A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards
Open accessAbstract
Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps. We show that the MS signal of histones can be used as a "proteomic ruler" because it is proportional to the amount of DNA in the sample, which in turn depends on the number of cells. As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell. We compare our protein quantifications with values derived via the use of stable isotope labeling by amino acids in cell culture and protein epitope signature tags in a method that combines spike-in protein fragment standards with precise isotope label quantification. The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method. We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements. The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset-even in retrospective analysis-and we demonstrate its usefulness with a series of mouse organ proteomes.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper introduces a label-free “proteomic ruler” that uses histone MS signal as a proxy for DNA amount to estimate total protein per cell and protein copy numbers without spike-in standards or cell counting. It reports agreement with spike-in/SILAC and biochemical estimates across cell lines, plasma, and mouse tissues.
Key findings
- In four human cell lines, histone peptides accounted for 2.07–4.03% of total MS signal; equating that to 6.5 pg DNA per diploid cell gave protein mass per cell within 1.24 ± 0.29-fold of cell-counting values.
- Ribosomal protein signal was 3.61–5.27% of total MS signal, and RNA estimates from this signal were within 1.01 ± 0.13-fold of biochemical RNA measurements.
- Reanalysis of a PrEST-SILAC dataset gave copy numbers deviating on average 1.5-fold from spike-in values; label-free estimates used 19.4 peptides per protein on average versus 4.7 for PrEST-SILAC, and larger deviations occurred when PrEST-SILAC used ≤2 peptides or label-free used ≤11 peptides.
- In mouse organs, ovary cells had 42 pg protein per cell and leg muscle about 675 pg per nucleus; liver averaged 464 ± 35 pg per cell, thymus 59 ± 31 pg, and dendritic cell subtypes 64 ± 14 to 95 ± 25 pg.
Methods
- Samples included human plasma, four cultured human cell lines (U87-MG, A549, PC-3, Hep-G2), and mouse tissues; proteins were processed by FASP with LysC/trypsin digestion, and DNA/RNA were released by RNase/DNase and measured by A260.
- LC-MS/MS used Q Exactive with HCD or LTQ Orbitrap with CID; data analyzed in MaxQuant/Andromeda against human and mouse UniProt proteomes, with 1% FDR and a Perseus plugin for absolute abundance calculations.
- Absolute scaling used the Total Protein Approach and histone proteomic ruler; comparisons were made with cell counting, DNA-based cell number estimates, biochemical RNA/protein measurements, PrEST-SILAC copy numbers, and retrospective dendritic cell proteome data.
Limitations
- The authors state the method requires eukaryotic whole-cell proteome data with unbiased chromatin representation and sufficient depth; histone fraction stabilized only from about 12,000 peptides onward. They also note that in syncytial muscle the ruler yields protein per nucleus rather than per cell.
- The authors acknowledge assumptions that allow omission of spike-ins, and they report typical agreement within about a factor of 2 with precision measurements or literature values.
- Evident from the text: validation is limited to a small set of cell lines, plasma, and selected mouse tissues; accuracy depends on known genome size/ploidy and on the assumed DNA-to-histone mass relationship; the retrospective dendritic cell dataset came from in-gel digestion, which the paper notes is incompatible with direct DNA quantification.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 34 cited sources checked · 0 supported · 0 flagged
- Findingown data
The proteomic ruler method is useful for mouse organ proteomes.
“we demonstrate its usefulness with a series of mouse organ proteomes.”
Quote in the paperSubstantial claim - Findingreasoning
The proteomic ruler method's quantitative results closely match those from a high-precision approach using SILAC, protein epitope signature tags, spiked protein fragment standards, and accurate isotope-label measurement.
“The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method.”
Quote in the paperSubstantial claim - Findingown data
The Total Protein Approach quantified human plasma proteins within expected physiological ranges across at least five orders of magnitude.
“We were able to quantify proteins within their expected physiological ranges over at least 5 orders of magnitude (Fig. 1, supplemental Table S1).”
Quote in the paperSubstantial claim - Findingown data
Including acetylation, phosphorylation, and methylation as variable modifications changed the cumulative histone fraction of total MS signal by only 5% to 10%.
“the fraction of the cumulative histone to total MS signal changed only by 5% to 10% (Fig. 4D).”
Quote in the paperSubstantial claim - Findingown data
Protein mass per cell calculated from DNA-derived cell numbers was very similar to that calculated from counted cell numbers.
“Dividing the total amount of protein input by these cell numbers, we obtained a protein mass per cell that was very similar to that obtained by dividing the total protein input amount by the counted cell numbers (supplemental Table S2).”
Quote in the paperSubstantial claim- Rests on an assumption: The analyzed cell lines have diploid genomes, so their DNA mass per cell is 6.5 pg.
- Findingreasoning
Within the first few thousand identified peptides, the histone fraction of the total MS signal is overestimated because histones contribute some of the most intense peptides.
“Within the first few thousand peptides, the histone fraction is overestimated because histones contribute some of the most intense peptides.”
Quote in the paperSubstantial claim - Findingown data
From a proteomic depth of about 12,000 or more peptides, the histone fraction of the total MS signal stabilizes within tight margins.
“From a depth of around 12,000 or more peptides, however, the histone fraction stabilizes within tight margins.”
Quote in the paperSubstantial claim - Findingown data
The MS-based estimate of cellular RNA amount was within a factor of 1.01 ± 0.13 of the biochemical measurements.
“Our results were within a factor of 1.01 ± 0.13 of the biochemical measurements”
Quote in the paperSubstantial claim - Findingreasoning
Using 6.5 pg as the DNA mass of diploid human cells, cellular protein masses were within a factor of 1.24 ± 0.29 of those from cell counting.
“Equating this fraction with 6.5 pg as the DNA mass of diploid human cells, we obtained cellular protein masses within a factor of 1.24 ± 0.29 compared with the value obtained via cell counting (Fig. 3B; supplemental Table S2).”
Quote in the paperSubstantial claim- Rests on an assumption: The analyzed cell lines have diploid genomes, so their DNA mass per cell is 6.5 pg.
- Findingown data
In the four-cell-line dataset, histone MS signal made up 2.07% to 4.03% of total MS signal.
“In our four-cell-line dataset, the histone MS signal amounted to 2.07% to 4.03% of the total MS signal.”
Quote in the paperSubstantial claim - Findingown data
The amount of eluted nucleotides correlates linearly with the amount of sample.
“We observed a linear correlation between the amount of the eluted nucleotides and the amount of the sample.”
Quote in the paperSubstantial claim - Findingown data
The summed MS signal for all ribosomal proteins was between 3.61% and 5.27% of the total MS signal across the cell lines.
“The summed MS signal for all ribosomal proteins amounted to values between 3.61% and 5.27% of the total MS signal across the cell lines.”
Quote in the paperSubstantial claim - Findingreasoning
The dendritic cell protein amounts translated to diameters of 8.5 to 9.7 μm for spherical cell shapes.
“These values translated to diameters of 8.5 to 9.7 μm for spherical cell shapes”
Quote in the paperNot a substantial claim - Findingown data
The minority of larger deviations occurred exclusively with PrEST-SILAC quantifications based on two or fewer peptides or label-free quantifications based on 11 or fewer peptides.
“we saw that the minority of larger deviations occurred exclusively with PrEST-SILAC quantifications based on two or fewer peptides or label-free quantifications based on 11 or fewer peptides (Fig. 5B).”
Quote in the paperNot a substantial claim - Findingreasoning
The estimated total protein amount per thymocyte is 59 ± 31 pg.
“as compared with our estimate of 59 ± 31 pg.”
Quote in the paperNot a substantial claim - Findingown data
Reanalysis of the same dataset using the proteomic ruler recapitulated the correct scaling of total protein mass.
“We recapitulated not only the correct scaling of the total protein mass”
Quote in the paperSubstantial claim - Findingown data
Histone proteomic-ruler-derived copy numbers of members of the pyruvate dehydrogenase complex and the TRiC chaperone closely match the expected 1:1 stoichiometry among subunits.
“Fig. 5C shows that our histone proteomic-ruler-derived copy numbers of members of the pyruvate dehydrogenase complex and the TRiC chaperone closely match the expected 1:1 stoichiometry among subunits.”
Quote in the paperSubstantial claim- Rests on an assumption: Many obligate protein complexes are well characterized in terms of composition and stoichiometry, with subunits expressed at equimolar levels.
- Findingown data
Proteomic ruler estimates for different dendritic cell subtypes ranged between 64 ± 14 and 95 ± 25 pg total protein per cell.
“our proteomic ruler estimates that ranged between 64 ± 14 and 95 ± 25 pg total protein per cell for the different dendritic cell subtypes (Fig. 3D).”
