expected from the relatively sparse evidence in prior literature. We found that the presence or absence of a cell-type specific DNA binding signature is distinct from, and indeed largely uncorrelated to, the degree of overlap between ChIP-seq peaks in different cell types, and tended to arise by two mechanisms: using established motifs in different frequencies, and by selective inclusion of motifs for distint TFs.ConclusionsWhile recent results have highlighted cell state features such as chromatin accessibility and gene expression in predicting TF binding, our results emphasize that, for some TFs, the DNA sequences of the binding sites contain substantial cell-type specific motifs.

Evolution of plant cell-type-specific cis-regulatory elements.

Cis-regulatory elements (CREs) are critical in regulating gene expression, and yet understanding of CRE evolution remains challenging. Here, we constructed a comprehensive single-cell atlas of chromatin accessibility in Oryza sativa, integrating data from 103,911 nuclei representing 126 discrete cell states across nine distinct organs. We used comparative genomics to compare cell-type resolved chromatin accessibility between O. sativa and 57,552 nuclei from four additional grass species (Zea mays, Sorghum bicolor, Panicum miliaceum, and Urochloa fusca). Accessible chromatin regions (ACRs) had different levels of conservation depending on the degree of cell-type specificity. We found a complex relationship between ACRs with conserved noncoding sequences, cell-type specificity, conservation, and tissue-specific switching. Additionally, we found that epidermal ACRs were less conserved compared to other cell types, potentially indicating that more rapid regulatory evolution has occurred in the L1-derived epidermal layer of these species. Finally, we identified and characterized a conserved subset of ACRs that overlapped the repressive histone modification H3K27me3, implicating them as potentially silencer-like CREs maintained by evolution. Collectively, this comparative genomics approach highlights the dynamics of plant cell-type-specific CRE evolution.

Cell Type Specificity of FcγRIIB Involving NLRP3 Inflammasome and Dectin-2 in a Mouse Model of IgA Nephropathy

Cell Type Specificity of FcγRIIB Involving NLRP3 Inflammasome and Dectin-2 in a Mouse Model of IgA Nephropathy

Partial chemogenetic inhibition of the locus coeruleus due to heterogeneous transduction of noradrenergic neurons preserved auditory salience processing in wild-type rats.

The acoustic startle reflex (ASR) and prepulse inhibition of the ASR (PPI) assess the efficiency of salience processing, a fundamental brain function that is impaired in many psychiatric conditions. Both ASR and PPI depend on noradrenergic transmission, yet the modulatory role of the locus coeruleus (LC) remains controversial. Clonidine (0.05 mg/kg, i.p.), an alpha2-adrenoreceptor agonist, strongly reduced the ASR amplitude. In contrast, chemogenetic LC inhibition only mildly suppressed the ASR and did affect the PPI in virus-transduced rats. The canine adenovirus type 2 (CAV2)-based vector carrying a gene cassette for the expression of inhibitory receptors (hM4Di) and noradrenergic cell-specific promoter (PRSx8) had high cell-type specificity (94.4±3.1%) but resulted in heterogeneous virus transduction of DbH-positive LC neurons (range: 9.2-94.4%). Clozapine-N-oxide (CNO; 1mg/kg, i.p.), a hM4Di actuator, caused the firing cessation of hM4Di-expressing LC neurons, yet complete inhibition of the entire population of LC neurons was not achieved. Case-based immunohistochemistry revealed that virus injections distal (> 150μm) to the LC core resulted in partial LC transduction, while proximal (< 50 μm) injections caused neuronal loss due to virus neurotoxicity. Neither the ASR nor PPI differed between the intact and virus-transduced rats. Our results suggest that a residual activity of virus-non-transduced LC neurons might have been sufficient for mediating an unaltered ASR and PPI. Our study highlights the importance of a case-based assessment of the virus efficiency, specificity, and neurotoxicity for targeted cell populations and of considering these factors when interpreting behavioral effects in experiments employing chemogenetic modulation.

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability. An example vignette along with gene and gene set weights for the 81 HPA SCTA cell types and the MSigDB collections are available at https://hrfrost.host.dartmouth.edu/SCGeneSetOpt/ .

ScEMB: Learning context representation of genes based on large-scale single-cell transcriptomics.

The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges. We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data. Trained on over 30 million single-cell transcriptomes, scEMB utilizes an innovative binning strategy that integrates data across multiple platforms, effectively preserving both gene expression hierarchies and cell-type specificity. In downstream tasks such as batch integration, clustering, and cell type annotation, scEMB demonstrates superior performance compared to existing models like scGPT and Geneformer. Notably, scEMB excels in silico correlation analysis, accurately predicting gene perturbation effects in CRISPR-edited datasets and microglia state transition, identifying a few known Alzheimer's disease (AD) risks genes in top gene list. Additionally, scEMB offers robust fine-tuning capabilities for domain-specific applications, making it a versatile tool for tackling diverse biological problems such as therapeutic target discovery and disease modeling. scEMB represents a powerful tool for extracting biologically meaningful insights from complex gene expression data. Its ability to model in silico perturbation effects and conduct correlation analyses in the embedding space highlights its potential to accelerate discoveries in precision medicine and therapeutic development.

