Quantitative Proteomics: Applications and Strategies
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Transcript of Quantitative Proteomics: Applications and Strategies
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Quantitative Proteomics: Applications and Strategies
October 2013
Gustavo de SouzaIMM, OUS
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A little history…
1985 – First use: up to a 3 kDa peptide could be ionized
1987 – Method to ionize intact proteins (up to 34 kDa) described
Instruments have no sequence capability
1989 – ESI is used for biomolecules (peptides)
Sequence capability, but low sensitivity
1994 – Term «Proteome» is coined
1995 – LC-MS/MS is implemented
«Gold standard» of proteomic analysis
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2DE-based approach
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2DE-based approach
““I see 1000 spots, but identify 50 only.”I see 1000 spots, but identify 50 only.”
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Gradient elution:200 nl/min
Column (75 mm)/spray tip (8 mm)
Reverse-phase C18 beads, 3 mm
Platin-wire2.0 kV
Sample Loading:500 nl/min
No precolumn or split
ESI
15 cm
Fenn et al., Science 246:64-71, 1989.
LC-MS
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MS-based quantitation
InletIon
SourceMass
AnalyzerDetector
MALDIES
Time-of-FlightQuadrupole
Ion TrapQuadrupole-TOF
LC
Peak intensities can vary up to 100x between duplicate runs.
Quatitative analysis MUST be carried on a single run.
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Ion Intensity = Ion abundance
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MS measure m/z
m/z
Inte
nsity
Sample 2Sample 1
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Isotopic Labeling
Unlabeled peptide:
Labeled peptide:
a) b) a) b)
18O16O
15N14N
13C12C
2H1H
Stable IsotopeElement
18O16O
15N14N
13C12C
2H1H
Stable IsotopeElement
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Enzymatic Labeling
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Metabolic Labeling
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SILAC
*
m/zm/z
Passage cells to allow incorporation of labelled AA
By 5 cell doublings cells have incorporated
*
m/zm/z
Grow SILAC labelled cells to desired number of cells for experiment
*
m/zm/z
Start SILAC labelling by growing cells in labelling media
(labelled AA / dialized serum)m/zm/z
Media with Normal AA ()
Media with Labelled AA (*)
X 3 X 3
Cells in normal culture media
Ong SE et al., 2002
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Chemical Labeling
Biotin Biotin tagtag
Linker Linker (heavy or light)(heavy or light)
Thiol specific Thiol specific reactive groupreactive group
ICAT Reagents:ICAT Reagents: Heavy reagent: d8Heavy reagent: d8--ICAT (ICAT (XX=deuterium)=deuterium)Light reagent: d0Light reagent: d0--ICAT (ICAT (XX=hydrogen)=hydrogen)
S
N N
O
N OO
O N IO OXX
XX
XX
XX
XX
XX
XX
XX
XX
XX
XX
XX
XX
XX
XX
XX
Gygi SP et al., 1999
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ICAT (Isotope-Coded Affinity Tag)
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ICAT
Cell State 1Cell State 1((All cysteines labeled with All cysteines labeled with
light ICATlight ICAT))
Cell State 2Cell State 2(All cysteines labeled (All cysteines labeled
with heavy ICAT)with heavy ICAT)
Rel
ativ
e A
bund
ance
Rel
ativ
e A
bund
ance
00
100100
Prote
in A
Prote
in A
Prote
in B
Prote
in B
Prote
in C
Prote
in C
Prote
in D
Prote
in D
Prote
in E
Prote
in E
Prote
in F
Prote
in F
. . . .