Quote in the paperSubstantial claim - Findingown data
The precisions of the proteomic ruler and PrEST-SILAC quantifications were comparable, judged by standard deviations from three replicates.
“and comparable precisions judged by the standard deviations from three replicates.”
Quote in the paperNot a substantial claim - Findingown data
Label-free quantification on average used 19.4 peptides along the entire protein length, whereas PrEST-SILAC quantification used 4.7 peptides on average.
“our label-free quantification on average made use of 19.4 peptides along the entire length of the proteins, whereas the PrEST-SILAC quantification used 4.7 peptides on average.”
Quote in the paperSubstantial claim - Findingown data
Total cellular protein mass values derived from the DNA-based method and from the histone proteomic ruler approach showed excellent agreement.
“There was excellent agreement between the total cellular protein mass values derived from the DNA-based method and our histone proteomic ruler approach (Fig. 3C; supplemental Table S3).”
Quote in the paperSubstantial claim - Findingown data
The estimated total protein amount per average liver cell is 464 ± 35 pg.
“This compares to our estimate of 464 ± 35 pg total protein per average liver cell”
Quote in the paperNot a substantial claim - Findingreasoning
The calculated 50 pg of protein per thymocyte from volume and assumed concentration compares with the estimate of 59 ± 31 pg.
“This translates to 50 pg of protein, as compared with our estimate of 59 ± 31 pg.”
Quote in the paperNot a substantial claim - Findingown data
Copy numbers of individual PrEST-quantified proteins were recapitulated within an average deviation of 1.5-fold.
“but also the copy numbers of the individual PrEST-quantified proteins within an average deviation of 1.5-fold (Fig. 5A; supplemental Table S4)”
Quote in the paperSubstantial claim - Findingown data
Leg muscle cells had around 675 pg of protein per nucleus.
“Leg muscle cells, in contrast, had around 675 pg of protein per nucleus.”
Quote in the paperNot a substantial claim - Findingown data
The average molecular mass of a protein is rather similar across tissues, with the notable exception of muscle tissues.
“This number is rather similar across tissues, with the notable exception of muscle tissues.”
Quote in the paperSubstantial claim - Findingown data
Despite large differences in cellular protein amounts, there was much less variation in the dependence of protein abundance on molecular mass, irrespective of tissue origin.
“Despite the huge differences in cellular protein amounts, we observed much less variation in the dependence of the abundance of a protein and its molecular mass, irrespective of the tissue of origin.”
Quote in the paperSubstantial claim - Findingown data
The quantitative results of the proteomic ruler approach are typically within a factor of 2 of precision measurements or literature values.
“We found the quantitative results of our proteomic ruler approach to be typically within a factor of 2 of precision measurements or literature values.”
Quote in the paperSubstantial claim - Premisecitation
Common absolute protein quantification workflows generally need labeled spike-in standards, cell counts, and protein concentration assays.
“Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [9] Schwanhausser, 2011, Global quantification of mammalian gene expression control, Nature source
Not checked in the free run.
- cites [10] Beck, 2011, The quantitative proteome of a human cell line, Mol. Syst. Biol source
Not checked in the free run.
- cites [11] Zeiler, 2012, A protein epitope signature tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines, Mol. Cell. Proteomics source
Not checked in the free run.
- Premisecitation
The DNA amount in a sample is determined by the number of cells.
“which in turn depends on the number of cells.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [23] Papadimitriou, 1993, Measurement of cell numbers in microtiter culture plates using the fluorescent dye Hoechst 33258, J. Immunol. Methods source
Not checked in the free run.
- cites [24] Shimada, 2010, Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells, Plant Methods source
Not checked in the free run.
- cites [25] Silva, 2013, Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines, Anal. Chem source
Not checked in the free run.
- Interpretationcitation
The mass-spectrometry signal from histones is proportional to the DNA amount in the sample.
“We show that the MS signal of histones can be used as a “proteomic ruler” because it is proportional to the amount of DNA in the sample”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29] van Holde, 1989
Not checked in the free run.
- Rests on an assumption: The summed MS signal from all histone-derived peptides is proportional to cumulative histone mass.
- Premisenone given
All methods that convert protein quantities to copies per cell need to know the number of cells analyzed.
“To convert protein quantities to copies per cell, all methods require knowledge of the number of cells used for the analysis.”
Quote in the paperNot a substantial claim - Premisecitation
Dye-based protein assays depend on the reactivity of a few amino acid residues-mainly tryptophan and tyrosine for Lowry and BCA, or a hydrophilic/hydrophobic balance for Bradford.
“Commonly used dye-based protein determination methods rely on the reactivity of few amino acid residues—mainly tryptophan and tyrosine (12) in the case of the Lowry and BCA assays, or a hydrophilic/hydrophobic balance of the proteins in the case of Bradford reagent (13).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [12] Wiechelman, 1988, Investigation of the bicinchoninic acid protein assay: identification of the groups responsible for color formation, Anal. Biochem source
Not checked in the free run.
- cites [13] Fountoulakis, 1992, Comparison of the Coomassie Brilliant Blue, bicinchoninic acid and Lowry quantitation assays, using non-glycosylated and glycosylated proteins, J. Biochem. Biophys. Methods source
Not checked in the free run.
- Premisereasoning
Accurate protein concentration measurement is a key and often limiting factor for overall accuracy.
“Accurate protein concentration measurements are thus an essential and often limiting factor for overall accuracy.”
Quote in the paperSubstantial claim- Rests on an assumption: Protein determination reagents can cross-react with non-protein cell components such as thiols, nucleic acids, and phospholipids.
- Interpretationreasoning
Accurate DNA content measurement in a proteomic sample can directly determine cell number.
“We show that an accurate determination of the DNA content in a proteomic sample helps to directly determine the number of cells.”
Quote in the paperSubstantial claim - Interpretationreasoning
The proteomic ruler method can be applied to any deep eukaryotic proteome dataset, including retrospective analysis.
“The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset—even in retrospective analysis”
Quote in the paperSubstantial claim - Premisecitation
The Total Protein Approach estimates absolute amounts of individual proteins or protein classes from their share of the total MS signal.
“we recently devised a method for estimating absolute amounts of individual proteins or protein classes based on the proportion of their MS signals to the total MS signal (16).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Wisniewski, 2012, Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma, Mol. Syst. Biol source
Not checked in the free run.
- Interpretationreasoning
The close agreement is explained by the proteomic ruler method avoiding error-prone steps such as cell counting or protein concentration measurement.
“We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements.”
Quote in the paperSubstantial claim - Premisereasoning
Errors in protein determination or cell counts propagate to the final readout even with precise MS, reducing accuracy without reducing precision.
“Any deviations in protein determination or cell counts will inevitably carry over to the final readout, even when very precise MS methods are used. This limits the overall accuracy, without showing up as a decrease in the precision of the quantification”
Quote in the paperSubstantial claim - Interpretationreasoning
The histone proteomic ruler permits estimation of total protein amount per cell.
“It serves as a proteomic ruler that allows the estimation of total protein amounts per cell.”
Quote in the paperSubstantial claim- Rests on an assumption: The summed MS signal from all histone-derived peptides is proportional to cumulative histone mass.
- Premisecitation
Choosing a non-optimal protein standard can cause systematic errors of up to twofold in dye-based protein determination.
“Systematic errors of up to a factor of 2 may therefore arise from the selection of a non-optimal protein standard (14).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] Noble, 2009, Quantitation of Protein source
Not checked in the free run.
- Interpretationreasoning
The histone MS signal can serve as a natural standard in whole-proteome data because DNA is wrapped around histones in a defined ratio.
“We then demonstrate that the MS signal derived from histones, around which DNA is wrapped in a defined ratio, can be used as a natural standard in a whole proteome dataset.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29] van Holde, 1989
Not checked in the free run.
- Rests on an assumption: The summed MS signal from all histone-derived peptides is proportional to cumulative histone mass.
- Interpretationreasoning
The quantitative readout can be absolutely scaled to copies per cell without cell counting or protein concentration determination.
“Thereby the quantitative readout can be absolutely scaled to copies per cell without the need for cell counting or protein concentration determination.”
- Also stated in: § RESULTS · plain
“This method requires neither cell counting nor the determination of protein concentration.”
Quote in the paperSubstantial claim- Rests on an assumption: The summed MS signal from all histone-derived peptides is proportional to cumulative histone mass.
- Also stated in: § RESULTS · plain
- Premisecitation
Ribosomal RNA typically accounts for about 80% of total RNA.
“Ribosomal RNA typically represents about 80% of total RNA (31)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] Warner, 1999, The economics of ribosome biosynthesis in yeast, Trends Biochem. Sci source
Not checked in the free run.
- Study design and conductown data
Post-FASP DNase and RNase digestion is suitable for measuring RNA and DNA content in a proteomic sample without extra preparative steps.
