The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index; 2 of the 34 sources its claims cite were checked for support.
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.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
27 Own interpretation · 27 Own result · 26 Not checked yet · 10 No basis given · 1 Supported so far · 1 Own method
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“which in turn depends on the number of cells.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
What the sources say
- Not checked yetcites [23] Papadimitriou, 1993, Measurement of cell numbers in microtiter culture plates using the fluorescent dye Hoechst 33258, J. Immunol. Methods source
- Supportedcites [24] Shimada, 2010, Normalization using ploidy and genomic DNA copy number allows absolute quantification of transcripts, proteins and metabolites in cells, Plant Methods source
In Results and Discussion, Table 1 expresses genomic DNA copy number per sample volume and genome copy number per cell, supporting the relationship between sample DNA and cell number. The paper also accounts for ploidy, which affects DNA copy number per cell. (LLM, conf=0.85)
- Supportedcites [25] Silva, 2013, Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines, Anal. Chem source
In Results and Discussion (“DNA concentration is a robust surrogate for cell number”), DNA concentration had a strong linear relationship with seeded cell number (R² = 0.98), supporting the claim that DNA amount depends on cell number. The Conclusions note that ploidy, nuclei per cell, and cell-cycle effects can alter the DNA-to-cell-number ratio. (LLM, conf=0.92)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we demonstrate its usefulness with a series of mouse organ proteomes.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We were able to quantify proteins within their expected physiological ranges over at least 5 orders of magnitude (Fig. 1, supplemental Table S1).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the fraction of the cumulative histone to total MS signal changed only by 5% to 10% (Fig. 4D).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The analyzed cell lines have diploid genomes, so their DNA mass per cell is 6.5 pg.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Within the first few thousand peptides, the histone fraction is overestimated because histones contribute some of the most intense peptides.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“From a depth of around 12,000 or more peptides, however, the histone fraction stabilizes within tight margins.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results were within a factor of 1.01 ± 0.13 of the biochemical measurements”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The analyzed cell lines have diploid genomes, so their DNA mass per cell is 6.5 pg.
- It may state two separate claims as one. 20
- Probably not a substantial claim. 9
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Yields were greater than 95% and were independent of the protein content (Fig. 2B)”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In triplicate analyses, MaxQuant identified about 7000 proteins in each of the cell lines (supplemental Table S1).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [36] Luber, 2010, Quantitative proteomics reveals subset-specific viral recognition in dendritic cells, Immunity source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Ovaries consist predominantly of small follicular cells and showed the least protein per cell (42 pg).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [23] Papadimitriou, 1993, Measurement of cell numbers in microtiter culture plates using the fluorescent dye Hoechst 33258, J. Immunol. Methods source
- Not checked yetcites [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 yetcites [25] Silva, 2013, Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines, Anal. Chem source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The total protein concentration in plasma varies around a typical value of 70 g/l within a narrow margin (22)”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [22] Kratz, 2004, Case records of the Massachusetts General Hospital. Weekly clinicopathological exercises. Laboratory reference values, N. Engl. J. Med source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
What the sources say
- Not checked yetcites [16] Wisniewski, 2012, Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma, Mol. Syst. Biol source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > Abstract
What the sources say
- Not checked yetcites [15] Crowe, 1993, Morphometric analysis of small intestinal mucosa. IV. Determining cell volumes, Virchows Archive A Pathol. Anat. Histopathol source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [26] Zhou, 2011, Proteomic reactors and their applications in biology, FEBS J source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“The human genome contains around 3.2 × 109 base pairs (28).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [28] 2004, Finishing the euchromatic sequence of the human genome, Nature source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [29] van Holde, 1989
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This depth of analysis is easily attainable with minimal sample fractionation and also with single run analyses on latest-generation machines (30).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which is reasonable given that non-hepatocytes contribute the same amount of DNA or histones but less overall protein mass.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which is expected to be slightly smaller than observed cell sizes, given the numerous dendrites projecting from the cell surfaces.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This observation underlines the benefits of approaches that rely on multiple independent quantifications instead of single peptide ratios”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- 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.
In the paper
“Perseus is part of the freely available MaxQuant suite (20).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [20] Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
What the sources say
- Not checked yetcites [38] Warringer, 2006, Evolutionary constraints on yeast protein size, BMC Evolutionary Biol source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, this information comes for free, in that it incorporates absolute quantification into any kind of in-depth proteome dataset, even in retrospective analysis.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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?