200200 400400 600600 800800200200 400400 600600 800800
m/zm/z
00
100100
NHNH22--EACDPLREACDPLR--COOHCOOH=Protein A=Protein A
Rel
ativ
e A
bund
ance
Rel
ativ
e A
bund
ance
TimeTime
Quantitate relative protein levels by measuring peak ratios
Identify proteins by sequence information (MS/MS scan)
CombineCombine
Optional fractionationOptional fractionation
Affinity separationAffinity separation
Analyze by LCAnalyze by LC--MS/MSMS/MS
ProteolyzeProteolyze
Thiol-specific group = binds to Cysteins
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ICAT
Thiol-specific group = binds to Cysteins
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Quantitation at MS1 level
m/z
Inte
nsity
Double sample complexity, i.e. instrument have more “features”to identify, i.e. decrease in identification rate
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iTRAQ (isobaric Tag for Relative and Absolute Quantitation)
Sample prep
Total mass of label= 145 Da ALWAYS
RecognizesArg or Lys
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iTRAQ
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iTRAQ
Multiplexing
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Metabolic VS Chemical Labeling
• Metabolic labeling
- 15N labeling
- SILAC
Living cells
Efficient labeling
Simple!
• Chemical methods
- many… but ICAT is prototype
Isolated protein sample
Depends on chemistry
Multi-step protocols
Require optimization
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Summary
Kolkman A et al., 2005
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Label-free
Mobile phase
A
A = 5% organic solvent in waterB = 95% organic solvent in water
B
C18 column, 25cm long
Time
20 s
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Label-free
Strassberger V et al., 2010
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Summary
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Summary
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Take home message
1. Quantitation can be done gel-free 2. Labeling can be performed at protein or peptide level,
during normal cell growth or in vitro
3. Quantitation can be achieved at MS1 or MS2 level
4. Method choice depends on experimental design, costs, expertise etc
5. In my PERSONAL OPINION, chemical label should be avoided at all costs unless heavy multiplexing is required
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Applications
State A State B
Light Isotope Heavy Isotope
Mix 1:1
Optional Protein Fractionation
Digest with Trypsin
Protein Identification and Quantitation by LC-MS
Upregulated protein - Peptide ratio >1
Arg-12C6
Arg-13C6
m/z
Arg-12C6
Arg-13C6
m/z
Control vs Tumor Cell?
Control vs drug treated cell?
Control vs knock-out cell?
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Applications – Cell Biology
Geiger T et al., 2012
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Applications – Cell Biology
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Applications – Immunology
Meissner et al, Science 2013
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Clinical Proteomics
A. Amyloid tissue stained in Congo Red; B. After LMD.
Wisniewski JR et al., 2012
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Interactomics
Schulze and Mann, 2004Schulze WX et al., 2005
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Signaling Pathways
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Take home message
1. Anything is possible!
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SILAC
October 2013
Gustavo de SouzaIMM, OUS
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SILAC
*
m/zm/z
Passage cells to allow incorporation of labelled AA
By 5 cell doublings cells have incorporated
*
m/zm/z
Grow SILAC labelled cells to desired number of cells for experiment
*
m/zm/z
Start SILAC labelling by growing cells in labelling media
(labelled AA / dialized serum)m/zm/z
Media with Normal AA ()
Media with Labelled AA (*)
X 3 X 3
Cells in normal culture media
Ong SE et al., 2002
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Importance of Dialyzed Serum
• non-dialzed serum contains free (unlabeled) amino acids!
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No alterations to cell phenotype
C2C12 myoblast cell line
Labeled cells behaved as expected under differentiation protocols
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Why SILAC is convenient?
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Why SILAC is convenient?
• Convenient - no extra step introduced to experiment, just special medium • Labeling is guaranteed close to 99%. All identified proteins in
principle are quantifiable
• Quantitation of proteins affected by different stimuli, disruption of genes, etc.
• Quantitation of post-translational modifications (phosphorylation, etc.)
• Identification and quantitation of interaction partners
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Catch 22
- SILAC custom formulation media (without Lys and/or Arg) $$$$$$
- Labeled amino acids – Lys4, Lys6, Lys8, Arg6, Arg10. Use formulation accordingly to media formula (RPMI Lys, 40mg/L)
***** When doing Arg labeling, attention to Proline conversion!
(50% of tryptic peptides in a random mixture predicted to contain 1 Pro)
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Proline Conversion!
