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Using Orthogonal Techniques for Protein-Peptide Separation to Generate Comprehensive HDMSe Mass Spectral Libraries from an E. coli Model System

Escherichia coli (K-12, MG1655) was grown under approximately 60 different growth conditions including variation of growth phases and carbon sources, limitation of nutrients and stress response induction. Individual culture conditions and a composite cell mixture were used to produce corresponding protein lysates. The resulting proteins were fractioned by denaturing electrophoresis using a prototype of the ELF platform from Sage Science to produce 12 in-solution protein fractions, spanning 5000 –150000 Da. Fractionated protein samples were reduced, alkylated, digested with trypsin, and desalted prior to LC-MS analysis. Peptides were analyzed by multidimensional 2D-RP/RP-HDMSe on a Waters nanoAcquity UPLC and a SYNAPT-G2S HDMS system using data-independent acquisition methods. Assignment of HDMSe spectra to peptide sequences and label-free quantitation were performed using TransOmics software.

Introduc4on  

Jus.n  D.  Topp3,  Michael  J.  Nold5,  Ezra  S.  Abrams2,  Charles  L.  Farnsworth1,  ScoP  Geromanos5,  Manor  Askenazi4,  Jeffrey  C.  Silva1  1Cell  Signaling  Technology,  Danvers  MA;  2Sage  Science  Inc.,  Beverly  MA;  3Gordon  College,  Wenham,  MA;  4The  Ionomix  Ini.a.ve,  Arlington,  MA;  5Waters  Corpora.on,  Milford,  MA  

Spectral  Library  Work  Flow  &  Results  

Spectral libraries are increasingly used for peptide identification as they can take full advantage of all spectral features, including relative ion intensities as well as precursor and fragment masses. Such libraries have been constructed by synthesizing individual peptides or by curating spectra from pre-existing proteomic data sets. Libraries generated from experimental data tend to emphasize peptides that are most likely to be observed in typical in vivo experiments and may even preserve characteristics of the underlying biological system. We report a third library generation strategy that builds upon the benefits of existing empirical strategies by implementing orthogonal, multi-dimensional protein and peptide separation techniques to acquire a comprehensive HDMSe spectral library data set for E. coli.

Methods  Figure 5. (A) Heat map of proteins identified from replicate analyses of the 11 protein fractions and (B) the sum total intensity of characterized proteins identified in each protein fraction using TransOmics analysis.

Spectral  Library  Results   Quan4ta4ve  and  Qualita4ve  Results  

Summary  Generation of spectral libraries by coupling the orthogonal techniques of intact protein fractionation with LC-HDMSe shows great promise. Future work will include refinement of the intact protein fractionation step, and utilization of 2D-RP/RP (pH 10/pH 3) LC-HDMSe to increase the percent sequence coverage of each protein, and increase the percent coverage of the E. coli proteome from a single multi-dimensional analysis study.

References  S.J. Geromanos etal (2009) Proteomics, 9: 1683-1695. J.C. Silva etal (2006) Molecular & Cellular Proteomics, 5: 589-607.

Figure 2. Analysis Workflow. Protein fractionation, reduction, alkylation, digestion and desalting (step 1, 2d), LC-HDMSe acquisition (step 2, 5d), TransOmics Data Processing and Analysis (step 3, 3d).

Quan4ta4ve  and  Qualita4ve  Results  

Figure 1. Common Media and Growth Phases (black), Carbohydrate Sources (blue), Stress Conditions (red) and Anaerobic Conditions (green) generated for the pooled Escherichia coli reference sample. The growth conditions that were evaluated for global protein measurement are noted as CST1, CST2, CST3 and CST4.

Escherichia  Coli  Growth  Condi4ons   Figure 3. Sage Science ELF Apparatus for protein fractionation (A) and PAGE analysis of fractionated samples using SDS and UREA buffers (B).

2  days   5  days   3  days  

Figure 4. HDMSe Acquisition. (A) Illustration of time-resolved precursor and product ion acquisition (LCMSe) with ion mobility separation (IMS) for HDMSe data acquisition, (B) Separation of +1 (black), +2 (blue) and +3 (red) precursors with IMS (C) IMS separation of co-eluting peptides from replicate analysis of ELF-Fraction07 of E. coli reference sample.

Figure 6. (A) Fraction of identified proteins from E. coli proteome (B) Distribution of protein coverage (C) Ion Map of AceA (mass, RT, drift time).

Figure 10. Self Organizing Map of the scaled relative intensity for all peptide features from the four E. coli growth conditions (see Figure 8).

aceB

aceA

gltA

aldA

Figure 11. Protein abundance observed among the four E. coli growth conditions (CST1, CST2, CST3 and CST4), including replicates, for select protein groups or pathways (Glyoxylate Shunt, Pyruvate Metabolism and Ribosomes). Relative protein abundance is estimated using the Top3 peptides calibrated against internal standard (yeast ADH1).

Pyruvate Metabolism

Glyoxylate Shunt

Ribosomes

aceE pflB aceF

mdh

rpsT rpsB rpsA rplM

Figure 7. HDMSe drift resolved fragment ion spectra for aceE and aceA for E. coli growth conditions (see Figure 8 for sample descriptions).

CST1

CST2

CST3

CST4

aceE aceA

without IMS with IMS without IMS with IMS

Figure 8. Distribution of variation among replicate injections of four E. coli growth conditions (LB, SOC, M9/0.4% glucose and M9/0.4% alanine).

Figure 9. Binary comparisons of the TransOmics features from the four growth conditions (log2(ratio) versus control intensity (denominator)).

A B

C

A B

Protein Fraction – Replicate Injection

Prot

ein

Log10(Protein Molecular Weight)

Sum

Inte

nsity

of a

ll Pe

ptid

es

A

B

C 30%

70%

Median %CV = 23.5% CST1 - LB

Median %CV = 19.2% CST3 - M9 and 0.4% glucose

Median %CV = 20.9% CST2 - SOC

Median %CV = 23.0% CST4 - M9 and 0.4% alanine

CST1 versus CST2 CST1 versus CST3 CST1 versus CST4

CST2 versus CST3 CST2 versus CST4 CST3 versus CST4

E. coli growth conditions (CST1, CST2, CST3 and CST4)

Scal

ed R

elat

ive

Inte

nsity

of P

eptid

e Fe

atur

es

+1

+2

+3

A B

log2(ratio)=1, green log2(ratio)=-1, red

Quantitation includes only those features with 30% replicate CV for each sample.

+1 +2

+3

Color coded by charge state

% Protein Coverage

Markers