Cell Type-specific Gene Research Articles

Predictive Prioritization of Enhancers Associated with Pancreas Disease Risk.

Genetic and epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies. Despite recent advances, linking enhancer elements to target genes and predicting transcriptional outcomes of enhancer dysfunction remain significant challenges. Using 3D chromatin conformation assays, we generated an extensive enhancer interaction dataset for the human pancreas, encompassing more than 20 donors and five major cell types, including both exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating a quantitative, genome-wide analysis of enhancer connectivity. With these tree models, we developed a machine learning algorithm to estimate the impact of enhancer perturbations on cell type-specific gene expression in the human pancreas. Orthogonal to our computational approach, we perturbed enhancer function in primary human pancreas cells using CRISPR interference and quantified the effects at the single-cell level through RNA FISH coupled with high-throughput imaging. Our enhancer tree models enabled the annotation of common germline risk variants associated with pancreas diseases, linking them to putative target genes in specific cell types. For pancreatic ductal adenocarcinoma, we found a stronger enrichment of disease susceptibility variants within acinar cell regulatory elements, despite ductal cells historically being assumed as the primary cell-of-origin. Our integrative approach-combining cell type-specific enhancer-promoter interaction mapping, computational models, and single-cell enhancer perturbation assays-produced a robust resource for studying the genetic basis of pancreas disorders.

Aging and age-related disorders, cell by cell.

Cell type-specific gene signatures in an aged brain imply biological links to age-related disorders.

Enhancer hijacking: Innovative ways of carcinogenesis

Enhancer elements are specific DNA sequences that play a crucial role in regulating gene expression. Located upstream or downstream in the genomic context, enhancers enhance the transcription of linked genes. This is achieved by providing binding sites for transcription factors and other proteins, acting as a nucleation point that promotes the assembly of the transcriptional machinery. What makes enhancers unique is that located even thousands of base pairs away, they can exert their function. They can act over long distances, looping to interact with the promoter region of their target genes. Enhancers are often involved in cell type-specific gene expression as different cells express different sets of genes by activating and repressing cell type-specific enhancers. Cancer cells seem to co-opt this mechanism and hijack it for their own survival. In this process, an aberrant enhancer element activates the transcription of oncogenes (genes that have the potential to cause cancer) due to some alterations in genomic structure or regulatory elements that happen in cancer, contributing to tumorigenesis and cancer progression. Enhancer hijacking often occurs through complex genomic rearrangements such as chromosomal translocations and other structural rearrangements such as deletions or amplifications.8 These alterations can bring a usually distant, far-located enhancer into proximity with an oncogene, leading to its inappropriate activation. This is often seen in various cancers, including leukemias and solid tumors. Enhancer hijacking and its effects on gene regulation in cancer are frequently studied using methods such as RNA sequencing, chromatin conformation capture, and CRISPR-Cas9. Finding an enhancer hijacking event may be used to inform treatment choices, especially in precision medicine settings, and may also function as a biomarker for particular cancer types.

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.

Variational inference of single cell time series.

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe genome-wide expression dynamics at the the single cell scale. However, when gene expression is affected jointly by time and cellular identity, analyzing such data - including conducting cell type annotation and modeling cell type-dependent dynamics - becomes challenging. To address this problem, we propose SNOW (SiNgle cell flOW map), a deep learning algorithm to deconvolve single cell time series data into time-dependent and time-independent contributions. SNOW has a number of advantages. First, it enables cell type annotation based on the time-independent dimensions. Second, it yields a probabilistic model that can be used to discriminate between biological temporal variation and batch effects contaminating individual timepoints, and provides an approach to mitigate batch effects. Finally, it is capable of projecting cells forward and backward in time, yielding time series at the individual cell level. This enables gene expression dynamics to be studied without the need for clustering or pseudobulking, which can be error prone and result in information loss. We describe our probabilistic framework in detail and demonstrate SNOW using data from three distinct time course scRNA-seq studies. Our results show that SNOW is able to construct biologically meaningful latent spaces, remove batch effects, and generate realistic time-series at the single-cell level. By way of example, we illustrate how the latter may be used to enhance the detection of cell type-specific circadian gene expression rhythms, and may be readily extended to other time-series analyses.

Novel Insights into Post-Myocardial Infarction Cardiac Remodeling through Algorithmic Detection of Cell-Type Composition Shifts.