In the citing paper
“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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
“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.”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
“C, pyruvate dehydrogenase complex. D, TRiC chaperonin.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
“we ranked all peptides by intensity and calculated the histone-MS fraction as a function of the number of identified peptides (Fig. 4E).”
§ A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards* > RESULTS
“D, histone MS signal fraction as a function of the depth of analysis, simulated by intensity-based ranking of peptides.”
Citations
Cites
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
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
Self-citationMass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
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
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 5,520
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
Self-citationFinishing the euchromatic sequence of the human genome
International Human Genome Sequencing Consortium (2004) · Nature · doi:10.1038/nature03001 · Cited by 4,971
Rochester, C. L., & Kradin, R. L. (2000) · New England Journal of Medicine · doi:10.1056/nejm200012213432509 · Cited by 3,875
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
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
Cited by
Sorted by citations
Items 301–400 of 665
Expression of transglutaminase 2 in human gut epithelial cells: Implications for coeliac disease
Amundsen, S. F., Stamnæs, J., Lundin, K. E. A., & Sollid, L. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287662 · Cited by 21
Distinct regulation of ATM signaling by DNA single-strand breaks and APE1
Zhao, H., Li, J., You, Z., Lindsay, H. D., & Yan, S. (2024) · Nature Communications · doi:10.1038/s41467-024-50836-6 · Cited by 22
Suh, J. L., Watts, B. E., Stuckey, J. I., Norris-Drouin, J. L., Cholensky, S. H., Dickson, B. M., An, Y., Mathea, S., Salah, E., Knapp, S., et al. (2018) · Biochemistry · doi:10.1021/acs.biochem.8b00150 · Cited by 21
The non-catalytic role of DNA polymerase epsilon in replication initiation in human cells
Vipat, S., Gupta, D., Jonchhe, S., Anderspuk, H., Rothenberg, E., & Moiseeva, T. N. (2022) · Nature Communications · doi:10.1038/s41467-022-34911-4 · Cited by 21
Lean-Proteome Strains – Next Step in Metabolic Engineering
Valgepea, K., Peebo, K., Adamberg, K., & Vilu, R. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00011 · Cited by 22
Temporal photoproximity labeling of ligand-activated EGFR neighborhoods using MultiMap
Lin, Z., Ngo, W., Chou, Y., Wu, H., Susa, K. J., Jun, Y., Bivona, T. G., Doudna, J. A., & Wells, J. A. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-02076-y · Cited by 23
Edwards, W., Greco, T. M., Miner, G. E., Barker, N. K., Herring, L. E., Cohen, S., Cristea, I. M., & Conlon, F. L. (2023) · Developmental Cell · doi:10.1016/j.devcel.2023.04.011 · Cited by 21
MHC cross-dressing in antigen presentation
MacNabb, B. W., & Kline, J. P. (2023) · Advances in immunology · doi:10.1016/bs.ai.2023.07.001 · Cited by 22
Proteomic landscape of the primary somatosensory cortex upon sensory deprivation
Kole, K., Lindeboom, R. G. H., Baltissen, M. P. A., Jansen, P. W. T. C., Vermeulen, M., Tiesinga, P. H. E., & Celikel, T. (2017) · GigaScience · doi:10.1093/gigascience/gix082 · Cited by 20
New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling
