Transcript Proteomics2_2012

Multiple flavors of mass analyzers
Single MS (peptide fingerprinting):
Identifies m/z of peptide only
Peptide id’d by comparison to database,
of predicted m/z of trypsinized proteins
Tandem MS/MS (peptide sequencing):
Pulls each peptide from the first MS
Breaks up peptide bond
Identifies each fragment based on m/z
Collision cell
Now multiple types of collision cells:
CID: collision induced dissociation
ETD: electron transfer dissociation
HCD: high-energy collision dissociation
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Intro to Mass Spec (MS)
Separate and identify peptide fragments by their Mass and Charge (m/z ratio)
Mass Spec
Ion source
Mass analyzer
MS Spectrum
Detector
Basic principles:
1. Ionize (i.e. charge) peptide fragments
2. Separate ions by mass/charge (m/z) ratio
3. Detect ions of different m/z ratio
4. Compare to database of predicted m/z fragments for each genome
2
Quantitative proteomics
Either absolute measurements or relatively comparisons
1.
Spectral counting
2.
Isotope labeling (SILAC)
3.
Isobaric tagging (iTRAQ & TMT)
4.
SRM
3
SILAC
(Stable Isotope Labeling with Amino acids in Cell culture)
Cells are grown separately in heavy (13C) or light (12C) amino acids (often K or R),
lysates are mixed, then analyzed in the same mass-spec run
Mass shift of one neutron allows deconvolution, and quantification,
of peaks in the same run.
Advantages / Challenges:
+ not affected by run-to-run variation
- need special media to incorporate heavy aa’s,
- can only compare (and quantify) few samples directly
- incomplete label incorporation can confound MS/MS identification
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Isobaric Tagging
iTRAQ or
Tandem Mass Tags, TMTs
Each peptide mix covalently tagged
with one of 4, 6, or 8 chemical
tags of identical mass
LTQ Velos
Orbitrap
Samples are then pooled and analyzed
in the same MS run
Collision before MS2 breaks tags –
Tags can be distinguished in the
small-mass range and quantified to
give relative abundance across
up to 8 samples.
Advantages / Challenges:
+ can analyze up to 8 samples,
same run
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- still need to deal with normalization
Selective Reaction Monitoring (SRM)
Targeted proteomics to quantify specific peptides with great accuracy
-
Specialized instrument capable of very sensitively measuring
the transition of precursor peptide and one peptide fragment
-
Typically dope in heavy-labeled synthetic peptides of precisely known
abundance to quantify
Advantages:
- best precision measurements
Disadvantages:
- need to identify ‘proteotypic’ peptides for doping controls
- expensive to make many heavy peptides of precise abundance
- limited number of proteins that can be analyzed
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How does each spectrum translate to amino acid sequence?
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Mann Nat Reviews MBC. 5:699:711
How does each spectrum translate to amino acid sequence?
PSMs: Peptide Spectrum Match
1.
De novo sequencing: very difficult and not widely used (but being developed)
for large-scale datasets
2.
Matching observed spectra to a database of theoretical spectra
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Theoretical spectra:
- in silico digestion of a known
protein database
- set of limited set of theoretical
spectra based on enzyme,
instrument sensitivity, others
- this reduces search space
- can miss some peptides
- comparisons based on several
different scores (eg.
correlation between obs.
and theoretical profiles)
9
Mann Nat Reviews MBC. 5:699:711
How does each spectrum translate to amino acid sequence?
1.
De novo sequencing: very difficult and not widely used (but being developed)
for large-scale datasets
2.
Matching observed spectra to a database of theoretical spectra
3.
Matching observed spectra to a spectral database of previously seen spectra
10
Nesvizhskii (2010) J. Proteomics, 73:20922123.
-
spectral matching is supposedly more accurate but …
limited to the number of peptides whose spectra have been observed before
With either approach, observed spectra are processed to:
group redundant spectra, remove bad spectra, recognized co-fragmentation,
improve z estimates
Many good spectra will not match a known sequence due to:
absence of a target in DB (*esp polymorphisms!), PTM modifies
spectrum, constrained DB search, incorrect m or z estimate.
