1. Big picture: “Antibody technology” as a platform 🧪

The lecturer frames antibodies not just as immune proteins, but as a technology platform that can be applied across many biological problems, as long as those problems can be “combined with antibodies.”

Example application areas mentioned:

• Viral infections
  • Engineering better neutralizing antibodies against viruses (e.g. SARS-CoV-2).
• Cancer
  • Identifying biomarkers on cancer stem cells, such as colon cancer stem cells.
• Vascular biology
  • Modulating blood vessels using antibody-based approaches.
• Prenatal diagnostics
  • Non-invasive detection of fetal cells in maternal blood using antibodies (see section 7).
• Neurological diseases
  • Designing antibodies that can cross the blood–brain barrier (BBB) to reach brain targets.
• Obesity and metabolic disease
  • Targeting CD36 as a biomarker for obesity-associated cardiovascular and metabolic risk.
• Aging
  • Long-standing interest in aging, again approached through antibody-based tools.

Core idea: antibodies are used as highly specific recognition tools to detect, isolate, or target particular cells and molecules across many biological systems.

2. Antibody structure and basic functions 🧩

An antibody (immunoglobulin, Ig) is described structurally and functionally:

2.1 Structural overview

• Composed of four polypeptide chains:
  • Two heavy chains (H) – shown in red.
  • Two light chains (L) – shown in yellow.
• Each chain has:
  • Variable domains (V) – at the tips, responsible for antigen recognition.
  • Constant domains (C) – more conserved, responsible for effector functions.

You can imagine the classical Y-shape:

• The two arms (Fab regions): mainly variable + some constant domains – bind antigen.
• The stem (Fc region): constant domains – interact with immune effector systems.

2.2 Two main functions

1. Antigen binding
   • The antigen (virus, protein, etc.) binds near the tips of the Y:
     • Formed by the variable regions of both heavy and light chains.
   • This region is extremely diverse between different antibodies.
2. Effector function via Fc
   • The constant (Fc) region binds Fc receptors on immune cells.
   • All antibodies of a given isotype must be recognized by Fc receptors, so:
     • The Fc region is highly conserved (little sequence diversity).
   • Through Fc, antibodies can:
     • Recruit phagocytes, activate complement, trigger ADCC, etc.

Key contrast:

• Variable region (Fab tips) = highly diverse → antigen specificity.
• Constant region (Fc + constant parts of Fab) = conserved → standardized effector interaction.

3. Genetic basis of antibody diversity: V(D)J recombination 🧬

To achieve diverse antigen binding but conserved effector functions, the immune system uses a modular genetic design in B cells.

3.1 Germline organization

In your germline DNA (genomic DNA), immunoglobulin genes are split into gene segments:

• For the heavy chain variable region (V_H):
  • Multiple V (variable) segments.
  • Multiple D (diversity) segments.
  • Multiple J (joining) segments.
• For the light chain variable region (V_L):
  • Multiple V segments.
  • Multiple J segments.
  • No D segment in light chains.

The constant regions (C) are encoded by other segments (grey in the lecture figure) and are comparatively uniform, so they are not the focus here.

3.2 Recombination in B cells

During B-cell development (maturation):

• A single B cell:
  • Chooses one V, one D, and one J segment for the heavy chain.
  • Chooses one V and one J segment for the light chain.
• These segments are recombined by a process involving homologous recombination and V(D)J recombinase activity.
• The result is a rearranged V(D)J exon that encodes the variable region.

Then:

• The rearranged gene is transcribed, spliced, and processed.
• The resulting mRNA is translated into the antibody heavy or light chain protein.

3.3 Combinatorial diversity – numbers

For the heavy chain, the lecture gives approximate segment counts:

• 65 V segments
• 27 D segments
• 6 J segments

If any combination of V–D–J can be used, the number of possible heavy chain variable regions by simple multiplication is:

• 65 × 27 × 6 = 10,530 heavy chain V-region combinations.

There is a similar (but somewhat smaller) combinatorial repertoire for the light chain.

When you consider:

• Many possible heavy chains × many possible light chains

→ You get on the order of ~10⁵ (~100,000) different antibody specificities from combinatorial V(D)J assembly alone.

