A study published in Science (2010) quantifies the E. coli proteome and transcriptome with single-molecule sensitivity in single cells. The researchers developed a yellow fluorescent protein (YFP) fusion library for E. coli, enabling the detection of protein and mRNA levels in individual cells. They found that protein number distributions follow a gamma distribution, with parameters that can be interpreted as transcription rate and burst size at low expression levels. At high expression levels, distributions are dominated by extrinsic noise. The study also found that protein and mRNA copy numbers for any given gene in a single cell are uncorrelated. The researchers used single-molecule fluorescence microscopy to image YFP reporter strains, allowing them to measure protein abundance and noise. They observed that protein levels vary widely, from 10^-1 to 10^4 molecules per cell. Essential genes have higher expression levels than non-essential genes. The study also found that mRNA copy numbers range from 0.05 to 5 per cell, with a moderate correlation between mRNA and protein levels. The researchers performed single-molecule RNA counting using fluorescence in situ hybridization (FISH) with a single universal Atto594-labeled 20-mer oligonucleotide probe. They found that mRNA noise scales inversely with mRNA mean number, and that mRNA Fano factors are close to 1.6, indicating non-Poissonian production or degradation. The study also examined the correlation between mRNA and protein levels in the same cells. They found that mRNA and protein levels are uncorrelated, suggesting a disconnect between proteome and transcriptome analyses. The researchers also found that expression properties are correlated with biological factors, such as gene characteristics and chromosome position. The study compared E. coli and yeast, finding that E. coli proteins generally have larger noise and Fano factors than yeast proteins. The results highlight the importance of single-cell proteome analysis for understanding stochastic gene expression and regulation. The study provides a quantitative and integral account of single-cell gene expression profiles.A study published in Science (2010) quantifies the E. coli proteome and transcriptome with single-molecule sensitivity in single cells. The researchers developed a yellow fluorescent protein (YFP) fusion library for E. coli, enabling the detection of protein and mRNA levels in individual cells. They found that protein number distributions follow a gamma distribution, with parameters that can be interpreted as transcription rate and burst size at low expression levels. At high expression levels, distributions are dominated by extrinsic noise. The study also found that protein and mRNA copy numbers for any given gene in a single cell are uncorrelated. The researchers used single-molecule fluorescence microscopy to image YFP reporter strains, allowing them to measure protein abundance and noise. They observed that protein levels vary widely, from 10^-1 to 10^4 molecules per cell. Essential genes have higher expression levels than non-essential genes. The study also found that mRNA copy numbers range from 0.05 to 5 per cell, with a moderate correlation between mRNA and protein levels. The researchers performed single-molecule RNA counting using fluorescence in situ hybridization (FISH) with a single universal Atto594-labeled 20-mer oligonucleotide probe. They found that mRNA noise scales inversely with mRNA mean number, and that mRNA Fano factors are close to 1.6, indicating non-Poissonian production or degradation. The study also examined the correlation between mRNA and protein levels in the same cells. They found that mRNA and protein levels are uncorrelated, suggesting a disconnect between proteome and transcriptome analyses. The researchers also found that expression properties are correlated with biological factors, such as gene characteristics and chromosome position. The study compared E. coli and yeast, finding that E. coli proteins generally have larger noise and Fano factors than yeast proteins. The results highlight the importance of single-cell proteome analysis for understanding stochastic gene expression and regulation. The study provides a quantitative and integral account of single-cell gene expression profiles.