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Typical SILAC experiment workflow
State A State B
Light Isotope Heavy Isotope
Mix 1:1
Optional Protein Fractionation
Digest with Trypsin
Protein Identification and Quantitation by LC-MS
Upregulated protein - Peptide ratio >1
Background protein - Peptide ratio 1:1
Arg-12C6
Arg-13C6
m/z
Arg-12C6
Arg-13C6
m/z
m/z
Arg-12C6
Arg-13C6
m/z
Arg-12C6
Arg-13C6
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Additional validation criteria
* Never use labelled Arg or Lys with same mass difference (Lys6/Arg6)
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Triple SILAC
Triple Encoding SILAC allows:
Monitoring of three cellular states simultaneously
Study of the dynamics of signal transduction cascades even in short time scales
m/z
Inte
nsity
32
Blagoev B et al., 2004
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Five time-point “multiplexing” profile
Blagoev B et al., 2004
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Quantitative phosphoproteomics in EGFR signaling
Blagoev B et al., 2004
SILAC-HeLa cells
0’ EGF
1’ EGF
5’ EGF
5’ EGF
10’ EGF
20’ EGF
0-5-10 min.Cytoplasmic ext.Nuclear extract
Lysis andFractionationAnf digestion
1-5-20 min.Cytoplasmic ext.Nuclear extract
SCX / TiO2
SCX / TiO2
SCX / TiO2
SCX / TiO2
Phospho-peptide
enrichment
44 LC-MS runs
4x (10 SCX-frac-tions +FT)
ID and quantitation
8x
8x
8x
8x
8x
8x
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MAP kinases activation
40
EGF (minutes)1 5 10 15 20
10
2
EGFr-pY1110ShcA-pY427ERK1-pY204ERK2-pY187EMS1-pS405
Rela
tive r
ati
os
Signal progression
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Spatial distribution of phosphorylation dynamics
Cytosolic STAT5 translocates to the nucleus upon phosphorylation
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Interactomics
Schulze and Mann, 2004Schulze WX et al., 2005
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Limitations
- Expensive
- Quantitation at MS1 level increased sample complexity
- Cells has to grow in culture. Not a choice for primary cells,tissues or body fluids.
- Cell lines have to be dyalized serum-friendly.
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SILAC-labeled organism
Sury MD et al., 2010
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Super-SILAC
Geiger T et al., 2010
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Spike-In SILAC
Geiger T et al., 2013
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Take home message
1. Arguably the best labeling strategies: easy to handle, no chemical steps, >98% incorporation low variability
2. Successfully used in the most diverse applications
3. Cells must be stable and growing in the media
4. There are decent alternative strategies for primary cells or organisms.
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Label-free
October 2013
Gustavo de SouzaIMM, OUS
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Label-free
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Label-free
Strassberger V et al., 2010
Time
10 s
Time
500 fmol peptide
100 fmol peptide
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Label-free
Kiyonami R. et al, Thermo-Finnigan application note 500, 2010.
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Label-free
Replicates
xx x
xx x
Ideal (low std)
Replicates
x
x
x
x
x
x
Reality (late 90’s)
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Label-free
Strassberger V et al., 2010
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Label-free
Neilson et al., Proteomics 2011
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Spectral Count
899.013
899.013
899.013
MS1 (or MS)
MS2 (or MS/MS)
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Spectral Count
Time
20 s
Time
Depending on how complex the sample is at a specificretention time, the machine might be busy (i.e., doing many MS2)or idle (i.e., few or none MS2)
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Limitation in Spectral Count
Time
Time
MS scan
MS2 scan2 counts
2 counts
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Area Under Curve measurement
Retention Time
AUC
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Area Under Curve measurement
MS2 scan
Ion intensityin one MS1
Retention Time
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Importance of Resolution for label-free
RT
m/z
RT
m/z
2+ 2+
3+ 3+
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Cox and Mann, Nature Biotechnol 26, 2008.