Erickson, K. E., Rukhlenko, O. S., Posner, R. G., Hlavacek, W. S., & Kholodenko, B. Ν. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.02.008 · Cited by 21
Zuzow, N., Ghosh, A., Leonard, M., Liao, J., Yang, B., & Bennett, E. J. (2018) · Molecular Biology of the Cell · doi:10.1091/mbc.e17-12-0714 · Cited by 21
Small Heat Shock Proteins: Protein Aggregation Amelioration and Neuro- and Age-Protective Roles
Albinhassan, T. H., Alharbi, B. M., AlSuhaibani, E. S., Mohammad, S., & Malik, S. S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26041525 · Cited by 19
Iron deficiency causes aspartate-sensitive dysfunction in CD8+ T cells
Teh, M. R., Gudgeon, N. H., Frost, J. N., Sinclair, L. V., Smith, A., Millington, C. L., Kronsteiner, B., Roberts, J., Marzullo, B. P., Murray, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60204-7 · Cited by 20
Single cell proteomic analysis defines discrete neutrophil functional states in human glioblastoma
Sadiku, P., Brenes, A. J., Mayer, R. L., Reyes, L., Coelho, P., van Stralen, G., Zhang, A., Sánchez-García, M. A., Watts, E., Liaquat, I., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67367-3 · Cited by 20
Zheng, L., Wang, R., Zhou, P., Pan, Y., Shen, R. F., & Lan, P. (2023) · Journal of Proteomics · doi:10.1016/j.jprot.2023.104894 · Cited by 19
Homogenous Phase Enrichment of Cysteine-Containing Peptides for Improved Proteome Coverage
Wiśniewski, J. R., & Pruś, G. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b01215 · Cited by 19
Self-citationMore than a Toxin: Protein Inventory of Clostridium tetani Toxoid Vaccines
Møller, J. R., Kraner, M., & Burkovski, A. (2019) · Proteomes · doi:10.3390/proteomes7020015 · Cited by 19
Cardiac mitochondrial function depends on BUD23 mediated ribosome programming
Baxter, M. D., Voronkov, M., Poolman, T. M., Galli, G. L. J., Pinali, C., Goosey, L. C., Knight, A., Krakowiak, K., Maidstone, R. J., Iqbal, M., et al. (2020) · eLife · doi:10.7554/elife.50705 · Cited by 19
Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World
Silvestre, D. D., Bergamaschi, A., Bellini, E. N., & Mauri, P. (2018) · Proteomes · doi:10.3390/proteomes6020027 · Cited by 19
Barber, J., Al‐Majdoub, Z. M., Couto, N. A. S., Howard, M., Elmorsi, Y. M., Scotcher, D., Alizai, N. K., de Wildt, S. N., Stader, F., Sepp, A., et al. (2023) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2023.106375 · Cited by 18
Orsburn, B. C. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00212 · Cited by 19
Chemical cross-linking and mass spectrometry enabled systems-level structural biology
Botticelli, L., Bakhtina, A. A., Kaiser, N. K., Keller, A., McNutt, S. W., Bruce, J. E., & Feixia Chu, C. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102872 · Cited by 18
Exploring the unmapped cysteine redox proteoform landscape
Cobley, J. N. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00152.2024 · Cited by 23
Esnault, S. J., Hebert, A. S., Jarjour, N. N., Coon, J. J., & Mosher, D. F. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00057 · Cited by 18
Pinho, N., Wiśniewski, J. R., Dias-Lopes, G., Vahia, L. S., Bombaça, A. C. S., Mesquita‐Rodrigues, C., Menna‐Barreto, R. F. S., Cupolillo, E., de Jesus, J. B., Padrón, G., et al. (2020) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008509 · Cited by 18
Self-citationSánchez, B. J., Lahtvee, P., Campbell, K., Kasvandik, S., Yu, R., Domenzain, I., Zelezniak, A., & Nielsen, J. (2021) · PROTEOMICS · doi:10.1002/pmic.202000093 · Cited by 18
Spinelli, L., Marchingo, J. M., Nomura, A., Damasio, M. P. S., & Cantrell, D. A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.691997 · Cited by 18
