Heterogeneous Gene Expression Research Articles

Spatial transcriptomics techniques and its applications in plant research

The heterogeneity of gene expression in plant cells plays a crucial role in determining the functional differences among tissues. Recent advancements in spatial transcriptome (ST) technology have significantly contributed to the study of specific biological questions in plants. This technology has been successfully applied to examine cell development, identification, and stress resistance. This review aims to explore the application of ST technology in plants by reviewing three aspects: the development of ST technology, its current application in plants, and future research directions. The review provides a systematic description of the development process of ST technology, with a focus on analyzing its progress in studying plant cell growth and differentiation, plant cell identification, and stress resistance. In addition, the challenges faced by ST technology in plant applications are summarized, along with proposed future directions for plant research, including the advantages of combining other omics technologies with ST technology to tackle scientific challenges in the field of plants.

Modification of Seurat v4 for the Development of a Phase Assignment Tool Able to Distinguish between G2 and Mitotic Cells.

Single-cell RNA sequencing (scRNAseq) is a rapidly advancing field enabling the characterisation of heterogeneous gene expression profiles within a population. The cell cycle phase is a major contributor to gene expression variance between cells and computational analysis tools have been developed to assign cell cycle phases to cells within scRNAseq datasets. Whilst these tools can be extremely useful, all have the drawback that they classify cells as only G1, S or G2/M. Existing discrete cell phase assignment tools are unable to differentiate between G2 and M and continuous-phase-assignment tools are unable to identify a region corresponding specifically to mitosis in a pseudo-timeline for continuous assignment along the cell cycle. In this study, bulk RNA sequencing was used to identify differentially expressed genes between mitotic and interphase cells isolated based on phospho-histone H3 expression using fluorescence-activated cell sorting. These gene lists were used to develop a methodology which can distinguish G2 and M phase cells in scRNAseq datasets. The phase assignment tools present in Seurat were modified to allow for cell cycle phase assignment of all stages of the cell cycle to identify a mitotic-specific cell population.

SpVC for the detection and interpretation of spatial gene expression variation

Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC’s accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.

Abstract 5527: Spatiotemporal analysis of growth factor regulation of osteosarcoma AKT and ERK signaling dynamics in an ex vivo lung metastasis model

Abstract In the present study, we investigated the relationships between spatiotemporal distribution of growth factors in the lung microenvironment, metastatic osteosarcoma cell signaling, and intratumoral gene expression heterogeneity. Osteosarcoma is an aggressive bone malignancy that commonly metastasizes to the lungs, resulting in significant patient mortality. During metastasis, disseminated cancer cells are exposed to microenvironment-derived growth factors that result in signaling pathway activation. Two such pathways are MAPK/ERK and PI3K/AKT, which regulate complex transcriptional programs underlying cell proliferation and survival decisions. In this study, we utilized precision cut lung slices (PCLS) obtained from mice with established osteosarcoma metastases to ex vivo model associations between the spatiotemporal distributions of growth factors in the lung, osteosarcoma signaling dynamics, and intratumoral gene expression heterogeneity, with single cell precision. To accomplish this, we engineered osteosarcoma cells that co-expressed genetically-encoded ERK and AKT sensors, engrafted them into PCLS tissues, then recorded sensor dynamics via live-cell microscopy. These tissues were subsequently fixed and immuno-stained for measurement of growth factor expression in resident lung cells and spatial correlation with observed tumor cell ERK-AKT signaling dynamics. Using this approach, we found that growth factor expression was diffusely increased within areas bordering osteosarcoma metastases. FGF2 and IGF1, which we have previously demonstrated to potently induce ERK signaling in osteosarcoma cells, were both significantly upregulated in these regions. However, despite relatively uniform overexpression of growth factors adjacent to tumors, individual osteosarcoma cells displayed stochastics patterns of ERK and AKT signaling, and downstream gene targets. These findings revealed that upregulation of lung growth factors, and the inherent systems-level properties of osteosarcoma signaling networks, can converge to promote intratumoral signaling and gene expression heterogeneity. Together, our results, using a novel PCLS and live-cell biosensor imaging approach, provide new insight into how spatial and temporal changes in microenvironmental signaling composition are integrated by metastatic osteosarcoma cells to promote heterogeneity. Citation Format: Rawan Makkawi, Ryan D. Roberts, Jeremy Copperman, Alexander E. Davies. Spatiotemporal analysis of growth factor regulation of osteosarcoma AKT and ERK signaling dynamics in an ex vivo lung metastasis model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5527.