-Label-free became reliable (*)
Importance of Resolution for label-free
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1. Retention time2. Peak intensity3. Monoisotopic mass accuracy
1
2
3
080711_Gustavo_Mtub_07 #1001 RT: 24.80 AV: 1 NL: 3.43E6T: FTMS + p NSI Full ms [300.00-2000.00]
791 792 793 794 795 796 797 798 799 800 801 802 803 804 805m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bun
danc
e
798.32
798.83
799.33
799.83800.32 802.13
803.40797.73793.82790.90 796.31 802.72805.84792.68 795.17
x
Cox and Mann, Nature Biotechnol 26, 2008.
Area Under Curve measurement
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Regarding Label-free…
- Calculate individual peptide “Intensity”. Protein Intensity = mean of peptides intensities
- LFQ normalization
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Data without Normalization
-7422 proteins identified
- 7105 proteins quantified(95.72%)
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How this was demonstrated?
Yeast model
Ghaemmagami S. et al., Nature 425, 2003
Huh WK. et al., Nature 425, 2003
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How this was demonstrated?
Ghaemmagami S. et al., Nature 425, 2003
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MaxQuant and Yeast
De Godoy LM. et al, 2008.-Label-free became reliable AND showed good correlation with a well-established model
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Label-free in primary cells
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Higher CD4+Higher CD8+
Pattern Recognition Receptors Pathway
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Infection with Sendai virus(activate RIG-I PRR)
RIG-I knockout
Label-free in primary cells
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Take home message
1. “Labe-free” represents a myriad of ANY method that does not use any labeling
2. Area Under Curve calculations are the most
appropriate
3. Reliability is heavily dependent in good instrumentation and good bioinformatics (MaxQuant)
4. Currently, almost as good as SILAC (yet slightly less accurate)
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SRM / MRM
October 2013
Gustavo de SouzaIMM, OUS
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A little history…
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So far, ID everything we can
Mobile phase
A B
C18 column, 25cm long
Time
20 s
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Targeted analysis
In some cases, the researcher don’t want the MSinstrument to waste time trying to sequence as much as possible, but just to “search” and sequence pre-determined peptides.
-Biomarker research-Tracking specific metabolic pathways-Tracking low abundant proteins in challenging sample (f.ex., in serum)
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Plasma dynamic range
Schiess R et al., 2009
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Improving detection through tergeting
Michalski A et al., 2011
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Biomarker
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Discovery phase
Screening the sample gives you the following info:
-For protein X most intense peptides (not all peptides from same protein have the same intensity)
- most common m/z format (+2, +3, PTM?)- their Retention times- their fragmentation profiles (does the +2fragments well?)
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Biomarker
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Shorter gradient = More complex MS1
As you decreaseseparation resolution,you increase the chance that two or more peptides withdifferent sequencesBUT very close m/zelutes at the sametime.
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SRM (Selected Reaction Monitoring)
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Different transitions from same peptide
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Performance with synthetic peptides
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Shorter gradient = More complex MS1
As you decreaseseparation resolution,you increase the chance that two or more peptides withdifferent sequencesBUT very close m/zelutes at the sametime.
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Number of biomarkers discovered so far by MS
0
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Spiking sinthetic labeled peptide for absolute quantitation
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Applying SRM to a proper model
Bacterial genomic structure
- 700-6000 genes- No alternative splicing- Limited PTM presence
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Discovery Phase
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Validation on metabolic network
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Validation on metabolic network
- It open possibilities to studymolecular function implicationsat metabolic level.
- Generate knockout, discoveryphase to visualize pahways possibly altered by the KO,targeted the candidate pathwaysfor in-depth quantitation.
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Take home message
- 1st step is to make the regular analysis to collect acquisitionfeatures for as many peptides as possible.
- Relevant in Biomarker research
- Very challenging for complex samples, very powerful for simpler organisms and for pure biology projects.
- Targeted analysis: ignore whole sample and focus in fewprotein.