“indicating that post-FASP digestion of a sample with DNase and RNase is a suitable method for determination of the RNA and DNA content in a proteomic sample that does not require additional preparative steps.”
Quote in the paperNot a substantial claim - Premisecitation
In eukaryotic ribosomes, the ratio of RNA to protein is about 1:1.
“in eukaryotic ribosomes there is a ratio of about 1:1 between RNA and protein (32).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32] Melnikov, 2012, One core, two shells: bacterial and eukaryotic ribosomes, Nat. Struct. Mol. Biol source
Not checked in the free run.
- Interpretationown data
The ratio of histone MS signal to total MS signal can estimate total cellular protein mass without additional measurements.
“This is close to the hypothesized value of 1 and implies that the ratio of histone MS signal to total MS signal allows the estimation of the total cellular protein mass without any additional measurements.”
Quote in the paperSubstantial claim- Rests on an assumption: The summed MS signal from all histone-derived peptides is proportional to cumulative histone mass.
- Premisereasoning
A diploid human cell contains about 6.5 pg DNA.
“We used a value of 6.5 pg for a diploid human cell to calculate cell numbers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28] 2004, Finishing the euchromatic sequence of the human genome, Nature source
Not checked in the free run.
- Interpretationown data
The surprisingly good performance of proteomic ruler quantifications is attributed to the higher average peptide count in label-free quantification, which might compensate for some principal limitations of the label-free approach.
“We attribute the surprisingly good performance of the proteomic ruler quantifications to the fact that our label-free quantification on average made use of 19.4 peptides along the entire length of the proteins, whereas the PrEST-SILAC quantification used 4.7 peptides on average.”
- Also stated in: § RESULTS · hedged
“This might compensate for some of the principal limitations of the label-free approach.”
Quote in the paperSubstantial claim - Also stated in: § RESULTS · hedged
- Interpretationown data
The histone proteomic ruler serves as a good proxy for estimating cellular protein masses in tissues.
“This demonstrates that the histone proteomic ruler serves as a good proxy for estimating cellular protein masses in tissues.”
Quote in the paperSubstantial claim - Premisecitation
Hepatocytes, the predominant cell type in liver, are roughly cubical cells with a 15-μm edge length.
“Hepatocytes, the predominant cell type in liver, are roughly cubical cells with a 15-μm edge length (34).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] 2000
Not checked in the free run.
- Premisecitation
Total cellular protein concentration typically lies within 20% to 30% (w/v), or 200 to 300 g/l, in many cell types and organisms.
“The total cellular protein concentration typically lies within a range of 20% to 30% (w/v) (i.e. 200 to 300 g/l) in many cell types and organisms (33).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [33] Brown, 1991, Total cell protein concentration as an evolutionary constraint on the metabolic control distribution in cells, J. Theor. Biol source
Not checked in the free run.
- Premisecitation
Thymocytes have an average volume of 250 μm3.
“Thymocytes are at the other end of the size scale with an average volume of 250 μm3 (35).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Salinas, 1972, Cell size distribution in the thymus as a function of age, J. Cell. Physiol source
Not checked in the free run.
- Premisecitation
Spiked-in PrESTs with isotopic labeling, cell counting, and total protein concentration determination previously yielded highly reliable copy number values for selected proteins.
“We previously employed spiked-in protein epitope signature tags (PrESTs) of known quantities in combination with isotopic labeling, cell counting, and total protein concentration determination to obtain highly reliable copy number values of selected proteins (11).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Zeiler, 2012, A protein epitope signature tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines, Mol. Cell. Proteomics source
Not checked in the free run.
- Premisecitation
Mature dendritic cells have diameters between 10 and 15 μm.
“Mature dendritic cells have diameters between 10 and 15 μm (37).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [37] Dumortier, 2005, Antigen presentation by an immature myeloid dendritic cell line does not cause CTL deletion in vivo, but generates CD8+ central memory-like T cells that can be rescued for full effector function, J. Immunol source
Not checked in the free run.
- Interpretationown data
The MS signal of ribosomal proteins can be used as a proteomic ruler to estimate cellular RNA amounts.
“indicating that the MS signal of ribosomal proteins can indeed be used as a proteomic ruler to estimate cellular RNA amounts.”
Quote in the paperSubstantial claim - Interpretationown data
Individual protein copy numbers will become more accurate with increased peptide coverage in deep datasets.
“Additionally, individual protein copy numbers will become more accurate with increased peptide coverage in deep datasets.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The necessary depth of proteomic analysis should be readily attainable with many experimental setups.
“but the necessary depth should be readily attainable with many experimental setups.”
Quote in the paperNot a substantial claim - Premisecitation
The combination of a protein's sequence, the protease specificity used for digestion, and the characteristics of the mass spectrometric analysis can introduce protein-specific biases.
“Because the combination of the sequence of a protein, the specificity of the protease used for digestion, and the characteristics of the mass spectrometric analysis can introduce protein-specific biases (39)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [39] Peng, 2012, Protease bias in absolute protein quantitation, Nat. Methods source
Not checked in the free run.
- Interpretationreasoning
Because muscle fibers are syncytial, multi-nucleated cells, the histone proteomic ruler delivered protein amounts per nucleus and not per cell in this case.
“Considering that muscle fibers are syncytial, multi-nucleated cells, the histone proteomic ruler delivered protein amounts per nucleus and not per cell in this particular case.”
Quote in the paperSubstantial claim - Interpretationreasoning
As a consequence of the trend toward smaller abundant proteins, the average molecular mass of a protein in a cell is much smaller than the nominal average of all protein sizes when abundances are not taken into account.
“As a consequence of this trend, the average molecular mass of a protein in a cell is much smaller than the nominal average of the sizes of all proteins when their abundances are not taken into account.”
Quote in the paperNot a substantial claim - Interpretationown data
A reasonable depth of proteomic analysis is needed to ensure a robust contribution of the histone MS signal.
“A reasonable depth of proteomic analysis is needed to ensure a robust contribution of the histone MS signal”
Quote in the paperSubstantial claim - Interpretationreasoning
Accurate absolute quantification is possible without spike-in standards through the proteomic ruler concept.
“In this paper, we propose that accurate absolute quantification is possible without the use of spike-in standards through the use of a concept we call the “proteomic ruler.””
Quote in the paperSubstantial claim- The authors state this as an assumption: A protein's cumulative mass in the proteomic sample is directly proportional to the MS signals summed over all peptides derived from it.
- Interpretationreasoning
The proteomic ruler approach eliminates several experimental steps such as cell counting and absolute protein concentration determination, which are themselves prone to errors.
“At the same time, it eliminates several experimental steps such as cell counting and absolute protein concentration determination, which are themselves prone to errors, in particular stemming from issues with protein determination assays.”
Quote in the paperSubstantial claim - Interpretationreasoning
The only prerequisite for the proteomic ruler is a eukaryotic, whole-cell proteome dataset in which the chromatin fraction is not over- or underrepresented as a result of sample handling.
“The only prerequisite is a eukaryotic, whole-cell proteome dataset where the chromatin fraction is not over- or underrepresented as a result of sample handling.”
Quote in the paperSubstantial claim
- May combine two claims. 20
- Classified as probably not a substantial claim. 9
May combine two claims.
Findingown dataRNase and DNase cleavage yields exceeded 95% and did not depend on protein content.
“Yields were greater than 95% and were independent of the protein content (Fig. 2B)”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataMaxQuant identified about 7000 proteins in each of four human cell lines in triplicate analyses.
“In triplicate analyses, MaxQuant identified about 7000 proteins in each of the cell lines (supplemental Table S1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationObserved similarities in dendritic cell sizes correlate with overall patterns of proteomic similarity at the level of individual proteins observed in the original study.
“Interestingly, our observed similarities in cell sizes correlate with overall patterns of proteomic similarity on the level of individual proteins that were observed in the original study (36).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [36] Luber, 2010, Quantitative proteomics reveals subset-specific viral recognition in dendritic cells, Immunity source
Not checked in the free run.
May combine two claims.
Findingown dataIndependent of tissue origin, low-abundance proteins had an average molecular mass of around 100 kDa, and this value decreased with increasing cellular abundance to around 40 kDa for the most abundant proteins.
“Independent of the tissue of origin, low-abundant proteins had an average molecular mass of around 100 kDa, and this value decreased with increasing cellular abundance of the proteins to around 40 kDa for the most abundant proteins.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn skeletal muscle cells, filaments and motorproteins such as titin and myosins are exceptions to the trend of abundant proteins being smaller, being both large (>150 kDa) and very abundant (>1 million copies per cell), resulting in a profound increase in the average molecular protein mass in muscle cells.
