Single-cell Gene Research Articles

Visualization of Endogenous RNA G-Quadruplex in Individual Genes in Single Cells.

Single-cell RNA imaging enables the detection of endogenous RNA and the analysis of its abundance, localization, and single-cell heterogeneity, which is powerful in the study of RNA biology. G-quadruplex (G4) is a kind of nucleic secondary structure and plays important roles in various biological processes. To reveal the regulating mechanism of monogenic G4s in a specific biological pathway, it is of great important to visualize endogenous RNA G4 in individual genes. Here, we describe Module Assembled Multifunctional Probes Assay (MAMPA) for single-cell visualization of monogenic RNA G4. This method works well in various cell lines and various RNA G4 analytes and has a low detection limit at ten copies per cell.

Homeotic <I>DUX4</i> genes shape dynamic inter-chromosomal contacts with nucleoli in human cells

Nucleoli shape inter-chromosomal contacts with genes controlling differentiation and cancer genesis. DUX4 genes specify transcription factor possessing two homeodomains. Previously, using Circular Chromosome Conformation Capture (4С) approach on population of cells, it was demonstrated that DUX4 gene clusters form frequent contacts with nucleoli. It was found also that these contacts are almost completely abolished after heat shock treatment. 4C approach as all ligation-mediated methods is capable to detect rather close interactions between chromatin loops in nuclei. In order to independently confirm the formation and the frequency of the contacts in single cells we used FISH approach. Here, we show that DUX genes in single cells form stable contacts in all tested HEK293T cells. The contacts after heat shock treatment reversibly retreat up to 1–3 μm distance. We conclude that inter-chromosomal contacts shaping by nucleoli are dynamic and stable providing both the initiation and maintenance of a differentiated state.

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells.

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

Cohesin prevents cross-domain gene coactivation

The contrast between the disruption of genome topology after cohesin loss and the lack of downstream gene expression changes instigates intense debates regarding the structure–function relationship between genome and gene regulation. Here, by analyzing transcriptome and chromatin accessibility at the single-cell level, we discover that, instead of dictating population-wide gene expression levels, cohesin supplies a general function to neutralize stochastic coexpression tendencies of cis-linked genes in single cells. Notably, cohesin loss induces widespread gene coactivation and chromatin co-opening tens of million bases apart in cis. Spatial genome and protein imaging reveals that cohesin prevents gene co-bursting along the chromosome and blocks spatial mixing of transcriptional hubs. Single-molecule imaging shows that cohesin confines the exploration of diverse enhancer and core promoter binding transcriptional regulators. Together, these results support that cohesin arranges nuclear topology to control gene coexpression in single cells.

Homeotic DUX4 Genes Shape Dynamic Inter-Chromosomal Contacts with Nucleoli in Human Cells.

Nucleoli form interchromosomal contacts with genes controlling differentiation and carcinogenesis. DUX4 genes specify transcription factor possessing two homeodomains. Previously, using Circular Chromosome Conformation Capture (4С) approach on population of cells, it was demonstrated that DUX4 gene clusters form frequent contacts with nucleoli. It was found also that these contacts are almost completely abolished after heat shock treatment. 4C approach as all ligation-mediated methods is capable to detect rather close interactions between chromatin loops in nuclei. In order to independently confirm the formation and the frequency of the contacts in single cells we used FISH approach. Here, we show that DUX genes in single cells form stable contacts in all tested HEK293T cells. During heat shock, DUX4 genes reversibly move 1-3 µm away from the nuclei. We conclude that interchromosomal contacts formed by nucleoli are strong, dynamic, and reversible, providing both the initiation and maintenance of a differentiated state.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

