Bulk Tissue Research Articles

Spatially resolved gene expression profiles of fibrosing interstitial lung diseases

Fibrosing interstitial lung diseases (ILDs) encompass a diverse range of scarring disorders that lead to progressive lung failure. Previous gene expression profiling studies focused on idiopathic pulmonary fibrosis (IPF) and bulk tissue samples. We employed digital spatial profiling to gain new insights into the spatial resolution of gene expression across distinct lung microenvironments (LMEs) in IPF, chronic hypersensitivity pneumonitis (CHP) and non-specific interstitial pneumonia (NSIP). We identified differentially expressed genes between LMEs within each condition, and across histologically similar regions between conditions. Uninvolved regions in IPF and CHP were distinct from normal controls, and displayed potential therapeutic targets. Hallmark LMEs of each condition retained distinct gene signatures, but these could not be reproduced in matched lung tissue samples. Based on these profiles and unsupervised clustering, we grouped previously unclassified ILD cases into NSIP or CHP. Overall, our work uniquely dissects gene expression profiles between LMEs within and across different types of fibrosing ILDs.

The gene expression signature of electrical stimulation in the human brain.

Direct electrical stimulation has been used for decades as a gold standard clinical tool to map cognitive function in neurosurgery patients 1-8 . However, the molecular impact of electrical stimulation in the human brain is unknown. Here, using state-of-the-art transcriptomic and epigenomic sequencing techniques, we define the molecular changes in bulk tissue and at the single-cell level in the human cerebral cortex following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Direct electrical stimulation surprisingly had a robust and consistent impact on the expression of genes related to microglia-specific cytokine activity, an effect that was replicated in mice. Using a newly developed deep learning computational tool, we further demonstrate cell type-specific molecular activation, which underscores the effects of electrical stimulation on gene expression in microglia. Taken together, this work challenges the notion that the immediate impact of electrical stimulation commonly used in the clinic has a primary effect on neuronal gene expression and reveals that microglia robustly respond to electrical stimulation, thus enabling these non-neuronal cells to sculpt and shape the activity of neuronal circuits in the human brain.

MetsDB: a knowledgebase of cancer metastasis at bulk, single-celland spatial levels.

Cancer metastasis, the process by which tumour cells migrate and colonize distant organs from a primary site, is responsible for the majority of cancer-related deaths. Understanding the cellular and molecular mechanisms underlying this complex process is essential for developing effective metastasis prevention and therapy strategies. To this end, we systematically analysed 1786 bulk tissue samples from 13 cancer types, 988463 single cells from 17 cancer types, and 40252 spots from 45 spatial slides across 10 cancer types. The results of these analyses are compiled in the metsDB database, accessible at https://relab.xidian.edu.cn/metsDB/. This database provides insights into alterations in cell constitutions, cell relationships, biological pathways, molecular biomarkers, and drug responses during cancer metastasis at bulk, single-cell, and spatial levels. Users can perform cell or gene searches to obtain multi-view and multi-scale metastasis-related data. This comprehensive resource is invaluable for understanding the metastasis process and for designing molecular therapies.

Unveiling the importance of heterotrophy for coral symbiosis under heat stress.

Global warming endangers reef-building corals as they lose their photosynthetic symbionts, which limits their ability to feed autotrophically. Consequently, heterotrophy, the capture of zooplankton, can become crucial for the energy budget of heat-stressed corals. However, it is difficult to assess the extent of the heterotrophic contribution in corals, as well as the dynamics of nutrient exchange between the host and its symbionts. In this pioneering study, we employed a suite of isotopic markers, including 13C- and 15N bulk tissue isotope measurements, compound-specific isotope analysis of amino acids (CSIA-AAs), and 13C- and 15N-labeled food incubations, to investigate nutrient acquisition and allocation in the coral Stylophora pistillata under controlled and heat-induced bleaching conditions. Bulk isotope values and inorganic carbon assimilation remained unchanged in the bleached corals compared to the control corals, overall indicating undisturbed autotrophic activity of the symbionts under heat stress. However, CSIA-AAs showed an increased dependence on heterotrophy for amino acid synthesis in both the host and the symbionts despite reduced assimilation of 15N-labeled food. Overall, these results suggest that although S. pistillata reduces its assimilation of heterotrophic food under heat stress, the acquisition of amino acids by the coral host and symbionts still relies on heterotrophy. This study emphasizes the importance of using multiple indicators to gain a comprehensive understanding of coral nutrition. It shows that coral dependence on heterotrophy is not only associated with a decline in autotrophic availability. Rather, it demonstrates the ability of S. pistillata to adapt its utilization of food sources to the prevailing environmental conditions.IMPORTANCEThis work highlights that every isotopic marker displays a piece of different information concerning the diet of the model coral S. pistillata. By combining all markers, we observed that although S. pistillata exhibited reduced heterotrophic assimilation under heat stress, amino acid acquisition and synthesis remained dependent on heterotrophy. The findings emphasize the adaptability of corals in utilizing different food sources, which is vital for their resilience and recovery in changing environmental conditions. This research underscores the complexity of coral symbiosis and highlights the need for multiple indicators to understand dietary dynamics comprehensively.

Kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq.

The term 'RNA-seq' refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, single cells or single nuclei. The kallisto, bustools and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data. Execution of this protocol requires basic familiarity with a command line environment. With this protocol, quantification of a moderately sized RNA-seq dataset can be completed within minutes.

Ferroptosis-associated alterations in diabetes following ischemic stroke: Insights from RNA sequencing

ObjectiveFerroptosis is an iron-dependent form of programmed cell death associated with lipid peroxidation. Though diabetes worsens cerebral injury and clinical outcomes in stroke, it is poorly understood whether ferroptosis contributes to diabetes-exacerbated stroke. This study aimed to identify ferroptosis-associated differentially expressed genes in ischemic stroke under diabetic condition and then explore their roles using comprehensive bioinformatics analyses. MethodsType 1 diabetes (T1D) model was established in male mice at 8–10 weeks of age by one intraperitoneal injection of streptozotocin (110 mg/kg). Ischemic stroke was induced by a transient 45-minute middle cerebral artery occlusion and evaluated three days thereafter. Ischemic brain cortex was dissected 24 h after the reperfusion and subjected to bulk tissue RNA sequencing followed by bioinformatics analysis and verification of key findings via quantitative real-time PCR. ResultsEnlarged infarct size was seen in diabetic, as compared with non-diabetic mice, in conjunction with worsened neurological behaviors. Both body and spleen weights were reduced in diabetic as compared with non-diabetic mice. There was a trend for reduced survival rate in diabetic mice following the stroke. In RNA sequencing analysis, we identified 1299 differentially expressed genes in ischemic brain between diabetic and non-diabetic mice, with upregulation and downregulation for 732 and 567 genes, respectively. Among these genes, 27 genes were associated with ferroptosis. Further analysis reveals that solute carrier family 25 member 28(SLC25A28) and sterol carrier protein 2(SCP2) were the top genes associated with ferroptosis in diabetic mice following ischemic stroke. In several bioinformatics analyses, we found SLC25A28, one of the top ferroptosis-related genes, is involved in several metabolic and regulatory pathways as well as the regulatory complexity of microRNAs and circular RNAs, which demonstrates the potential role of SLC25A28 in diabetes-exacerbated stroke. Drug network analysis suggests SLC25A28 as a potential therapeutic target for ameliorating ischemic injury in diabetes. ConclusionsOur bulk RNA sequencing and bioinformatics analyses show that altered ferroptosis signaling pathway was associated with the exacerbation of experimental stroke injury under diabetic condition. Especially, additional investigation into the mechanisms of SLC25A28 and SCP2 in diabetes-exacerbated stroke will be explored in the future study.

Organogenesis in a Broad Spectrum of Grape Genotypes and Agrobacterium-Mediated Transformation of the Podarok Magaracha Grapevine Cultivar.