“Notably, in skeletal muscle cells, filaments and motorproteins such as titin and myosins are notable exceptions to the trend of abundant proteins being smaller, as they are both large (>150 kDa) and very abundant (>1 million copies per cell) in this tissue, resulting in a profound increase in the average molecular protein mass in a muscle cell (Fig. 6C, circles).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOvaries consist predominantly of small follicular cells and showed the least protein per cell, at 42 pg.
“Ovaries consist predominantly of small follicular cells and showed the least protein per cell (42 pg).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe new proteomic ruler method gives quantitative results comparable to existing approaches directly from deep eukaryotic proteome datasets, with no extra experimental steps.
“Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationDNA concentration is proportional to cell count and has been used to normalize enzyme activities, transcript and protein amounts, and metabolome data.
“DNA concentration was shown to be proportional to the cell count and was successfully used to normalize enzyme activities, transcript and protein amounts, and metabolome data (23–25).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [23] Papadimitriou, 1993, Measurement of cell numbers in microtiter culture plates using the fluorescent dye Hoechst 33258, J. Immunol. Methods source
Not checked in the free run.
- cites [24] Shimada, 2010, Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells, Plant Methods source
Not checked in the free run.
- cites [25] Silva, 2013, Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines, Anal. Chem source
Not checked in the free run.
May combine two claims.
PremisereasoningBecause cells are not uniform, scaling by cell number can be inaccurate; a 25% change in the diameter of a spherical cell corresponds to a twofold change in volume.
“However, cells are not necessarily uniform; therefore scaling by cell numbers may be inaccurate, as a 25% variation of the diameter of a sphere-shaped cell corresponds to a 2-fold change in cell volume.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationPlasma total protein concentration is typically about 70 g/l and varies within a narrow range.
“The total protein concentration in plasma varies around a typical value of 70 g/l within a narrow margin (22)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Kratz, 2004, Case records of the Massachusetts General Hospital. Weekly clinicopathological exercises. Laboratory reference values, N. Engl. J. Med source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe proteomic ruler method puts the MS readout on an absolute scale and permits per-cell copy-number estimates for individual proteins.
“As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe Total Protein Approach relates this share to total protein mass; per-cell copy numbers require separate determination or estimation of total protein mass per cell.
“We termed the method the Total Protein Approach, because we relate this proportion to a total protein mass. To obtain copy numbers, we specifically used the total protein mass per cell, which needs to be determined or estimated separately.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Wisniewski, 2012, Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma, Mol. Syst. Biol source
Not checked in the free run.
May combine two claims.
PremisecitationVisual cell counting is problematic in tissues; reported calculated cell volumes for intestinal mucosa enterocytes differ by up to fivefold.
“In tissues, not only are cell sizes variable, but visual counting of cells is also problematic. For instance, up to 5-fold differences in calculated cell volumes have been reported for enterocytes of the intestinal mucosa (15).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [15] Crowe, 1993, Morphometric analysis of small intestinal mucosa. IV. Determining cell volumes, Virchows Archive A Pathol. Anat. Histopathol source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationThe FASP method is reactor-based and permits sequential sample processing and separation of reaction products.
“In contrast to in-solution or in-gel approaches, the FASP method is reactor based (26) and allows sequential processing of the sample and separation of reaction products.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Zhou, 2011, Proteomic reactors and their applications in biology, FEBS J source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationThe human genome has about 3.2 × 10^9 base pairs.
“The human genome contains around 3.2 × 109 base pairs (28).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28] 2004, Finishing the euchromatic sequence of the human genome, Nature source
Not checked in the free run.
May combine two claims.
Interpretationown dataExcept for histone H3, the share of MS signal from histone peptides with PTMs is low and can be ignored in overall data scaling.
“This indicates that, with the exception of histone H3, the fraction of the MS signal derived from histone peptides that have PTMs is low and can be neglected in the overall data scaling process.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationIn eukaryotic cells, histones package DNA in chromatin, and DNA mass is approximately equal to total histone mass.
“In eukaryotic cells, DNA is packaged in chromatin by histones, and the mass of the DNA is about equal to the combined mass of histones (29).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29] van Holde, 1989
Not checked in the free run.
May combine two claims.
PremisecitationThe proteomic depth needed for a stable histone fraction is easily attainable with minimal sample fractionation and with single-run analyses on latest-generation instruments.
“This depth of analysis is easily attainable with minimal sample fractionation and also with single run analyses on latest-generation machines (30).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [30] Nagaraj, 2012, System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap, Mol. Cell. Proteomics source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe estimate of 464 ± 35 pg total protein per average liver cell is reasonable because non-hepatocytes contribute the same amount of DNA or histones but less overall protein mass.
“which is reasonable given that non-hepatocytes contribute the same amount of DNA or histones but less overall protein mass.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe translated dendritic cell diameters are expected to be slightly smaller than observed cell sizes because of the numerous dendrites projecting from the cell surfaces.
“which is expected to be slightly smaller than observed cell sizes, given the numerous dendrites projecting from the cell surfaces.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationown dataThis observation underlines the benefits of approaches that rely on multiple independent quantifications instead of single peptide ratios.
“This observation underlines the benefits of approaches that rely on multiple independent quantifications instead of single peptide ratios”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataFor proteins quantified with more than a few peptides, the proteomic ruler approach could offer surprisingly high accuracy, making it an attractive alternative to label-based methods.
“We conclude that for those proteins quantified with more than a few peptides, the proteomic ruler approach could offer a surprisingly high level of accuracy, making it an attractive alternative to label-based methods.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationnone givenUsing the MS signal derived from histones and relating it to a known amount of DNA per cell provides accurate estimates of total protein amount per cell that can be used as scaling factors for calculating cellular copy numbers of any protein of interest.
“Using the MS signal derived from histones and relating it to a known amount of DNA per cell provides accurate estimates of the total protein amount per cell that can be used as scaling factors for calculating cellular copy numbers of any protein of interest.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: A protein's cumulative mass in the proteomic sample is directly proportional to the MS signals summed over all peptides derived from it.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPerseus is part of the freely available MaxQuant suite.
“Perseus is part of the freely available MaxQuant suite (20).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe dependence of protein size on abundance was observed in earlier studies and is thought to reflect an evolutionary advantage of decreasing the size of abundant proteins for biosynthetic cost reasons.
“This dependence was observed in earlier studies and is thought to reflect the evolutionary advantage of decreasing the size of abundant proteins for reasons of biosynthetic cost (38).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [38] Warringer, 2006, Evolutionary constraints on yeast protein size, BMC Evolutionary Biol source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe information from the proteomic ruler comes for free, incorporating absolute quantification into any kind of in-depth proteome dataset, even in retrospective analysis.
“Importantly, this information comes for free, in that it incorporates absolute quantification into any kind of in-depth proteome dataset, even in retrospective analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenProteins with a high fraction of theoretical peptides per sequence length and a high number of actually identified peptides, most of which are group-unique, are expected to yield better quantification.
“proteins having a high fraction of theoretical peptides per sequence length and a high number of actually identified peptides, most of which are group-unique, are expected to yield better quantification.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Protein determination reagents can cross-react with non-protein cell components such as thiols, nucleic acids, and phospholipids.
“An additional, often ignored source of errors is the cross-reactivity of the reagents with non-proteinaceous cell components such as thiols, nucleic acids, and phospholipids.”
The paper relies on this without support.
The summed MS signal from all histone-derived peptides is proportional to cumulative histone mass.
“This value reflects the cumulative histone mass.”
The paper relies on this without support.
The analyzed cell lines have diploid genomes, so their DNA mass per cell is 6.5 pg.
“We used a value of 6.5 pg for a diploid human cell to calculate cell numbers.”
The authors state this as an assumption.
A protein's cumulative mass in the proteomic sample is directly proportional to the MS signals summed over all peptides derived from it.
“our copy number calculation assumes a direct proportionality between a protein's cumulative mass in the proteomic sample and the MS signals summed over all peptides derived from it (see Eq. 3).”
The paper relies on this without support.
Many obligate protein complexes are well characterized in terms of composition and stoichiometry, with subunits expressed at equimolar levels.
“Many obligate protein complexes are well characterized in terms of their composition and stoichiometry with subunits expressed at equimolar levels.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DNA and histones are good proxies for each other because the number of histones per cell is proportional to the amount of DNA per cell, same for all 2N human cells (not applicable for cell lines with different karyotypes).
“Also, DNA and histones are good proxies for each other as the number of histones per cell is proportional toward amount of DNA per cell, that is, same for all 2N human cells (Wiśniewski et al, 2014) (note: not applicable for cell lines with different karyotypes)”
From Gene‐specific correlation of RNA and protein levels in human cells and tissues (2016) · cites this paper as Wiśniewski et al, 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
TRiC results are assigned to different Figure 5 panels
MinorThe text says Fig. 5C shows results for both complexes, whereas the caption assigns TRiC to panel D.