Richards, A., Chen, K., Wilburn, D. B., Stevenson, E. J., Polacco, B. J., Searle, B. C., & Swaney, D. L. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00960 · Cited by 19
Lin, Y., Li, J., Zhao, H., McMahon, A., McGhee, K., & Yan, S. (2023) · eLife · doi:10.7554/elife.82324 · Cited by 18
Holland, D. O., & Johnson, M. E. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006022 · Cited by 18
Autophagy-regulated mitochondrial inheritance controls early CD8+ T cell fate commitment
Borsa, M., Lechuga‐Vieco, A. V., Kayvanjoo, A. H., Jenkins, E. M., Yazicioglu, Y. F., Compeer, E. B., Richter, F. C., Rapp, S., Mitchell, R., Youdale, T., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01835-2 · Cited by 18
Carvalho, L. B., Teigas-Campos, P. A. D., Jorge, S., Protti, M., Mercolini, L., Dhir, R., Wiśniewski, J. R., Lodeiro, C., Santos, H. M., & Capelo, J. L. (2023) · Talanta · doi:10.1016/j.talanta.2023.124953 · Cited by 17
Self-citationA systems approach reveals species differences in hepatic stress response capacity
Russomanno, G., Sison-Young, R. L. C., Livoti, L. A., Coghlan, H., Jenkins, R. E., Kunnen, S. J., Fisher, C., Reddyhoff, D., Gardner, I. B., Rehman, A. H., et al. (2023) · Toxicological Sciences · doi:10.1093/toxsci/kfad085 · Cited by 17
Mijit, M., Kpenu, E. K., Chowdhury, N. N., Gampala, S., Wireman, R., Liu, S., Babb, O., Georgiadis, M. M., Wan, J., Fishel, M. L., et al. (2023) · Redox Biology · doi:10.1016/j.redox.2023.102977 · Cited by 17
Nicholas, B., Bailey, A. G., Staples, K. J., Wilkinson, T. M. A., Elliott, T. J., & Skipp, P. J. S. (2022) · PLoS Pathogens · doi:10.1371/journal.ppat.1009894 · Cited by 19
Proteomic quantification of perturbation to pharmacokinetic target proteins in liver disease
Vasilogianni, A., El‐Khateeb, E., Al‐Majdoub, Z. M., Alrubia, S., Rostami‐Hodjegan, A., Barber, J., & Achour, B. (2022) · Journal of Proteomics · doi:10.1016/j.jprot.2022.104601 · Cited by 17
High-resolution kinetic characterization of the RIG-I-signaling pathway and the antiviral response
Burkart, S. S., Schweinoch, D., Frankish, J., Sparn, C., Wüst, S., Urban, C., Merlo, M., Magalhães, V. G., Piras, A., Pichlmair, A., et al. (2023) · Life Science Alliance · doi:10.26508/lsa.202302059 · Cited by 18
Ross, S. H., Rollings, C. M., & Cantrell, D. A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.712402 · Cited by 17
Ciuffa, R., Uliana, F., Mannion, J., Mehnert, M., Tenev, T., Marulli, C., Satanowski, A., Keller, L. M. L., Rodilla-Ramírez, P. N., Ori, A., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2117175119 · Cited by 17
Current approaches and challenges in targeted absolute quantification of membrane proteins
Trötschel, C., & Poetsch, A. (2014) · PROTEOMICS · doi:10.1002/pmic.201400427 · Cited by 16
Giguère, S., Guise, A. J., Beltran, P. M. J., Joshi, P., Greco, T. M., Schenck, O. L., Kong, J., & Cristea, I. M. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.054619 · Cited by 16
Lombard‐Banek, C., & Schiel, J. E. (2020) · Molecules · doi:10.3390/molecules25061396 · Cited by 16
Föll, M. C., Volkmann, V., Enderle-Ammour, K., Timme, S., Wilhelm, K., Guo, D., Olga Vitek, A., Bronsert, P., & Schilling, O. (2022) · Clinical Proteomics · doi:10.1186/s12014-022-09347-z · Cited by 15
Zhang, T., Zou, P., Fang, Y., & Li, Y. (2022) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2335 · Cited by 15
Dolgalev, G. V., Safonov, T. A., Arzumanian, V. A., Kiseleva, O. I., & Poverennaya, E. V. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032081 · Cited by 17
Molecules interact. But how strong and how much?