Abstract 7418: LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data

Abstract Gene fusion resulted from genomic translocation plays important oncogenic roles including but not limited to gene activation and changing protein coding sequences. Historically, fusion gene analysis has been limited to bulk levels resulting in mixed oncogene activation models. Single cell Nanopore RNA sequencing (scNanoRNAseq) has the unique strength in detecting full-length chimeric transcripts in thousands of single cells in parallel. However, robust computational tools for detecting fusion genes in single cells from scNanoRNAseq data are missing. Here, we developed LongFuse, a fast and accurate tool, to detect fusion genes and their splicing isoforms in single cells from scNanoRNAseq data. LongFuse implements an XOR logic gate algorithm to speed up fusion candidate searching and stringent criteria to eliminate false discoveries due to trans-splicing events and randomly ligated chimeras. We applied LongFUSE onto scNanoRNAseq data of both human prostate cancer cell line, VCaP, and tumor tissues with known TMPRSS2-ERG fusion. Our results showed that both cancer cell line and prostate tumors harbored a mixture of fusion positive and fusion negative tumor cells. Gene expression analysis revealed transcriptional differences between fusion positive and negative cells including genes involved in cancer cell proliferation and invasion programs such as ELL2, ZBTB16, IGF1R, ANK3, MYPOP and PLPP1 genes. LongFUSE also calculates the splicing isoforms of fusion gene transcripts, allowing prediction of protein sequences of fusion gene transcripts. Taken together, our data demonstrated that LongFUSE is a robust computational tool for detecting gene fusions and their splicing isoforms at single cell level from scNanoRNAseq data. Citation Format: Cheng-Kai Shiau, Yueying He, Hsiao-Yun Lin, Lina Lu, Xiao-Dong Lu, Jindan Yu, Ruli Gao. LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data [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 7418.

Single-cell atlas of rainbow trout peripheral blood leukocytes and profiling of their early response to infectious pancreatic necrosis virus.

The recent development of single cell sequencing technologies has revolutionized the state-of-art of cell biology, allowing the simultaneous measurement of thousands of genes in single cells. This technology has been applied to study the transcriptome of single cells in homeostasis and also in response to pathogenic exposure, greatly increasing our knowledge of the immune response to infectious agents. Yet the number of these studies performed in aquacultured fish species is still very limited. Thus, in the current study, we have used the 10x Genomics single cell RNA sequencing technology to study the response of rainbow trout (Oncorhynchus mykiss) peripheral blood leukocytes (PBLs) to infectious pancreatic necrosis virus (IPNV), an important trout pathogen. The study allowed us to obtain a transcriptomic profile of 12 transcriptionally distinct leukocyte cell subpopulations that included four different subsets of B cells, T cells, monocytes, two populations of dendritic-like cells (DCs), hematopoietic progenitor cells, non-specific cytotoxic cells (NCC), neutrophils and thrombocytes. The transcriptional pattern of these leukocyte subpopulations was compared in PBL cultures that had been exposed in vitro to IPNV for 24h and mock-infected cultures. Our results revealed that monocytes and neutrophils showed the highest number of upregulated protein-coding genes in response to IPNV. Interestingly, IgM+IgD+ and IgT+ B cells also upregulated an important number of genes to the virus, but a much fainter response was observed in ccl4 + or plasma-like cells (irf4 + cells). A substantial number of protein-coding genes and genes coding for ribosomal proteins were also transcriptionally upregulated in response to IPNV in T cells and thrombocytes. Interestingly, although genes coding for ribosomal proteins were regulated in all affected PBL subpopulations, the number of such genes transcriptionally regulated was higher in IgM+IgD+ and IgT+ B cells. A further analysis dissected which of the regulated genes were common and which were specific to the different cell clusters, identifying eight genes that were transcriptionally upregulated in all the affected groups. The data provided constitutes a comprehensive transcriptional perspective of how the different leukocyte populations present in blood respond to an early viral encounter in fish.

Single-cell gene and isoform expression analysis reveals signatures of ageing in haematopoietic stem and progenitor cells

Single-cell approaches have revealed that the haematopoietic hierarchy is a continuum of differentiation, from stem cell to committed progenitor, marked by changes in gene expression. However, many of these approaches neglect isoform-level information and thus do not capture the extent of alternative splicing within the system. Here, we present an integrated short- and long-read single-cell RNA-seq analysis of haematopoietic stem and progenitor cells. We demonstrate that over half of genes detected in standard short-read single-cell analyses are expressed as multiple, often functionally distinct, isoforms, including many transcription factors and key cytokine receptors. We observe global and HSC-specific changes in gene expression with ageing but limited impact of ageing on isoform usage. Integrating single-cell and cell-type-specific isoform landscape in haematopoiesis thus provides a new reference for comprehensive molecular profiling of heterogeneous tissues, as well as novel insights into transcriptional complexity, cell-type-specific splicing events and consequences of ageing.