We present data on the ability for organogenesis in 22 genotypes of grapevine and developed a direct organogenesis protocol for the cultivar Podarok Magaracha and the rootstock Kober 5BB. The protocol does not require replacement of culture media and growth regulators, and the duration is 11 weeks. The cultivation of explants occurs on modified MS medium with the addition of 2.0 mg L-1 benzyladenine and indole-3-butyric acid (0.15 mg L-1 for the rootstock Kober 5BB or 0.05 mg L-1 for the cultivar Podarok Magaracha). The direct organogenesis protocol consists of three time periods: (1) culturing explants for 2 weeks in dark conditions for meristematic bulk tissue, (2) followed by 4 weeks of cultivation in light conditions for regeneration, and (3) 5 weeks of cultivation in dark conditions for shoot elongation. Based on this protocol, conditions for the Agrobacterium-mediated transformation of the Podarok Magaracha cultivar were developed with an efficiency of 2.0% transgenic plants per 100 explants. Two stably transformed lines with integration into the genome of the pBin35SGFP plasmid construction, confirmed by Southern blotting, were obtained.

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/ .

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data.

Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities such as bulk ATAC-seq data.

Single-Cell RNA Sequencing Revealing that MMP12+ Macrophages are Associated with Cancer Liver Metastasis.

We aimed to explore the MMP12+ macrophages function in liver metastasis of Colorectal cancer [CRC]. CRC has a high incidence, and a great many patients develop liver metastases. Some studies have found that macrophages may participate in the liver metastasis of CRC. This study aimed to determine the factors and major signaling pathways of MMP12+ macrophages affecting liver metastasis of CRC. The single-cell RNA sequencing [scRNA-seq] data of CRC and bulk transcriptome data were downloaded. After filtering scRNA-seq data, dimensionality reduction and clustering were performed to identify different cell subgroups. The FindAll- Markers function was used to calculate the differentially expressed genes in each cell subgroup, and the genes in the promising set were uploaded to the DAVID database to analyze the biological processes to which these genes were enriched. Differentially expressed genes among macrophage subgroups were selected by the AverageExpression function. Then, the CIBERSORT algorithm was used to compute the proportion of each macrophage subgroup in each bulk tissue and determine the most significant macrophage subgroup. The dynamic changes of gene expression in macrophage subgroup were computed by Pseudotime. Finally, CellChat was applied to investigate the effect of the macrophage subgroup on epithelial cells and the ligand-receptor effect of B cells and T cells. Clustering scRNA-seq data showed a larger proportion of macrophages in liver metastases. The proportion of MMP12+ macrophage subtypes increased gradually among normal, tumor, and liver metastasis groups, and MMP12+ macrophages were associated with angiogenesis, cell migration, and inhibited T cell proliferation. The Pseudotime showed higher expression levels of genes related to angiogenesis and enhanced TGF-β signaling pathway and the negative regulation of T cell proliferation with the occurrence of liver metastasis in MMP12+ macrophages. MMP12+ macrophages can promote the proliferation of epithelial cells and inhibit the activation of T cells and B cells. MMP12+ macrophages promoted liver metastasis of CRC by influencing angiogenesis, TGF-β signaling pathway expression, and regulation of T cells and B cells.

Intraventricular haemorrhage in premature infants: the role of immature neuronal salt and water transport.

Intraventricular haemorrhage is a common complication of premature birth. Survivors are often left with cerebral palsy, intellectual disability and/or hydrocephalus. Animal models suggest that brain tissue shrinkage, with subsequent vascular stretch and tear, is an important step in the pathophysiology, but the cause of this shrinkage is unknown. Clinical risk factors for intraventricular haemorrhage are biomarkers of hypoxic-ischaemic stress, which causes mature neurons to swell. However, immature neuronal volume might shift in the opposite direction in these conditions. This is because immature neurons express the chloride, salt and water transporter NKCC1, which subserves regulatory volume increases in non-neural cells, whereas mature neurons express KCC2, which subserves regulatory volume decreases. When hypoxic-ischaemic conditions reduce active ion transport and increase the cytoplasmic membrane permeability, the effects of these transporters are diminished. Consequentially, mature neurons swell (cytotoxic oedema), whereas immature neurons might shrink. After hypoxic-ischaemic stress, in vivo and in vitro multi-photon imaging of perinatal transgenic mice demonstrated shrinkage of viable immature neurons, bulk tissue shrinkage and blood vessel displacement. Neuronal shrinkage was correlated with age-dependent membrane salt and water transporter expression using immunohistochemistry. Shrinkage of immature neurons was prevented by prior genetic or pharmacological inhibition of NKCC1 transport. These findings open new avenues of investigation for the detection of acute brain injury by neuroimaging, in addition to prevention of neuronal shrinkage and the ensuing intraventricular haemorrhage, in premature infants.