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Result: peptide-to-spectral match (PSM)
A major problem in proteomics is bad PSM calls
… therefore statistical measures are critical
Methods of estimating significance of PSMs:
p- (or E-) value: compare score S of best PSM against distribution of
all S for all spectra to all theoretical peptides
FDR correction methods:
1.Benjamini & Hochberg (or the related Q value) FDR correction
2.Estimate the null distribution of RANDOM PSMs:
- match all spectra to real (‘target’) DB and to fake (‘decoy’) DB
- often decoy DB is the same peptides in the library but reverse
sequence
one measure of FDR: 2*(# decoy hits) / (# decoy hits + # target hits)
3. Use #2 above to calculate posterior probabilities for EACH PSM
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3. Use #2 above to calculate posterior probabilities for EACH PSM
- mixture model approach: take the distribution of ALL scores S
- this is a mixture of ‘correct’ PSMs and ‘incorrect’ PSMs
- but we don’t know which are correct or incorrect
- scores from decoy comparison are included, which can provide
some idea of the distribution of ‘incorrect’ scores
-EM or Bayesian approaches can then estimate the proportion of correct vs.
incorrect PSM … based on each PSM score, a posterior probability is calculated
FDR can be done at the level of PSM identification … but often done
at the level of Protein identification
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Error in PSM identification can amplify FDR in Protein identification
Some methods
combine PSM FDR
to get a protein FDR
Nesvizhskii (2010) J. Proteomics, 73:20922123.
Often focus on proteins identified by at least 2 different PSMs
(or proteins with single PSMs of very high posterior probability)
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Some practical guidelines for analyzing proteomics results
1.
Know that abundant proteins are much easier to identify
2.
# of peptides per protein is an important consideration
- proteins ID’d with >1 peptide are more reliable
- proteins ID’d with 1 peptide observed repeatedly are more reliable
- note than longer proteins are more likely to have false PSMs
3.
Think carefully about the p-value/FDR and know how it was calculated
4.
Know that proteomics is no where near saturating
… many proteins will be missed
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Insights from Proteomic studies
1.
Identifying protein mixtures, modified proteins, protein interactions
Nature Methods 2011
LS-MS/MS on 4 ES lines and 4 iPS lines, using isobaric tags for quantification &
phospho-proteomics
Identified 7,956 proteins and 10,499 PTM sites
Insights from Proteomic studies
1.
Identifying protein mixtures, modified proteins, protein interactions
2.
Correlating protein abundance to RNA abundance
Most proteomic studies show no to poor correlation between mRNA and protein ..
Greenbaum et al, 2003
Washburn et al, 2003
Fu et al., 2009
However, most prior studies limited by:
- developing proteomic technology
- lack of biological replicates
- no paired mRNA and protein samples
- no dynamics … comparing the wrong time points?
Tandem Mass Spectrometry (MS)
Follow dynamic changes in
transcript abundance &
protein abundance in cells
responding to 0.7M NaCl
LTQ Velos
Orbitrap
Pseudo-steady states +
acclimation phase
Violet Lee, Josh Coon
Scott Topper
Same samples for RNA/protein
Biological triplicate
Full time courses
for ~2500 proteins
770 significant protein changes
(FDR < 0.05)
Correlation is high at induced transcripts
R2 = 0.77
Comparing maximum average-log2 changes in mRNA and protein
for all changing mRNAs (FDR <0.05)
Lee & Topper et al. 2011. Mol Sys Biol
Correlation is lousy at repressed transcripts
R2 = 0.77
R2 = 0.09
Comparing maximum average log2 changes in mRNA and protein
for all changing mRNAs (FDR <0.05)
Lee & Topper et al. 2011. Mol Sys Biol
Insights from Proteomic studies
1.
Identifying protein mixtures, modified proteins, protein interactions
2.
Correlating protein abundance to RNA abundance
3.
Protein-QTL analysis
Nature Genetics 2007
LC-MS/MS to quantify 569 proteins in 98 F2 segregants of two strains
Detected 24 linkages
Genetics Nov 2012
MALDI-TOF analysis of plasma proteins in 455 F2 intercrossed mice
69 out of 175 proteins identified could be mapped to QTL
7 – 10 page (1.5 spacing) final paper
(with additional pages for figures and references)
in manuscript format
due Next Wednesday Dec. 5
Insights from Proteomic studies
1.
Identifying protein mixtures, modified proteins, protein interactions
2.
Correlating protein abundance to RNA abundance
3.
Protein-QTL analysis
4.
Allele-specific protein abundance
Molecular Systems Biology 2012