Juri Rappsilber

Proteomics is critically dependent on optimal sample preparation. Particularly, the interface between protein digestion and mass spectrometric analysis has a large influence on the overall quality and sensitivity of the analysis. We here describe a novel procedure in which a very small disk of beads embedded in a Teflon meshwork is placed as a microcolumn into pipet tips. Termed Stage, for STop And Go Extraction, the procedure has been implemented with commercially available material (C18 Empore Disks (3M, Minneapolis, MN)) as frit and separation material. The disk is introduced in a simple and fast process yielding a convenient and completely reliable procedure for the production of self-packed microcolumns in pipet tips. It is held in place free of obstacles solely by the narrowing tip, ensuring optimized loading and elution of analytes. Five disks are conveniently placed in 1 min, adding <0.1 cent in material costs to the price of each tip. The system allows fast loading with low backpressure (>300 micro/min for the packed column using manual force) while eliminating the possibility of blocking. The loading capacity of C18-StageTips (column bed: 0.4 mm diameter, 0.5 mm length) is 2-4 microg of protein digest, which can be increased by using larger diameter or stacked disks. Five femtomole of tryptic BSA digest could be recovered quantitatively. We have found that the Stage system is well-suited as a universal sample preparation system for proteomics.

To estimate absolute protein contents in complex mixtures, we previously defined a protein abundance index (PAI) as the number of observed peptides divided by the number of observable peptides per protein (Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome. Res. 12, 1231–1245). Here we report that PAI values obtained at different concentrations of serum albumin show a linear relationship with the logarithm of protein concentration in LC-MS/MS experiments. This was also the case for 46 proteins in a mouse whole cell lysate. For absolute quantitation, PAI was converted to exponentially modified PAI (emPAI), equal to 10PAI minus one, which is proportional to protein content in a protein mixture. For the 46 proteins in the whole lysate, the deviation percentages of the emPAI-based abundances from the actual values were within 63% on average, similar or better than determination of abundance by protein staining. emPAI was applied to comprehensive protein expression analysis and to a comparison study between gene and protein expression in a human cancer cell line, HCT116. The values of emPAI are easily calculated and add important quantitation information to proteomic experiments; therefore we suggest that they should be reported in large scale proteomic identification projects. To estimate absolute protein contents in complex mixtures, we previously defined a protein abundance index (PAI) as the number of observed peptides divided by the number of observable peptides per protein (Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome. Res. 12, 1231–1245). Here we report that PAI values obtained at different concentrations of serum albumin show a linear relationship with the logarithm of protein concentration in LC-MS/MS experiments. This was also the case for 46 proteins in a mouse whole cell lysate. For absolute quantitation, PAI was converted to exponentially modified PAI (emPAI), equal to 10PAI minus one, which is proportional to protein content in a protein mixture. For the 46 proteins in the whole lysate, the deviation percentages of the emPAI-based abundances from the actual values were within 63% on average, similar or better than determination of abundance by protein staining. emPAI was applied to comprehensive protein expression analysis and to a comparison study between gene and protein expression in a human cancer cell line, HCT116. The values of emPAI are easily calculated and add important quantitation information to proteomic experiments; therefore we suggest that they should be reported in large scale proteomic identification projects. Proteomic LC-MS approaches combined with genome-annotated databases currently allow identification of thousands of proteins from complex mixtures (1Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Google Scholar). Approaches have also been developed for relative quantitation using stable isotope labeling (2Oda Y. Huang K. Cross F.R. Cowburn D. Chait B.T. Accurate quantitation of protein expression and site-specific phosphorylation.Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 6591-6596Google Scholar, 3Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.Nat. Biotechnol. 1999; 17: 994-999Google Scholar, 4Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Google Scholar). Recently not only comprehensive quantitation studies between two states (5MacCoss M.J. Wu C.C. Liu H. Sadygov R. Yates III, J.R. A correlation algorithm for the automated quantitative analysis of shotgun proteomics data.Anal. Chem. 2003; 75: 6912-6921Google Scholar, 6Foster L.J. De Hoog C.L. Mann M. Unbiased quantitative proteomics of lipid rafts reveals high specificity for signaling factors.