Recent advances in single cell sequencing have led to an increased focus on the role of cell-type composition in phenotypic presentation and disease progression. Cell-type composition research in the heart is challenging due to large, frequently multinucleated cardiomyocytes that preclude most single cell approaches from obtaining accurate measurements of cell composition. Our in silico studies reveal that ignoring cell type composition when calculating differentially expressed genes (DEGs) can have significant consequences. For example, a relatively small change in cell abundance of only 10% can result in over 25% of DEGs being false positives. We have implemented an algorithmic approach that uses snRNAseq datasets as a reference to accurately calculate cell type compositions from bulk RNAseq datasets through robust data cleaning, gene selection, and multi-sample cross-subject and cross-cell-type deconvolution. We applied our approach to cardiomyocyte-specific α1A adrenergic receptor (CM-α1A-AR) knockout mice. 8-12 week-old mice (either WT or CM-α1A-KO) were subjected to permanent left coronary artery (LCA) ligation or sham surgery (n=4 per group). Transcriptomes from the infarct border zones were collected 3 days later and analyzed using our algorithm to determine cell-type abundances, corrected differential expression calculations using DESeq2, and validated these findings using RNAscope. Uncorrected DEGs for the CM-α1A-KO X LCA interaction term featured many cell-type specific genes such as Timp4 (fibroblasts) and Aplnr (cardiomyocytes) and overall GO enrichment for terms pertaining to cardiomyocyte differentiation (P=3.1E-4). Using our algorithm, we observe a striking loss of cardiomyocytes and gain in fibroblasts in the α1A-KO + LCA mice that was not recapitulated in WT + LCA animals, although we did observe a similar increase in macrophage abundance in both conditions. This recapitulates prior results that showed a much more severe heart failure phenotype in CM-α1A-KO + LCA mice. Following correction for cell-type, our DEGs now highlight a novel set of genes enriched for GO terms such as cardiac contraction (P=3.7E-5) and actin filament organization (P=6.3E-5). Our algorithm identifies and corrects for cell-type abundance in bulk RNAseq datasets opening new avenues for research on novel genes and pathways as well as an improved understanding of the role of cardiac cell types in cardiovascular disease.

YTHDF1 mediates translational control by m6A mRNA methylation in adaptation to environmental challenges.

Animals adapt to environmental challenges with long-term changes at the behavioral, circuit, cellular, and synaptic levels which often require new protein synthesis. The discovery of reversible N6-methyladenosine (m6A) modifications of mRNA has revealed an important layer of post-transcriptional regulation which affects almost every phase of mRNA metabolism and therefore translational control. Many in vitro and in vivo studies have demonstrated the significant role of m6A in cell differentiation and survival, but its role in adult neurons is understudied. We used cell-type specific gene deletion of Mettl14, which encodes one of the subunits of the m6A methyltransferase, and Ythdf1, which encodes one of the cytoplasmic m6A reader proteins, in dopamine D1 receptor expressing or D2 receptor expressing neurons. Mettl14 or Ythdf1 deficiency blunted responses to environmental challenges at the behavioral, cellular, and molecular levels. In three different behavioral paradigms, gene deletion of either Mettl14 or Ythdf1 in D1 neurons impaired D1-dependent learning, whereas gene deletion of either Mettl14 or Ythdf1 in D2 neurons impaired D2-dependent learning. At the cellular level, modulation of D1 and D2 neuron firing in response to changes in environments was blunted in all three behavioral paradigms in mutant mice. Ythdf1 deletion resembled impairment caused by Mettl14 deletion in a cell type-specific manner, suggesting YTHDF1 is the main mediator of the functional consequences of m6A mRNA methylation in the striatum. At the molecular level, while striatal neurons in control mice responded to elevated cAMP by increasing de novo protein synthesis, striatal neurons in Ythdf1 knockout mice didn't. Finally, boosting dopamine release by cocaine drastically increased YTHDF1 binding to many mRNA targets in the striatum, especially those that encode structural proteins, suggesting the initiation of long-term neuronal and/or synaptic structural changes. While the m6A-YTHDF1 pathway has similar functional significance at cellular level, its cell type specific deficiency in D1 and D2 neurons often resulted in contrasting behavioral phenotypes, allowing us to cleanly dissociate the opposing yet cooperative roles of D1 and D2 neurons.