“Fig. 5C shows that our histone proteomic-ruler-derived copy numbers of members of the pyruvate dehydrogenase complex and the TRiC chaperone closely match the expected 1:1 stoichiometry among subunits.”
“C, pyruvate dehydrogenase complex. D, TRiC chaperonin.”
Figure 4 panel for the analysis-depth result is inconsistent
MinorThe text assigns the analysis-depth result to Fig. 4E, but the figure caption assigns that result to panel D.
“we ranked all peptides by intensity and calculated the histone-MS fraction as a function of the number of identified peptides (Fig. 4E).”
“D, histone MS signal fraction as a function of the depth of analysis, simulated by intensity-based ranking of peptides.”
Cites
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works
Self-citationUniversal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352 · see citing works
Self-citationMass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955 · see citing works
Self-citationGlobal quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · see citing works
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101065j · Cited by 5,350 · see citing works
Self-citationCox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180 · see citing works
Self-citationFinishing the euchromatic sequence of the human genome
International Human Genome Sequencing Consortium (2004) · Nature · doi:10.1038/nature03001 · Cited by 4,971 · see citing works
Rochester, C. L., & Kradin, R. L. (2000) · New England Journal of Medicine · doi:10.1056/nejm200012213432509 · Cited by 3,875 · see citing works
Ishihama, Y., Oda, Y., Tabata, T., Sato, T., Nagasu, T., Rappsilber, J., & Mann, M. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500061-mcp200 · Cited by 2,055 · see citing works
Self-citationThe economics of ribosome biosynthesis in yeast
Warner, J. R. (1999) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(99)01460-7 · Cited by 1,933 · see citing works
The Proteomics Identifications (PRIDE) database and associated tools: status in 2013
Vizcaíno, J. A., Côté, R. G., Csordas, A., Dianes, J. A., Fabregat, A., Foster, J. M., Griss, J., Alpi, E., Birim, M., Contell, J., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1262 · Cited by 1,918 · see citing works
Cabot, R. C., Scully, R. E., Mark, E. J., McNeely, W. F., McNeely, B. U., Harris, W. H., & Rosenberg, A. E. (1990) · New England Journal of Medicine · doi:10.1056/nejm199008233230808 · Cited by 896 · see citing works
The quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816 · see citing works
Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present
Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-6203-4 · Cited by 801 · see citing works
Next-generation proteomics: towards an integrative view of proteome dynamics
Altelaar, M. A. F., Muñoz, J., & Heck, A. J. R. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3356 · Cited by 776 · see citing works
Wiechelman, K. J., Braun, R. D., & Fitzpatrick, J. D. (1988) · Analytical Biochemistry · doi:10.1016/0003-2697(88)90383-1 · Cited by 766 · see citing works
Kratz (2004) · doi:10.1056/nejmcpc049016 · Cited by 593 · see citing works
Quantitative proteomics reveals subset-specific viral recognition in dendritic cells
Luber, C. A., Cox, J., Lauterbach, H., Fancke, B., Selbach, M., Tschopp, J., Akira, S., Wiegand, M., Hochrein, H., O'Keeffe, M., et al. (2010) · Immunity · doi:10.1016/j.immuni.2010.01.013 · Cited by 576 · see citing works
Self-citationChapter 8 Quantitation of Protein
Noble, J. E., & Bailey, M. J. A. (2009) · Methods in Enzymology · doi:10.1016/s0076-6879(09)63008-1 · Cited by 492 · see citing works
One core, two shells: bacterial and eukaryotic ribosomes
Melnikov, S., Ben-Shem, A., Garreau de Loubresse, N., Jenner, L., Yusupova, G., & Yusupov, M. (2012) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2313 · Cited by 477 · see citing works
Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans
Malmström, J., Beck, M., Schmidt, A., Lange, V., Deutsch, E. W., & Aebersold, R. (2009) · Nature · doi:10.1038/nature08184 · Cited by 421 · see citing works
Nagaraj, N., Alexander Kulak, N., Cox, J., Neuhauser, N., Mayr, K., Hoerning, O., Vorm, O., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.013722 · Cited by 392 · see citing works
Self-citationWiśniewski, J. R., & Mann, M. (2012) · Analytical Chemistry · doi:10.1021/ac300006b · Cited by 300 · see citing works
Self-citationExtensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma
Wiśniewski, J. R., Ostasiewicz, P., Duś, K., Zielińska, D. F., Gnad, F., & Mann, M. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.44 · Cited by 248 · see citing works
Self-citationZeiler, M., Straube, W. L., Lundberg, E., Uhlen, M., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.009613 · Cited by 168 · see citing works
Self-citationWiśniewski, J. R., Duś‐Szachniewicz, K., & Mann, M. (2012) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201200046 · Cited by 149 · see citing works
Self-citationBrown, G. C. (1991) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(05)80422-9 · Cited by 131 · see citing works
Silva, L. P., Lorenzi, P. L., Purwaha, P., Yong, V., Hawke, D. H., & Weinstein, J. N. (2013) · Analytical Chemistry · doi:10.1021/ac401559v · Cited by 111 · see citing works
Protease bias in absolute protein quantitation
Peng, M., Taouatas, N., Cappadona, S., van Breukelen, B., Mohammed, S., Scholten, A., & Heck, A. J. R. (2012) · Nature Methods · doi:10.1038/nmeth.2031 · Cited by 90 · see citing works
Fountoulakis, M., Juranville, J.-F., & Manneberg, M. (1992) · Journal of Biochemical and Biophysical Methods · doi:10.1016/0165-022x(94)90078-7 · Cited by 88 · see citing works
Beck, M., Claassen, M., & Aebersold, R. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.09.002 · Cited by 83 · see citing works
Evolutionary constraints on yeast protein size
Warringer (2006) · BMC Evolutionary Biology · doi:10.1186/1471-2148-6-61 · Cited by 71 · see citing works
Towards Cellular Systems in 4D
Peer Bork, B., & Serrano, L. G. (2005) · Cell · doi:10.1016/j.cell.2005.05.001 · Cited by 68 · see citing works
Morphometric analysis of small intestinal mucosa
Niazi, N. M., Leigh, R. J., Crowe, P. T., & Marsh, M. N. (1984) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/bf00704250 · Cited by 58 · see citing works
Proteomic Sample Preparation from Formalin Fixed and Paraffin Embedded Tissue
Wiśniewski, J. R. (2013) · Journal of Visualized Experiments · doi:10.3791/50589 · Cited by 50 · see citing works
Self-citationMeasurement of cell numbers in microtiter culture plates using the fluorescent dye Hoechst 33258
Papadimitriou, E., & Lelkes, P. I. (1993) · Journal of Immunological Methods · doi:10.1016/0022-1759(93)90405-v · Cited by 47 · see citing works
Handbook of systems biology : concepts and insights
Walhout, A. J. M., Vidal, M., & Dekker, J. (2013) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.1016/c2010-0-67190-2 · Cited by 41 · see citing works
Dumortier, H., van Mierlo, G. J. D., Egan, D., van Ewijk, W., Toes, R. E. M., Offringa, R., & Melief, C. J. M. (2005) · The Journal of Immunology · doi:10.4049/jimmunol.175.2.855 · Cited by 39 · see citing works
James, M., Verhofste, M., Franklin, C., Beilman, G. J., & Goldman, C. D. (2010) · World Journal of Emergency Surgery · doi:10.1186/1749-7922-5-21 · Cited by 38 · see citing works
Proteomic reactors and their applications in biology
Zhou, H., Ning, Z., Wang, F., Seebun, D., & Figeys, D. (2011) · The FEBS Journal · doi:10.1111/j.1742-4658.2011.08292.x · Cited by 34 · see citing works
Morphometric analysis of small intestinal mucosa. IV. Determining cell volumes
Crowe, P. T., & Marsh, M. N. (1993) · Virchows Archiv A Pathological Anatomy and Histopathology · doi:10.1007/bf01606454 · Cited by 28 · see citing works
Proteomic Analysis of Cellular Systems
Hein, M. Y., Sharma, K., Cox, J., & Mann, M. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00001-0 · Cited by 28 · see citing works
Self-citationCell size distribution in the thymus as a function of age
Salinas, F. A., Smith, L. H., & Goodman, J. W. (1972) · Journal of Cellular Physiology · doi:10.1002/jcp.1040800304 · Cited by 14 · see citing works
Shimada, H., Obayashi, T., Takahashi, N., Matsui, M., & Sakamoto, A. (2010) · Plant Methods · doi:10.1186/1746-4811-6-29 · Cited by 10 · see citing works
