PROTEORED Multicentric Study QUANTITATIVE PROTEOMICS METHOD SPECTRAL COUNT 2010 UNIVERSITY OF BARCELONA Salamanca 16 th March

Objectives: Test each laboratory abilities to perform quantitative proteomic analysis. Comparison of methodologies for relative quantitative analysis of proteomes. The study should provide data to assess and compare performance of different methodologies and intra- and inter-lab reproducibility of these. Evaluation of data reporting and data sharing tools (MIAPE documents, standard formats, public repositories).MIAPE

Samples: Each participant laboratory will receive two protein mixture samples, labeled A and B, containing each 100 µg of total protein. 100 micrograms of each protein mixture A and B dissolved in 6M Urea /1% CHAPS, at 6 micrograms/microliter concentration. Samples contain: A mixture of around 150 E. Coli proteins (identical in each sample). This mixture has been prepared by fractionation of the cytoplasmatic proteome of E.Coli. It contains soluble proteins, of a wide range of pI and Mw. Four spiked mammalian proteins: CYC_HORSE (Cytochrome C, Mw 12362), added at the ~ 1 pmol/ 1 mg total protein level. CYC_HORSE MYG_HORSE (Apomyoglobin, Mw 16952), at ~ 200 fmol / 1 mg total protein MYG_HORSE ALDOA_RABIT (Aldolase, Mw 39212), at ~ 25 fmol / 1 mg total protein ALDOA_RABIT ALBU_BOVIN (Serum albumin, Mw 66430), at ~ 1 fmol / 1 mg total protein ALBU_BOVIN These four proteins have been spiked in different amounts in samples A and B, with ratios ranging from 1.5:1 to 5:1 between the two samples.

Purpose of the analysis: The intended purpose of the analysis is to measure the ratios between samples A and B for the four spiked proteins. The “matrix” E. Coli proteins, which should be unchanged, will provide a measure of dispersion for the method used. The samples can be also used to test methods for absolute quantitation, if desired. In order to evaluate reproducibility in an homogenous dataset, we ask to perform a minimum of 4 replicate analysis of the samples. (Depending on the method of choice this would demand a maximum of LC-MS runs).LC-MS

Methods: Sample complexity has been chosen to allow for the analysis of the mixture on single LC-MS runs. In principle, there is no need for pre-fractionation. A long enough gradient ( min) gradient is suggested, but this of course will strongly depend on the MS instrument available for analysis.LC-MS 1-2 micrograms of total protein per run should be enough to cover the range of abundances of the spiked proteins in the samples. Again, this will depend a lot on the instrument used, and should be adjusted by each Lab. according to their expertise. The sample is primarily intended to test non-targeted relative quantitation methodologies. Both label-based methods (ICPL, iTRAQ, TMT, O18,...) and label-free methods (based on spectral counts, Hi3, “LCMS Image analysis”...) can be performed and tested to analyze the samples. Some of them will require LC-MS runs, while others (i.e. 8-plex iTRAQ) could require a single run to provide comparable measurements of reproducibility. Try to choose the number of replicate analysis in a way that 4 independent measurements of each A:B ratio are obtained, so that comparable statistics can be calculated.iTRAQTMTlabel-freeLC-MSiTRAQ The sample can be also used if desired to test targeted methods, such MRM methods for relative or absolute quantitation. The concentration of the spiked proteins is probably too high to provide a real challenge for those methods, but it can still be useful for test purposes (one can test accuracy, sensitivity on serial dilutions of the sample...) The amount of sample provided, as well as the concentration of the spiked proteins, should allow also a 2D-DIGE analysis of the samples, although this is not the main purpose of the experiment.2D-DIGE

Quantitative Proteomic Approaches Label free – Spectral counting – Ion current based (Extracted ion chromatograms) – Other Stable isotope labeling – Stable isotope label reagent as ICAT and ITRAQ – Metabolic labeling (SILAC, 15N) – Others Shotgun Proteomics Digestion of proteins and separation of peptides – Extensive chromatographic separation (one or mutliple dimensional separations, columns,..) Data acquisition – Data-dependent acquisition (Automated acquisition of MS/MS spectra from as many precursor ions as possible) Data analysis – Automated interpretation of the MS/MS spectra (DB search)

Spectral Counting Summary Spectral count correlates well with protein abundance Fold change can be calculated and statistically evaluated Simple and straightforward implementation Sensitive to protein abundance changes – for abundant proteins 2 fold change easily detected with high confidence Limitations The response to increasing protein amount is saturable Noisy data at low spectral counts – large difference in spectral count necessary to determine significant change Fu et al, 2006