Abstract 2961: Investigating the role of aging and treatment-induced senescence in the metastatic liver cancer tumor microenvironment

Abstract Liver senescence, marked by the progressive decline in hepatic cell function and regenerative capacity, plays a vital role in the context of aging, chronic liver diseases, and cancer progression, particularly when influenced by anti-cancer therapies. This study is designed to extensively characterize liver senescence under two specific conditions: replicative aging and therapy-induced changes. We examined liver samples from 20 individuals, covering a wide age range (young, <40 years; old, >60 years), and 16 cases of colorectal cancer (CRC) liver metastasis (ages 37-66). Our integrative approach melds single-nucleus RNA-sequencing (snRNA-seq) and ATAC-sequencing (snATAC-seq) to investigate the transcriptomic and epigenetic landscapes across a spectrum of liver cell types, including hepatocytes, cholangiocytes, stellate cells, liver sinusoidal endothelial cells, and diverse immune cells. Key findings include an upregulation of senescence markers such as CDKN1A, CDKN2A, and HMGB1, particularly in specific hepatocytes from older individuals and various cell types in the CRC liver metastasis group. Furthermore, we noted a distinctive senescence-associated secretory phenotype (SASP) across multiple immune cell types in our treated CRC liver metastasis group and upregulation of treatment-induced senescence gene signatures, such as SenCan. Moreover, hepatic stellate cells from older subjects exhibited augmented senescence-related gene signatures (SenSig) and transcription factors (SenMayo), with myofibroblast-like and FAP+ stellate cells showing a marked senescence phenotype, evidenced by elevated SASP molecule expression (CCL2, SERPINE1, GLB1, IGFBP7, IGFBP3).Incorporating advanced high-resolution imaging techniques, our study delves into the detailed morphological characteristics and spatial distribution of senescence markers within the liver in the setting of replicative aging and treatment-induced senescence. This study sheds light on the heterogeneity and intricacies of senescence-associated gene expression within liver cell populations. By combining comprehensive multiomic data with advanced high-resolution spatial profiling, we aim to decipher unique senescence phenotypes in the context of both replicative and treatment-induced senescence. Our work highlights the significance of cellular senescence in liver pathologies and cancer progression, suggesting new directions for therapeutic interventions that target these complex dynamics. Citation Format: Kelsey Gallant, Alla Karpova, Xiang Li, Michael Iglesia, Andrew Houston, Daniel Rapp, Chien-Wei Peng, Clara Liu, John Herdon, Feng Chen, Ryan Fields, Li Ding. Investigating the role of aging and treatment-induced senescence in the metastatic liver cancer tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2961.

Abstract 3501: Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis

Abstract Chordoma, a rare bone cancer characterized by limited actionable clinical targets, exhibits a pivotal hallmark in the activation of the developmental transcription factor gene TBXT (Brachyury) through its association with highly active enhancers. TBXT amplification emerges as a major causative factor in familial chordomas, yet the amplification mechanism and its consequential impact on genomic instability remain inadequately elucidated. In this study, we conducted a comprehensive analysis of multiple genomic and epigenomic sequencing datasets, including WGS-seq, H3K27ac ChIP-seq, and ATAC-seq in six chordoma cell lines, and HiC data in two of them, to unravel the mechanisms of structural variations and their biological implications. Notably, our focus extends to JHC7 cells, which exhibit extrachromosomal amplification of a 2.4M region encompassing TBXT and the associated enhancers. The existence of such extrachromosomal DNA (ecDNA) in JHC7 was further validated through DNA FISH and Sanger sequencing. Comparing JHC7 with CH22, a chordoma cell line lacking TBXT amplification, revealed heightened chromatin relaxation and increased DNA rearrangements in JHC7. Interestingly, FISH results showed significant genomic ploidy heterogeneity within JHC7 cells, a finding demonstrated by nucleus FACS sorting. Single-cell multiomic analysis, combining snRNA-seq and snATAC-seq, demonstrated heterogeneous gene expression and distinct copy number variations among JHC7 cells. However, the amplification of the TBXT locus was observed in nearly all cells examined, indicating the rapid genomic evolution in the cultured JHC7 cells. These results collectively show that TBXT is extensively amplified through the formation of ecDNA, providing a fitness advantage to chordoma cells and concurrently exacerbating genomic instability. The findings in this study provide an insight for understanding the pathogenesis of chordoma and may inform the development of targeted therapeutic strategies. Citation Format: Bo Zhao, Martin Barrio Ines, Lingqun Ye, Xizi Wu, Alice Soragni, Andrew Futreal, Kadir Akdemir. Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3501.

Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer.

