scholarly journals Analysis of Phase-Specific Gene Expression at the Single-Cell Level in the White-Opaque Switching System ofCandida albicans

2001 ◽  
Vol 183 (12) ◽  
pp. 3761-3769 ◽  
Author(s):  
Anja Strauß ◽  
Sonja Michel ◽  
Joachim Morschhäuser
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Reporter Gene ◽  
Single Cells ◽  
Phenotypic Switching ◽  
Specific Gene ◽  
Single Cell Level ◽  
Switching System ◽  
Cell Type ◽  
Cell Level

ABSTRACT The opportunistic fungal pathogen Candida albicanscan switch spontaneously and reversibly between different cell forms, a capacity that may enhance adaptation to different host niches and evasion of host defense mechanisms. Phenotypic switching has been studied intensively for the white-opaque switching system of strain WO-1. To facilitate the molecular analysis of phenotypic switching, we have constructed homozygous ura3 mutants from strain WO-1 by targeted gene deletion. The two URA3 alleles were sequentially inactivated using theMPA R -flipping strategy, which is based on the selection of integrative transformants carrying a mycophenolic acid (MPA) resistance marker that is subsequently deleted again by site-specific, FLP-mediated recombination. To investigate a possible cell type-independent switching in the expression of individual phase-specific genes, two different reporter genes that allowed the analysis of gene expression at the single-cell level were integrated into the genome, using URA3 as a selection marker. Fluorescence microscopic analysis of cells in which aGFP reporter gene was placed under the control of phase-specific promoters demonstrated that the opaque-phase-specificSAP1 gene was detectably expressed only in opaque cells and that the white-phase-specific WH11 gene was detectably expressed only in white cells. WhenMPA R was used as a reporter gene, it conferred an MPA-resistant phenotype on opaque but not white cells in strains expressing it from the SAP1 promoter, which was monitored at the level of single cells by a significantly enlarged size of the corresponding colonies on MPA-containing indicator plates. Similarly, white but not opaque cells became MPA resistant whenMPA R was placed under the control of the WH11 promoter. The analysis of these reporter strains showed that cell type-independent phase variation in the expression of the SAP1 and WH11 genes did not occur at a detectable frequency. The expression of these phase-specific genes of C. albicans in vitro, therefore, is tightly linked to the cell type.

scholarly journals Generation of genetic tools for gauging multiple gene expression at single cell level

2021 ◽  
Author(s):  
Marta Mellini ◽  
Massimiliano Lucidi ◽  
Francesco Imperi ◽  
Paolo Visca ◽  
Livia Leoni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Single Cell ◽  
Fluorescent Protein ◽  
Single Cells ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Multiple Gene ◽  
Genetic Tools

Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple gene expression at the single cell level has limited the understanding of phenotypic heterogeneity. In order to investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple gene expression at the single cell level has been generated. This tool, named pRGC, consists in a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP) and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population, without the need of image-processing for spectral crosstalk correction. In addition, two pRGC variants have been generated for either i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome, that is suitable for long-term experiments in the absence of antibiotic selection, or ii) replication in bacterial genera other than Pseudomonas. The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity. IMPORTANCE Within a bacterial population single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial sub-populations with distinct phenotypes. The analysis of gene expression at the single cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple gene expression at the single cell level by fluorescent microscopy, without the need of advanced image-processing procedures. A proof of concept has been provided by investigating iron-uptake and iron-storage gene expression in response to iron availability in P. aeruginosa.

