Patent Application
20230081546
The invention relates to a method of determining, in particular localising and/or quantifying, mitochondrial fission, fusion and depolarisation events in a cell.
Mitochondria are highly dynamic organelles that do not operate in a stagnant or isolated manner. Rather, they function in a highly energetic and networked fashion, continuously subjected to rapid remodelling events referred to as fission (fragmentation) and fusion. These events serve as a critical quality control mechanism, not only to adapt and respond to changing metabolic demands but also to enable separation of damaged mitochondria from those in an interconnected state and thereby exposing them to specific degradation. The elimination of damaged mitochondria, mediated through a mitochondrial specific autophagy referred to as mitophagy, decreases the risk of mitochondrial DNA (mtDNA) mutation accumulation. It also improves the electron transport chain (ETC) efficiency. Failure to eliminate damaged mitochondria is a hallmark of neurodegenerative diseases such as Parkinson's disease, while certain heritable diseases, including Charcot-Marie-Tooth Type IIA, are related to dysregulated mitochondrial dynamics. This underscores the important role mitochondrial fission and fusion play, not only in maintaining mitochondrial homeostasis, but also in preserving overall cellular viability. There is therefore a need to quantify these dynamic changes accurately.
Previous attempts to better describe and unravel the interplay between mitochondrial dynamics and cell death onset have largely focused on either the change in the mitochondrial network of the entire cell or the rate at which fission and fusion occurs. However, the accurate description of the quantitative relationship between fission and fusion dynamics in a three-dimensional cellular context, in order to detect deviation from its equilibrium, has remained challenging. Moreover, although it is becoming increasingly clear that intracellular localisation of mitochondria is indicative of regional specific functions, for example the reliance of cellular organelles on mitochondrial ATP provision, it remains largely uncertain where in the mitochondrial network depolarisation is most likely to occur. It therefore remains to be determined which areas of the mitochondrial network are preferentially depolarised to facilitate either transportation or degradation and how these areas relate to the mitochondrial morphometric parameters usually employed.
Current methods typically rely on the manual comparison of two time-lapse image frames in order to observe where fission, fusion or depolarisation occurred. This very labour-intensive approach makes it challenging to gain comprehensive insights into the mitochondrial dynamics of a whole three-dimensional sample. It is usually unclear whether mitochondrial fission and fusion events are changing spatio-temporally, thereby shifting the equilibrium towards either fission or fusion. It is also often not clear whether a cell with a more extensively fused mitochondrial network is in transition or whether it has established a new equilibrium between fission and fusion by means of a newly adapted net contribution of fusion events. Similarly, a cell with greater mitochondrial fragmentation may establish a new equilibrium between fission and fusion as an adaptive response, in order to maintain and preserve this fragmented morphology. Although mitochondrial photoactivation provides a highly selective tool to quantitatively assess mitochondrial dynamics, it does not reveal the relative contribution of fission and fusion events to the observed dynamics. For example, mitochondrial fusion is often enhanced during adaptations to metabolic perturbations, particularly in the perinuclear region, and may protect mitochondria from degradation. On the other hand, a degree of fragmentation is required and desirable to allow, for example, mitochondrial transport in neurons to reach synaptic connections.
There is therefore a need for a method that is able to localise and quantify the number of mitochondrial events in large three-dimensional (3D) time-lapse sample sets, and that also allows quantitative description of the fission/fusion equilibrium as well as the extent of depolarisation.
According to a first aspect of the invention, there is provided a method of determining mitochondrial fission, fusion and/or depolarisation events in a cell. Using a three-dimensional time lapse image sequence of a cell, the method identifies which of the mitochondria in a cell had depolarised or undergone fission or fusion in the interval between the acquisition of the earlier and later images
The method may comprise the steps of:
The method may comprise the further step(s) of:
The fluorescence intensity of the two z-stacks of images may be normalised between steps (i) and (ii).
Between steps (iii) and (iv), each of the labelled structures may be separated into an array of z-stacks for each of the binarised z-stacks, where each z-stack in each of the arrays contains only one labelled voxel structure.
