Mitochondrial event localiser (MEL) to quantitativelydescribe fission, fusion and depolarisation in the three-dimensional space

Abstract

Mitochondrial fission and fusion play an important role not only in maintaining mitochondrial homeostasis but also in preserving overall cellular viability. However, quantitative analysis based on the three-dimensional localisation of these highly dynamic mitochondrial events in the cellular context has not yet been accomplished. Moreover, it remains largely uncertain where in the mitochondrial network depolarisation is most likely to occur. We present the mitochondrial event localiser (MEL), a method that allows high-throughput, automated and deterministic localisation and quantification of mitochondrial fission, fusion and depolarisation events in large three-dimensional microscopy time-lapse sequences. In addition, MEL calculates the number of mitochondrial structures as well as their combined and average volume for each image frame in the time-lapse sequence. The mitochondrial event locations can subsequently be visualised by superposition over the fluorescence micrograph z-stack. We apply MEL to both control samples as well as to cells before and after treatment with hydrogen peroxide (H2O2). An average of 9.3/7.2/2.3 fusion/fission/depolarisation events per cell were observed respectively for every 10 sec in the control cells. With peroxide treatment, the rate initially shifted toward fusion with and average of 15/6/3 events per cell, before returning to a new equilibrium not far from that of the control cells, with an average of 6.2/6.4/3.4 events per cell. These MEL results indicate that both pre-treatment and control cells maintain a fission/fusion equilibrium, and that depolarisation is higher in the post-treatment cells. When individually validating mitochondrial events detected with MEL, for a representative cell for the control and treated samples, the true-positive events were 47%/49%/14% respectively for fusion/fission/depolarisation events. We conclude that MEL is a viable method of quantitative mitochondrial event analysis.

DRP1-mediated mitochondrial fission is essential to maintain cristae morphology and bioenergetics

“…The MEL algorithm processes a fluorescence microscopy time-lapse sequence of z-stack images of Mitotracker- labeled mitochondria and produces event counts per frame and 3D locations indicating where mitochondrial events are likely to have occurred at each time step(Theart et al, 2020). These locations can subsequently be superimposed on the z-stacks to indicate the different mitochondrial events.…”

Predicting mitochondrial fission, fusion and depolarisation event locations from a single z-stack

“…The localisation of mitochondrial fission, fusion and depolarisation events, especially in threedimensions, has, until, recently been done manually. The mitochondrial event localiser (MEL), developed by Theart et al [18], is an automatic, deterministic and high-throughput analysis system. MEL uses two sequential frames of a time-lapse sequence of fluorescence microscopy z-stack images that was stained for mitochondria (with TMRE) as input and provides the three-dimensional locations of mitochondrial fission, fusion and depolarisation events as output.…”

“…MEL was used to localise mitochondrial fission, fusion and depolarisation events for 44 threedimensional distinct fluorescence microscopy z-stack time-lapse sequences of healthy, untreated, cells. The parameters proposed by Theart et al [18] were used to create the dataset for the training of the neural network.…”

“…A review tool was developed by Theart et al [18], which was used to validate the legitimacy of the events localised by MEL by manually comparing the relevant frames of the time-lapse sequence to the localised events. False-positive events were removed form the dataset through this process.…”

“…Until recently, the occurrence and locations of these events were determined through manual visual inspection by comparing two frames of a time-lapse sequence. Recently, an automatic method for comparing two frames of a time-lapse sequence to localise these events has been developed [18]. This automatic method was used to generate the training data for the neural networks discussed in this paper.…”