Hydrophobic amino acids (e.g. tryptophan, leucine, phenylalanine) → strong influence
Positively charged amino acids (arginine, lysine) → better detection in positive mode MS

👉 Makes sense because:

Positive mode ESI favors protonated species

🧠 Extra insight:

Local sequence context matters (pairs of amino acids)
Some peptides are:
- ❌ Non-flyers
- ⚠️ Weak flyers
- ✅ Strong flyers

🔄 4. Quantification workflow in proteomics

Standard MS workflow:

Chromatography
MS1 → detect precursor ions
DDA (data-dependent acquisition)
MS2 → fragment ions
Peptide identification
Protein inference
Quantification

⚠️ Problem:

Proteins are inferred from peptides → so: 👉 Accurate peptide quantification = accurate protein quantification

🏷️ 5. Quantification strategies

🧪 A. Spike-in (internal standards)

Add a known, isotopically labeled peptide
Same chemistry → same behavior
Compare intensities

👉 Gives absolute quantification

🧬 B. Metabolic labeling (SILAC)**

Stable Isotope Labeling with Amino Acids in Cell Culture

How it works:

Grow cells in:
- Light amino acids
- Heavy amino acids
Proteins incorporate labels naturally
Mix samples → analyze together

🔥 Advantage:

Direct comparison (same MS run)
High accuracy

🧷 C. Chemical labeling (TMT / iTRAQ)**

Principle:

Attach tags to peptides after digestion

Key components of tags:

Reactive group → binds peptide
Reporter group → used for quantification
Balance group → keeps total mass constant

🧠 Trick:

Same mass in MS1 → looks identical
Different reporter ions in MS2 → enables quantification

🔬 Example methods:

Tandem Mass Tags

Multiplexing (analyze many samples at once)
Reduces instrument variation

Isobaric Tags for Relative and Absolute Quantification

Similar concept
Uses NHS ester chemistry (targets amines)

⚖️ D. Label-free quantification**

No labeling → relies on computational methods

📈 1. MaxLFQ

MaxQuant

Assumption: 👉 Most proteins do NOT change

Then:

Normalize intensities across samples
Detect differences

🔝 2. Top-N method (e.g., Top 3)

Take top 3 most intense peptides per protein
Average them

👉 Gives:

Relative abundance per protein

🧮 3. iBAQ

Intensity-Based Absolute Quantification

Formula:

Sum of peptide intensities
Divide by number of possible peptides

👉 Corrects for protein length

🧠 6. MALDI-TOF advanced applications

🧫 A. Imaging mass spectrometry

How it works:

Slice tissue
Scan many tiny spots
Record spectra at each location

👉 Build:

2D maps
3D molecular images

🧬 What you see:

Distribution of metabolites
Biomarkers in tissue

🦠 B. Microbial identification

Each microorganism has a unique spectral fingerprint
Compare to database

👉 Fast identification without sequencing

🎯 7. Big-picture takeaways

💡 Key limitations:

MS signal ≠ concentration
Ionization variability is the main challenge

🔧 Solutions:

Labeling (SILAC, TMT, iTRAQ)
Internal standards
Label-free algorithms
AI prediction models

🧠 What you should understand:

Why MS is not inherently quantitative
How labeling fixes that problem
Difference between:
- Absolute vs relative quantification
- Label-based vs label-free
Role of MS1 vs MS2 in quantification

🚀 Final intuition

Think of MS like this:

It’s not a “scale” that weighs molecules directly It’s more like a “microphone” — some molecules just speak louder than others

Quantification methods are basically: 👉 Ways to normalize the volume

Quiz

Score: 0/31 (0%)

Q0. Why is mass spectrometry not inherently quantitative?

Because MS cannot distinguish different masses

Because different molecules ionize, fly, and remain stable with different efficiencies

Because MS only works for proteins and not small molecules

Because MS2 fragmentation destroys all quantitative information

Q1. A compound at very low concentration gives a much stronger MS1 peak than another compound at much higher concentration. What is the best explanation?

The low-concentration compound must have a larger molecular weight

The high-concentration compound was necessarily absent from the sample

The two compounds likely have different ionization efficiencies or ion stabilities

The detector can only detect low-concentration compounds

Q2. In peptide ESI-MS, why can peptides at the same concentration produce very different MS1 intensities?