Single-Cell Dissection of Primary Quadruplet- and Triplet-Resistant Multiple Myeloma

Background: With the advent of triplet and quadruplet induction therapies, response rates and survival outcomes have improved significantly in multiple myeloma. However, a minority of patients does not achieve deep responses to these anti-myeloma therapies and survival outcomes for these patients remain dismal. The molecular mechanisms of primary therapeutic resistance remain poorly understood and novel therapeutic approaches are needed for these high-risk patients. Methods: We generated single-cell gene expression and chromatin accessibility profiles at the time of diagnosis from 16 patients with multiple myeloma using the 10X Genomics Multiome platform. Viably frozen CD138+ bone marrow plasma cells were thawed and washed before removing dead cells and isolating nuclei according to the manufacturer's protocols. RNA- and ATAC-seq libraries were constructed per the manufacturer's user guides. GEX and ATAC libraries were sequenced separately on an Illumina HiSeq 4000 instrument before demultiplexing, alignment to the reference genome, and post-alignment quality control. The Cell Ranger pipelines with default parameters were used to trim reads, align, count unique molecular identifiers, and call cells, transcripts, and peaks. Downstream processing was carried out using Seurat and Signac. Doublets were identified by DoubletFinder and batch effects were corrected using Harmony. After quality control for feature counts and mitochondrial gene expression, there were a total of 16188 single cells: 6139 cells from 6 patients resistant to quadruplet induction (Dara-Ixa-Rd), 4841 cells from 4 patients resistant to triplet induction (VRd), and 5208 cells from 6 patients responding to quadruplet / triplet induction. Primary resistance was defined as not achieving at least a very good partial response (VGPR+ after a minimum of 4 cycles of therapy). Expression of the up-regulated resistance genes in single cells was measured by calculating a module score, with higher numbers indicating higher expression (Tirosh et al. Science 2016;352(6282):189-196). We used a score cut-off of 0.200 to define increased expression of resistance genes. Results: Half of the patients were female, the median age at diagnosis was 61 years (42-80), and half of them had high-risk cytogenetics. There was differential gene expression between cells from quadruplet-resistant and -responsive patients (1272 genes, FDR<0.05). Likewise, there was differential gene expression triplet-resistant and -responsive patients (1354 genes, FDR<0.05). There were 212 genes up- and 222 down-regulated in both comparisons (shared resistance genes), an overlap unlikely to have arisen by chance (p<2.22x10-16). Functional annotation of these shared differentially expressed genes revealed up-regulation of GTPase (FDR=4.01x10-4), kinase (FDR=4.39x10-3), and transferase (FDR=1.65x10-3) activity in cells from resistant patients. Conversely, there was down-regulation of genes involved in ubiquitin (FDR=6.63x10-13) and ubiquitin-like protein ligase binding (FDR=2.50x10-12) as well as unfolded protein binding (FDR=1.21x10-10). Expectedly, the mean expression of the shared up-regulated resistance genes was higher among quadruplet- (score=0.261) and triplet-resistant patients (score=0.212) compared to responding patients (score=0.096, Figure 1A). This difference in gene expression was driven by larger subpopulations of cells with increased expression of resistance genes: Quadruplet-resistant (39.2% of cells) and triplet-resistant (37.5% of cells), compared to patients responding to induction therapy (17.2% of cells, Figure 1B). The subpopulations of cells with increased expression of resistance genes were characterized by increased accessibility in Spi-B (FDR=2.55x10-2) and RAR-γ (FDR=1.86x10-3) binding sites. The shared up-regulated genes included known drug resistance genes such as IKZF1 and viable therapeutic targets including CCND3, RASA1, AKT3, CDK8, and ATM. Conclusions: Single-cell interrogation of the transcriptome and epigenome of patients with primary resistant multiple myeloma suggests an overexpression of actionable drug targets and increased cell proliferation in cell subpopulations. There is biological rationale for the investigation of novel individualized therapeutic approaches like IKZF1 degradation and targeted kinase inhibition in this patient population. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Single-Cell Transcriptome Analysis Reveals Different Immune Signatures in HPV- and HPV + Driven Human Head and Neck Squamous Cell Carcinoma.