A 3D Gaussian filter may be applied to each z-stack in the first array to create a second array of z-stacks for each of the binarised z-stacks.
Voxels not located on the edges of the labelled voxel structures in the first arrays of z-stacks may be removed to create a third array of z-stacks for each of the binarised z-stacks.
The voxel structures which may have undergone fission or fusion may be determined in step (iv) by:
Step (a) may be performed by calculating the overlapping volume of each voxel structure in the binarised first z-stack of images and each structure in the binarised second z-stack of images.
In step (b), the information from step (a) may be used to identify labelled structures in the second z-stack of images that are associated with labelled structures in the first z-stack of images and thereby to identify structures that are associated with each other in the first z-stack of images; and to identify labelled structures in the first z-stack of images that are associated with labelled structures in the second z-stack of images, thereby to identify structures that are associated with each other in the second z-stack of images.
The identification in step (iv) of the voxel structures that had undergone depolarisation may be performed by determining that a labelled voxel structure in the binarised z-stack for the first z-stack of images does not have an associated labelled voxel structure in the binarised z-stack for the second z-stack of images.
The filtering out of false fission and fusion structure pairs in step (v) may be performed by:
The locations of the fission, fusion and/or depolarisation events within the cell may be indicated in step (vi) by generating an output z-stack of images which shows the location of the fission, fusion and/or depolarisation events that occurred in the interval between the acquisition of the first z-stack of images and the acquisition of the second z-stack of images.
More particularly, the method may comprise the steps of:
Even more particularly, the method may comprise the steps of:
The time interval between the acquisition of the first z-stack of images and the acquisition of the second z-stack of images may be in the range of from about 1 s to about 90 s, and more preferably in the range of from about 5 s to about 60 s.
The images may be micrographs.
The cell may be stained for mitochondria prior to acquisition of the z-stacks of images.
The cell may be from a human or animal subject.
A computer may be used to implement the method or a part thereof.
According to a further aspect of the invention, there is provided a method of diagnosing a disease or disorder which is associated with an increase or decrease in mitochondrial fission, fusion and/or depolarisation events relative to a healthy state, the method comprising determining the location and/or quantity of mitochondrial fission, fusion and/or depolarisation events as described above, and comparing these events to predetermined reference mitochondrial fission, fusion and/or depolarisation events of a healthy or diseased state.
According to a further aspect of the invention, there is provided a method of screening a compound or composition for its potential use in the treatment or prevention of a disease or disorder which is associated with an increase or decrease in mitochondrial fission, fusion and/or depolarisation events relative to a healthy state, the method comprising contacting a human or animal cell with the compound or composition, determining the location and quantity of mitochondrial fission, fusion and/or depolarisation events in a cell as described above, and comparing these to predetermined reference mitochondrial fission, fusion and depolarisation events of a healthy or diseased state.
The disease or disorder may be a neurodegenerative disease, cancer or ischaemic heart disease.
According to further aspects of the invention, there are provided computer-implemented methods of the methods described above.
According to a further aspect of the invention, there is provided a computer program product configured to perform any of the methods described above, the computer program product comprising a computer-readable medium having stored computer-readable program code for performing the steps of the method(s) described above.
The patent application file contains at least one drawing executed in color. Copies of this patent application publication with the color drawings will be provided by the Office upon request and payment of the necessary fee.
A method of determining the location and quantity of mitochondrial fission, fusion and depolarisation events that occur in a cell is described herein. Using a three-dimensional time lapse image sequence of a cell, the method can identify which of the mitochondria in a cell have depolarised or undergone fission or fusion in the interval between the acquisition of the earlier and later images; can indicate the locations of the fission, fusion and/or depolarisation events; and can generate a count of the number of mitochondrial fission, fusion and/or depolarisation events. The method can be used to diagnose a disease or condition associated with an increase or decrease in mitochondrial fission, fusion and depolarisation events. The method can also be used to screen a compound or composition for use in preventing or treating a disease or condition associated with an increase or decrease in mitochondrial fission, fusion and/or depolarisation events. Some or all of the steps of the method can be computer-implemented, and a computer program product configured to perform the method is also provided.