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 5813-5818Google Scholar) but also protein-protein (7Blagoev B. Kratchmarova I. Ong S.E. Nielsen M. Foster L.J. Mann M. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling.Nat. Biotechnol. 2003; 21: 315-318Google Scholar, 8Ranish J.A. Yi E.C. Leslie D.M. Purvine S.O. Goodlett D.R. Eng J. Aebersold R. The study of macromolecular complexes by quantitative proteomics.Nat. Genet. 2003; 33: 349-355Google Scholar), protein-peptide (9Schulze W.X. Mann M. A novel proteomic screen for peptide-protein interactions.J. Biol. Chem. 2004; 279: 10756-10764Google Scholar), and protein-drug (10Oda Y. Owa T. Sato T. Boucher B. Daniels S. Yamanaka H. Shinohara Y. Yokoi A. Kuromitsu J. Nagasu T. Quantitative chemical proteomics for identifying candidate drug targets.Anal. Chem. 2003; 75: 2159-2165Google Scholar) interaction analyses have been reported. So far, however, a comprehensive approach for determining protein concentrations in one sample has not been established. Protein concentrations are one of the most basic and important parameters in quantitative proteomics because the kinetics/dynamics of the cellular proteome is described in terms of changes in the concentrations of proteins in particular compartments. Biological experiments often require at least some information on protein abundance for correct interpretation. In the past, crude quantitative information could be drawn from the intensity of gel staining in comparison to a known amount of marker protein. However, in complex mixture analysis, individual proteins cannot be stained individually, and usually all information about protein abundance is lost. So far, isotope-labeled synthetic peptides have been used as internal standards for absolute quantitation of particular proteins of interest (11Barr J.R. Maggio V.L. Patterson Jr., D.G. Cooper G.R. Henderson L.O. Turner W.E. Smith S.J. Hannon W.H. Needham L.L. Sampson E.J. Isotope dilution–mass spectrometric quantification of specific proteins: model application with apolipoprotein A-I.Clin. Chem. 1996; 42: 1676-1682Google Scholar, 12Gerber S.A. Rush J. Stemman O. Kirschner M.W. Gygi S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 6940-6945Google Scholar). This approach is in principle applicable to comprehensive analysis but is hampered by the high cost of isotope-labeled peptides as well as the difficulty of quantitative digestion of proteins in-gel (13Havlis J. Shevchenko A. Absolute quantification of proteins in solutions and in polyacrylamide gels by mass spectrometry.Anal. Chem. 2004; 76: 3029-3036Google Scholar). Even a single nano-LC-MS/MS analysis can easily generate a long list of identified proteins with the help of database searching, and additional information can be extracted, such as the hit rank in identification, the probability score, the number of identified peptides per protein, ion counts of identified peptides, LC retention times, and so on. Qualitatively some parameters, such as the hit rank, the score, and the number of peptides per protein (14Corbin R.W. Paliy O. Yang F. Shabanowitz J. Platt M. Lyons Jr., C.E. Root K. McAuliffe J. Jordan M.I. Kustu S. Soupene E. Hunt D.F. Toward a protein profile of Escherichia coli: comparison to its transcription profile.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 9232-9237Google Scholar), can be considered as indicators for protein abundance in the analyzed sample. Among them, the integrated ion counts of the peptides identifying each protein would be the most direct parameter to describe the abundance and has been used to compare protein expression in different states (15Lasonder E. Ishihama Y. Andersen J.S. Vermunt A.M. Pain A. Sauerwein R.W. Eling W.M. Hall N. Waters A.P. Stunnenberg H.G. Mann M. Analysis of the Plasmodium falciparum proteome by high-accuracy mass spectrometry.Nature. 2002; 419: 537-542Google Scholar). However, a mass spectrometer is not as versatile as an absorbance detector because of limited linearity and possibly because of background and ionization suppression effects (16Shen Y. Zhao R. Berger S.J. Anderson G.A. Rodriguez N. Smith R.D. High-efficiency nanoscale liquid chromatography coupled on-line with mass spectrometry using nanoelectrospray ionization for proteomics.Anal. Chem. 2002; 74: 4235-4249Google Scholar). Therefore, it is necessary to normalize these parameters to obtain at least approximate quantitative information. The first approach to achieve this, to our knowledge, was to use the number of peptides per protein normalized by the theoretical number of peptides (so-called protein abundance index (PAI) 1The abbreviations used are: PAI, protein abundance index; emPAI, exponentially modified protein abundance index; SILAC, stable isotope labeling with amino acids in cell culture; SCX, strong cation exchange chromatography; HSA, human serum albumin 1The abbreviations used are: PAI, protein abundance index; emPAI, exponentially modified protein abundance index; SILAC, stable isotope labeling