Proteomics and Mass Spectrometry – Lecture 1 (TAIBMS module)

Human Genome & Human Proteome

• Genome = complete genetic material of an organism

  • Humans: nuclear genome (23 pairs of chromosomes) + mitochondrial genome

  • Contains both protein–coding genes and non-coding DNA

  • Landmark projects

  • Human Genome Project (2001)

  • Human Genome Project–Write (2016)

• Proteome = entire set of proteins that is or can be expressed by a cell, tissue or organism

  • Dynamic, varies with cell type, developmental stage, external stimuli, disease status

  • Human Proteome Atlas resources

  • Organ & tissue proteomes

  • Cell-cycle–dependent proteome

  • Organelle proteome

• Ongoing debate on gene/ protein numbers

  • Latest (Nature 2018) tally ≈ 21\,306 protein-coding genes

  • Historical estimates ranged from \approx6\,700 to \approx100\,000

Why Study Proteins? — The ’Omics Cascade

• Flow of biological information

  • DNA → RNA → Protein → Metabolite

• Each level has its own discipline/ technology

  • Genome → Next-Generation Sequencing (NGS)

  • Transcriptome → RNA-Seq

  • Proteome → Mass Spectrometry-based proteomics

  • Metabolome → Metabolomics

• Proteome adds post-transcriptional & post-translational complexity beyond the genome

  • Isoforms, splice variants, PTMs, turnover, cellular localisation, interactions, temporal regulation

Proteome Complexity

• One gene ≠ one protein; multiple mechanisms increase proteome size relative to genome size

  • Alternative splicing

  • RNA editing

  • Post-translational modifications (PTMs): phosphorylation, glycosylation, ubiquitination, etc.

• Illustration (Virág et al., 2020): complexity massively increases from genome → transcriptome → proteome

Information Gleaned from Proteomics

• Protein isoform identification

• Differential gene expression at the protein level

• Spatiotemporal dynamics (where & when proteins appear)

• PTM mapping & PTM dynamics

• Protein–protein / protein–nucleic-acid / protein–metabolite interactions

• Cellular localisation (sub-organellar resolution)

• Protein stability & turnover rates

• Insight into regulation beyond transcription (translational & post-translational control)

• Health vs disease “snapshot” of the protein environment

Proteomics Techniques – Overview

• Two-Dimensional Gel Electrophoresis (2D-GE)

• Two-Dimensional Difference Gel Electrophoresis (2D-DIGE)

• Mass Spectrometry (MS)

Two-Dimensional Gel Electrophoresis (2D-GE)

• 1st dimension: Isoelectric Focusing (IEF)

  • Proteins migrate in a pH gradient until net charge = 0 (their isoelectric point, pI)

• 2nd dimension: SDS-PAGE (orthogonal separation)

  • Proteins separated by molecular weight (MW)

• Visualisation: stain (e.g.
  Coomassie, SYPRO Ruby)

• Each spot ≈ individual polypeptide chain (ex: entire E. coli proteome shown in classic figure)

• Advantages

  • Resolves hundreds → thousands of proteins in one gel ➔ discovery/ comparative profiling

  • Can estimate relative abundance by spot intensity

• Disadvantages

  • Spot matching across gels is difficult; small positional shifts hinder quantification

  • Low-abundance, very acidic/basic, very hydrophobic or extremely large/small proteins may be under-represented

  • Extensive computer-based image analysis required

• Biomedical example

  • Ott et al. (2001) used parallel 2-D-PAGE of paired colorectal tissue → patient-specific tumour profiling

• Classroom exercise (illustrated)

  • Given MW & pI, locate spots A–G; reinforces concept of bi-dimensional separation

Two-Dimensional Difference Gel Electrophoresis (2D-DIGE)

• Principle: direct fluorescent labelling of proteins before IEF

  • Cy2, Cy3, Cy5 dyes are mass/charge-matched but have distinct excitation/ emission spectra

  • Each sample (≥2) labelled with different dye; pooled “internal standard” (Cy2) added to every gel

• All labelled proteins co-migrate on one gel → eliminates gel-to-gel variability

• Workflow

  1. Label extracts with CyDye DIGE Fluor minimal dyes

  2. Mix labelled samples + Cy2-labelled normalisation pool

  3. Perform 2-D separation

  4. Scan gel at dye-specific wavelengths

  5. Use software for image analysis, spot matching, statistics

• Example: Alasmari et al. (2021)

  • Compared serum proteomes of cannabis users vs controls

  • >121 differentially expressed proteins (fold-change > 1.5, p<0.05) → identified by MS

• Advantages

  • Internal Cy2 standard → rigorous normalisation

  • Multiplexing (≤3 samples per gel) reduces total gels and matching workload

  • Improved quantification accuracy; detects expression changes & PTMs

• Disadvantages

  • Dye-labelling may reduce detection of very low-abundance proteins

  • In-gel digestion + MS still limits ultra-low abundance IDs

  • Reagents & scanners are relatively expensive

Mass Spectrometry (MS) – Fundamentals

• Analytical technique that measures mass-to-charge ratio (m/z) of ions in the gas phase

• 2002 Nobel Prize in Chemistry awarded for "soft ionisation" (MALDI & ESI) that allowed analysis of biomacromolecules