Because cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer's genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introduces a methodological framework, combining heterogeneous gene expression data, random forest selection, and pathway analysis, alongside clinical information and Cox regression analysis, to discover prognostic biomarkers. Heterogeneous gene expression data for colorectal cancer were collected from TCGA-COAD (RNA-seq), and GSE17536 and GSE39582 (microarray), and were integrated with Entrez Gene IDs. Using Cox regression analysis and random forest, genes with consistent hazard ratios and significantly affecting patient survivability were chosen. Predictive accuracy was evaluated using ROC curves. Pathway analysis identified potential RNA biomarkers. The authors identified 28 RNA biomarkers. Pathway analysis revealed enrichment in cancer-related pathways, notably EGFR downstream signaling and IGF1R signaling. Three RNA biomarkers (ZEB1-AS1, PI4K2A, and ITGB8-AS1) and two clinical biomarkers (stage and age) were chosen for a prognostic model, improving predictive performance compared to using clinical biomarkers alone. Despite biomarker identification challenges, this study underscores integration of heterogenous gene expression data for discovery.

Lactate in breast cancer cells is associated with evasion of hypoxia-induced cell cycle arrest and adverse patient outcome.

Tumor hypoxia is a common microenvironmental factor in breast cancers, resulting in stabilization of Hypoxia-Inducible Factor 1 (HIF-1), the master regulator of hypoxic response in cells. Metabolic adaptation by HIF-1 results in inhibition of citric acid cycle, causing accumulation of lactate in large concentrations in hypoxic cancers. Lactate can therefore serve as a secondary microenvironmental factor influencing cellular response to hypoxia. Presence of lactate can alter the hypoxic response of breast cancers in many ways, sometimes in opposite manners. Lactate stabilizes HIF-1 in oxidative condition, as well as destabilizes HIF-1 in hypoxia, increases cellular acidification, and mitigates HIF-1-driven inhibition of cellular respiration. We therefore tested the effect of lactate in MDA-MB-231 under hypoxia, finding that lactate can activate pathways associated with DNA replication, and cell cycling, as well as tissue morphogenesis associated with invasive processes. Using a bioengineered nano-patterned stromal invasion assay, we also confirmed that high lactate and induced HIF-1α gene overexpression can synergistically promote MDA-MB-231 dissemination and stromal trespass. Furthermore, using The Cancer Genome Atlas, we also surprisingly found that lactate in hypoxia promotes gene expression signatures prognosticating low survival in breast cancer patients. Our work documents that lactate accumulation contributes to increased heterogeneity in breast cancer gene expression promoting cancer growth and reducing patient survival.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BackgroundThe term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.MethodsWe propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.ResultsWe observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.ConclusionOverall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Bet-hedging and variability in plant development: seed germination and beyond.

When future conditions are unpredictable, bet-hedging strategies can be advantageous. This can involve isogenic individuals producing different phenotypes, under the same environmental conditions. Ecological studies provide evidence that variability in seed germination time has been selected for as a bet-hedging strategy. We demonstrate how variability in germination time found in Arabidopsis could function as a bet-hedging strategy in the face of unpredictable lethal stresses. Despite a body of knowledge on how the degree of seed dormancy versus germination is controlled, relatively little is known about how differences between isogenic seeds in a batch are generated. We review proposed mechanisms for generating variability in germination time and the current limitations and new possibilities for testing the model predictions. We then look beyond germination to the role of variability in seedling and adult plant growth and review new technologies for quantification of noisy gene expression dynamics. We discuss evidence for phenotypic variability in plant traits beyond germination being under genetic control and propose that variability in stress response gene expression could function as a bet-hedging strategy. We discuss open questions about how noisy gene expression could lead to between-plant heterogeneity in gene expression and phenotypes. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas.

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

The genetic regulation of the gastric transcriptome is associated with metabolic and obesity-related traits and diseases.