with amino acids in cell culture; SCX, strong cation exchange chromatography; HSA, human serum albumin), and this was applied to human spliceosome complex analysis (17Rappsilber J. Ryder U. Lamond A.I. Mann M. Large-scale proteomic analysis of the human spliceosome.Genome. Res. 2002; 12: 1231-1245Google Scholar). PAI is superior to the number of identified peptides because it takes account of the fact that, for the same number of molecules, larger proteins and proteins with many peptides in the preferred mass range for mass spectrometry will generate more observed peptides. Independently Sanders et al. (18Sanders S.L. Jennings J. Canutescu A. Link A.J. Weil P.A. Proteomics of the eukaryotic transcription machinery: identification of proteins associated with components of yeast TFIID by multidimensional mass spectrometry.Mol. Cell. Biol. 2002; 22: 4723-4738Google Scholar) developed a similar index. The number of peptides, spectra counts, or the total of the peptide probability scores in LC/LC-MS/MS analysis can also be used for relative quantitation (19Liu H. Sadygov R.G. Yates III, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.Anal. Chem. 2004; 76: 4193-4201Google Scholar, 20Cox B.J. Kislinger T. Wigle D.A. Brown K. Manning D. Jurisica I. Emili A. Rossant J. Proceedings of the 52nd ASMS Conference on Mass Spectrometry and Allied Topics, May 23–27, 2004, Nashville, Abstr. ThPS352. American Society for Mass Spectrometry, Santa Fe, NM2004Google Scholar, 21Allet N. Barrillat N. Baussant T. Boiteau C. Botti P. Bougueleret L. Budin N. Canet D. Carraud S. Chiappe D. Christmann N. Colinge J. Cusin I. Dafflon N. Depresle B. Fasso I. Frauchiger P. Gaertner H. Gleizes A. Gonzalez-Couto E. Jeandenans C. Karmime A. Kowall T. Lagache S. Mahe E. Masselot A. Mattou H. Moniatte M. Niknejad A. Paolini M. Perret F. Pinaud N. Ranno F. Raimondi S. Reffas S. Regamey P.O. Rey P.A. Rodriguez-Tome P. Rose K. Rossellat G. Saudrais C. Schmidt C. Villain M. Zwahlen C. In vitro and in silico processes to identify differentially expressed proteins.Proteomics. 2004; 4: 2333-2351Google Scholar). Here we further develop the PAI strategy to determine protein abundance from nano-LC-MS/MS experiments and present a modified form, emPAI, the exponential form of PAI minus one. In experiments with labeled complex mixtures, into which we spiked in synthetic peptides, we show emPAI to be roughly proportional to protein abundance. RPMI 1640 medium (Invitrogen) containing [13C6]Leu (Cambridge Isotope Laboratories, Andover, MA) was prepared according to the SILAC protocol of Ong et al. (4Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Google Scholar). Mouse neuroblastoma neuro2a cells were cultured in this medium for [13C6]Leu labeling. Whole cells were lysed using in the of a mixture cells were in a RPMI 1640 medium as described previously (10Oda Y. Owa T. Sato T. Boucher B. Daniels S. Yamanaka H. Shinohara Y. Yokoi A. Kuromitsu J. Nagasu T. Quantitative chemical proteomics for identifying candidate drug targets.Anal. Chem. 2003; 75: 2159-2165Google Scholar). Whole proteins were with of containing mixture and from cell lysates were and in containing mixtures were and with and as described previously L.J. De Hoog C.L. Mann M. Unbiased quantitative proteomics of lipid rafts reveals high specificity for signaling factors.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 5813-5818Google Scholar). solutions were with and and using J. Ishihama Y. Mann M. and for and sample in proteomics.Anal. Chem. 2003; 75: Scholar), which were prepared by a automated with by strong cation exchange chromatography was using with Y. Sato T. T. N. K. Nagasu T. Y. Quantitative mouse proteomics using isotope as internal Biotechnol. Scholar), and the were using to LC-MS/MS for peptide containing at least one and one were the of peptides from proteins expressed in neuro2a containing and were to sample In peptides with basic were the of by The peptides were using a with and were by analysis, peptide mass and were for and A containing equal of each peptide was spiked into the peptide mixtures from neuro2a different were spiked so that intensity of peptides to labeled peptides were between and were analyzed by nano-LC-MS/MS using a a or a with a and an with with were into a with a cell to an with Y. J. Andersen J.S. Mann M. with for A. 2002; Scholar). A was on an and a with a was used to the and to the The was and the was a The of A and and The linear of in in in and in was used this A of was applied the as described previously Y. J. Andersen J.S. Mann M. with for A. 2002; Scholar). For experiments with the were for to and were for was for to the previously For the per one were For and two per one were in the automated The was for one and for one on in and for one and for one on in The range was for and A database was used for protein identification the protein The number of was to and peptide scores to were used for peptide identification of was for [13C6]Leu SILAC to determine the ion counts in for absolute concentrations of proteins using known of synthetic peptides. is developed