Weimer, K., Zámbó, B., & Gógl, G. (2023) · BioEssays · doi:10.1002/bies.202300007 · Cited by 16
Abdul-Khalek, N., Wimmer, R., Overgaard, M. T., & Echers, S. G. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.07.027 · Cited by 18
Zheng, L., Boeren, S. A., Liu, C., Bakker, W., Wang, H., Rietjens, I. M. C. M., & Saccenti, E. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.132859 · Cited by 15
Burat, B., Reynaerts, A., Baiwir, D., Fléron, M., Gohy, S. T., Eppe, G., Leal, T., & Mazzucchelli, G. (2022) · Cells · doi:10.3390/cells11152358 · Cited by 15
Defining and refining the cysteine redoxome through sulfur chemical biology
Carroll, K. S., & Yang, J. (2026) · Nature Chemical Biology · doi:10.1038/s41589-026-02145-w · Cited by 17
Concomitant deletion of Ptpn6 and Ptpn11 in T cells fails to improve anticancer responses
Ventura, P., Gakovic, M., Fischer, B. A., Spinelli, L., Rota, G., Pathak, S., Khameneh, H. J., Zenobi, A., Thomson, S., Birchmeier, W., et al. (2022) · EMBO Reports · doi:10.15252/embr.202255399 · Cited by 14
Constant-complexity stochastic simulation algorithm with optimal binning
Sanft, K. R., & Othmer, H. G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4928635 · Cited by 14
Michalsen, M. M., King, A. S., Istok, J. D., Crocker, F. H., Fuller, M. E., Kucharzyk, K. H., & Gander, M. J. (2019) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2019.121529 · Cited by 14
Ayash, M., Abukhalaf, M., Thieme, D., Proksch, C., Heilmann, M., Schattat, M., & Hoehenwarter, W. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.744103 · Cited by 14
Systems-level conservation of the proximal TCR signaling network of mice and humans
Nicolas, P., Ollier, J., Mori, D., Voisinne, G., Celis‐Gutierrez, J., Grégoire, C., Perroteau, J., Vivien, R., Camus, M., Burlet‐Schiltz, O., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20211295 · Cited by 15
Proteomics Unveils Fibroblast–Cardiomyocyte Lactate Shuttle and Hexokinase Paradox in Mouse Muscles
Rakus, D., Gizak, A., & Wiśniewski, J. R. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b01149 · Cited by 14
Self-citationKarim, M. R., Wang, R., Zheng, L., Dong, X., Shen, R. F., & Lan, P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21062119 · Cited by 14
Ziemian, S., Green, C., Sourbron, S. P., Jošt, G., Schütz, G., & Hines, C. D. G. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4401 · Cited by 14
Vaughan, R. M., Kupai, A., Foley, C. A., Sagum, C. A., Tibben, B. M., Eden, H. E., Tiedemann, R. L., Berryhill, C. A., Patel, V., Shaw, K. M., et al. (2020) · Epigenetics & Chromatin · doi:10.1186/s13072-020-00366-4 · Cited by 15
Genome concentration limits cell growth and modulates proteome composition in Escherichia coliPreprint
Mäkelä, J., Papagiannakis, A., Lin, W.-H., Lanz, M. C., Glenn, S., Swaffer, M. P., Marinov, G. K., Skotheim, J. M., & Jacobs, C. (2024) · eLife · doi:10.7554/elife.97465 · Cited by 14
Glycolysis model shows that allostery maintains high ATP and limits accumulation of intermediates
Choe, M., Einav, T., Phillips, R., & Titov, D. V. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.03.037 · Cited by 13
Benchmarking informatics workflows for data-independent acquisition single-cell proteomics
Wang, J., Huang, Y., Lu, F., Xu, Q., Yang, Z., Jiang, Y., Shi, S.-W., Pan, J., Yang, Y., & Fang, Q. (2025) · Nature Communications · doi:10.1038/s41467-025-65174-4 · Cited by 13
Single‐Cell and Spatial Omics: Methods and Applications
Cen, X., Huang, X., Deng, E., Gong, X., Tan, N., Ye, J. T., Wang, Y., Eils, R., Luo, Q., Li, Y., et al. (2026) · MedComm · doi:10.1002/mco2.70713 · Cited by 14
In-Cell Dynamics: The Next Focus of All-Atom Simulations
Samuel, P. P., Alaeen, S., & Pogorelov, T. V. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c05166 · Cited by 13