Tissue-specific gene expression and gene regulation lead to a better understanding of tissue-specific physiology and pathophysiology. We analyzed the transcriptome and genetic regulatory profiles of two distinct gastric sites, corpus and antrum, to identify tissue-specific gene expression and its regulation. Gastric corpus and antrum mucosa biopsies were collected during routine gastroscopies from up to 431 healthy individuals. We obtained genotype and transcriptome data and performed transcriptome profiling and expression quantitative trait locus (eQTL) studies. We further used data from genome-wide association studies (GWAS) of various diseases and traits to partition their heritability and to perform transcriptome-wide association studies (TWAS). The transcriptome data from corpus and antral mucosa highlights the heterogeneity of gene expression in the stomach. We identified enriched pathways revealing distinct and common physiological processes in gastric corpus and antrum. Furthermore, we found an enrichment of the single nucleotide polymorphism (SNP)-based heritability of metabolic, obesity-related, and cardiovascular traits and diseases by considering corpus- and antrum-specifically expressed genes. Particularly, we could prioritize gastric-specific candidate genes for multiple metabolic traits, like NQO1 which is involved in glucose metabolism, MUC1 which contributes to purine and protein metabolism or RAB27B being a regulator of weight and body composition. Our findings show that gastric corpus and antrum vary in their transcriptome and genetic regulatory profiles indicating physiological differences which are mostly related to digestion and epithelial protection. Moreover, our findings demonstrate that the genetic regulation of the gastric transcriptome is linked to biological mechanisms associated with metabolic, obesity-related, and cardiovascular traits and diseases. NEW & NOTEWORTHY We analyzed the transcriptomes and genetic regulatory profiles of gastric corpus and for the first time also of antrum mucosa in 431 healthy individuals. Through tissue-specific gene expression and eQTL analyses, we uncovered unique and common physiological processes across both primary gastric sites. Notably, our findings reveal that stomach-specific eQTLs are enriched in loci associated with metabolic traits and diseases, highlighting the pivotal role of gene expression regulation in gastric physiology and potential pathophysiology.

New generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution

Numerous methods for bulk RNA sequence deconvolution have been developed to identify cellular targets of diseases by understanding the composition of cell types in disease-related tissues. However, issues of heterogeneity in gene expression between subjects and the shortage of reference single-cell RNA sequence data remain to achieve accurate bulk deconvolution. In our study, we investigated whether a new data generative method named sc-CMGAN and benchmarking generative methods (Copula, CTGAN and TVAE) could solve these issues and improve the bulk deconvolutions. We also evaluated the robustness of sc-CMGAN using three deconvolution methods and four public datasets. In almost all conditions, the generative methods contributed to improved deconvolution. Notably, sc-CMGAN outperformed the benchmarking methods and demonstrated higher robustness. This study is the first to examine the impact of data augmentation on bulk deconvolution. The new generative method, sc-CMGAN, is expected to become one of the powerful tools for the preprocessing of bulk deconvolution.

Prospects of Identifying Alternative Splicing Events from Single-Cell RNA Sequencing Data

Background: The advent of single-cell RNA sequencing (scRNA-seq) technology has offered unprecedented opportunities to unravel cellular heterogeneity and functions. Yet, despite its success in unraveling gene expression heterogeneity, accurately identifying and interpreting alternative splicing events from scRNA-seq data remains a formidable challenge. With advancing technology and algorithmic innovations, the prospect of accurately identifying alternative splicing events from scRNA-seq data is becoming increasingly promising Objective: This perspective aims to uncover the intricacies of splicing at the single-cell level and their potential implications for health and disease. It seeks to harness scRNA-seq's transformative power in revealing cell-specific alternative splicing dynamics and aims to propel our understanding of gene regulation within individual cells to new heights. Methods: The perspective grounds its method on recent literature along with the experimental protocols of single-cell RNA-seq and methods to identify and quantify the alternative splicing events from scRNA-seq data. Results: This perspective outlines the promising potential, challenges, and methodologies for leveraging different scRNA-seq technologies to identify and study alternative splicing events, with a focus on advancing our understanding of gene regulation at the single-cell level. Conclusion: This perspective explores the prospects of utilizing scRNA-seq data to identify and study alternative splicing events, highlighting their potential, challenges, methodologies, biological insights, and future directions.

Transcription-replication interactions reveal bacterial genome regulation.

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells4. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription-replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory contextof a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.

GTP-dependent regulation of heterochromatin fluctuations at subtelomeric regions in Saccharomyces cerevisiae.

In eukaryotes, single cells in a population display different transcriptional profiles. One of the factors regulating this heterogeneity is the chromatin state in each cell. However, the mechanisms of epigenetic chromatin regulation of specific chromosomal regions remain unclear. Therefore, we used single-cell tracking system to analyze IMD2. IMD2 is located at the subtelomeric region of budding yeast, and its expression is epigenetically regulated by heterochromatin fluctuations. Treatment with mycophenolic acid, an inhibitor of de novo GTP biosynthesis, triggered a decrease in GTP, which caused heterochromatin fluctuations at the IMD2 locus. Interestingly, within individually tracked cells, IMD2 expression state underwent repeated switches even though IMD2 is positioned within the heterochromatin region. We also found that 30% of the cells in a population always expressed IMD2. Furthermore, the addition of nicotinamide, a histone deacetylase inhibitor, or guanine, the GTP biosynthesis factor in salvage pathway of GTP biosynthesis, regulated heterogeneity, resulting in IMD2 expression being uniformly induced or suppressed in the population. These results suggest that gene expression heterogeneity in the IMD2 region is regulated by changes in chromatin structure triggered by slight decreases in GTP.

