Toward absolute viability measurements for bacteria

Abstract

We aim to develop a quantitative viability method that distinguishes individual quiescent from dead cells and is measured in time (ns) as a referenceable, comparable quantity. We demonstrate that fluorescence lifetime imaging of an anionic, fluorescent membrane voltage probe fulfills these requirements for Streptococcus mutans. A random forest machine-learning model assesses whether individual S. mutans can be correctly classified into their original populations: stationary phase (quiescent), heat killed and inactivated via chemical fixation. We compare the results to intensity using three models: lifetime variables (τ 1 , τ 2 and p 1 ), phasor variables (G, S) or all five variables, with the five variable models having the most accurate classification. This initial work affirms the potential for using fluorescence lifetime of a membrane voltage probe as a viability marker for quiescent bacteria, and future efforts on other bacterial species and fluorophores will help refine this approach.

Identifying lipid particle sub-types in live Caenorhabditis elegans with two-photon fluorescence lifetime imaging

“…The Nile Red 2p-FLIM offers a novel approach to examine living, intact specimens with imaging speed about 10× faster than BCARS, providing direct insight in the dynamics of lipid homeostasis. Our measurement protocol and analysis pipeline can be directly applied to other FLIM instruments such as frequency-domain FLIM ( Raspe et al, 2016 ; Dunkers et al, 2021 ) or real-time pixel phasor displayed FLIM ( Sorrells et al, 2021 ). Applying advanced deep learning algorithms such as U-Net convolutional neural network (CNN) ( Smolen and Wooley, 2022 ) could possibly further improve the specificity and accuracy of Nile Red 2p-FLIM.…”

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

“…The most common type of classification, binary classification, can be used, for example, to differentiate cancer (positive) from everything else (negative) without paying attention to specific details of the tissue. More recent studies have investigated ML cells classification, some of them being listed in table 2 [148][149][150][151][152][153]. They classify the type of cells or their states, such as activated and quiescent for T-cells.…”

“…ML algorithms may be able to pick up on hidden links between features that we do not see. For example, phasor plot variables [ 69 ] have been used together with lifetime by [ 149 ], showing better results than with phasor or lifetime features alone.…”