All peptides fragment before MS1

Different peptide sequences have different chemical properties that affect ionization and detectability

Peptides with the same concentration always give identical signal

Only peptides containing cysteine can be detected

Q3. Which amino acid types were highlighted as important for peptide MS response in the deep learning model?

Only glycine and alanine

Hydrophobic/aromatic amino acids and positively charged amino acids

Only negatively charged amino acids

Only cysteine-containing peptides

Q4. Why do arginine and lysine often improve peptide detection in positive-mode ESI?

They are acidic and repel protons

They are basic and can be protonated

They prevent peptide digestion

They make peptides invisible in MS1

Q5. What does the term 'non-flyer' refer to in the context of peptide MS?

A peptide that cannot be digested by trypsin

A peptide predicted or observed to be poorly detected in the mass spectrometer

A peptide that has no molecular mass

A peptide that only appears in MALDI imaging

Q6. In a bottom-up proteomics workflow, what is usually quantified first before inferring protein abundance?

Whole intact chromosomes

Peptide ions

Only carbohydrates

Only the MALDI matrix

Q7. What is the main purpose of using an isotopically labeled spike-in peptide?

To prevent all peptide fragmentation

To provide a known reference that behaves almost identically to the target peptide

To remove the need for chromatography

To convert all peptides into proteins

Q8. What type of labeling is SILAC?

Metabolic labeling

Post-digestion chemical labeling

MALDI matrix labeling

Database labeling

Q9. In SILAC, why can light and heavy samples be mixed before digestion and MS analysis?

Because the labeled and unlabeled versions of the same peptide behave similarly in MS

Because heavy peptides cannot be ionized

Because SILAC removes all biological variation

Because proteins are not digested in SILAC

Q10. What is the key principle behind isobaric tags such as TMT and iTRAQ?

Different samples have different precursor masses in MS1

Tagged peptides from different samples have the same total mass but release different reporter ions in MS2

They prevent peptide ionization

They are used only for DNA sequencing

Q11. What is the role of the balance group in isobaric labeling?

It digests the protein into peptides

It keeps the total tag mass constant when reporter groups differ

It destroys the reporter ion

It prevents MS2 fragmentation

Q12. Why is accurate one-to-one mixing important in TMT or iTRAQ workflows?

Because incorrect mixing can distort quantitative comparisons

Because inaccurate mixing prevents trypsin from cutting proteins

Because mixing changes the genetic code

Because reporter ions disappear if samples are mixed

Q13. What assumption is central to MaxLFQ label-free quantification?

All proteins change equally between samples

Most proteins or peptides remain relatively constant between samples

Only one peptide per protein can be detected

MS intensities are always absolute concentrations

Q14. What does relative Top 3 quantification use to estimate protein abundance?

The three least intense peptides for each protein

The average of the three most intense precursor ions for each protein

Only reporter ions from TMT

The number of chromosomes in the organism

Q15. MS1 peak intensity can always be directly converted into compound concentration without correction.

True

False

Q16. Two peptides present at the same concentration can produce MS signals differing by several orders of magnitude.

True

False

Q17. Hydrophobic and aromatic amino acids may influence peptide detectability in MS.

True

False

Q18. Arginine and lysine are especially relevant in positive-mode ESI because they can contribute to protonation.

True

False

Q19. Protein quantification in bottom-up proteomics usually depends on peptide-level measurements.

True

False

Q20. A spike-in peptide should ideally be chemically similar or identical to the target peptide except for isotopic labeling.

True

False

Q21. SILAC labels peptides by chemically tagging them after digestion.

True

False

Q22. TMT and iTRAQ are examples of isobaric labeling methods.

True

False

Q23. In isobaric labeling, quantification mainly comes from reporter ions observed in MS2.

True

False

Q24. The NHS ester reactive group in iTRAQ reacts with amines.

True

False

Q25. MaxLFQ and Top 3 quantification answer exactly the same type of quantitative question.

True

False

Q26. iBAQ normalizes peptide intensity by the number of theoretically observable peptides for a protein.

True

False

Q27. MALDI-TOF imaging can map the spatial distribution of selected ions across tissue sections.

True

False

Q28. MALDI-TOF microbial profiling relies on characteristic spectral patterns that can act like molecular fingerprints.

True

False

Q29. The lecture emphasized that HRMS-based peptide principles are useful examples, but similar MS/MS principles can also apply to other organic polymers.

True

False

Q30. Label-free quantification requires no assumptions or normalization.

True

False