Processing and Analyzing High-Throughput Microfluidic Enzymology Data: A Practical Guide To Rate Fitting and Quality Control
- 1University of California, Berkeley
Abstract
High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.
Related
- A Gravity-driven Microfluidic Platform for High-throughput Culture and Inflammatory Stimulation of Human Articular Chondrocytes.
- Processing, analysing and modelling kinetic data in the era of high-throughput single-molecule biophysics
- Protocol for analyzing tumor-infiltrating bystander CD8+ T cells in mice using adoptive transfer of P14 TCR-transgenic CD8+ T cells
- Protocol for imaging and analyzing the synaptic coverage of astrocytes with super-resolution STED microscopy in mouse brain slices
- Butyrate Triggers Neuronal Signaling via Release of Colonic Serotonin in a Microfluidic Model of Gut-Brain Axis
BibTeX
@misc{freitas2026processing,
title = {{Processing and Analyzing High-Throughput Microfluidic Enzymology Data: A Practical Guide To Rate Fitting and Quality Control}},
author = {N. Freitas and J. S. Zhang and D. F. Muir and H. S. Saunders and D. Aidlen and M. M. Pinney},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.753063},
url = {https://www.biorxiv.org/content/10.64898/2026.09.21.753063v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Processing and Analyzing High-Throughput Microfluidic Enzymology Data: A Practical Guide To Rate Fitting and Quality Control
AU - N. Freitas
AU - J. S. Zhang
AU - D. F. Muir
AU - H. S. Saunders
AU - D. Aidlen
AU - M. M. Pinney
PY - 2026
DA - 2026/09/21
PB - bioRxiv
DO - 10.64898/2026.09.21.753063
UR - https://www.biorxiv.org/content/10.64898/2026.09.21.753063v1
AB - High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.
ER -