by and at To the number of observable peptides per protein, proteins were in and the obtained peptides were with the range of the mass In the retention our were calculated according to the of of peptide retention in liquid chromatography on the of amino Natl. Acad. Sci. U. S. A. Scholar) and et al. Y. N. T. of peptide retention Scholar) with our on peptides. that were or were was in to the peptide number and was used to all to The is at the number of observed peptides per protein, of were and and the by were from to using the of The PAI is defined as and are the number of observed peptides per protein and the number of observable peptides per protein, (17Rappsilber J. Ryder U. Lamond A.I. Mann M. Large-scale proteomic analysis of the human spliceosome.Genome. Res. 2002; 12: 1231-1245Google Scholar). The emPAI is defined as the protein contents in and percentages are described as Protein content Protein content is the of the protein, and is the of emPAI values for all identified The for emPAI is in To the of the parameters, a deviation was defined as values are larger than values or values are larger than cells were at in with of the the cells were for with experiments were using according to was used to gene and two of were and on gene of human serum albumin peptides were analyzed by and the number of identified peptides was in and the number of identified peptides with as the amount at larger of to However, in the the is the number of peptides not have a linear relationship to the protein the number of peptides a linear relationship to the logarithm of the amount from to A similar was obtained on an with the and This that each was well in and that the of by the could be this In this were used to all different states from the same peptide all peptides different states and such as and peptides with peptides by that the number of peptides on and the correlation with the logarithm of protein abundance. that these are not to the particular used but are a more Recently two similar the number of peptides to the concentration of proteins (19Liu H. Sadygov R.G. Yates III, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.Anal. Chem. 2004; 76: 4193-4201Google Scholar, G. M. M. J. B. Proceedings of the 52nd ASMS Conference on Mass Spectrometry and Allied Topics, May 23–27, 2004, Nashville, Abstr. American Society for Mass Spectrometry, Santa Fe, NM2004Google Scholar). of analyzed the it to that are also with a linear relationship between the logarithm of protein concentration and the number of peptides. it is not the logarithm of protein concentration with the number of observed peptides, and in case this relationship is to be to a of processes and only In it is a that the mass spectrometric peptide from the digestion of a protein are For to of a protein often of large protein and by often not To of the PAI index in complex mixtures, we known of proteins in a whole cell lysate. peptides from mouse neuroblastoma neuro2a cells with [13C6]Leu (4Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Google Scholar) were by a single LC-MS/MS with the and proteins were identified on peptides. For accurate absolute quantitation, we spiked synthetic peptides containing into this sample one for each protein, and the peptides containing peptides were not because they in in the ion 46 proteins in mass from to were in the range from to in the sample as in proteins identified and in mouse neuro2a hit concentrations were by isotope using proteins and synthetic of observed protein protein, protein protein protein protein, protein protein protein protein protein protein protein, protein protein protein protein protein protein Protein concentrations were by isotope using proteins and synthetic peptides. in a In complex protein mixtures, two additional should be is the of protein on the number of peptides. larger proteins generate more peptides. Therefore, observable peptides were used for as previously that we used the peptide retention as an additional The is the mixture A large number of peptides in total cell lysate, and the number of observed peptides could to some be by the random for ion suppression and of the the that is a linear relationship between and the number of observed peptides normalized by the number of observable peptides per protein different proteins were into one with parameters, PAI most with logarithm of protein amount deviation by number of peptides divided by protein deviation a similar to PAI that it well the peptide can generate peptides in the correct mass range for mass used for protein abundance such as and the number of peptides with protein abundance deviation and deviation PAI can estimate the abundance relationship between it cannot the Therefore, we a emPAI, that is the exponential form of PAI minus and that is proportional to protein content as in E. To the absolute total protein were as by and the of 46 proteins neuro2a proteins were calculated using in the emPAI-based concentrations were with the actual values and the deviation from to with an of The in is Mouse is not in the but in which was not used for