scholarly journals Using array hybridization to monitor gene expression at the single cell level

2002 ◽  
Vol 53 (379) ◽  
pp. 2315-2323 ◽  
Author(s):  
S. Brandt
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Array Hybridization

Unmasking cellular response of a bloom-forming alga to virus infection by resolving expression profiling at a single-cell level

2017 ◽  
Author(s):  
Shilo Rosenwasser ◽  
Miguel J. Frada ◽  
David Pilzer ◽  
Ron Rotkopf ◽  
Assaf Vardi
Keyword(s):  
Gene Expression ◽  
Viral Infection ◽  
Single Cell ◽  
Host Defense ◽  
Host Response ◽  
Expression Profiles ◽  
Life Cycles ◽  
Viral Gene ◽  
Single Cell Level ◽  
Cell Level

AbstractMarine viruses are major evolutionary and biogeochemical drivers of microbial life in the ocean. Host response to viral infection typically includes virus-induced rewiring of metabolic network to supply essential building blocks for viral assembly, as opposed to activation of anti-viral host defense. Nevertheless, there is a major bottleneck to accurately discern between viral hijacking strategies and host defense responses when averaging bulk population response. Here we use Emiliania huxleyi, a bloom-forming alga and its specific virus (EhV), as one of the most ecologically important host-virus model system in the ocean. Using automatic microfluidic setup to capture individual algal cells, we quantified host and virus gene expression on a single-cell resolution during the course of infection. We revealed high heterogeneity in viral gene expression among individual cells. Simultaneous measurements of expression profiles of host and virus genes at a single-cell level allowed mapping of infected cells into newly defined infection states and uncover a yet unrecognized early phase in host response that occurs prior to viral expression. Intriguingly, resistant cells emerged during viral infection, showed unique expression profiles of metabolic genes which can provide the basis for discerning between viral resistant and sensitive cells within heterogeneous populations in the marine environment. We propose that resolving host-virus arms race at a single-cell level will provide important mechanistic insights into viral life cycles and will uncover host defense strategies.

Circulating melanoma cells isolated from clinical blood samples and characterized by full-length mRNA sequencing at single-cell level.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10539-10539 ◽  
Author(s):  
Yu-Chieh Wang ◽  
Daniel Ramskold ◽  
Shujun Luo ◽  
Robin Li ◽  
Qiaolin Deng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Melanoma Cells ◽  
Clinical Samples ◽  
Single Cell Level ◽  
Cell Level ◽  
Blood Samples

10539 Background: Melanoma is the most aggressive type of skin cancer. Late-stage melanoma is highly metastatic and currently lacks effective treatment. This discouraging clinical observation highlights the need for a better understanding of the molecular mechanisms underlying melanoma initiation and progression and for developing new therapeutic approaches based on novel targets. Although genome-wide transcriptome analyses have been frequently used to study molecular alterations in clinical samples, it has been technically challenging to obtain the transcriptomic profiles at single-cell level. Methods: Using antibody-mediated magnetic activated cell separation (MACS), we isolated and individualized putative circulating melanoma cells (CMCs) from the blood samples of the melanoma patients at advance stages. The transcriptomic analysis based on a novel and robust mRNA-Seq protocol (Smart-Seq) was established and applied to the putative CMCs for single-cell profiling. Results: We have discovered distinct gene expression patterns, including new putative markers for CMCs. Meanwhile, the gene expression profiles derived of the CMC candidates isolated from the patient’s blood samples are closely-related to the expression profiles of other cells originated from human melanocytes, including normal melanocytes in primary culture and melanoma cell lines. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which provides advantage for better analyzing transcript isoforms and SNPs. Conclusions: Our results suggest that the techniques developed in this research for cell isolation and transcriptomic analyses can potentially be used for addressing many biological and clinical questions requiring genomewide transcriptome profiling in rare cells.

scholarly journals A bead-based microfluidic approach to integrated single-cell gene expression analysis by quantitative RT-PCR

RSC Advances ◽  
2015 ◽  
Vol 5 (7) ◽  
pp. 4886-4893 ◽  
Author(s):  
Hao Sun ◽  
Tim Olsen ◽  
Jing Zhu ◽  
Jianguo Tao ◽  
Brian Ponnaiya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cell Level ◽  
Rt Pcr ◽  
Cell Level ◽  
P Gene ◽  
Cell Gene Expression ◽  
Cell Gene

Gene expression analysis at the single-cell level is critical to understanding variations among cells in heterogeneous populations.

Visualizing interleukin 2 gene expression at the single cell level

1989 ◽  
Vol 19 (9) ◽  
pp. 1619-1624 ◽  
Author(s):  
Dominique Emilie ◽  
Michel Peuchmaur ◽  
Marc Barad ◽  
HéLèNe Jouin ◽  
Marie-Christine Maillot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Interleukin 2 ◽  
Single Cell Level ◽  
Cell Level

scholarly journals In situelectro-sequencing in three-dimensional tissues

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

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.

scholarly journals Nanopore Long-Read RNAseq Reveals Widespread Transcriptional Variation Among the Surface Receptors of Individual B cells

2017 ◽  
Author(s):  
Ashley Byrne ◽  
Anna E. Beaudin ◽  
Hugh E. Olsen ◽  
Miten Jain ◽  
Charles Cole ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Surface Receptors ◽  
B1a Cells ◽  
A Genome ◽  
Long Read ◽  
Gene Expression Quantification ◽  
Expression Quantification

ABSTRACTUnderstanding gene regulation and function requires a genome-wide method capable of capturing both gene expression levels and isoform diversity at the single cell level. Short-read RNAseq, while the current standard for gene expression quantification, is limited in its ability to resolve complex isoforms because it fails to sequence full-length cDNA copies of RNA molecules. Here, we investigated whether RNAseq using the long-read single-molecule Oxford Nanopore MinION sequencing technology (ONT RNAseq) would be able to identify and quantify complex isoforms without sacrificing accurate gene expression quantification. After successfully benchmarking our experimental and computational approaches on a mixture of synthetic transcripts, we analyzed individual murine B1a cells using a new cellular indexing strategy. Using the Mandalorion analysis pipeline we developed, we identified thousands of unannotated transcription start and end sites, as well as hundreds of alternative splicing events in these B1a cells. We also identified hundreds of genes expressed across B1a cells that displayed multiple complex isoforms, including several B cell specific surface receptors and the antibody heavy chain (IGH) locus. Our results show that not only can we identify complex isoforms, but also quantify their expression, at the single cell level.

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