Background Head and neck squamous cell carcinoma (HNSCC) is a significant health problem and related to poor long-term outcomes, indicating more research to be done to deeply understand the underlying pathways. Objective This current study aimed in the assessment of the viral- (especially human papilloma virus [HPV]) and carcinogen-driven head and neck squamous cell carcinoma (HNSCC) microenvironment based on single-cell sequencing analysis. Methods Data were downloaded from GEO database (GSE139324), including 131224 cells from 18 HP- HNSCC patients and 8 HPV+ HNSCC patients. Following data normalization, all highly variable genes in single cells were identified, and batch correction was applied. Differentially expressed genes were identified using Wilcoxon rank sum test. A gene enrichment analysis was performed in each cell cluster using KEGG analysis. Single-cell pseudotime trajectories were constructed with MONOCLE (version 2.6.4). Cell-cell interactions were analyzed with CellChat R package. Additionally, cell-cell communication patterns in key signal pathways were compared in different tissue groups. A hidden Markov model (HMM) was used to predict gene expression states (on or off) throughout pseudotime. Five-year overall survival outcomes were compared in both HPV+ and HPV- subsets. Results 20,978 high-quality individual cells passed quality control. RNA-seq data were used from 522 HNSCC primary tumor samples. 1,137 differentially expressed genes between HPV+ and HPV- HNSCC patients were investigated. 96 differentially expressed genes were associated with overall survival and highly enriched in B cell associated biological process. Cell composition differed between types of samples. MHC-I, MHC-II, and MIF signaling pathways were found to be most relevant. Within these pathways, some cells were either signal receiver or signal sender, depending on sample type, respectively. Six genes were obtained, AREG and TGFBI (upregulation), CD27, CXCR3, MS4A1, and CD19 (downregulation), whose expression and HPV types were highly associated with worse overall survival. AREG and TGFBI were pDC marker genes, CXCR3 and CD27 were significantly expressed in T cell-related cells, while MS4A1 and CD19 were mainly expressed in B naïve cells. Conclusions This study revealed dynamic changes in cell percentage and heterogeneity of cell subtypes of HNSCC. AREG, TGFBI, CD27, CXCR3, MS4A1, and CD19 were associated with worse overall survival in HPV-related HNSCC. Especially B-cell related pathways were revealed as particularly relevant in HPV-related HNSCC. These findings are a basis for the development of biomarkers and therapeutic targets in respective patients.

Abstract 6214: Detecting and characterizing extrachromosomal DNA in medulloblastoma at single-cell resolution

Abstract Extrachromosomal circular DNA (ecDNA) is an important driver of aggressive cancers, including medulloblastoma (MB), the most common malignant pediatric brain tumor. To assess the clinical import of ecDNA in MB, we applied computational methods to detect ecDNA to the genomes of a cohort of 468 MB patients and 31 MB model systems. Among patients, ecDNA was detected in 18% of tumors and carried threefold greater risk of mortality. Between sequential patient biopsies at initial diagnosis and subsequent relapse, we observed frequent structural variation at ecDNA loci and generation of new ecDNA sequences. Among model systems, ecDNA was found in 19 of 31 genomes (61%). Although ecDNA was far more prevalent among MB models than patients, the ecDNA genomic sequence was conserved between most patient-derived xenograft (PDX) models and their origin human tumors. EcDNA promotes high oncogene copy number, accelerated evolution of drug resistance and poor prognosis. Mechanistically, these behaviors are hypothesized to be consequences of intratumoral heterogeneity of ecDNA copy number; however, there are currently no methods to quantify the abundance and distribution of ecDNA across single cells in a tumor. We have previously identified a primary TP53-mutant SHH medulloblastoma tumor, which harbors multiple distinct lineages of ecDNA originating from different chromosomes. We have now simultaneously analyzed the accessible chromatin and gene expression in single cells of the human tumor and of a orthotopic mouse xenograft (PDX) derived for this tumor, using multiome single-cell ATAC-seq and gene expression (10X Genomics). By measuring scATAC-seq read depth at the ecDNA loci relative to the rest of the genome, we estimate ecDNA copy number in single cells. We observe substantial variation in the ecDNA copy number of cells in both the human tumor and the PDX, and strong association with transcription of ecDNA-amplified genes. We anticipate that this approach will illuminate mechanisms of ecDNA-mediated drug resistance, by revealing the sensitivity and population dynamics of ecDNA-containing cells under treatment pressure. Citation Format: Owen Chapman, Sunita Sridhar, Jon D. Larson, Robert J. Wechsler-Reya, Jill Mesirov, Lukas Chavez. Detecting and characterizing extrachromosomal DNA in medulloblastoma at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6214.

Dynamic spreading of chromatin-mediated gene silencing and reactivation between neighboring genes in single cells.

