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Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs.

Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra GJ, Vermeulen M - Nucleic Acids Res. (2014)

Bottom Line: While recent developments in genomic sequencing technology have enabled comprehensive transcriptome analyses of single cells, single cell proteomics has thus far been restricted to targeted studies.Absolute protein amounts in single eggs are highly consistent, thus indicating a tight regulation of global protein abundance.Comparison between the single-cell proteome and transcriptome reveal poor expression correlation.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands Cancer Genomics Netherlands, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.

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Comparison of single-cell proteomes and transcriptomes. (A) Scatterplot of the mean protein and mRNA levels for genes quantified in all individual proteomes and transcriptomes (4798 genes) reveals a poor correlation between protein and mRNA abundance. (B) Unsupervised correlation-based clustering of global proteomes (left panel) and transcriptomes (right panel) indicate tight regulation of global proteome and transcriptome. (C) Scatterplot of protein and mRNA CV (4798 genes) reveals a lack of correlation between protein and mRNA variation. Genes are colored that show similar mRNA and protein variation (green, high variation; blue, low variation) and that show differential mRNA and protein variation (red, low protein and high mRNA variation; orange, high protein and low mRNA variation). Genes outside these categories are in black. (D) Enriched GO terms for the gene groups as depicted in C. Y-axis represents the –log FDR. See also Supplementary Table S4.
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Figure 3: Comparison of single-cell proteomes and transcriptomes. (A) Scatterplot of the mean protein and mRNA levels for genes quantified in all individual proteomes and transcriptomes (4798 genes) reveals a poor correlation between protein and mRNA abundance. (B) Unsupervised correlation-based clustering of global proteomes (left panel) and transcriptomes (right panel) indicate tight regulation of global proteome and transcriptome. (C) Scatterplot of protein and mRNA CV (4798 genes) reveals a lack of correlation between protein and mRNA variation. Genes are colored that show similar mRNA and protein variation (green, high variation; blue, low variation) and that show differential mRNA and protein variation (red, low protein and high mRNA variation; orange, high protein and low mRNA variation). Genes outside these categories are in black. (D) Enriched GO terms for the gene groups as depicted in C. Y-axis represents the –log FDR. See also Supplementary Table S4.

Mentions: Raw mass spectrometry data were analyzed using MaxQuant (37), using default settings and with the algorithms ‘match between runs’ and ‘iBAQ’ enabled, and searched against the in-house generated database. The resulting identified proteins were filtered for contaminants and reverse hits. Proteins detected in the data from the unfractionated samples were filtered to be at least quantified in all nine triplicates (1670 proteins, Figure 1). Proteins detected in the quintuplicate egg analysis had to be quantified in all five replicates (5837 proteins, Figure 2) and for the comparison with mRNA levels the genes also needed to be quantified in all mRNA replicates (4612 proteins, Figure 3). For the combined egg and embryo analysis, proteins had to be quantified in either all replicates of the egg or all replicates of the embryo (5658 proteins, Figure 4). Missing values were semi-random imputed using Perseus (default settings, MaxQuant software package), based on the assumption that they were not detected because they were under or close to the detection limit. Unsupervised correlation-based clustering was performed in R. Identification of significant regulated proteins between egg and embryo 10.5 was done using an adapted t-test that is corrected for multiple testing by permutation-based estimation of the false discovery rate (FDR; default settings, Perseus of the MaxQuant software package). Gene ontology (GO) term enrichment were calculated with DAVID (38) for clusters of genes against the background of proteins identified and quantified in the corresponding sample. All proteomics data are available on the PRIDE repository (PXD000902).


Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs.

Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra GJ, Vermeulen M - Nucleic Acids Res. (2014)

Comparison of single-cell proteomes and transcriptomes. (A) Scatterplot of the mean protein and mRNA levels for genes quantified in all individual proteomes and transcriptomes (4798 genes) reveals a poor correlation between protein and mRNA abundance. (B) Unsupervised correlation-based clustering of global proteomes (left panel) and transcriptomes (right panel) indicate tight regulation of global proteome and transcriptome. (C) Scatterplot of protein and mRNA CV (4798 genes) reveals a lack of correlation between protein and mRNA variation. Genes are colored that show similar mRNA and protein variation (green, high variation; blue, low variation) and that show differential mRNA and protein variation (red, low protein and high mRNA variation; orange, high protein and low mRNA variation). Genes outside these categories are in black. (D) Enriched GO terms for the gene groups as depicted in C. Y-axis represents the –log FDR. See also Supplementary Table S4.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150773&req=5

Figure 3: Comparison of single-cell proteomes and transcriptomes. (A) Scatterplot of the mean protein and mRNA levels for genes quantified in all individual proteomes and transcriptomes (4798 genes) reveals a poor correlation between protein and mRNA abundance. (B) Unsupervised correlation-based clustering of global proteomes (left panel) and transcriptomes (right panel) indicate tight regulation of global proteome and transcriptome. (C) Scatterplot of protein and mRNA CV (4798 genes) reveals a lack of correlation between protein and mRNA variation. Genes are colored that show similar mRNA and protein variation (green, high variation; blue, low variation) and that show differential mRNA and protein variation (red, low protein and high mRNA variation; orange, high protein and low mRNA variation). Genes outside these categories are in black. (D) Enriched GO terms for the gene groups as depicted in C. Y-axis represents the –log FDR. See also Supplementary Table S4.
Mentions: Raw mass spectrometry data were analyzed using MaxQuant (37), using default settings and with the algorithms ‘match between runs’ and ‘iBAQ’ enabled, and searched against the in-house generated database. The resulting identified proteins were filtered for contaminants and reverse hits. Proteins detected in the data from the unfractionated samples were filtered to be at least quantified in all nine triplicates (1670 proteins, Figure 1). Proteins detected in the quintuplicate egg analysis had to be quantified in all five replicates (5837 proteins, Figure 2) and for the comparison with mRNA levels the genes also needed to be quantified in all mRNA replicates (4612 proteins, Figure 3). For the combined egg and embryo analysis, proteins had to be quantified in either all replicates of the egg or all replicates of the embryo (5658 proteins, Figure 4). Missing values were semi-random imputed using Perseus (default settings, MaxQuant software package), based on the assumption that they were not detected because they were under or close to the detection limit. Unsupervised correlation-based clustering was performed in R. Identification of significant regulated proteins between egg and embryo 10.5 was done using an adapted t-test that is corrected for multiple testing by permutation-based estimation of the false discovery rate (FDR; default settings, Perseus of the MaxQuant software package). Gene ontology (GO) term enrichment were calculated with DAVID (38) for clusters of genes against the background of proteins identified and quantified in the corresponding sample. All proteomics data are available on the PRIDE repository (PXD000902).

Bottom Line: While recent developments in genomic sequencing technology have enabled comprehensive transcriptome analyses of single cells, single cell proteomics has thus far been restricted to targeted studies.Absolute protein amounts in single eggs are highly consistent, thus indicating a tight regulation of global protein abundance.Comparison between the single-cell proteome and transcriptome reveal poor expression correlation.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands Cancer Genomics Netherlands, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.

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Related in: MedlinePlus