Interlaboratory benchmarking of differential scanning fluorimetry reveals sources of variability in protein thermal stability parameters
- 1Institut Pasteur; Universite Paris Cite
Abstract
Differential scanning fluorimetry (DSF) is widely used to assess protein thermal stability and to compare protein variants, ligands, or formulations with limited sample consumption. Yet, the reproducibility of DSF derived parameters across laboratories, instruments and analysis workflows remains poorly defined. Here, we report a two phase interlaboratory benchmark using a shared VHH sample across 26 laboratories, 28 instruments and seven DSF instrument models. In Phase 1, participants applied their routine acquisition and analysis workflows, whereas in Phase 2, acquisition conditions were harmonised. Datasets were analysed locally and then centrally reprocessed with MoltenProt. Participant reported apparent melting temperatures (Tm) showed good within instrument repeatability but moderate inter instrument dispersion. Harmonisation of acquisition conditions did not substantially reduce this dispersion. Conversely, centralised reprocessing narrowed the distribution among retained datasets, indicating contributions from analysis workflows and data acceptance criteria in addition to instrument dependent factors. Comparison with differential scanning calorimetry (DSC) further showed that apparent Tm depended strongly on the fluorescence readout. For this VHH, single wavelength fluorescence at 350 nm showed the closest agreement with the DSC transition temperature, whereas the 350/330 nm ratio and barycentric mean generally yielded higher values. Furthermore, DSF derived apparent unfolding enthalpies were more variable than apparent Tm. Together, these findings show that DSF can be highly repeatable within a defined instrument and workflow, but that quantitative comparability across instruments depends on instrument specific effects, the choice of fluorescence readout and analysis strategy. Improved comparability will require transparent reporting of acquisition and analysis conditions, explicit data quality criteria, and well characterised reference materials to monitor instrument dependent offsets.
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BibTeX
@misc{brule2026interlaboratory,
title = {{Interlaboratory benchmarking of differential scanning fluorimetry reveals sources of variability in protein thermal stability parameters}},
author = {S. Brule and M. Chevreuil and O. Burastero and O. Abian and G. Anderluh and M. Aumont-Nicaise and M. Bagdonas and H. Birchenough and A. K. Buell and T. Charnavets and A. Crousilles and N. Danilenko and M. Epe and E. Fabini and A. Feoli and M. Garcia-Alai and R. Gasper and E. Griss Pascualli and J. Houser and T. Jowitt and A. Kuntzmann and A. Langer and K. Lapouge and K. Lau and A. Le Roy and B. Lopez-Mendez and R. Malempre and C. Mas and A. Matagne and D. Matulis and A. H. Mueller and S. Pasqualato and E. Paulenova and G. Sbardella and W. Streicher and A. Struve Garcia and P. O. Tsvetkov and S. Uebel and M. Vandevenne and S. Vega and A. Velazquez-Campoy and R. W. Walton and Z},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758416},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758416v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Interlaboratory benchmarking of differential scanning fluorimetry reveals sources of variability in protein thermal stability parameters
AU - S. Brule
AU - M. Chevreuil
AU - O. Burastero
AU - O. Abian
AU - G. Anderluh
AU - M. Aumont-Nicaise
AU - M. Bagdonas
AU - H. Birchenough
AU - A. K. Buell
AU - T. Charnavets
AU - A. Crousilles
AU - N. Danilenko
AU - M. Epe
AU - E. Fabini
AU - A. Feoli
AU - M. Garcia-Alai
AU - R. Gasper
AU - E. Griss Pascualli
AU - J. Houser
AU - T. Jowitt
AU - A. Kuntzmann
AU - A. Langer
AU - K. Lapouge
AU - K. Lau
AU - A. Le Roy
AU - B. Lopez-Mendez
AU - R. Malempre
AU - C. Mas
AU - A. Matagne
AU - D. Matulis
AU - A. H. Mueller
AU - S. Pasqualato
AU - E. Paulenova
AU - G. Sbardella
AU - W. Streicher
AU - A. Struve Garcia
AU - P. O. Tsvetkov
AU - S. Uebel
AU - M. Vandevenne
AU - S. Vega
AU - A. Velazquez-Campoy
AU - R. W. Walton
AU - Z
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758416
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758416v1
AB - Differential scanning fluorimetry (DSF) is widely used to assess protein thermal stability and to compare protein variants, ligands, or formulations with limited sample consumption. Yet, the reproducibility of DSF derived parameters across laboratories, instruments and analysis workflows remains poorly defined. Here, we report a two phase interlaboratory benchmark using a shared VHH sample across 26 laboratories, 28 instruments and seven DSF instrument models. In Phase 1, participants applied their routine acquisition and analysis workflows, whereas in Phase 2, acquisition conditions were harmonised. Datasets were analysed locally and then centrally reprocessed with MoltenProt. Participant reported apparent melting temperatures (Tm) showed good within instrument repeatability but moderate inter instrument dispersion. Harmonisation of acquisition conditions did not substantially reduce this dispersion. Conversely, centralised reprocessing narrowed the distribution among retained datasets, indicating contributions from analysis workflows and data acceptance criteria in addition to instrument dependent factors. Comparison with differential scanning calorimetry (DSC) further showed that apparent Tm depended strongly on the fluorescence readout. For this VHH, single wavelength fluorescence at 350 nm showed the closest agreement with the DSC transition temperature, whereas the 350/330 nm ratio and barycentric mean generally yielded higher values. Furthermore, DSF derived apparent unfolding enthalpies were more variable than apparent Tm. Together, these findings show that DSF can be highly repeatable within a defined instrument and workflow, but that quantitative comparability across instruments depends on instrument specific effects, the choice of fluorescence readout and analysis strategy. Improved comparability will require transparent reporting of acquisition and analysis conditions, explicit data quality criteria, and well characterised reference materials to monitor instrument dependent offsets.
ER -