Papoin, J., Yan, H., Leduc, M., Gall, M. L., Narla, A., Palis, J., Steiner, L. A., Gallagher, P. G., Hillyer, C. D., Gautier, E., et al. (2023) · American Journal of Hematology · doi:10.1002/ajh.27145 · Cited by 13
Integrating the analysis of human biopsies using post‐translational modifications proteomics
Bhardwaj, S., Bulluss, M., D’Aubeterre, A., Derakhshani, A., Penner, R., Mahajan, M., Mahajan, V. B., & Dufour, A. (2024) · Protein Science · doi:10.1002/pro.4979 · Cited by 15
Ghosh, S., Tuz, A. A., Stenzel, M., Singh, V., Richter, M., Soehnlein, O., Lange, E., Heyer, R., Cibir, Z., Beer, A., et al. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100858 · Cited by 13
Cutler, R. R., Corveleyn, L., Ctortecka, C., Licausi, F., Cantlon-Bruce, J., Jácome, A. S. V., Deforce, D. L. D., Vijg, J., Dhaenens, M., Papanastasiou, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66031-0 · Cited by 13
Unfair competition governs the interaction of pCPI-17 with myosin phosphatase (PP1-MYPT1)
Filter, J. J., Williams, B. C., Eto, M., Shalloway, D. I., & Goldberg, M. L. (2017) · eLife · doi:10.7554/elife.24665 · Cited by 13
Grebennikov, D. S., Bouchnita, A., Volpert, V. A., Bessonov, N. M., Meyerhans, A., & Bocharov, G. A. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01213 · Cited by 13
Generation and proteome profiling of PBMC-originated, iPSC-derived lentoid bodies
Ali, M., Kabir, F., Raskar, S., Renuse, S., Na, C. H., Delannoy, M. R., Khan, S. Y., & Riazuddin, S. (2020) · Stem Cell Research · doi:10.1016/j.scr.2020.101813 · Cited by 13
Quantitative proteomics identifies PTP1B as modulator of B cell antigen receptor signaling
Schwarz, J., Grundmann, L., Kokot, T., Kläsener, K., Fotteler, S., Medgyesi, D., Köhn, M., Reth, M. G., & Warscheid, B. (2021) · Life Science Alliance · doi:10.26508/lsa.202101084 · Cited by 13
A critical evaluation of ultrasensitive single-cell proteomics strategies
Nalehua, M. R., & Zaia, J. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05171-6 · Cited by 13
Sharma, N., LaRusch, J., Sosnay, P. R., Gottschalk, L., López, A. P., Pellicore, M. J., Evans, T. A., Davis-Marcisak, E. F., Atalar, M., Na, C. H., et al. (2016) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00363.2016 · Cited by 13
Kirk, I. K., Weinhold, N., Belling, K., Skakkebæk, N. E., Jensen, T. S., Leffers, H. C. B., Juul, A., & Brunak, S. (2017) · Cell Systems · doi:10.1016/j.cels.2017.01.001 · Cited by 14
Fine-tuning BACH2 dosage balances stemness and effector function to enhance antitumor T cell therapy
Conti, A. G., Evans, A. C., von Linde, T., Deguit, C. D. T., Whiteside, S. K., Wesolowski, A. J., Imianowski, C. J., Yamashita-Kanemaru, Y., Dahmani, L., Chapman, J., et al. (2026) · Nature Immunology · doi:10.1038/s41590-025-02389-z · Cited by 12
Prospects and challenges in using neuronal extracellular vesicles in biomarker research
Rocha, D. A. S., Santos, L. E., Fonseca, P. B. D., & Felice, F. G. D. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13918 · Cited by 12
Yang, S., Wang, W., Rao, Q., Xu, Y., Zhang, S., Qu, Y., Zhuang, Q.-C., Mao, J., Sun, L., Geng, D., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-012331 · Cited by 12
Hakobyan, A., Liesack, W., & Glatter, T. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00216 · Cited by 12
Wang, W., Mulder, T., Cai, J., Zhang, C., Wong, S., Yan, Z., Khojasteh, S. C., & Zhang, D. (2020) · Xenobiotica · doi:10.1080/00498254.2020.1795949 · Cited by 12
Asensio, J. O., Verheijen, M. C. T., & Caiment, F. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.04.017 · Cited by 12
Aulin, L. B. S., Tandar, S. T., van Zijp, T., van Ballegooie, E., van der Graaf, P. H., Saleh, M. A. A., Välitalo, P. A. J., & van Hasselt, J. G. C. (2022) · Clinical Pharmacokinetics · doi:10.1007/s40262-022-01186-3 · Cited by 12