A developmental mechanism to regulate alternative polyadenylation in an adult stem cell lineage.

Alternative cleavage and polyadenylation (APA) often results in production of mRNA isoforms with either longer or shorter 3' UTRs from the same genetic locus, potentially impacting mRNA translation, localization, and stability. Developmentally regulated APA can thus make major contributions to cell type-specific gene expression programs as cells differentiate. During Drosophila spermatogenesis, ∼500 genes undergo APA when proliferating spermatogonia differentiate into spermatocytes, producing transcripts with shortened 3' UTRs, leading to profound stage-specific changes in the proteins expressed. The molecular mechanisms that specify usage of upstream polyadenylation sites in spermatocytes are thus key to understanding the changes in cell state. Here, we show that upregulation of PCF11 and Cbc, the two components of cleavage factor II (CFII), orchestrates APA during Drosophila spermatogenesis. Knockdown of PCF11 or cbc in spermatocytes caused dysregulation of APA, with many transcripts normally cleaved at a proximal site in spermatocytes now cleaved at their distal site, as in spermatogonia. Forced overexpression of CFII components in spermatogonia switched cleavage of some transcripts to the proximal site normally used in spermatocytes. Our findings reveal a developmental mechanism where changes in expression of specific cleavage factors can direct cell type-specific APA at selected genes.

A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

Integrated Single-Cell Multiomic Profiling of Caudate Nucleus Suggests Key Mechanisms in Alcohol Use Disorder.

Alcohol use disorder (AUD) is likely associated with complex transcriptional alterations in addiction-relevant brain regions. We characterize AUD-associated differences in cell type-specific gene expression and chromatin accessibility in the caudate nucleus by conducting a single-nucleus RNA-seq assay and a single-nucleus RNA-seq + ATAC-seq (multiome) assay on caudate tissue from 143 human postmortem brains (74 with AUD). We identified 17 cell types. AUD was associated with a higher proportion of microglia in an activated state and more astrocytes in a reactive state. There was widespread evidence for differentially expressed genes across cell types with the most identified in oligodendrocytes and astrocytes, including genes involved in immune response and synaptic regulation, many of which appeared to be regulated in part by JUND and OLIG2. Microglia-astrocyte communication via interleukin-1 beta, and microglia-astrocyte-oligodendrocyte interaction via transforming growth factor beta 1 were increased in individuals with AUD. Expression quantitative trait loci analysis revealed potential driver genes of AUD, including ADAL, that may protect against AUD in medium spiny neurons and interneurons. This work provides a thorough profile of the effects of AUD in the human brain and identifies several promising genes for further study.

Members of an array of zinc finger proteins specify distinct Hox chromatin boundaries.

Partitioning of repressive from actively transcribed chromatin in mammalian cells fosters cell-type specific gene expression patterns. While this partitioning is reconstructed during differentiation, the chromatin occupancy of the key insulator, CTCF, is unchanged at the developmentally important Hox clusters. Thus, dynamic changes in chromatin boundaries must entail other activities. Given its requirement for chromatin loop formation, we examined cohesin-based chromatin occupancy without known insulators, CTCF and MAZ, and identified a family of zinc finger proteins (ZNFs), some of which exhibit tissue-specific expression. Two such ZNFs foster chromatin boundaries at the Hox clusters that are distinct from each other and from MAZ. PATZ1 was critical to the thoracolumbar boundary in differentiating motor neurons and mouse skeleton, while ZNF263 contributed to cervicothoracic boundaries. We propose that these insulating activities act with cohesin, alone or combinatorially, with or without CTCF, to implement precise positional identity and cell fate during development.

TransTEx: novel tissue-specificity scoring method for grouping human transcriptome into different expression groups.