Spectral count reflects relative abundance of a protein (r2 ≥0.99) Issues to address: - Variability of Spectral counts - Sensitivity of Spectral count to protein abundance changes - How to determine relative changes between two samples Variability of Spectral counting LCMSMS analysis of replicate SCX fractions of K562 cell lysates, G-test Old W. et al, MCP 2005 How to determine relative changes between two samples Fold change determination Old W. et al, MCP 2005 Practical issue – no peptides found in one of the compared samples Data discontinuity (spectral count – integers) – not amenable to Student t-test Differences in sampling depth Fold change determination. RSC = log2[(n2 + f)/(n1 + f)] + log2[(t1 - n1 + f)/(t2 - n2 + f)] n1, n2 - spectral counts for sample 1 and 2 t1, t2 – total spectral count (sampling depth) for samples 1 and 2 f – correction factor 1.25 (Beissbarth et al – Bioinformatics 2004) Observed RSC correlates well with expected RSC for standard proteins spiked into complex samples (Old W. et al, MCP 2005)

100 micrograms of each protein mixture A and B are dissolved in 6M Urea /1% CHAPS, at 6 micrograms/microliter concentration. Samples were kept at -20ºC. Precipitation with TCA/ACETONE Re suspended in 100 uL 0.3 % SDS/50 mM Tris HCl pH 8.0/200 mM DTT 5 uL(5ug) Sample digested with trypsin O/N at 1/100 ratio Separate with nanoHPLC (4 replicas 1uL) MS/MS LTQ VelosOrbitrap

Analysis Spectra analyzed Proteored A Proteored A Proteored A Proteored A Proteored B Proteored B Proteored B Proteored B TOTAL SEQUEST PARAMS peptide_mass_tolerance = 0.07 fragment_ion_tolerance = 0.6 diff_search_options = M C X

ItemLC-MS runA-1A-2A-3A-4B-1B-2B-3B-4 Total Sample A-B (Combined AB1- 4 runs)** 1Number of MS/MS spectra acquired Number of total assigned peptides id Number of unique peptides id Number of E Coli proteins id. (total) Number of E Coli single hit- proteins id Number of Spiked proteins id FDR* Total Number of proteins quantitated5 9Number of proteins quantitated > 3 peptides2 10Number of proteins quantitated > 2 peptides3 11Number of proteins quantitated 1 peptide A/B ratio 12Average of A/B ratios for E Coli proteins 13Standard deviation A/B ratios 14% CV A/B ratios E Coli proteins

The Normalized Spectrum Counts bar chart shows a protein's relative abundance across different samples. The y-axis is the normalized count of the spectra matching any of the peptides in the protein. This count depends upon the protein, peptide, required mods and search filters set on the Samples page. Each bar along the x-axis is for a different biological sample. The bars are color coded. Each sample category is colored a different color. The bar chart can be used as a visual confirmation of a differential expression flagged by the Quantitative Analysis in the Samples view.

FDR= Proteins

SAMPLE A1 SAMPLE A2 SAMPLE A3 SAMPLE A4 SAMPLE B1 SAMPLE B2 SAMPLE B3 SAMPLE B4Av ASTD AAv BSTD BA/BB/A ALDOA_RABIT ALBU_BOVIN MYG_HORSE CIC_HORSE

ALBUMIN_BOVIN SAMPLE A2 Xcorr 0.88 DeltaCn 0.46 SAMPLE A1 Xcorr 3.16 DeltaCn 0.43 SAMPLE A1 Xcorr 3.23 DeltaCn 0.64 SAMPLE B2 Xcorr 2.9 DeltaCn 0.57 P-value=0.52

ALDOA_RABIT SAMPLE A2 Xcorr 2.12 DeltaCn 0.5 SAMPLE A3 Xcorr 5.33 DeltaCn 0.78 SAMPLE B3 Xcorr 5.35 DeltaCn 0.77 P-value=

CYC_HORSE SAMPLE A1 Xcorr 4.78 DeltaCn 0.66 SAMPLE B3 Xcorr 5.46 DeltaCn 0.66 P-value=

MYG_HORSE SAMPLE B1 Xcorr 3.87 DeltaCn 0.55 SAMPLE A3 Xcorr 4.66 DeltaCn 0.72 P-value=

FDR= 0 219

CIC_HORSE MYG_HORSE ALDOA_RABIT

Conclusions: Spectral count can be an easy way to try to perform quantitative proteome analysis, but : Needs the ability to perform different LC runs with very low dispersion. The response to increasing protein amount is saturable. Noisy data at low spectral counts – large difference in spectral count necessary to determine significant change.

M José Fidalgo Eva Olmedo Francisco Fernández Josep M Estanyol Oriol Bachs Proteomic Facility University of Barcelona

SAMPLE A1 SAMPLE A2 SAMPLE A3 SAMPLE A4 SAMPLE B1 SAMPLE B2 SAMPLE B3 SAMPLE B4M ADES AM BDES BA/BB/A ALDOA_RABIT ALBU_BOVIN MYG_HORSE CIC_HORSE fmol/microgram E. Coli protein MWABB/AA/B ALDOA_RABIT BSA_BOVIN MYG_HORSE CYC_HORSE