Ionisation & Charge

• Organic molecules gain or lose protons (H$^+$) under controlled pH → become ions

  • Acidic conditions: \text{M} + H^+ \rightarrow \text{[M+H]}^{+} (cation)

  • Basic conditions: \text{M} - H^+ \rightarrow \text{[M-H]}^{-} (anion)

• Charged species are manipulated by electromagnetic fields (acceleration, focusing, deflection)

  • In liquid = electrophoresis; in vacuum = mass spectrometry

Calculating m/z

\text{m/z} = \frac{m+z}{z}

• Example for peptide of m=1200\,\text{Da}

  • z=1 → (1200+1)/1 = 1201

  • z=2 → (1200+2)/2 = 601

  • z=3 → (1200+3)/3 = 401

• Multiple isotope peaks (e.g. ^{13}\text{C}, 1.1 % natural abundance) create a pattern spaced \approx1 Da

Fragmentation (Tandem MS / MS–MS)

• Selected parent ion accelerated into inert gas (e.g. N$_2$) → collision-induced dissociation (CID)

• Breaks along peptide backbone → series of b-, y- (and a-, c-, z-) ions; pattern defines sequence

• Example spectrum depicts y7, y8, etc.

Peptide-Centric Strategy

• Proteins are enzymatically digested (commonly trypsin cuts C-terminal to Lys (K) & Arg (R))

  • Yields 7–20 residue peptides: soluble, singly/multiply charged, informative fragmentation

• Peptide identification

  • Compare observed m/z list or fragment pattern against database (in silico digested proteome) ⇒ “peptide mass fingerprinting” / “peptide mapping”

Signal Intensity & Quantification

• Peak area or height ≈ ion count → correlates with peptide abundance (may be non-linear; matrix dependent)

• Ionisation efficiency varies between molecules (matrix effects)

Core Components of an MS System

1. Ion source – generates ions

   • Electrospray Ionisation (ESI): ions from solution; compatible with LC; handles complex mixtures

   • Matrix-Assisted Laser Desorption/Ionisation (MALDI): laser pulses from crystalline matrix; simple mixtures, imaging

2. Mass analyser – separates ions by m/z

   • Quadrupole, Ion Trap, Time-of-Flight (TOF), Orbitrap, Fourier-Transform Ion Cyclotron (FT-ICR)

3. Detector – counts ions at each m/z

4. (Optional) Collision cell – fragmentation for MS-MS

5. Up-front separation – Liquid Chromatography (LC) or capillary electrophoresis for complex samples (LC-MS)

Bird’s-Eye Workflow Example

• Source → Vacuum interface → Mass filter → Collision cell (on/off) → Mass analyser → Detector (output spectrum)

Integrating Gels & Mass Spectrometry

• Spots from 2D-GE / 2D-DIGE excised → in-gel tryptic digestion → LC-MS-MS

• Database search (Mascot, SEQUEST, etc.) matches spectra → identifies protein; relative spot intensity gives abundance

Proteomics Pipeline (Summary Workflow)

1. Sample collection / culture

2. Disruption & solubilisation (lysis buffers, detergents)

3. Complexity reduction (fractionation, 2D-GE/DIGE, LC)

4. Protease digestion (bottom-up) or top-down intact MS

5. Peptide clean-up (desalting, SPE)

6. Mass-spectrometric analysis (instrument run)

7. Data processing (peak picking, identification, quantification)

8. Biological interpretation (pathways, biomarkers, personalised medicine)

Current & Emerging Applications

• Clinical biomarker discovery (blood, urine, tears)

• Disease pathogenesis & personalised medicine

• Host–pathogen interaction mapping

• Drug-target identification & mode-of-action studies

• Neuroproteomics; prediction of clinical outcomes

• Competing/ complementary technologies: deep RNA-Seq, single-molecule proteomics, “next-generation proteomics”

Learning Outcomes (Revisited)

• Define proteome and contrast with genome / transcriptome

• Explain biomedical importance of proteomics

• Describe core experimental approaches: 2D-GE, 2D-DIGE, Mass Spectrometry

• Outline fundamental MS principles: ionisation, m/z determination, fragmentation, instrumentation

Key Numerical / Statistical Facts & Equations

• \text{Current human protein-coding genes} \approx 21\,306

• \text{m/z} = (m+z)/z ; 1\,\text{proton} \equiv 1\,\text{Da of mass and +1 charge}

• Differential expression threshold example: fold-change > 1.5, p < 0.05 (Alasmari 2021)

Ethical / Practical Considerations

• Data volume & privacy in personalised proteomics

• Sample handling & standardisation critical; PTMs may change post-collection

• Cost–benefit of high-end MS instruments vs clinical utility

References & Further Reading (selected from lecture)

• Alberts B. “Molecular Biology of the Cell” (E-book)

• Cooper A. “Biophysical Chemistry” Chapter 7 – Electrophoresis

• Virág D. et al. 2020. Current Trends in PTM Analysis (Chromatographia 83)

• HUPO & EBI on-line courses (What is Proteomics?)

• Biomedical examples: Hariu 2017 (MALDI-TOF in blood cultures); Della Corte 2008 (2D-DIGE platelet secretome)