Although human tissues carry out common molecular processes, gene expression patterns can distinguish different tissues. Traditional informatics methods, primarily at the gene level, overlook the complexity of alternative transcript variants and protein isoforms produced by most genes, changes in which are linked to disease prognosis and drug resistance. We developed TransTEx (Transcript-level Tissue Expression), a novel tissue-specificity scoring method, for grouping transcripts into four expression groups. TransTEx applies sequential cut-offs to tissue-wise transcript probability estimates, subsampling-based P-values and fold-change estimates. Application of TransTEx on GTEx mRNA-seq data divided 199166 human transcripts into different groups as 17999 tissue-specific (TSp), 7436 tissue-enhanced, 36783 widely expressed (Wide), 79191 lowly expressed (Low), and 57757 no expression (Null) transcripts. Testis has the most (13466) TSp isoforms followed by liver (890), brain (701), pituitary (435), and muscle (420). We found that the tissue specificity of alternative transcripts of a gene is predominantly influenced by alternate promoter usage. By overlapping brain-specific transcripts with the cell-type gene-markers in scBrainMap database, we found that 63% of the brain-specific transcripts were enriched in nonneuronal cell types, predominantly astrocytes followed by endothelial cells and oligodendrocytes. In addition, we found 61 brain cell-type marker genes encoding a total of 176 alternative transcripts as brain-specific and 22 alternative transcripts as testis-specific, highlighting the complex TSp and cell-type specific gene regulation and expression at isoform-level. TransTEx can be adopted to the analysis of bulk RNA-seq or scRNA-seq datasets to find tissue- and/or cell-type specific isoform-level gene markers. TransTEx database: https://bmi.cewit.stonybrook.edu/transtexdb/ and the R package is available via GitHub: https://github.com/pallavisurana1/TransTEx.

Abstract We138: Single-nucleus and bulk multiomic analyses reveal the dynamics of individual cardiac cell types after myocardial infarction and the underlying gene regulatory networks

Cardiovascular diseases are the leading causes of death in the world, among which myocardial infarction (MI) is the most fatal form. Following MI, the death of cardiomyocytes induces cardiac remodeling, which is mainly mediated by cardiac fibroblasts but also involves other cardiac cell types. A deeper understanding of the dynamics of individual cell types among different stages after myocardial infarction may help identify novel therapeutic strategies. Here, through single-nucleus multiomic (snRNAseq and snATACseq) analysis of uninjured mouse hearts and mouse hearts at different time points (2 h, 1 d, 3 d, 7 d, and 4 w) after MI, we identified multiple cardiac cell types, including cardiomyocyte, fibroblast, epicardial cell, endothelial cell, mural cell, and immune cell, and their dynamic gene expression and chromatin accessibility after MI. Features specific to the acute and chronic responses of different cell types to MI were identified. Cell type-specific single-nucleus gene regulatory networks (snGRNs) constructed through integrated analysis of snRNAseq and snATACseq data revealed transcription factors possibly responsible for the gene expression changes in individual cell types. The cardiac fibroblast GRN was further enriched using bulk RNAseq and ATACseq data collected at more time points (uninjured, and 2 h, 12 h, 1 d, 3 d, 7 d, 2 w, and 4 w after MI) and non-coding RNA transcriptomics. Promoter and enhancer activities and chromatin interactions in cardiac fibroblasts were further verified by CUT&Tag targeting corresponding histone markers and Hi-C, respectively. The refined cardiac fibroblast GRN identified multiple key transcription factors that likely govern cardiac fibroblast activation and differentiation after MI.

Adult sex change leads to extensive forebrain reorganization in clownfish

BackgroundSexual differentiation of the brain occurs in all major vertebrate lineages but is not well understood at a molecular and cellular level. Unlike most vertebrates, sex-changing fishes have the remarkable ability to change reproductive sex during adulthood in response to social stimuli, offering a unique opportunity to understand mechanisms by which the nervous system can initiate and coordinate sexual differentiation.MethodsThis study explores sexual differentiation of the forebrain using single nucleus RNA-sequencing in the anemonefish Amphiprion ocellaris, producing the first cellular atlas of a sex-changing brain.ResultsWe uncover extensive sex differences in cell type-specific gene expression, relative proportions of cells, baseline neuronal excitation, and predicted inter-neuronal communication. Additionally, we identify the cholecystokinin, galanin, and estrogen systems as central molecular axes of sexual differentiation. Supported by these findings, we propose a model of sexual differentiation in the conserved vertebrate social decision-making network spanning multiple subtypes of neurons and glia, including neuronal subpopulations within the preoptic area that are positioned to regulate gonadal differentiation.ConclusionsThis work deepens our understanding of sexual differentiation in the vertebrate brain and defines a rich suite of molecular and cellular pathways that differentiate during adult sex change in anemonefish.