The lecturer emphasizes: this is actually not that much, given:

• Each virus presents multiple distinct epitopes (e.g. RBD, N protein, M protein in SARS-CoV-2), each requiring different antibodies.

So more diversity mechanisms are needed.

4. Junctional diversification: “sloppy” joining to boost diversity 🧨

V(D)J recombination is not perfectly precise. This imperfection is actually exploited:

4.1 Imperfect recombination

When a V segment is joined to a D segment, and D to J, extra nucleotides can be inserted at the junctions.

• Example from the lecture:
  • If you insert one extra nucleotide between V and D:
    • You induce a frameshift.
    • The reading frame changes, so the downstream amino acid sequence (often in the CDR3 region) becomes completely different.
• You can also add more than one nucleotide, creating many new sequence possibilities.

This process is often referred to as junctional diversity:

• It dramatically alters the CDR3 loop, which is a key determinant of antigen specificity.

4.2 Impact on diversity

Because each recombination event can have different numbers and sequences of inserted nucleotides, junctional diversification can:

• Boost the theoretical repertoire from ~10⁵ (combinatorial only) to millions of distinct antibodies.

So, the total diversity is:

1. Choice of V, D, J segments (combinatorial).
2. Random insertions/deletions at junctions (junctional).
3. Different heavy–light pairings.

5. Somatic hypermutation and affinity maturation 🎯

Even millions of naïve antibody variants are sometimes not enough, especially when high affinity and fine specificity are needed (e.g. neutralizing antibodies).

The immune system adds another layer: somatic hypermutation (SHM) and affinity maturation.

5.1 Somatic hypermutation

• After a B cell has:
  • Successfully rearranged its V(D)J segment, and
  • Expressed the antibody on its surface
• If its B-cell receptor (surface Ig) binds an antigen (e.g. SARS-CoV-2 protein), a signal is generated.
  • This involves T helper cells and other immune components (germinal center reaction).

Activated B cells then:

• Enter a phase of rapid proliferation.
• During these cell divisions:
  • The mutation rate in the variable region is artificially increased.
  • The process is targeted mostly to the variable regions, not the constant regions.
  • These are called somatic hypermutations:
    • Somatic (in body cells, not germline).
    • Hypermutation (mutations occur at a higher rate than normal DNA replication).

Mechanistically:

• Although not fully understood, special DNA-editing enzymes (e.g. AID in known biology) introduce mutations preferentially into the V region.

5.2 Affinity maturation in germinal centers

In germinal centers (specialized structures in lymph nodes and other lymphoid tissues):

1. B cells with mutated antibodies compete for binding to antigen.
2. Those with higher affinity:
   • Bind antigen more strongly.
   • Receive stronger survival and proliferation signals (from antigen and T helper cells).
3. Lower-affinity clones:
   • Receive weaker signals.
   • Are less likely to survive or proliferate.

Over time:

• The population of B cells is enriched for high-affinity variants.
• The average affinity of the antibody response increases – this is affinity maturation.

Result:

• Antibodies become highly specific and high affinity for their target antigen.
• This explains why booster exposures (or boosting by infection followed by vaccination) can give much better antibody responses.

6. When the immune system fails – and how to mimic it in the lab 🧫

Despite this sophisticated system, people still get sick:

• Pathogens can evade immunity.
• Immune systems can be compromised or insufficient.

The lecturer’s research interest is in:

• Creating new antibodies with new specificities to “help” the immune system:
  • For obese patients (e.g. CD36 diagnostics),
  • For cancer,
  • For infections, etc.

To do this, they need to mimic key aspects of the immune system in vitro.

6.1 Focusing on the variable region in engineering

In the lab:

• They mainly work with the variable part of the antibody:
  • Because antigen binding is determined by the variable region.
• The constant region is treated like LEGO bricks:
  • Once you have a useful variable region (e.g. as an scFv or Fab), you can later “snap it onto” a chosen constant region (e.g. an IgG1 Fc) to give desired effector functions.

Thus, antibody engineering projects often:

1. Generate or select variable region sequences with desired binding.
2. Clone these into various expression constructs (with different constant regions, linker formats, tags, etc.).

7. Practical/experimental concepts and methodologies discussed 🔬

Even though this is “part 1” of the lecture and doesn’t go deep into detailed protocols yet, it introduces several conceptual experimental approaches where antibodies are used.