Reporter gene expression at single-cell level characterized with real-time RT-PCR, chemiluminescence, fluorescence, and electrochemical imaging

FEBS Letters ◽  
2010 ◽  
Vol 584 (18) ◽  
pp. 4000-4008 ◽  
Author(s):  
Hitoshi Shiku ◽  
Daisuke Okazaki ◽  
Junya Suzuki ◽  
Yasufumi Takahashi ◽  
Tatsuya Murata ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Reporter Gene ◽  
Reporter Gene Expression ◽  
Single Cell Level ◽  
Rt Pcr ◽  
Cell Level ◽  
Electrochemical Imaging

scholarly journals Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zi-Hang Wen ◽  
Jeremy L. Langsam ◽  
Lu Zhang ◽  
Wenjun Shen ◽  
Xin Zhou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Group ◽  
Specific Gene ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Expression Matrix ◽  
Study Gene Expression ◽  
Expression Matrix

AbstractSingle-cell RNA-seq (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene or cell related information that users provide to increase the performance. Availability: Bfimpute is implemented in R and is freely available at https://github.com/maiziezhoulab/Bfimpute.

scholarly journals Gene Expression Profiling of CD93-Selected CP-CML Stem Cells Confirms Their Quiescent Character and Biomarker Potential

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4231-4231
Author(s):  
Gillian A. Horne ◽  
Chinmay Rajiv Munje ◽  
Ross Kinstrie ◽  
Eduardo Gómez-Castañeda ◽  
Helen Wheadon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Single Cells ◽  
Gene Clustering ◽  
Single Cell Level ◽  
Cell Level ◽  
Advisory Committees ◽  
Two Populations

Abstract The introduction of BCR-ABL tyrosine kinase inhibitors has revolutionized the treatment of chronic myeloid leukemia (CML). A major clinical aim remains the identification and elimination of low-level disease persistence, termed "minimal residual disease". Disease persistence suggests, that despite targeted therapeutic approaches, BCR-ABL-independent mechanisms exist which sustain the survival of a small population of cells, termed leukemic stem cells (LSC). We previously identified CD93 expression as a promising biomarker of LSC in chronic phase (CP)-CML. Our group has described the long term self-renewal potential of Lin-CD34+93+ CP-CML cells compared to their Lin-CD34+93- counterparts through LTCIC assays (n=3, p<0.0001) and NSG engraftment models (3.5-30-fold increased in engraftment with Lin-CD34+93+ cells, p<0.03). We hypothesized that CD93+-selected cells would represent a more immature functional phenotype compared to CD93- selected cells. The aim of this study was to characterize differences in the gene expression profile between CD93+ and CD93- CML LSC populations and determine heterogeneity of each population at a single cell level. To interrogate this, we initially identified CP-CML subpopulations with the greatest functional capability compared to normal. Normal and CP-CML samples were FACS-sorted into HSC/LSC, CMP, GMP, and MEP sub-populations. Results suggest a significant change in functional status between normal and CP-CML subpopulations within the HSC/LSC compartment (lin-CD34+CD38-CD45RA-CD90+), where CML LSC demonstrated significantly increased proliferation (14 fold expansion; P<0.001) compared to normal HSC (no expansion) after 5 days in vitro culture in physiological growth factors. In addition, equivalent numbers of CML LSC produce ~4-fold more colonies in colony forming cell (CFC) assays than normal HSC (329±56 versus 86±17 per 2,000 cells, respectively (p<0.05)). Furthermore, fluorescence in situ hybridization demonstrated that >90% of lin-CD34+CD38-CD45RA-CD90+ CML LSC from all patient samples were BCR-ABL positive. Subsequent experiments were confined to the LSC population. We hypothesized that lin-CD34+CD38-CD90+CD93- CML cells would have a more mature gene expression profile compared to lin-CD34+CD38-CD90+CD93+ cells. CP-CML cells were sorted into (1) lin-CD34+, (2) lin-CD34+CD38-CD90+CD93- and (3) lin-CD34+CD38-CD90+CD93+ populations. RNA was harvested at baseline from bulk populations (1) to (3) and cDNA was generated from single cells using the Fluidigm C1 autoprep system. Using Fluidigm technology, quantitative PCR of 90 lineage-specific and cell survival genes was performed within all populations of cells (1) to (3) in 'bulk' samples (n=3), and at single cell level (n=123 CD93+, n=120 CD93-single cells; n=3 samples in total). Bulk sample analysis demonstrated a significant increase in expression of lineage commitment genes within the lin-CD34+CD38-CD90+CD93- population, as shown by increased expression of GATA1 (p=0.0007), and CBX8 (p=0.0002). The lin-CD34+CD38-CD90+CD93+ population displayed a less lineage-restricted profile with increased expression of CDK6 (p=0.05), HOXA6 (ns), CDKN1C (ns) and CKIT (p=0.0014), compared to the lin-CD34+CD38-CD90+CD93- population. Furthermore, the two populations could be segregated by differential gene expression through gene clustering. At a single cell level, differences were noted in the frequency of expression between lin-CD34+CD38-CD90+CD93- and lin-CD34+CD38-CD90+CD93+ populations, particularly in GATA1, TPOR, and VWF. Although a statistically significant change was demonstrated in gene expression between the lin-CD34+CD38-CD90+CD93- and lin-CD34+CD38-CD90+CD93+ populations in a number of genes, we were not able to segregate the populations by differential expression using gene clustering. This highlights the heterogeneous nature of the cell populations and the inability to distinctly characterize between the two populations at a single cell level. Our results validate CD93 as a potential biomarker to separate the primitive CP-CML LSC population and highlight key lineage and cell survival pathways that are altered in CML LSC. The results demonstrate the heterogeneity seen within gene expression at the single cell level, which may allow for further insight into the CML LSC compartment with further analyses. Disclosures Wheadon: GlaxoSmithKline: Research Funding. Copland:Shire: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; ARIAD: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria.

