1. DSC applied to the study of
blood serum samples
Adrian Velazquez-Campoy
2MEUM, Institut Pasteur, Paris, September 2016

2. Talking about calorimetry of complex samples…
Lavoisier and Laplace (S. XVIII) determined the specific heat of various substances, in reasonably good agreement with modern standards.
They measured the heat generated by a guinea pig and determined the amount of carbon dioxide in the exhaled air. The results were accurate enough to conclude that respiration was a form of combustion.

3. Differential scanning calorimetry (DSC)
DSC measures the heat capacity of a sample through the heat exchange that takes place during the controlled increase in temperature.
DSC directly measures intrinsic thermal properties of the sample, it is noninvasive and it requires no chemical modifications or extrinsic probes.

4. Differential scanning calorimetry (DSC)
Traditionally DSC was used by dedicated specialists.
Now with the ready availability of sensitive, stable, user-friendly instruments, DSC has become part of the standard repertoire of methods available to the biophysical chemist for the study of macromolecular conformation and interactions.

5. DSC in protein biophysics allows an exhaustive characterization of structural stability and the features of the conformational landscape.
Tm, Hcal, HVH, absolute CP
Nature of native state and thermal unfolding process (cooperativity, intermediate states…)
Temperature stability profile, phase diagrams,…
Interaction with ligands and solutes (pH, ionic strength, co-solutes, co-solvents, ligands…)

6. Recently, DSC has been recognized as a novel tool for diagnosing and monitoring diseases.
DSC profiles from plasma samples of patients exhibited marked differences in the thermodynamic profile.
Garbett et al. Semin. Nephrol :
Garbett et al. Exp. Mol. Pathol :

7. The thermogram is represented by the weighted sum of the most abundant plasma proteins.
Garbett et al. Biophys. J :

8. Serum is a complex mix of biological (macro)molecules.
healthy
patient
Protein modification, up- or down-regulation or interaction with metabolites or ligands as a consequence of certain pathology result in an altered thermal denaturation profile.
Serum is a complex mix of biological (macro)molecules.
New physical tool to probe the plasma proteome, in terms of thermal stability, rather than size, shape or charge.

9. Interactome concept
Serum profile is sensitive to mass (concentration) of each protein, which has a characteristic thermogram.
Serum profile is sensitive to interactions and modifications in plasma proteins associated with diseased state.
In response to a certain illness, potential protein up- or down-regulation and/or modification together with the presence of peptides and metabolites may result in new interactions involving the main plasma proteins (e.g., albumin and immunoglobulin).

10. Challenge: How to do this?
Goal: Use thermograms to differentiate between diseased and healthy subjects.
Challenge: How to do this?
Solution: Defining useful thermogram metrics.
Metrics that represent and quantify aspects of the curve (e.g. peak height, peak temperature, first/second moments, distance between curves, Pearson coefficient…).
Systemic lupus
Lyme disease
Rheumatoid arthritis
Garbett et al. Biophys. J :

12. Aim of the study
Blood serum samples from patients with gastric adenocarcinoma (GAC) at different stages of tumor development.
Goal: To find differences in thermograms from healthy subjects and patients, and classify patients at different tumor stages.
Procedure: Multiparametric phenomenological approach for analyzing the thermogram.

13. Certain transitions are altered by disease.
healthy
patient
healthy
patient
Deconvolution of serum thermogram into individual apparent transitions.
Individual transitions do not necessary correspond to individual serum proteins.
Certain transitions are altered by disease.

14. Multiparametric deconvolution of serum thermograms
𝑇 𝑐 𝐴 𝑤
𝐶 𝑃 (𝑇)= 𝐶 𝑃,0 + 4𝐴 𝑒𝑥𝑝 − 𝑇− 𝑇 𝑐 𝑤 𝑒𝑥𝑝 − 𝑇− 𝑇 𝑐 𝑤 2
𝐶 𝑃 (𝑇)= 𝐶 𝑃,0 + 𝑖= 𝐴 𝑖 𝑒𝑥𝑝 − 𝑇− 𝑇 𝑐,𝑖 𝑤 𝑖 𝑒𝑥𝑝 − 𝑇− 𝑇 𝑐,𝑖 𝑤 𝑖 2
𝐴 𝑖 , 𝑇 𝑐,𝑖 , 𝑤 𝑖 𝑖=1,…,6
𝐴𝑈𝐶= 𝑗 𝐶 𝑃 𝑇 𝑗 𝑇 𝑎𝑣 = 𝑗 𝐶 𝑃 𝑇 𝑗 𝑇 𝑗 𝑗 𝐶 𝑃 𝑇 𝑗
𝑚 𝑘 = 𝑗 𝐶 𝑃 𝑇 𝑗 𝑇 𝑗 − 𝑇 𝑎𝑣 𝑘 𝑗 𝐶 𝑃 𝑇 𝑗 𝑔 1 = 𝑚 3 𝑚

15. 𝐴 𝑖 / 𝐴 2 𝑇 𝑐,𝑖 𝑤 𝑖 Healthy Stage I GAC Stage II GAC Stage III GAC
𝐴𝑈𝐶 𝑛 = j 𝐶 𝑃 𝑇 𝑗 𝐴 𝐴 𝑖,𝑛 = 𝐴 𝑖 𝐴 2
𝐴𝑃 𝑛 = i= 𝐴 𝑖 𝐴 𝑖+1 𝐴 with 𝐴 7 =𝐴 1

16. * **
Healthy
Stage I
Stage II
Stage III
AUCn
28 ± 5
46 ± 6
66 ± 9
100 ± 20
APn
1.3 ± 0.5
4 ± 1
6 ± 1
14 ± 5
Tav
68.1 ± 0.2
68.5 ± 0.3
69.2 ± 0.3
69.5 ± 0.4 g1
0.14 ± 0.04
0.05 ± 0.01
0.03 ± 0.01
0.02 ± 0.01

17. Conclusions
The results of this work validate the use of DSC as a novel technique for GAC diagnosis and classification.
New graphical tools and value ranges for the parameters have been developed and established allowing the discrimination from healthy and GAC subjects in different stages.

18. Future challenges Extension to other diseases.
Use of different types of samples (cerebral spinal fluid, tissues…).
Link differences between thermograms to specific alterations in serum composition and interactome.

19. Thanks for your attention!!!
Sonia Vega
Olga Abian
Rafael Claveria-Gimeno
Angel Lanas
Digestive Pathology Group
Hospital Clinico Universitario
Natalia Markova
Ronan O’Brien
Thanks for your attention!!!