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Sorted by citations
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P. G., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 7,977 · see citing works
Self-citationThe MaxQuant computational platform for mass spectrometry-based shotgun proteomics
Tyanova, S., Temu, T., & Cox, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.136 · Cited by 4,689 · see citing works
Self-citationOn the Dependency of Cellular Protein Levels on mRNA Abundance
Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221 · see citing works
A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation
Patel, A., Lee, H. O., Jawerth, L. M., Maharana, S., Jahnel, M., Hein, M. Y., Stoynov, S., Mahamid, J., Saha, S., Franzmann, T. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.047 · Cited by 2,927 · see citing works
Self-citationMass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334 · see citing works
Self-citationA Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances
Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I. A., Weisswange, I., Mansfeld, J., Buchholz, F., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.053 · Cited by 1,270 · see citing works
Self-citationMitochondrial proteins: from biogenesis to functional networks
Pfanner, N., Warscheid, B., & Wiedemann, N. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0092-0 · Cited by 1,028 · see citing works
diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition
Meier, F., Brunner, A., Frank, M., Ha, A., Bludau, I., Voytik, E., Kaspar‐Schoenefeld, S., Lubeck, M., Raether, O., Bache, N., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-00998-0 · Cited by 902 · see citing works
Self-citationA deep proteome and transcriptome abundance atlas of 29 healthy human tissues
Wang, D., Eraslan, B., Wieland, T., Hallström, B., Hopf, T., Zolg, D. P., Zecha, J., Asplund, A., Li, L., Meng, C., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188503 · Cited by 750 · see citing works
A pathway for mitotic chromosome formation
Gibcus, J. H., Samejima, K., Goloborodko, A. A., Samejima, I., Naumova, N., Nuebler, J., Kanemaki, M. T., Xie, L., Paulson, J. R., Earnshaw, W. C., et al. (2018) · Science · doi:10.1126/science.aao6135 · Cited by 709 · see citing works
m6A enhances the phase separation potential of mRNA
Ries, R. J., Zaccara, S., Klein, P., Olarerin-George, A. O., Namkoong, S., Pickering, B. F., Patil, D. P., Kwak, H., Lee, J. H., & Jaffrey, S. R. (2019) · Nature · doi:10.1038/s41586-019-1374-1 · Cited by 710 · see citing works
Phase Separation of FUS Is Suppressed by Its Nuclear Import Receptor and Arginine Methylation
Hofweber, M., Hutten, S., Bourgeois, B. M. R., Spreitzer, E., Niedner, A., Schifferer, M., Ruepp, M., Simons, M., Niessing, D., Madl, T., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.004 · Cited by 654 · see citing works
Global, quantitative and dynamic mapping of protein subcellular localization
Itzhak, D. N., Tyanova, S., Cox, J., & Borner, G. H. H. (2016) · eLife · doi:10.7554/elife.16950 · Cited by 595 · see citing works
Self-citationZhu, Y., Piehowski, P. D., Zhao, R., Chen, J., Shen, Y., Moore, R. J., Shukla, A. K., Petyuk, V. A., Campbell-Thompson, M., Mathews, C. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03367-w · Cited by 587 · see citing works
The role of PINK1–Parkin in mitochondrial quality control
Narendra, D. P., & Youle, R. J. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01513-9 · Cited by 576 · see citing works
Gene‐specific correlation of RNA and protein levels in human cells and tissues
Edfors, F., Danielsson, F., Hallström, B. M., Käll, L., Lundberg, E., Pontén, F., Forsström, B., & Uhlén, M. (2016) · Molecular Systems Biology · doi:10.15252/msb.20167144 · Cited by 493 · see citing works
Burgstaller, G., Oehrle, B., Gerckens, M., White, E. S., Schiller, H. B., & Eickelberg, O. (2017) · European Respiratory Journal · doi:10.1183/13993003.01805-2016 · Cited by 483 · see citing works
An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes
Bekker‐Jensen, D. B., Kelstrup, C. D., Batth, T. S., Buch-Larsen, S. C., Haldrup, C., Bramsen, J. B., Sørensen, K. D., Høyer, S., Orntoft, T. F., Andersen, C. L., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.05.009 · Cited by 467 · see citing works
Definition of a High-Confidence Mitochondrial Proteome at Quantitative Scale
Morgenstern, M., Stiller, S. B., Lübbert, P., Peikert, C. D., Dannenmaier, S., Drepper, F., Weill, U., Höß, P., Feuerstein, R., Gebert, M., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.014 · Cited by 451 · see citing works
The Curious Case of the HepG2 Cell Line: 40 Years of Expertise
Arzumanian, V. A., Kiseleva, O. I., & Poverennaya, E. V. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222313135 · Cited by 387 · see citing works
Social network architecture of human immune cells unveiled by quantitative proteomics
Rieckmann, J. C., Geiger, R., Hornburg, D., Wolf, T., Kveler, K., Jarrossay, D., Sallusto, F., Shen-Orr, S. S., Lanzavecchia, A., Mann, M., et al. (2017) · Nature Immunology · doi:10.1038/ni.3693 · Cited by 376 · see citing works
Self-citationMeier, F., Geyer, P. E., Winter, S. V., Cox, J., & Mann, M. (2018) · Nature Methods · doi:10.1038/s41592-018-0003-5 · Cited by 341 · see citing works
Self-citationFornasiero, E. F., Mandad, S., Wildhagen, H., Alevra, M., Rammner, B., Keihani, S., Opazo, F., Urban, I., Ischebeck, T., Sakib, M. S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06519-0 · Cited by 340 · see citing works
Schuler, B., Soranno, A., Hofmann, H., & Nettels, D. (2016) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-062215-010915 · Cited by 335 · see citing works
Saha, S., Weber, C. A., Nousch, M., Adame-Arana, O., Hoege, C., Hein, M. Y., Nishimura, E. O., Mahamid, J., Jahnel, M., Jawerth, L. M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.006 · Cited by 335 · see citing works
Self-citationScheltema, R. A., Hauschild, J.-P., Lange, O., Hornburg, D., Denisov, E. V., Damoc, E., Kuehn, A., Makarov, A., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.043489 · Cited by 321 · see citing works
Self-citationQuantitative high-confidence human mitochondrial proteome and its dynamics in cellular context
Morgenstern, M., Peikert, C. D., Lübbert, P., Suppanz, I., Klemm, C., Alka, O., Steiert, C., Naumenko, N., Schendzielorz, A. B., Melchionda, L., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.11.001 · Cited by 318 · see citing works
Quantitative Analysis of Human Red Blood Cell Proteome
Bryk, A. H., & Wiśniewski, J. R. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00025 · Cited by 290 · see citing works
Self-citationBioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280 · see citing works
Region and cell-type resolved quantitative proteomic map of the human heart
Doll, S., Dreßen, M., Geyer, P. E., Itzhak, D. N., Braun, C., Doppler, S. A., Meier, F., Deutsch, M.-A., Lahm, H., Lange, R., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01747-2 · Cited by 265 · see citing works
Self-citationAntigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens
Khodadoust, M. S., Olsson, N., Wagar, L. E., Haabeth, O. A. W., Chen, B., Swaminathan, K., Rawson, K., Liu, C. L., Steiner, D. F., Lund, P. J., et al. (2017) · Nature · doi:10.1038/nature21433 · Cited by 256 · see citing works
Kühl, I., Miranda, M., Atanassov, I., Kuznetsova, I., Hinze, Y., Mourier, A., Filipovska, A., & Larsson, N. (2017) · eLife · doi:10.7554/elife.30952 · Cited by 251 · see citing works
The cytotoxic T cell proteome and its shaping by the kinase mTOR
Hukelmann, J. L., Anderson, K. E., Sinclair, L. V., Grzes, K. M., Murillo, A. B., Hawkins, P. T., Stephens, L. R., Lamond, A. I., & Cantrell, D. A. (2016) · Nature Immunology · doi:10.1038/ni.3314 · Cited by 249 · see citing works
Comprehensive Proteomic Analysis of Human Erythropoiesis
Gautier, E., Ducamp, S., Leduc, M., Salnot, V., Guillonneau, F., Dussiot, M., Hale, J., Giarratana, M., Raimbault, A., Douay, L., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.06.085 · Cited by 248 · see citing works
Proteome complexity and the forces that drive proteome imbalance
Harper, J. W., & Bennett, E. J. (2016) · Nature · doi:10.1038/nature19947 · Cited by 234 · see citing works