In mammalian cells genes that are in close proximity can be transcriptionally coupled: silencing or activating one gene can affect its neighbors. Understanding these dynamics is important for natural processes, such as heterochromatin spreading during development and aging, and when designing synthetic gene regulation circuits. Here, we systematically dissect this process in single cells by recruiting and releasing repressive chromatin regulators at dual-gene synthetic reporters, and measuring how fast gene silencing and reactivation spread as a function of intergenic distance and configuration of insulator elements. We find that silencing by KRAB, associated with histone methylation, spreads between two genes within hours, with a time delay that increases with distance. This fast KRAB-mediated spreading is not blocked by the classical cHS4 insulators. Silencing by histone deacetylase HDAC4 of the upstream gene can also facilitate background silencing of the downstream gene by PRC2, but with a days-long delay that does not change with distance. This slower silencing can sometimes be stopped by insulators. Gene reactivation of neighboring genes is also coupled, with strong promoters and insulators determining the order of reactivation. Our data can be described by a model of multi-gene regulation that builds upon previous knowledge of heterochromatin spreading, where both gene silencing and gene reactivation can act at a distance, allowing for coordinated dynamics via chromatin regulator recruitment.

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors.

e20540 Background: Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Methods: Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients’ single-cell (SC) tumor transcriptomics. Our approach is a two-step process: first, the prediction models are trained on large-scale bulk-expression profiles of cancer cell lines and then, in a second step, the models’ performance is further optimized by training on SC-expression profiles of cancer cell lines. Results: First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patients’ tumor’s SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patients’ cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. Conclusions: In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma.

BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, but effective early detection and prognostication methods are lacking. MethodsThe Cox regression model was built to stratify the HCC patients. The single-cell RNA sequencing data analysis and gene set enrichment analysis were employed to investigate the biological function of identified markers. PLCB1 gain- or loss-of-function experiments were performed, and obtained HCC samples were analyzed using quantitative real-time PCR and immunohistochemistry assay to validate the biological function of identified markers.ResultsIn this study, we developed a model using optimized markers for HCC recurrence prediction. Specifically, we screened out 8 genes through a series of data analyses, and built a multivariable Cox model based on their expression. The risk stratifications using the Eight-Gene Cox (EGC) model were closely associated with the recurrence-free survivals (RFS) in both training and three validation cohorts. We further demonstrated that this risk stratification could serve as an independent predictor in predicting HCC recurrence, and that the EGC model could outperform other models. Moreover, we also investigated the cell-type-specific expression patterns of the eight recurrence-related genes in tumor microenvironment using single-cell RNA sequencing data, and interpreted their functional roles from correlation and gene set enrichment analyses, in vitro and in vivo experiments. Particularly, PLCB1 and SLC22A7 were predominantly expressed in malignant cells, and they were predicted to promote angiogenesis and to help maintain normal metabolism in liver, respectively. In contrast, both FASLG and IL2RB were specifically expressed in T cells, and were highly correlated with T cell marker genes, suggesting that these two genes might assist in maintaining normal function of T cell-mediated immune response in tumor tissues.ConclusionIn conclusion, the EGC model and eight identified marker genes could not only facilitate the accurate prediction of HCC recurrence, but also improve our understanding of the mechanisms behind HCC recurrence.

Stationary and Time-Dependent Molecular Distributions in Slow-Fast Feedback Circuits

Biochemical reaction systems, in particular those that govern the expression of genes in single cells, involve discrete numbers of molecules and are inherently stochastic. This study concerns reaction systems with two molecular species and an arbitrary number of reactions, the rates of which conform to a scaling of the classical type in the first species (but not the second). For limiting values of the scaling parameter, the first species is highly abundant, evolves slowly, and buffers the relatively fast fluctuation of the second, scarce, species. The scale separation facilitates the construction of asymptotic approximations to the molecular distributions: the large-time behavior is described by a WKB approximation, which is further resolved into a tractable mixture of metastable modes; the earlier-time dynamics are described by quasi-stationary and linear noise approximations. The paper presents the theory and the algebraic recipes that are required to implement it. The framework is applied on two reaction systems with positive feedback: a delayed feedback circuit and a gene autoregulation model with cooperative binding of the protein to the promoter. For the former, the approximation scheme results into a mixture of Gaussian/Poisson modes for the inactive/active protein distribution. For the latter, the analysis elucidates the effects of bursty gene expression, promoter responsivity, and protein sequestration on bimodality of protein distributions.

Expression Patterns of Energy-Related Genes in Single Cells Uncover Key Isoforms and Enzymes That Gain Priority Under Nanoparticle-Induced Stress.