The myelin water imaging transcriptome: myelin water fraction regionally varies with oligodendrocyte-specific gene expression

Identifying sensitive and specific measures that can quantify myelin are instrumental in characterizing microstructural changes in neurological conditions. Neuroimaging transcriptomics is emerging as a valuable technique in this regard, offering insights into the molecular basis of promising candidates for myelin quantification, such as myelin water fraction (MWF). We aimed to demonstrate the utility of neuroimaging transcriptomics by validating MWF as a myelin measure. We utilized data from a normative MWF brain atlas, comprised of 50 healthy subjects (mean age = 25 years, range = 17–42 years) scanned at 3 Tesla. Magnetic resonance imaging data included myelin water imaging to extract MWF and T1 anatomical scans for image registration and segmentation. We investigated the inter-regional distributions of gene expression data from the Allen Human Brain Atlas in conjunction with inter-regional MWF distribution patterns. Pearson correlations were used to identify genes with expression profiles mirroring MWF. The Single Cell Type Atlas from the Human Protein Atlas was leveraged to classify genes into gene sets with high cell type specificity, and a control gene set with low cell type specificity. Then, we compared the Pearson correlation coefficients for each gene set to determine if cell type-specific gene expression signatures correlate with MWF. Pearson correlation coefficients between MWF and gene expression for oligodendrocytes and adipocytes were significantly higher than for the control gene set, whereas correlations between MWF and inhibitory/excitatory neurons were significantly lower. Our approach in integrating transcriptomics with neuroimaging measures supports an emerging technique for understanding and validating MRI-derived markers such as MWF.

Deciphering cellular heterogeneity in Spodoptera frugiperda midgut cell line through single cell RNA sequencing

Using the 10x Genomics Chromium single-cell RNA sequencing (scRNA-seq) platform, we discovered unexpected heterogeneity in an established cell line developed from the midgut of the Fall armyworm, Spodoptera frugiperda, a major global pest. We analyzed the sequences of 18,794 cells and identified ten unique cellular clusters, including stem cells, enteroblasts, enterocytes and enteroendocrine cells, characterized by the expression of specific marker genes. Additionally, these studies addressed an important knowledge gap by investigating the expression of genes coding for respiratory and midgut membrane insecticide targets classified by the Insecticide Resistance Action Committee. Dual-fluorescence tagging method, fluorescence microscopy and fluorescence-activated cell sorting confirmed the expression of midgut cell type-specific genes. Stem cells were isolated from the heterogeneous population of SfMG-0617 cells. Our results, validated by KEGG and Gene Ontology analyses and supported by Monocle 3.0, advance the fields of midgut cellular biology and establish standards for scRNA-seq studies in non-model organisms.

Thyroid hormone T4 mitigates traumatic brain injury in mice by dynamically remodeling cell type specific genes, pathways, and networks in hippocampus and frontal cortex

The complex pathology of mild traumatic brain injury (mTBI) is a main contributor to the difficulties in achieving a successful therapeutic regimen. Thyroxine (T4) administration has been shown to prevent the cognitive impairments induced by mTBI in mice but the mechanism is poorly understood. To understand the underlying mechanism, we carried out a single cell transcriptomic study to investigate the spatiotemporal effects of T4 on individual cell types in the hippocampus and frontal cortex at three post-injury stages in a mouse model of mTBI. We found that T4 treatment altered the proportions and transcriptomes of numerous cell types across tissues and timepoints, particularly oligodendrocytes, astrocytes, and microglia, which are crucial for injury repair. T4 also reversed the expression of mTBI-affected genes such as Ttr, mt-Rnr2, Ggn12, Malat1, Gnaq, and Myo3a, as well as numerous pathways such as cell/energy/iron metabolism, immune response, nervous system, and cytoskeleton-related pathways. Cell-type specific network modeling revealed that T4 mitigated select mTBI-perturbed dynamic shifts in subnetworks related to cell cycle, stress response, and RNA processing in oligodendrocytes. Cross cell-type ligand-receptor networks revealed the roles of App, Hmgb1, Fn1, and Tnf in mTBI, with the latter two ligands having been previously identified as TBI network hubs. mTBI and/or T4 signature genes were enriched for human genome-wide association study (GWAS) candidate genes for cognitive, psychiatric and neurodegenerative disorders related to mTBI. Our systems-level single cell analysis elucidated the temporal and spatial dynamic reprogramming of cell-type specific genes, pathways, and networks, as well as cell-cell communications as the mechanisms through which T4 mitigates cognitive dysfunction induced by mTBI.