Quantitative analysis of T cell proteomes and environmental sensors during T cell differentiation
Howden, A. J. M., Hukelmann, J. L., Brenes, A., Spinelli, L., Sinclair, L. V., Lamond, A. I., & Cantrell, D. A. (2019) · Nature Immunology · doi:10.1038/s41590-019-0495-x · Cited by 231 · see citing works
Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells
Tan, C. S. H., Go, K. D., Bisteau, X., Dai, L., Yong, C. H., Prabhu, N., Ozturk, M. B., Lim, Y. T., Sreekumar, L., Lengqvist, J., et al. (2018) · Science · doi:10.1126/science.aan0346 · Cited by 224 · see citing works
Antigen receptor control of methionine metabolism in T cells
Sinclair, L. V., Howden, A. J. M., Brenes, A. J., Spinelli, L., Hukelmann, J. L., Macintyre, A. N., Liu, X., Thomson, S., Taylor, P. M., Rathmell, J. C., et al. (2019) · eLife · doi:10.7554/elife.44210 · Cited by 222 · see citing works
Dynamics in protein translation sustaining T cell preparedness
Wolf, T., Jin, W., Zoppi, G., Vogel, I. A., Akhmedov, M., Bleck, C. K. E., Beltraminelli, T., Rieckmann, J. C., Ramirez, N. J., Benevento, M., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0714-5 · Cited by 220 · see citing works
Self-citationA Historical Review of Brain Drug Delivery
Pardridge, W. M. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14061283 · Cited by 219 · see citing works
Increasing cell size remodels the proteome and promotes senescence
Lanz, M. C., Zatulovskiy, E. A., Swaffer, M. P., Zhang, L., Ilerten, I., Zhang, S., You, D. S., Marinov, G. K., McAlpine, P. L., Elias, J. E., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.07.017 · Cited by 218 · see citing works
Marchingo, J. M., Sinclair, L. V., Howden, A. J. M., & Cantrell, D. A. (2020) · eLife · doi:10.7554/elife.53725 · Cited by 202 · see citing works
Multibatch TMT reveals false positives, batch effects and missing values
Brenes, A., Hukelmann, J., Bensaddek, D., & Lamond, A. I. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra119.001472 · Cited by 196 · see citing works
Barkovits, K., Pacharra, S., Pfeiffer, K., Steinbach, S., Eisenacher, M., Marcus, K., & Uszkoreit, J. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra119.001714 · Cited by 188 · see citing works
Wiśniewski, J. R., Vildhede, A., Norén, A., & Artursson, P. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.01.016 · Cited by 185 · see citing works
Self-citationAnalysis of human acetylation stoichiometry defines mechanistic constraints on protein regulation
Hansen, B. K., Gupta, R., Baldus, L., Lyon, D. B., Narita, T., Lammers, M., Choudhary, C., & Weinert, B. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09024-0 · Cited by 184 · see citing works
Wu, P., Heins, Z. J., Muller, J., Katsnelson, L., de Bruijn, I., Abeshouse, A. A., Schultz, N. D., Fenyö, D., & Gao, J. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir119.001673 · Cited by 177 · see citing works
Byrne, K. M., Monsefi, N., Dawson, J. C., Degasperi, A., Wills, J. C., Volinsky, N., Dobrzyński, M., Birtwistle, M. R., Tsyganov, M. A., Kiyatkin, A. B., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.01.003 · Cited by 174 · see citing works
EDF1 coordinates cellular responses to ribosome collisions
Sinha, N. K., Ordureau, A., Best, K., Saba, J. A., Zinshteyn, B., Sundaramoorthy, E., Fulzele, A., Garshott, D. M., Denk, T., Thoms, M., et al. (2020) · eLife · doi:10.7554/elife.58828 · Cited by 174 · see citing works
Cell-Type-Resolved Quantitative Proteomics of Murine Liver
Azimifar, S. B., Nagaraj, N., Cox, J., & Mann, M. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.11.002 · Cited by 173 · see citing works
Self-citationCong, Y., Liang, Y., Motamedchaboki, K., Huguet, R., Truong, T., Zhao, R., Shen, Y., López‐Ferrer, D., Zhu, Y., & Kelly, R. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.9b04631 · Cited by 164 · see citing works
Efficient and robust proteome-wide approaches for cross-linking mass spectrometry
Klykov, O. V., Steigenberger, B. A., Pektaş, S., Fasci, D., Heck, A. J. R., & Scheltema, R. A. (2018) · Nature Protocols · doi:10.1038/s41596-018-0074-x · Cited by 163 · see citing works
Wigger, L., Barovic, M., Brunner, A., Marzetta, F., Schöniger, E., Mehl, F., Kipke, N., Friedland, D., Burdet, F., Kessler, C., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00420-9 · Cited by 158 · see citing works
Self-citationNano-LC in Proteomics: Recent Advances and Approaches
Wilson, S. R., Vehus, T. S., Berg, H. S., & Lundanes, E. (2015) · Bioanalysis · doi:10.4155/bio.15.92 · Cited by 156 · see citing works
Multiscale photocatalytic proximity labeling reveals cell surface neighbors on and between cells
Lin, Z., Schaefer, K., Lui, I., Yao, Z. W., Fossati, A., Swaney, D. L., Palar, A., Šali, A., & Wells, J. A. (2024) · Science · doi:10.1126/science.adl5763 · Cited by 155 · see citing works
Update on Selenoprotein Biosynthesis
Bulteau, A., & Chavatte, L. (2015) · Antioxidants and Redox Signaling · doi:10.1089/ars.2015.6391 · Cited by 152 · see citing works
Depletion interactions modulate the binding between disordered proteins in crowded environments
Zosel, F., Soranno, A., Buholzer, K., Nettels, D., & Schuler, B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921617117 · Cited by 150 · see citing works
Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data
Sinitcyn, P. G., Rudolph, J. D., & Cox, J. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013516 · Cited by 138 · see citing works
Self-citationRangaraju, S., Dammer, E. B., Raza, S. A., Gao, T., Xiao, H., Betarbet, R. S., Duong, D. M., Webster, J. A., Hales, C. M., Lah, J. J., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0266-4 · Cited by 133 · see citing works
Unrestrained ESCRT-III drives micronuclear catastrophe and chromosome fragmentation
Vietri, M., Schultz, S. W., Bellanger, A., Jones, C. M., Petersen, L. I., Raiborg, C., Skarpen, E., Pedurupillay, C. R. J., Kjos, I., Kip, E., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0537-5 · Cited by 133 · see citing works
Systematic quantitative analysis of ribosome inventory during nutrient stress
An, H., Ordureau, A., Körner, M., Paulo, J. A., & Harper, J. W. (2020) · Nature · doi:10.1038/s41586-020-2446-y · Cited by 133 · see citing works
Cuadrado, E., van den Biggelaar, M., de Kivit, S., Chen, Y.-Y., Slot, M., Doubal, I., Meijer, A., van Lier, R. A. W., Borst, J., & Amsen, D. (2018) · Immunity · doi:10.1016/j.immuni.2018.04.008 · Cited by 132 · see citing works
Quantitative Proteomics of All 14 Renal Tubule Segments in Rat
Limbutara, K., Chou, C., & Knepper, M. A. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020010071 · Cited by 130 · see citing works
Spatial proteomics in three-dimensional intact specimens
Bhatia, H. S., Brunner, A.-D., Öztürk, F., Kapoor, S., Rong, Z., Mai, H., Thielert, M., Ali, M., Al-Maskari, R., Paetzold, J. C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.021 · Cited by 129 · see citing works
Self-citationHistone Interaction Landscapes Visualized by Crosslinking Mass Spectrometry in Intact Cell Nuclei
Fasci, D., van Ingen, H., Scheltema, R. A., & Heck, A. J. R. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000924 · Cited by 128 · see citing works
In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale
Theillet, F. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00937 · Cited by 126 · see citing works
Mühlhofer, M., Berchtold, E., Stratil, C., Csaba, G., Kunold, E., Bach, N. C., Sieber, S. A., Haslbeck, M., Zimmer, R., & Büchner, J. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.109 · Cited by 125 · see citing works
Peptide Level Turnover Measurements Enable the Study of Proteoform Dynamics
Zecha, J., Meng, C., Zolg, D. P., Samaras, P., Wilhelm, M., & Küster, B. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000583 · Cited by 123 · see citing works
Ctortecka, C., Clark, N. M., Boyle, B. W., Seth, A., Mani, D. R., Udeshi, N. D., & Carr, S. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49651-w · Cited by 121 · see citing works
Mitochondrial translation is required for sustained killing by cytotoxic T cells
Lisci, M., Barton, P. R., Randzavola, L. O., Ma, C. Y., Marchingo, J. M., Cantrell, D. A., Paupe, V., Prudent, J., Stinchcombe, J. C., & Griffiths, G. M. (2021) · Science · doi:10.1126/science.abe9977 · Cited by 120 · see citing works