scholarly journals Tracking single-cell gene regulation in dynamically controlled environments using an integrated microfluidic and computational setup

2016 ◽  
Author(s):  
Matthias Kaiser ◽  
Florian Jug ◽  
Olin Silander ◽  
Siddharth Deshpande ◽  
Thomas Pfohl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Environmental Conditions ◽  
Single Cells ◽  
Fine Tuning ◽  
Growth Media ◽  
Single Cell Level ◽  
Cell Level ◽  
Operon Regulation

AbstractBacteria adapt to changes in their environment by regulating gene expression, often at the level of transcription. However, since the molecular processes underlying gene regulation are subject to thermodynamic and other stochastic fluctuations, gene expression is inherently noisy, and identical cells in a homogeneous environment can display highly heterogeneous expression levels. To study how stochasticity affects gene regulation at the single-cell level, it is crucial to be able to directly follow gene expression dynamics in single cells under changing environmental conditions. Recently developed microfluidic devices, used in combination with quantitative fluorescence time-lapse microscopy, represent a highly promising experimental approach, allowing tracking of lineages of single cells over long time-scales while simultaneously measuring their growth and gene expression. However, current devices do not allow controlled dynamical changes to the environmental conditions which are needed to study gene regulation. In addition, automated analysis of the imaging data from such devices is still highly challenging and no standard software is currently available. To address these challenges, we here present an integrated experimental and computational setup featuring, on the one hand, a new dual-input microfluidic chip which allows mixing and switching between two growth media and, on the other hand, a novel image analysis software which jointly optimizes segmentation and tracking of the cells and allows interactive user-guided fine-tuning of its results. To demonstrate the power of our approach, we study the lac operon regulation in E. coli cells grown in an environment that switches between glucose and lactose, and quantify stochastic lag times and memory at the single cell level.

TAMI-68. DECONVOLUTION OF IMMUNOTHERAPY-TREATED GLIOBLASTOMA IDENTIFIES CELLULAR HETEROGENEITY AND PLASTICITY AT THE SINGLE-CELL LEVEL

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi212-vi212
Author(s):  
Josephine Hendriksen ◽  
Aidan Flynn ◽  
Simone Maarup ◽  
Hans Poulsen ◽  
Ulrik Lassen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Checkpoint ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Molecular Heterogeneity ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Cell Potential