Identification and Characterization of ATOH7-Regulated Target Genes and Pathways in Human Neuroretinal Development.

The proneural transcription factor atonal basic helix-loop-helix transcription factor 7 (ATOH7) is expressed in early progenitors in the developing neuroretina. In vertebrates, this is crucial for the development of retinal ganglion cells (RGCs), as mutant animals show an almost complete absence of RGCs, underdeveloped optic nerves, and aberrations in retinal vessel development. Human mutations are rare and result in autosomal recessive optic nerve hypoplasia (ONH) or severe vascular changes, diagnosed as autosomal recessive persistent hyperplasia of the primary vitreous (PHPVAR). To better understand the role of ATOH7 in neuroretinal development, we created ATOH7 knockout and eGFP-expressing ATOH7 reporter human induced pluripotent stem cells (hiPSCs), which were differentiated into early-stage retinal organoids. Target loci regulated by ATOH7 were identified by Cleavage Under Targets and Release Using Nuclease with sequencing (CUT&RUN-seq) and differential expression by RNA sequencing (RNA-seq) of wildtype and mutant organoid-derived reporter cells. Additionally, single-cell RNA sequencing (scRNA-seq) was performed on whole organoids to identify cell type-specific genes. Mutant organoids displayed substantial deficiency in axon sprouting, reduction in RGCs, and an increase in other cell types. We identified 469 differentially expressed target genes, with an overrepresentation of genes belonging to axon development/guidance and Notch signaling. Taken together, we consolidate the function of human ATOH7 in guiding progenitor competence by inducing RGC-specific genes while inhibiting other cell fates. Furthermore, we highlight candidate genes responsible for ATOH7-associated optic nerve and retinovascular anomalies, which sheds light to potential future therapy targets for related disorders.

Molecular basis of neurodegeneration in a mouse model of Polr3-related disease.

Pathogenic variants in subunits of RNA polymerase (Pol) III cause a spectrum of Polr3-related neurodegenerative diseases including 4H leukodystrophy. Disease onset occurs from infancy to early adulthood and is associated with a variable range and severity of neurological and non-neurological features. The molecular basis of Polr3-related disease pathogenesis is unknown. We developed a postnatal whole-body mouse model expressing pathogenic Polr3a mutations to examine the molecular mechanisms by which reduced Pol III transcription results primarily in central nervous system phenotypes. Polr3a mutant mice exhibit behavioral deficits, cerebral pathology and exocrine pancreatic atrophy. Transcriptome and immunohistochemistry analyses of cerebra during disease progression show a reduction in most Pol III transcripts, induction of innate immune and integrated stress responses and cell type-specific gene expression changes reflecting neuron and oligodendrocyte loss and microglial activation. Earlier in the disease when integrated stress and innate immune responses are minimally induced, mature tRNA sequencing revealed a global reduction in tRNA levels and an altered tRNA profile but no changes in other Pol III transcripts. Thus, changes in the size and/or composition of the tRNA pool have a causal role in disease initiation. Our findings reveal different tissue- and brain region-specific sensitivities to a defect in Pol III transcription.

SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.

The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, technical errors, and dropout phenomena in scRNA-seq data pose significant obstacles to GRN inference, making the design of accurate GRN inference algorithms still essential. The recent growth of both single-cell and spatial transcriptomic sequencing data enables the development of supervised deep learning methods to infer GRNs on these diverse single-cell datasets. In this study, we introduce a novel deep learning framework based on shared factor neighborhood and integrated neural network (SFINN) for inferring potential interactions and causalities between transcription factors and target genes from single-cell and spatial transcriptomic data. SFINN utilizes shared factor neighborhood to construct cellular neighborhood network based on gene expression data and additionally integrates cellular network generated from spatial location information. Subsequently, the cell adjacency matrix and gene pair expression are fed into an integrated neural network framework consisting of a graph convolutional neural network and a fully-connected neural network to determine whether the genes interact. Performance evaluation in the tasks of gene interaction and causality prediction against the existing GRN reconstruction algorithms demonstrates the usability and competitiveness of SFINN across different kinds of data. SFINN can be applied to infer GRNs from conventional single-cell sequencing data and spatial transcriptomic data. SFINN can be accessed at GitHub: https://github.com/JGuan-lab/SFINN.