Cellular responses to nanoparticles (NPs) have been largely studied in cell populations, providing averaged values that often misrepresent the true molecular processes that occur in the individual cell. To understand how a cell redistributes limited molecular resources to achieve optimal response and survival requires single-cell analysis. Here we applied multiplex single molecule-based fluorescence in situ hybridization (fliFISH) to quantify the expression of 10 genes simultaneously in individual intact cells, including glycolysis and glucose transporter genes, which are critical for restoring and maintaining energy balance. We focused on individual gill epithelial cell responses to lithium cobalt oxide (LCO) NPs, which are actively pursued as cathode materials in lithium-ion batteries, raising concerns about their impact on the environment and human health. We found large variabilities in the expression levels of all genes between neighboring cells under the same exposure conditions, from only a few transcripts to over 100 copies in individual cells. Gene expression ratios among the 10 genes in each cell uncovered shifts in favor of genes that play key roles in restoring and maintaining energy balance. Among these genes are isoforms that can secure and increase glycolysis rates more efficiently, as well as genes with multiple cellular functions, in addition to glycolysis, including DNA repair, regulation of gene expression, cell cycle progression, and proliferation. Our study uncovered prioritization of gene expression in individual cells for restoring energy balance under LCO NP exposures. Broadly, our study gained insight into single-cell strategies for redistributing limited resources to achieve optimal response and survival under stress.

Single-cell transcriptomics of human iPSC differentiation dynamics reveal a core molecular network of Parkinson\u2019s disease

Parkinson’s disease (PD) is the second-most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. The underlying mechanisms are only partly understood and there is no treatment to reverse PD progression. Here, we investigated the disease mechanism using mDA neurons differentiated from human induced pluripotent stem cells (hiPSCs) carrying the ILE368ASN mutation within the PINK1 gene, which is strongly associated with PD. Single-cell RNA sequencing (RNAseq) and gene expression analysis of a PINK1-ILE368ASN and a control cell line identified genes differentially expressed during mDA neuron differentiation. Network analysis revealed that these genes form a core network, members of which interact with all known 19 protein-coding Parkinson’s disease-associated genes. This core network encompasses key PD-associated pathways, including ubiquitination, mitochondrial function, protein processing, RNA metabolism, and vesicular transport. Proteomics analysis showed a consistent alteration in proteins of dopamine metabolism, indicating a defect of dopaminergic metabolism in PINK1-ILE368ASN neurons. Our findings suggest the existence of a network onto which pathways associated with PD pathology converge, and offers an inclusive interpretation of the phenotypic heterogeneity of PD.

Single-Cell Visualization of Monogenic RNA G-quadruplex and Occupied G-quadruplex Ratio through a Module-Assembled Multifunctional Probes Assay (MAMPA).

G-quadruplexes (G4s), non-canonical nucleic acid secondary structure, regulate many biological functions and are considered potential molecular targets for cancer therapeutics. However, due to the lack of analytical methods, the regulating mechanism of monogenic G4s is still unclear. Here, we developed a Module Assembled Multifunctional Probes Assay (MAMPA) for visualizing endogenous G4s in individual genes in single cells. Two modular probes separately recognize G4 structures and the adjacent RNA sequences, and the module assembly enables imaging of G4s in an individual RNA with high specificity. Through imaging G4s in several individual genes, we found that G4s were steadily occupied by G4 Binding Proteins (G4BPs) in various mRNAs in every cell line and defined "Occupied G4 Ratio". We demonstrated MAMPA was suitable for most experimental situations and found that Occupied G4 Ratios had the potential to become a new parameter for the study of G4s in living cells.

Single‐Cell Visualization of Monogenic RNA G‐quadruplex and Occupied G‐quadruplex Ratio through a Module‐Assembled Multifunctional Probes Assay (MAMPA)

AbstractG‐quadruplexes (G4s), non‐canonical nucleic acid secondary structure, regulate many biological functions and are considered potential molecular targets for cancer therapeutics. However, due to the lack of analytical methods, the regulating mechanism of monogenic G4s is still unclear. Here, we developed a Module Assembled Multifunctional Probes Assay (MAMPA) for visualizing endogenous G4s in individual genes in single cells. Two modular probes separately recognize G4 structures and the adjacent RNA sequences, and the module assembly enables imaging of G4s in an individual RNA with high specificity. Through imaging G4s in several individual genes, we found that G4s were steadily occupied by G4 Binding Proteins (G4BPs) in various mRNAs in every cell line and defined “Occupied G4 Ratio”. We demonstrated MAMPA was suitable for most experimental situations and found that Occupied G4 Ratios had the potential to become a new parameter for the study of G4s in living cells.