Hosp, F., Gutiérrez-Ángel, S., Schaefer, M. H., Cox, J., Meissner, F., Hipp, M. S., Hartl, F. U., Klein, R., Dudanova, I., & Mann, M. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.097 · Cited by 117 · see citing works
Self-citationItzhak, D. N., Davies, C. T. R., Tyanova, S., Mishra, A., Williamson, J. C., Antrobus, R., Cox, J., Weekes, M. P., & Borner, G. H. H. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.08.063 · Cited by 113 · see citing works
Self-citationSun, X.-M., Bowman, A., Priestman, M., Bertaux, F., Martínez-Segura, A., Tang, W., Whilding, C., Dormann, D., Shahrezaei, V., & Marguerat, S. (2020) · Current Biology · doi:10.1016/j.cub.2020.01.053 · Cited by 112 · see citing works
Nightingale, K., Lin, K., Ravenhill, B. J., Davies, C. T. R., Nobre, L., Fielding, C. A., Růčková, E., Fletcher-Etherington, A., Soday, L., Nichols, H., et al. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.07.011 · Cited by 111 · see citing works
Ryan, D. G., Knatko, E. V., Casey, A. M., Hukelmann, J. L., Naidu, S. D., Brenes, A. J., Ekkunagul, T., Baker, C. P., Higgins, M., Tronci, L., et al. (2021) · iScience · doi:10.1016/j.isci.2022.103827 · Cited by 111 · see citing works
Interplay between compartmentalized NAD + synthesis and consumption: a focus on the PARP familyPreprint
Cohen, M. S. (2020) · Genes & Development · doi:10.1101/gad.335109.119 · Cited by 109 · see citing works
Hoogendijk, A. J., Pourfarzad, F., Aarts, C. E. M., Tool, A. T. J., Hiemstra, I. H., Grassi, L., Frontini, M., Meijer, A. B., van den Biggelaar, M., & Kuijpers, T. W. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.082 · Cited by 108 · see citing works
Vildhede, A. M., Wiśniewski, J. R., Norén, A., Karlgren, M., & Artursson, P. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00334 · Cited by 106 · see citing works
Self-citationGautier, E., Leduc, M., Cochet, S., Bailly, K., Lacombe, C., Mohandas, N., Guillonneau, F., Nemer, W. E., & Mayeux, P. (2018) · Blood Advances · doi:10.1182/bloodadvances.2018023515 · Cited by 106 · see citing works
Petrosius, V., Aragon-Fernandez, P., Üresin, N., Kovács, G. E., Phlairaharn, T., Furtwängler, B., Beeck, J. O. D., Skovbakke, S. L., Goletz, S., Thomsen, S. F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41602-1 · Cited by 106 · see citing works
Rinschen, M. M., Gödel, M., Grahammer, F., Zschiedrich, S., Helmstädter, M., Kretz, O., Zarei, M., Braun, D. A., Dittrich, S., Pahmeyer, C., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.04.059 · Cited by 105 · see citing works
Crozier, L., Foy, R., Adib, R., Kar, A., Holt, J. A., Pareri, A. U., Valverde, J. M., Diazgranados, R. R., Weston, W. A., Wilson, R., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.10.016 · Cited by 105 · see citing works
A conserved phosphatase destroys toxic glycolytic side products in mammals and yeast
Collard, F., Baldin, F., Gerin, I., Bolsée, J., Noël, G., Graff, J., Veiga‐da‐Cunha, M., Stroobant, V., Vertommen, D., Houddane, A., et al. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2104 · Cited by 103 · see citing works
Quantitative interactomics in primary T cells unveils TCR signal diversification extent and dynamics
Voisinne, G., Kersse, K., Chaoui, K., Lu, L., Chaix, J., Zhang, L., Menoita, M. G., Girard, L., Ounoughene, Y., Wang, H., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0489-8 · Cited by 101 · see citing works
Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease
Cifani, P., & Kentsis, A. (2017) · PROTEOMICS · doi:10.1002/pmic.201600079 · Cited by 101 · see citing works
Determinants and Regulation of Protein Turnover in Yeast
Martin-Perez, M., & Villén, J. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.008 · Cited by 99 · see citing works
Prasad, B., Achour, B., Artursson, P., Hop, C. E. C. A., Lai, Y., Smith, P. C., Barber, J., Wiśniewski, J. R., Spellman, D. S., Uchida, Y., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1537 · Cited by 99 · see citing works
Self-citationRibosome Abundance Control Via the Ubiquitin–Proteasome System and Autophagy
An, H., & Harper, J. W. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.06.001 · Cited by 98 · see citing works
Sacco, F., Humphrey, S. J., Cox, J., Mischnik, M., Schulte, A. M., Klabunde, T., Schäfer, M., & Mann, M. (2016) · Nature Communications · doi:10.1038/ncomms13250 · Cited by 97 · see citing works
Self-citationInterleukin-2 shapes the cytotoxic T cell proteome and immune environment–sensing programs
Rollings, C. M., Sinclair, L. V., Brady, H. J. M., Cantrell, D. A., & Ross, S. H. (2018) · Science Signaling · doi:10.1126/scisignal.aap8112 · Cited by 97 · see citing works
A Temporal Proteomic Map of Epstein-Barr Virus Lytic Replication in B Cells
Ersing, I., Nobre, L., Wang, L. W., Soday, L., Ma, Y., Paulo, J. A., Narita, Y., Ashbaugh, C. W., Jiang, C., Grayson, N. E., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.04.062 · Cited by 97 · see citing works
A timeline of stable isotopes and mass spectrometry in the life sciences
Lehmann, W. D. (2016) · Mass Spectrometry Reviews · doi:10.1002/mas.21497 · Cited by 96 · see citing works
Filter-Aided Sample Preparation
Wiśniewski, J. R. (2016) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2016.09.013 · Cited by 96 · see citing works
Self-citationThe cellular economy of the Saccharomyces cerevisiae zinc proteome
Wang, Y., Weisenhorn, E., MacDiarmid, C. W., Andreini, C., Bucci, M. D., Taggart, J., Banci, L., Russell, J. D., Coon, J. J., & Eide, D. J. (2018) · Metallomics · doi:10.1039/c8mt00269j · Cited by 95 · see citing works
Membranes, energetics, and evolution across the prokaryote-eukaryote divide
Lynch, M., & Marinov, G. K. (2017) · eLife · doi:10.7554/elife.20437 · Cited by 92 · see citing works
Systematic discovery of biomolecular condensate-specific protein phosphorylation
Sridharan, S., Hernandez-Armendariz, A., Kurzawa, N., Potel, C. M., Memon, D., Beltrão, P., Bantscheff, M., Huber, W. K., Cuylen‐Haering, S., & Savitski, M. M. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01062-y · Cited by 92 · see citing works
Wiśniewski, J. R., Duś‐Szachniewicz, K., Ostasiewicz, P., Ziolkowski, P. P., Rakus, D., & Mann, M. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00523 · Cited by 91 · see citing works
Self-citationCanagliflozin Impairs T-Cell Effector Function via Metabolic Suppression in Autoimmunity
Jenkins, B., Blagih, J., Ponce-Garcia, F. M., Canavan, M., Gudgeon, N. H., Eastham, S., Hill, D., Hanlon, M. M., Ma, E. H., Bishop, E. L., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2023.05.001 · Cited by 88 · see citing works
Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring
Lawless, C., Holman, S. W., Brownridge, P., Lanthaler, K., Harman, V. M., Watkins, R. H., Hammond, D. E., Miller, R. L., Sims, P. F. G., Grant, C. M., et al. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.054288 · Cited by 87 · see citing works
BAP1 constrains pervasive H2AK119ub1 to control the transcriptional potential of the genomePreprint
Fursova, N. A., Turberfield, A. H., Blackledge, N. P., Findlater, E. L., Lastuvkova, A., Huseyin, M. K., Dobrinić, P., & Klose, R. J. (2021) · Genes & Development · doi:10.1101/gad.347005.120 · Cited by 87 · see citing works
- Authors
- Wiśniewski, J. R., Hein, M. Y., Cox, J., & Mann, M.
- Affiliations
- Max Planck Institute of Biochemistry (DE)
- Corresponding author
- Wiśniewski, Jacek R.
- Mann, Matthias
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 13
- Issue
- 12
- Pages
- 3497-3506
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC4256500
- OpenAlex
- W2152983548
- Cited by
- 690
- References
- 44
- Citations by year
- 2026: 58 · 2025: 77 · 2024: 82 · 2023: 67 · 2022: 69 · 2021: 57 · 2020: 73 · 2019: 40 · 2018: 62 · 2017: 39 · 2016: 35 · 2015: 25 · 2014: 5 · 2012: 1
- FWCI
- 14.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- European Commission (201648)
- Max-Planck-Gesellschaft
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- absolute protein quantification, histones, DNA content
- MeSH terms
- Amino Acids, Animals, Cell Count, DNA, Histones, Humans, Isotope Labeling, Reference Standards, Gene Expression, Proteome, Cell Line, Tumor, Mice
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