7.1 Using bacteria and bacteriophages (display systems – high-level)

The lecturer mentions that to mimic the immune system, they often use:

• Genetic tools
• Plasmids
• Bacteria and bacterial viruses

This is pointing toward display technologies (like phage display), where:

• The antibody variable region is expressed in a non-B cell host (e.g. E. coli, bacteriophages).
• Full IgG antibodies are often hard to express correctly in bacteria, so:
  • They typically express smaller fragments (e.g. scFv, Fab), which still include the variable regions necessary for antigen binding.
• These bacterial or phage systems allow:
  • Construction of large libraries of antibody variants.
  • Selection (“panning”) for those that bind a target antigen.

In this part of the lecture, the detailed phage display workflow is not yet explained, but the conceptual point is:

We can partly recreate the B-cell “selection” process using genetic libraries in bacteria/phages and select for binders in vitro.

7.2 Antibody-based prenatal diagnostics 👶🩸

Concept:

• During pregnancy (already by week 7–8), rare fetal cells enter the maternal bloodstream.
• In principle, you can:
  • Draw a blood sample from the pregnant woman.
  • Use those fetal cells to perform whole-genome sequencing of the fetus, looking for genetic diseases.

Practical challenge:

• The blood sample contains:
  • Billions of maternal nucleated cells (all containing maternal DNA).
  • Only a tiny number of fetal cells.
• For fetal genomic analysis, you must avoid sequencing the mother’s DNA.
• Therefore, you need a way to “fish out” the rare fetal cells from the mixture.

Antibody-based solution:

• Identify markers specific (or highly enriched) on fetal cells.
• Use antibodies against these markers to:
  • Capture and enrich the fetal cells from maternal blood.
• After enrichment:
  • Perform DNA extraction and sequencing on these rare cells to obtain fetal genomic information.

This is an example of rare-cell isolation using antibodies – conceptually similar to immunomagnetic separation or FACS-based enrichment.

7.3 CD36 as a biomarker in obesity and metabolic disease ⚖️❤️

The lecturer describes CD36 as:

• A molecule whose levels in blood are increased in obese patients, particularly when they are at risk of comorbidities like:
  • Cardiovascular disease
  • Diabetes, etc.

Research goal:

• Develop antibodies against CD36 to create a diagnostic test.

Conceptual diagnostic use:

• Measure CD36 levels with an antibody-based assay (e.g. ELISA or similar immunoassay).
• Stratify patients:
  • Obese but with low CD36 → potentially lower risk of additional diseases.
  • Obese with high CD36 → higher risk, might warrant clinical attention and lifestyle interventions.

So CD36 is used as a biomarker to personalize risk assessment in obesity.

7.4 Antibodies and the blood–brain barrier (BBB) 🧠

Neurological disease application:

• Normally, antibodies do not efficiently cross the BBB.
• The group is interested in:
  • Finding or engineering antibodies that can cross from blood into the brain, and
  • Target brain cells and processes.

Conceptually, this involves:

• Identifying receptor-mediated transport mechanisms at the BBB.
• Designing antibodies or antibody fragments that:
  • Bind BBB transporters, get shuttled across, and still retain specificity for brain targets (often in bispecific or “transport + target” formats).

Specific methods are not detailed here, but the key experimental theme is:

Use antibody engineering to overcome physiological barriers (like the BBB).

8. How these pieces connect conceptually 🧠➡️💉

Putting it all together:

1. Natural system:
   • V(D)J recombination + junctional diversity + somatic hypermutation + clonal selection → huge and adaptable antibody repertoire.
2. Failures / limitations:
   • Sometimes the natural immune system does not generate the right antibodies, or not in time or in sufficient amounts.
3. Mimicking in the lab:
   • Use genetic engineering, bacteria, and bacteriophages to build vast libraries of variable regions.
   • Apply selection pressures in vitro (binding to cancer markers, CD36, fetal cell markers, brain transporters, etc.) analogous to selection in germinal centers.
4. Application:
   • Once good binders are found, combine them with suitable constant regions and turn them into:
     • Diagnostics (e.g. CD36 tests, prenatal fetal cell capture),
     • Therapeutics (e.g. viral neutralization antibodies, cancer-targeting antibodies),
     • Research tools (e.g. BBB-crossing antibodies to study brain function).