A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development.

Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called "pseudo-genome divergence quantification" (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.

Dissecting the impact of differentiation stage, replicative history, and cell type composition on epigenetic clocks

Epigenetic clocks, built on DNA methylation patterns of bulk tissues, are powerful age predictors, but their biological basis remains incompletely understood. Here, we conducted a comparative analysis of epigenetic age in murine muscle, epithelial, and blood cell types across lifespan. Strikingly, our results show that cellular subpopulations within these tissues, including adult stem and progenitor cells as well as their differentiated progeny, exhibit different epigenetic ages. Accordingly, we experimentally demonstrate that clocks can be skewed by age-associated changes in tissue composition. Mechanistically, we provide evidence that the observed variation in epigenetic age among adult stem cells correlates with their proliferative state, and, fittingly, forced proliferation of stem cells leads to increases in epigenetic age. Collectively, our analyses elucidate the impact of cell type composition, differentiation state, and replicative potential on epigenetic age, which has implications for the interpretation of existing clocks and should inform the development of more sensitive clocks.

GPR65 contributes to constructing immunosuppressive microenvironment in glioma

Glioma, especially glioblastoma patients, present highly heterogeneous and immunosuppressive microenvironment, leading to their poor response to treatment and survival. Targeting the tumor microenvironment is considered a promising therapeutic strategy. M2 macrophages are highly infiltrated in glioma tissue, even up to 50% of the total number of bulk tissue cells. Here, we identified GPR65 as the hub gene of the M2 macrophage-related module in glioma through WGCNA analysis. The expression and prognosis analysis suggested that GPR65 was positively correlated with the malignancy and poor prognosis of glioma, and the heterogeneity analysis found that GPR65 was highly expressed in the vascular proliferation area of glioma, which matched the spatial expression characteristics of M2 macrophages. We further verified that GPR65 was highly expressed in macrophages but not tumor cells in the glioma microenvironment by single-cell data analysis and immunofluorescence. Most importantly, we found that inhibition of GPR65 was sufficient to reduce macrophages’ polarization response to glioma cell and break the malignant cooperation with glioma cells. Our study reports the expression characteristics and malignant behavior of GPR65 in the glioma microenvironment, which provides a new alternative target of treatment to glioma microenvironment.

A Curated Compendium of Transcriptomic Data for the Exploration of Neocortical Development.

Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of ~30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex. Applying joint matrix decomposition (SJD) to mouse, macaque and human data in this collection, we defined transcriptome dynamics that are conserved across mammalian neurogenesis and which elucidate the evolution of outer, or basal, radial glial cells. Decomposition of adult human neocortical data identified layer-specific signatures in mature neurons and, in combination with transfer learning methods in NeMO Analytics, enabled the charting of their early developmental emergence and protracted maturation across years of postnatal life. Interrogation of data from cerebral organoids demonstrated that while broad molecular elements of in vivo development are recapitulated in vitro, many layer-specific transcriptomic programs in neuronal maturation are absent. We invite computational biologists and cell biologists without coding expertise to use NeMO Analytics in their research and to fuel it with emerging data (carlocolantuoni.org).

The Microscope and Beyond: Current Trends in the Characterization of Kidney Allograft Rejection From Tissue Samples.

The Banff classification is regularly updated to integrate recent advances in the characterization of kidney allograft rejection, gathering novel diagnostic, prognostic, and theragnostic data into a diagnostic and pathogenesis-based framework. Despite ongoing research on noninvasive biomarkers of kidney rejection, the Banff classification remains, to date, biopsy-centered, primarily relying on a semiquantitative histological scoring system that overall lacks reproducibility and granularity. Besides, the ability of histopathological injuries and transcriptomics analyses from bulk tissue to accurately infer the pathogenesis of rejection is questioned. This review discusses findings from past, current, and emerging innovative tools that have the potential to enhance the characterization of allograft rejection from tissue samples. First, the digitalization of pathological workflows and the rise of deep learning should yield more reproducible and quantitative results from routine slides. Additionally, novel histomorphometric features of kidney rejection could be discovered with an overall genuine clinical implementation perspective. Second, multiplex immunohistochemistry enables in-depth in situ phenotyping of cells from formalin-fixed samples, which can decipher the heterogeneity of the immune infiltrate during kidney allograft rejection. Third, transcriptomics from bulk tissue is gradually integrated into the Banff classification, and its specific context of use is currently under extensive consideration. Finally, single-cell transcriptomics and spatial transcriptomics from formalin-fixed and paraffin-embedded samples are emerging techniques capable of producing up to genome-wide data with unprecedented precision levels. Combining all these approaches gives us hope for novel advances that will address the current blind spots of the Banff system.

