Characterization of Amyloid Structures in Aging C. Elegans Using Fluorescence Lifetime Imaging

10.3791/61004 ◽  
2020 ◽  
Author(s):  
Maria Lucia Pigazzini ◽  
Christian Gallrein ◽  
Manuel Iburg ◽  
Gabriele Kaminski Schierle ◽  
Janine Kirstein
Keyword(s):  
Fluorescence Lifetime ◽  
Fluorescence Lifetime Imaging ◽  
C Elegans ◽  
Lifetime Imaging ◽  
Amyloid Structures

scholarly journals Fluorescence lifetime imaging for the characterization of the biochemical composition of atherosclerotic plaques

2011 ◽  
Vol 16 (9) ◽  
pp. 096018 ◽  
Author(s):  
Jennifer Phipps ◽  
Yinghua Sun ◽  
Ramez Saroufeem ◽  
Nisa Hatami ◽  
Michael C. Fishbein ◽  
...  
Keyword(s):  
Fluorescence Lifetime ◽  
Biochemical Composition ◽  
Atherosclerotic Plaques ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging

scholarly journals Fast Fluorescence Lifetime Imaging for Longitudinal Studies of Protein Aggregation in Living C. Elegans

2018 ◽  
Vol 114 (3) ◽  
pp. 350a
Author(s):  
Tessa Sinnige ◽  
Romain F. Laine ◽  
Kai Yu Ma ◽  
Amanda J. Haack ◽  
Peter Gaida ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Longitudinal Studies ◽  
Fluorescence Lifetime ◽  
Fluorescence Lifetime Imaging ◽  
C Elegans ◽  
Lifetime Imaging

Abstract 15508: Random Forest Classifier-incoporated Intravascular Optical Coherence Tomography-fluorescence Lifetime Imaging (oct-flim) Provides Automated Characterization of Key Biochemical Components of Coronary Atherosclerotic Plaques

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Sunwon Kim ◽  
Hyeong Soo Nam ◽  
Woo Jae Kang ◽  
Joon Woo Song ◽  
Hyun Jung Kim ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Fluorescence Lifetime ◽  
Biochemical Characterization ◽  
Random Forest Classifier ◽  
Atherosclerotic Plaques ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging ◽  
Biochemical Components

Introduction: Fluorescence lifetime imaging (FLIm) allows label-free biochemical characterization of atheroma, however, multispectral FLIm produces massive data throughput making image interpretation problematic. We investigated whether machine learning algorithm can be applied to intravascular OCT-FLIm for automated biochemical characterization of coronary plaques. Methods and Results: We built a fully-integrated, high-speed OCT-FLIm system and a low-profile, dual-modal imaging catheter that can provide high-resolution OCT images and correctly co-registered multispectral fluorescence lifetime (FL) readouts: ch.1 and ch.2 FL, and FL intensity ratio (ch.2/ch.1). Rapid intracoronary imaging (10-20 mm/s, 100 rps) was safely performed in atheromatous pigs. There were significant differences in FL measurements according to plaque types (high-risk vs. fibrotic: p<0.001). Multispectral FL measurements sampled selectively from histologically-proven plaque components (lipid, macrophage, lipid+macrophage, SMC) were analyzed. Each component was distinguishable from one another either by difference of FLs or intensity ratio. Random forest classifier (RFC), trained with component-labeled multispectral FL dataset, accurately classified key biochemical components of atherosclerotic plaques. RFC-determined biochemical characteristics of target plaque were consistent across two repeated imaging data (intraclass correlation, p<0.0001) and corroborated closely with those derived from quantitative immunohistochemistries. Conclusions: Our OCT-FLIm incorporating RFC-based systematic multispectral FL analysis could provide high-resolution plaque imaging with automated biochemical characterization in beating coronary environment. The present imaging strategy enabling comprehensive characterization of multiple components of atherosclerotic plaques will open a new avenue in the field of cardiovascular imaging.

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