Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms
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Summary
Using single molecule counting and computational image analysis, it is shown that transcript abundance correlates with cellular volume at the single cell level due to increased global transcription in larger cells due to increased global transcription in larger cells.
- Type
- book
- Published
- 2015-04-09
- Cited by
- 469
- References
- 66
- Access
- Open access
- OpenAlex
- https://openalex.org/W25866248
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15466914
Keywords
CLARITY, Government (linguistics), Flexibility (engineering), Variance (accounting), Business
References
- Detection of individual endogenous RNA transcripts in situ using multiple singly labeled probes.
- Central dogma at the single-molecule level in living cells
- Altered patterns of ribonucleic acid synthesis during the cell cycle: a mechanism compensating for variation in gene concentration.
- Validation of noise models for single-cell transcriptomics
- Single-RNA counting reveals alternative modes of gene expression in yeast
- Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
- Genetic determinants and cellular constraints in noisy gene expression
- Cyclin mRNA Stability Does Not Vary During the Cell Cycle
- Mitochondrial Variability as a Source of Extrinsic Cellular Noise
- Coordinating genome expression with cell size.
- Connecting Variability in Global Transcription Rate to Mitochondrial Variability
- Accounting for technical noise in single-cell RNA-seq experiments
- Reprogramming to a muscle fate by fusion recapitulates differentiation
- Nuclear Lamin-A Scales with Tissue Stiffness and Enhances Matrix-Directed Differentiation
- A coordinated global control over cellular transcription.
- Noise in protein expression scales with natural protein abundance
- Control of Transcription by Cell Size
- Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells
- Transcript Dynamics of Pro-Inflammatory Genes Uncovered by RNA-Seq Analysis of Subcellular RNA Fractions
- Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells
Cited by
- Structure of silent transcription intervals and noise characteristics of mammalian genes
- MYC: connecting selective transcriptional control to global RNA production
- Single molecule approaches for quantifying transcription and degradation rates in intact mammalian tissues.
- Quantitative Microscopy based on Single-Molecule Fluorescence
- Differential context-specific impact of individual core promoter elements on transcriptional dynamics
- Connecting growth with gene expression: of noise and numbers.
- GLOBAL-SCALE ANALYSIS OF THE DYNAMIC TRANSCRIPTIONAL ADAPTATIONS WITHIN SKELETAL MUSCLE DURING HYPERTROPHIC GROWTH
- Targeted Delivery of Gold Nanoparticle Contrast Agents for Reporting Gene Detection by Magnetic Resonance Imaging
- A passive-flow microfluidic device for imaging latent HIV activation dynamics in single T cells
- Full-length single-cell RNA-seq applied to a viral human cancer: applications to HPV expression and splicing analysis in HeLa S3 cells
- The Biosynthetic Basis of Cell Size Control
- Defining cell types and states with single-cell genomics
- Half dozen of one, six billion of the other: What can small- and large-scale molecular systems biology learn from one another?
- Cell-to-cell variability in the propensity to transcribe explains correlated fluctuations in gene expression
- METHOD Open Access
- MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
- Quantitative spatial analysis of transcripts in multinucleate cells using single-molecule FISH
- Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos
- Control of Transcript Variability in Single Mammalian Cells.
- Gene expression variability in clonal populations: Causes and consequences
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