Abstract Glioblastoma is the most aggressive cancer originating in the brain with an average survival of 15 months. One of the characteristics of glioblastoma is the high level of intra-tumor heterogeneity (ITH), but the composition and complexity at the single-cell level is poorly understood. Here, we aimed to assess the effects and consequences of immune checkpoint inhibitor (ICI) on the cellular and molecular heterogeneity of glioblastoma tumors at the single cell level. In collaboration with the phase I trials unit at Rigshospitalet, we performed paired molecular analysis of glioma cells from primary and relapse surgery after ICI treatment. Samples were analyzed using single-cell RNA sequencing (scRNA-seq) as well as bulk RNA sequencing and whole exome DNA sequencing. In an effort to trace cellular lineages we developed and refined methods to a identify copy number changes using scRNA-seq. To this end, we identified clonal and subclonal tumor cell populations in each sample. We found high levels of ITH prior to treatment, both with respect to the glioblastoma subtype enrichment and the cell type-specific gene expression. Using expression-based cell-type classification, we found defined recurrent cell-type populations present at both surgery time points. The immune checkpoint treatment had consequences on the cellular phenotypes and proportions of tumor cells, suggesting a level of plasticity in the neoplastic cells. Moreover, we identified examples of clonal dynamics and sweeps following ICI treatment, pointing to potential treatment response and resistance in these populations. We additionally found a recurrent pattern of a small population showing high levels of cell cycle activation and simultaneously expressing markers of stem cell potential. In summary, we pursued single cell-focused analysis of ICI treated glioblastoma patients to study the cellular and molecular heterogeneity within and between glioblastoma patients, which pointed to recurrent patterns of cellular responses following ICI treatment.

scholarly journals High-throughput imaging of mRNA at the single-cell level in human primary immune cells

2020 ◽  
Author(s):  
Manasi Gadkari ◽  
Jing Sun ◽  
Adrian Carcamo ◽  
Hugh Alessi ◽  
Zonghui Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cells ◽  
Single Cells ◽  
Transcript Abundance ◽  
Hybridization Chain Reaction ◽  
Single Cell Level ◽  
Cell Level ◽  
Experimental Conditions ◽  
General Applicability

AbstractMeasurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

scholarly journals Discovering single-cell eQTLs from scRNA-seq data only

2021 ◽  
Author(s):  
Tianxing Ma ◽  
Haochen Li ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Negative Binomial ◽  
Single Cells ◽  
Single Cell Level ◽  
Cell Type ◽  
Paired Data ◽  
Cell Level ◽  
Specific Tumor ◽  
Cellular Context ◽  
Cell Type Specific

eQTL studies are essential for understanding genomic regulation. Effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a unique powerful tool for utilizing the huge scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.

scholarly journals Single-cell alternative polyadenylation analysis delineates GABAergic neuron types

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yang Yang ◽  
Anirban Paul ◽  
Thao Nguyen Bach ◽  
Z. Josh Huang ◽  
Michael Q. Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulation Of Gene Expression ◽  
Alternative Polyadenylation ◽  
Gabaergic Neuron ◽  
Gabaergic Interneurons ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Cell Type Specific

Abstract Background Alternative polyadenylation (APA) is emerging as an important mechanism in the post-transcriptional regulation of gene expression across eukaryotic species. Recent studies have shown that APA plays key roles in biological processes, such as cell proliferation and differentiation. Single-cell RNA-seq technologies are widely used in gene expression heterogeneity studies; however, systematic studies of APA at the single-cell level are still lacking. Results Here, we described a novel computational framework, SAPAS, that utilizes 3′-tag-based scRNA-seq data to identify novel poly(A) sites and quantify APA at the single-cell level. Applying SAPAS to the scRNA-seq data of phenotype characterized GABAergic interneurons, we identified cell type-specific APA events for different GABAergic neuron types. Genes with cell type-specific APA events are enriched for synaptic architecture and communications. In further, we observed a strong enrichment of heritability for several psychiatric disorders and brain traits in altered 3′ UTRs and coding sequences of cell type-specific APA events. Finally, by exploring the modalities of APA, we discovered that the bimodal APA pattern of Pak3 could classify chandelier cells into different subpopulations that are from different laminar positions. Conclusions We established a method to characterize APA at the single-cell level. When applied to a scRNA-seq dataset of GABAergic interneurons, the single-cell APA analysis not only identified cell type-specific APA events but also revealed that the modality of APA could classify cell subpopulations. Thus, SAPAS will expand our understanding of cellular heterogeneity.

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