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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Yeast Signaling01:28

Yeast Signaling

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Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...
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Transcription01:10

Transcription

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Overview
Transcription is the process of synthesizing RNA from a DNA sequence by RNA polymerase. It is the first step in producing a protein from a gene sequence. Additionally, many other proteins and regulatory sequences are involved in the proper synthesis of messenger RNA (mRNA). Regulation of transcription is responsible for the differentiation of all the different types of cells and often for the proper cellular response to environmental signals.
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations.

Mariona Nadal-Ribelles1,2,3,4, Saiful Islam1,2, Wu Wei1,2,5

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We developed yeast single-cell RNA sequencing (yscRNA-seq) to analyze microbial populations. This method reveals gene expression variability and its role in cellular adaptation and fitness.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Microbiology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
  • Few scRNA-seq methods exist for microbial clonal populations due to unique genomic features like dense transcript spacing and overlapping transcription.
  • Yeast presents challenges for RNA analysis due to its resilient cell wall and complex RNA pool.

Purpose of the Study:

  • To develop a sensitive, scalable, and inexpensive method for yeast single-cell RNA sequencing (yscRNA-seq).
  • To digitally count transcript start sites in a strand- and isoform-specific manner.
  • To investigate gene expression patterns and their functional implications in clonal yeast populations.

Main Methods:

  • Developed yeast single-cell RNA sequencing (yscRNA-seq) for digital transcript counting.
  • Ensured strand- and isoform-specific detection of RNA.
  • Combined yscRNA-seq with index sorting to correlate cell size and RNA content.

Main Results:

  • YscRNA-seq detects low-abundance noncoding RNAs and over half the protein-coding genes per cell.
  • Observed negative correlation for sense-antisense pairs and co-expression of paralogs and divergent transcripts in clonal cells.
  • Found a linear relationship between cell size and RNA content, with high variability in metabolic gene expression.

Conclusions:

  • Stochastic expression of metabolic genes primes yeast cells for environmental challenges, enhancing fitness.
  • Functional transcript diversity provides a selective advantage within transcriptionally heterogeneous populations.
  • YscRNA-seq offers a powerful tool for dissecting cellular heterogeneity in microbial systems.