Interlaboratory Variability in the Madin–Darby Canine Kidney Cell Proteome
Harwood, M. D., Zettl, K., Weinheimer, M., Pilla-Reddy, V., Shen, H., Jacobs, F., Chu, X., Huth, F., Nakakariya, M., Chothe, P. P., et al. (2023) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c00108 · Cited by 12
Self-citationEffects of Dimerization on the Deacylase Activities of Human SIRT2
Yang, J., Nicely, N. I., & Weiser, B. P. (2023) · Biochemistry · doi:10.1021/acs.biochem.3c00381 · Cited by 12
In vitro assay to determine SUMOylation sites on protein substrates
McManus, F. P., Altamirano, C. D., & Thibault, P. (2016) · Nature Protocols · doi:10.1038/nprot.2016.023 · Cited by 14
Single-molecule dynamics of the TRiC chaperonin system in vivo
Li, R., Dalheimer, N., Müller, M. B. D., & Hartl, F. U. (2026) · Nature · doi:10.1038/s41586-025-10073-3 · Cited by 11
Boulanger, D. S. M., Douglas, L. R., Duriez, P. J., Kang, Y., Dalchau, N., James, E., & Elliott, T. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.956603 · Cited by 11
Nakamura, H., Kono, N., Mori, M., Masunaga, H., Numata, K., & Arakawa, K. (2023) · Biomacromolecules · doi:10.1021/acs.biomac.2c01474 · Cited by 11
Kinetic modeling of leucine-mediated signaling and protein metabolism in human skeletal muscle
McColl, T. J., & Clarke, D. C. (2023) · iScience · doi:10.1016/j.isci.2023.108634 · Cited by 12
Ortiz, R. C., Knerler, S. A., Fridman, L. B., Mercado, A., Price, A. C., Rosado‐Franco, J. J., Wilkins, H. N., Flores, B. R., Orsburn, B. C., & Williams, D. W. (2024) · Fluids and Barriers of the CNS · doi:10.1186/s12987-023-00507-3 · Cited by 11
Dai, S., Liu, S., Zhou, C., Yu, F., Zhu, G., Zhang, W., Deng, H., Burlingame, A. L., Yu, W., Wang, T., et al. (2023) · Molecular Plant · doi:10.1016/j.molp.2023.03.013 · Cited by 11
Ribonucleic Acid-Mediated Control of Protein Translation Under Stress
Payea, M. J., Dar, S. A., Malla, S., & Maragkakis, M. (2023) · Antioxidants and Redox Signaling · doi:10.1089/ars.2023.0233 · Cited by 11
Gizak, A., Grenda, M., Mamczur, P., Wiśniewski, J., Sucharski, F., Silberring, J., McCubrey, J. A., Wiśniewski, J. R., & Rakus, D. (2015) · Oncotarget · doi:10.18632/oncotarget.4044 · Cited by 11
Self-citationOrsburn, B. C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.05.574437 · Cited by 11
An isoform of the giant protein titin is a master regulator of human T lymphocyte trafficking
Toffali, L., D’Ulivo, B., Giagulli, C., Montresor, A., Zenaro, E., Delledonne, M., Rossato, M., Iadarola, B., Sbarbati, A., Bernardi, P., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112516 · Cited by 11
Hogeling, S. M., Lê, D. M., Rose, N. L., Kwon, M. C., Wierenga, A. T. J., van den Heuvel, F. A. J., van den Boom, V., Kuchnio, A., Philippar, U., Huls, G. A., et al. (2024) · Haematologica · doi:10.3324/haematol.2024.285616 · Cited by 10
Dynamic Organellar Mapping in yeast reveals extensive protein localization changes during ER stress
Platzek, A., Odehnalová, K., Schessner, J. P., Borner, G. H. H., & Schuck, S. (2025) · Nature Communications · doi:10.1038/s41467-025-66946-8 · Cited by 10
Autophagolysosomal exocytosis inverts Src kinase onto the cell surface in cancer
Delaveris, C. S., Loudermilk, R. P., Pandey, A. K., Remesh, S. G., Peters-Clarke, T. M., Ganjave, S. D., Dougherty, W., Delavan, H. M., Wang, C., Ling, J., et al. (2026) · Science · doi:10.1126/science.aec1778 · Cited by 11
Systematic differences in protein stability underlie species-specific developmental tempo
Matsuda, M., Hammarén, H. M., Lázaro, J., Savitski, M. M., & Ebisuya, M. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.07.012 · Cited by 10
Huang, X., Xing, X., Ng, C. N., & Yobaş, L. (2019) · Micromachines · doi:10.3390/mi10070488 · Cited by 10
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