STIGMA: Single-cell tissue-specific gene prioritization using machine learning

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

Application progress of single-cell sequencing technology in mesenchymal stem cells research.

Single-Cell Sequencing (SCS) technology plays an important role in the field of Mesenchymal Stem Cells (MSCs) research. This paper comprehensively describes the application of SCS technology in the field of MSCs research, including (1) SCS enables more precise MSCs characterization and biomarker definition. (2) SCS reveals the prevalent gene expression heterogeneity among different subclusters within MSCs, which contributes to a more comprehensive understanding of MSCs function and diversity in developmental, regenerative, and pathological contexts. (3) SCS provides insights into the dynamic transcriptional changes experienced by MSCs during differentiation and the complex web of important signaling pathways and regulatory factors controlling key processes within MSCs, including proliferation, differentiation and regulation, and interactions mechanisms. (4) The analytical methods underpinning SCS data are rapidly evolving and converging with the field of histological research to systematically deconstruct the functions and mechanisms of MSCs. This review provides new perspectives for unraveling the biological properties, heterogeneity, differentiation potential, biological functions, and clinical potential of MSCs at the single-cell level.

Single-cell RNA sequencing reveals heterogeneity and differential expression of the maternal–fetal interface during ICP and normal pregnancy

Objective Intrahepatic cholestasis of pregnancy (ICP) can cause adverse perinatal outcomes. Previous studies have demonstrated that the placenta of an ICP pregnancy differs in morphology and gene expression from the placenta of a normal pregnancy. To date, however, the genetic mechanism by which ICP affects the placenta is poorly understood. Therefore, the aim of this study was to investigate the differences in main cell types, gene signatures, cell ratio, and functional changes in the placenta between ICP and normal pregnancy. Methods Single-cell RNA sequencing (scRNA-seq) technology was used to detect the gene expression of all cells at the placental maternal–fetal interface. Two individuals were analyzed – one with ICP and one without ICP. The classification of cell types was determined by a graph-based clustering algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R software phyper () function and DAVID website. The differentially expressed genes (DEGs) encoding transcription factors (TFs) were identified using getorf and DIAMOND software. Results We identified 14 cell types and 22 distinct cell subtypes that showed unique functional properties. Additionally, we found differences in the proportions of fibroblasts 1, helper T (Th) cells, extravillous trophoblasts, and villous cytotrophoblasts, and we observed heterogeneity of gene expression between ICP and control placentas. Furthermore, we identified 263 DEGs that belonged to TF families, including zf-C2H2, HMGI/HMGY, and Homeobox. In addition, 28 imprinted genes were preferentially expressed in specific cell types, such as PEG3 and PEG10 in trophoblasts as well as DLK1 and DIO3 in fibroblasts. Conclusions Our results revealed the differences in cell-type ratios, gene expression, and functional changes between ICP and normal placentas, and heterogeneity was found among cell subgroups. Hence, the imbalance of various cell types affects placental activity to varying degrees, indicating the complexity of the cell networks that form the placental tissue system, and this alteration of placental function is associated with adverse events in the perinatal period.

Single-Molecule Analysis of Transcription Dynamics to Understand the Relationship Between Epigenetic Alterations and Transcriptional Variability.

Heterogeneity in gene expression largely stems from the discontinuous nature of transcription, with transcripts being produced in bursts with defined frequencies. This cell-to-cell variability in transcription within isogenic cell populations is a known phenomenon across numerous genes. Multiple gene regulatory and epigenetic factors have been identified as key contributors to this pulsatile gene activity. Understanding the effects of epigenetic modulation on transcriptional cell-to-cell variability and kinetics of transcriptional activity is crucial for interpreting changes in treatment responsiveness. We present a detailed protocol that guides the assessment of fluctuations in gene expression induced by epigenetic modulation using single-molecule RNA in situ hybridization (smRNA FISH) combined with confocal microscopy imaging, data analysis, and quantification in breast cancer cells. Through smRNA FISH labeling, both mature and nascent transcripts are identified. Subsequently, the number of mature transcripts and the intensity and frequency of nascent transcripts are quantified, and these measurements are used to calculate the burst size and frequency for the labeled gene. By following this step-by-step methodology, insights are obtained into the intricate relationship between epigenetic alterations and the dynamic nature of gene expression in breast cancer cells.