Network Analysis of Enhancer-Promoter Interactions Highlights Cell-Type-Specific Mechanisms of Transcriptional Regulation Variation.

Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we generated enhancer-promoter interaction (EPI) networks and investigated the presence of disease-associated variants that fall within these regions. Our study analyzed six neuronal cell types across neural differentiation, allowing us to examine closely related cell types and across differentiation stages. Our results expand upon previous findings of cell-type specificity of enhancer, promoter, and transcription factor binding sites. Notably, we find that regulatory regions within EPI networks can identify the enrichment of variants associated with neuropsychiatric disorders within specific cell types and network sub-structures. This enrichment within sub-structures can allow for a better understanding of potential mechanisms by which variants may disrupt transcription. Together, our findings suggest that EPIs can be leveraged to better understand cell-type-specific regulatory architecture and used as a selection method for disease-associated variants to be tested in future functional assays. Combined with these future functional characterization assays, EPIs can be used to better identify and characterize regulatory variants' effects on such networks and model their mechanisms of gene regulation disruption across different disorders. Such findings can be applied in practical settings, such as diagnostic tools and drug development.

The transcriptome of playfulness is sex-biased in the juvenile rat medial amygdala: a role for inhibitory neurons.

Social play is a dynamic behavior known to be sexually differentiated; in most species, males play more than females, a sex difference driven in large part by the medial amygdala (MeA). Despite the well-conserved nature of this sex difference and the importance of social play for appropriate maturation of brain and behavior, the full mechanism establishing the sex bias in play is unknown. Here, we explore "the transcriptome of playfulness" in the juvenile rat MeA, assessing differences in gene expression between high- and low-playing animals of both sexes via bulk RNA-sequencing. Using weighted gene co-expression network analysis (WGCNA) to identify gene modules combined with analysis of differentially expressed genes (DEGs), we demonstrate that the transcriptomic profile in the juvenile rat MeA associated with playfulness is largely distinct in males compared to females. Of the 13 play-associated WGCNA networks identified, only two were associated with play in both sexes, and very few DEGs associated with playfulness were shared between males and females. Data from our parallel single-cell RNA-sequencing experiments using amygdala samples from newborn male and female rats suggests that inhibitory neurons drive this sex difference, as the majority of sex-biased DEGs in the neonatal amygdala are enriched within this population. Supporting this notion, we demonstrate that inhibitory neurons comprise the majority of play-active cells in the juvenile MeA, with males having a greater number of play-active cells than females, of which a larger proportion are GABAergic. Through integrative bioinformatic analyses, we further explore the expression, function, and cell-type specificity of key play-associated modules and the regulator "hub genes" predicted to drive them, providing valuable insight into the sex-biased mechanisms underlying this fundamental social behavior.

HKDC1 promotes liver cancer stemness under hypoxia through stabilizing β-catenin.

Hexokinases (HKs), a group of enzymes catalyzing the first step of glycolysis, have been shown to play important roles in liver metabolism and tumorigenesis. Our recent studies identified hexokinase domain containing 1 (HKDC1) as a top candidate associated with liver cancer metastasis. We aimed to compare its cell-type specificity with other HKs upregulated in liver cancer and investigate the molecular mechanisms underlying its involvement in liver cancer metastasis. We found that, compared to HK1 and HK2, the other 2 commonly upregulated HKs in liver cancer, HKDC1 was most strongly associated with the metastasis potential of tumors and organoids derived from 2 liver cancer mouse models we previously established. RNA in situ hybridization and single-cell RNA-seq analysis revealed that HKDC1 was specifically upregulated in malignant cells in HCC and cholangiocarcinoma patient tumors, whereas HK1 and HK2 were widespread across various tumor microenvironment lineages. An unbiased metabolomic profiling demonstrated that HKDC1 overexpression in HCC cells led to metabolic alterations distinct from those from HK1 and HK2 overexpression, with HKDC1 particularly impacting the tricarboxylic acid cycle. HKDC1 was prometastatic in HCC orthotopic and tail vein injection mouse models. Molecularly, HKDC1 was induced by hypoxia and bound to glycogen synthase kinase 3β to stabilize β-catenin, leading to enhanced stemness of HCC cells. Overall, our findings underscore HKDC1 as a prometastatic HK specifically expressed in the malignant compartment of primary liver tumors, thereby providing a mechanistic basis for targeting this enzyme in advanced liver cancer.