New Publication on Deep Learning to identify metabolic pathways of cells from label-free imaging

Congratulations to Walsh Lab members Linghao Hu and Daniela de Hoyos, as well as our collaborators Yuanjiu Lei and A. Phillip West (Jackson Laboratory) on our publication in APL Bioengineering, “3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data“. In this paper, we show how label-free autofluroescence lifetime imaging of …

Publication: “Label-free spatially maintained measurements of metabolic phenotypes in cells”

We have a new publication (https://doi.org/10.3389/fbioe.2023.1293268) on our work related to autofluorescence lifetime microscopy for imaging cellular metabolism. In this paper, we define NAD(P)H and FAD fluorescence lifetime changes with perturbations to inhibit or enhance the metabolic pathways of glycolysis, oxidative phosphorylation, and glutaminolysis. Additionally, we develop and test conventional feature-based machine learning models to …

Paper published on FLIM analysis methods for machine learning

Congratulations to Blanche ter Hofstede and Linghao Hu on the publication of their paper, “Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes” in Frontiers in Bioinformatics! In this paper, we compared two different fluorescence lifetime imaging analysis techniques, phasor analysis and exponential …