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15–20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

Synaptic gene expression changes in frontotemporal dementia due to the MAPT 10+ 16 mutation.

Mutations in the MAPT gene encoding tau protein can cause autosomal dominant neurodegenerative tauopathies including frontotemporal dementia (often with Parkinsonism). In Alzheimer's disease, the most common tauopathy, synapse loss is the strongest pathological correlate of cognitive decline. Recently, Positron Emission Tomography (PET) imaging with synaptic tracers revealed clinically relevant loss of synapses in primary tauopathies; however, the molecular mechanisms leading to synapse degeneration in primary tauopathies remain largely unknown. In this study, we examined post-mortem brain tissue from people who died with frontotemporal dementia with tau pathology (FTDtau) caused by the MAPT intronic exon 10+ 16 mutation, which increases splice variants containing exon 10 resulting in higher levels of tau with four microtubule-binding domains. We used RNA sequencing and histopathology to examine temporal cortex and visual cortex, to look for molecular phenotypes compared to age, sex and RNA integrity matched participants who died without neurological disease (n= 12 FTDtau10+ 16 and 13 controls). Bulk tissue RNA sequencing reveals substantial downregulation of gene expression associated with synaptic function. Upregulated biological pathways in human MAPT 10+ 16 brain included those involved in transcriptional regulation, DNA damage response and neuroinflammation. Histopathology confirmed increased pathological tau accumulation in FTDtau10 + 16 cortex as well as a loss of presynaptic protein staining and region-specific increased colocalization of phospho-tau with synapses in temporal cortex. Our data indicate that synaptic pathology likely contributes to pathogenesis in FTDtau10 + 16 caused by the MAPT 10+ 16 mutation.

Decoding the genetic landscape of juvenile dermatomyositis: insights from phosphorylation-associated single nucleotide polymorphisms.

Genome-wide association studies (GWASs) have identified genetic susceptibility loci associated with juvenile dermatomyositis (JDM). Single nucleotide polymorphisms related to phosphorylation (phosSNPs) are critical nonsynonymous mutations exerting substantial influence on gene expression regulation. The aim of this study was to identify JDM susceptibility genes in the GWAS loci by the use of phosSNPs. We explored quantitative trait loci (QTLs) among the phosSNPs associated with JDM using data from eQTL (bulk tissues and single-cell) and pQTL studies. For gene expression and protein levels significantly influenced by JDM-associated phosSNPs, we assessed their associations with JDM through MR analyses. Additionally, we conducted differential expression gene analyses, incorporating single-cell transcriptomic profiling of 6 JDM cases and 11 juvenile controls (99,396 cells). We identified 31 phosSNPs situated in the 6p21 locus that were associated with JDM. Half of these phosSNPs showed effects on gene expression in various cells and circulating protein levels. In MR analyses, we established associations between the expression levels of pivotal JDM-associated genes, including MICB, C4A, HLA-DRB1, HLA-DRB5, and PSMB9, in skin, muscle, or blood cells and circulating levels of C4A, with JDM. Utilizing single-cell eQTL data, we identified a total of 276 association signals across 14 distinct immune cell types for 28 phosSNPs. Further insights were gained through single-cell differential expression analysis, revealing differential expression of PSMB9, HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1 in immune cells. The present study pinpointed phosSNPs within susceptibility genes for JDM and unraveled the intricate relationships among these SNPs, gene expression levels, and JDM.

Sample Preparation in Bulk Tissue Samples Using the Arctis Plasma FIB DualBeam to Enable Molecular Imaging

Sample Preparation in Bulk Tissue Samples Using the Arctis Plasma FIB DualBeam to Enable Molecular Imaging