protein is not in to is that the number of observed peptides would using or of more for that these of compare with protein abundance by gel staining and with the used to total protein amount of in the to different proteins of the for Scholar). as are proteins known to the emPAI of proteins could also be in the In the emPAI approach to a accurate estimate for comprehensive absolute In this we used the and on the to the number of a was the deviation from the linear relationship between emPAI and the protein concentrations A and to the random sampling effects This was more an ion was used because the limited in a for more and a larger deviation was observed for more used an a linear ion that has a and a with the A linear ion mass Mass 2002; Scholar, M.W. A ion mass Mass 2002; Scholar), to the of the The use of the of emPAI in comparison to However, the of with not better correlation between emPAI and protein abundance. This could be because of the limited of to in the linear also the of sample by using multidimensional chromatography Y. Sato T. T. N. K. Nagasu T. Y. Quantitative mouse proteomics using isotope as internal Biotechnol. Scholar, A.J. Eng J. D.M. E. D.R. Yates III, J.R. analysis of protein complexes using mass Biotechnol. 1999; 17: Scholar). in in emPAI To the of the of sample on emPAI we used and in with and obtained in total identified proteins with peptides from neuro2a The correlation between emPAI values of the 46 proteins and protein abundances was as in that PAI values of proteins of 46 proteins were more than one in this analysis, only two proteins PAI values of more than one in the analysis This that the emPAI was not However, it would be to emPAI some proteins are observed this in our analysis of the proteome was because the were prepared from cells (15Lasonder E. Ishihama Y. Andersen J.S. Vermunt A.M. Pain A. Sauerwein R.W. Eling W.M. Hall N. Waters A.P. Stunnenberg H.G. Mann M. Analysis of the Plasmodium falciparum proteome by high-accuracy mass spectrometry.Nature. 2002; 419: 537-542Google Scholar). proteins the of protein identification because of ionization suppression and detector as well as the limited of LC The of proteins is therefore to the identification and can be by LC-MS gel by and LC-MS as in our proteome study or albumin for proteome a will also the of emPAI also the of the sample on the emPAI-based the whole cell of neuro2a different and and were analyzed by For proteins with identified peptides in all values of the were obtained were for and for emPAI values on the as The emPAI is a and easily obtained index that can be used to protein expression from LC-MS/MS applied this approach to obtain for comparison with gene expression in human cancer A expression for a single LC-MS identified proteins on peptides with gene with protein in a total of for the expression comparison A correlation was observed in as from studies on yeast (19Liu H. Sadygov R.G. Yates III, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.Anal. Chem. 2004; 76: 4193-4201Google Scholar, S.P. Y. Aebersold R. between protein and abundance in Cell. Biol. 1999; Scholar). most of the were is well known that, such as Escherichia cells the expression of proteins not only by transcription but also at the of of and and by of of proteins not associated with K. the of in eukaryotic Scholar, W.H. of protein gene Scholar). in a comparison study between gene and protein expression using emPAI for E. we not such a deviation of D. and M. Mann, gene and protein expression are not accurate to a for it is to obtain a as also that the protein quantitation of our simple emPAI is similar or better than the in determining expression in have a scale for absolute protein abundance emPAI is easily calculated from the information of database such as it is to this approach to previously or to add quantitative information additional emPAI can also be used for relative quantitation in approaches cannot be applied because of quantitative changes that are large for accurate of because labeling is not or because not allow chemical labeling In such emPAI values of proteins in one sample can compare with in and the from the emPAI correlation between two can be as or

Gemin3 is a DEAD-box RNA helicase that binds to the Survival of Motor Neurons (SMN) protein and is a component of the SMN complex, which also comprises SMN, Gemin2, Gemin4, Gemin5, and Gemin6. Reduction in SMN protein results in Spinal muscular atrophy (SMA), a common neurodegenerative disease. The SMN complex has critical functions in the assembly/restructuring of diverse ribonucleoprotein (RNP) complexes. Here we report that Gemin3 and Gemin4 are also in a separate complex that contains eIF2C2, a member of the Argonaute protein family. This novel complex is a large approximately 15S RNP that contains numerous microRNAs (miRNAs). We describe 40 miRNAs, a few of which are identical to recently described human miRNAs, a class of small endogenous RNAs. The genomic sequences predict that miRNAs are likely to be derived from larger precursors that have the capacity to form stem-loop structures.