Throughout the specification, the term “binarise” is intended to refer to a process of taking a grayscale image, where each pixel stores a value in some range (such as 0 to 255), and converting it to a black and white, or true and false, representation where most commonly the structure of interest is white (true) and the background is black (false).
The word “comprise” or variations such as “comprises” or “comprising” is understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers.
The term “depolarisation” refers to the loss of the mitochondrial voltage gradient.
The term “Gaussian filter” refers to a filter whose impulse response is a Gaussian function (or an approximation to it). In the context of image processing this is also sometimes referred to as Gaussian blur, since it is usually used to blur the image or to reduce noise.
The term “normalise” is intended to refer to a process of changing the range of pixel intensity values in such a way to improve the consistency of the image quality and pixel intensity histogram between various images of different samples.
The term “voxel structure” refers to an array of contiguous discrete elements into which a representation of a three-dimensional object is divided. Each discrete element within the array is a “voxel”.
“Z-stacking” (also known as focus stacking) is a digital image processing method which combines multiple images taken at different focal distances to provide a composite image with a greater depth of field (i.e. the thickness of the plane of focus) than any of the individual source images. Consequently, a z-stack is a composite image formed from multiple images (e.g. micrographs) taken at different focal distances.
The method includes the steps of:
The method can also include one or more of the following steps, a shown in
The fluorescence intensity of the two z-stacks of images can optionally be normalised between steps (i) and (ii).
Between steps (iii) and (iv), each of the labelled structures can optionally be separated into an array of z-stacks for each of the binarised z-stacks, where each z-stack in each of the arrays contains only one labelled voxel structure. A 3D Gaussian filter can optionally be applied to each z-stack in the first array to create a second array of z-stacks for each of the binarised z-stacks.
Voxels not located on the edges of the labelled voxel structures in the first arrays of z-stacks can optionally be removed to create a third array of z-stacks for each of the binarised z-stacks.
The voxel structures which may have undergone fission or fusion can optionally be determined in step (iv) by:
Step (a) can optionally be performed by calculating the overlapping volume of each voxel structure in the binarised first z-stack of images and each structure in the binarised second z-stack of images.
In step (b), the information from step (a) can optionally be used to identify labelled structures in the second z-stack of images that are associated with labelled structures in the first z-stack of images and thereby to identify structures that are associated with each other in the first z-stack of images; and to identify labelled structures in the first z-stack of images that are associated with labelled structures in the second z-stack of images, thereby to identify structures that are associated with each other in the second z-stack of images.
The identification in step (iv) of the voxel structures that had undergone depolarisation can optionally be performed by determining that a labelled voxel structure in the binarised z-stack for the first z-stack of images does not have an associated labelled voxel structure in the binarised z-stack for the second z-stack of images.
The filtering out of false fission and fusion structure pairs in step (v) can optionally be performed by:
The locations of the fission, fusion and/or depolarisation events within the cell can optionally be indicated in step (vi) by generating an output z-stack of images which shows the location of the fission, fusion and/or depolarisation events that occurred in the interval between the acquisition of the first z-stack of images and the acquisition of the second z-stack of images.
In one embodiment (shown in
In another embodiment, the method comprises the steps of:
The time interval between the acquisition of the first z-stack of images and the acquisition of the second z-stack of images may be in the range of from about 1 s to about 90 s, and more preferably in the range of from about 5 s to about 60 s, from about 5 s to about 40 s, or from about 5 s to about 20 s.
When implemented by a computer, step (i) of the above method does not need to be performed on the computer. Instead, the z-stack images can be uploaded, downloaded or otherwise acquired by the computer. Similarly, the computer program product need not be configured to perform this step.
The method automatically localises the mitochondrial events occurring between two micrograph frames in a time-lapse sequence. The number of mitochondrial structures, their combined and average volume, as well as the number of events at each frame in the time-lapse sequence can also be calculated. The results of this automatic analysis allow the quantitative assessment of fission, fusion and depolarisation localisation with high accuracy and precision.
The cell may be from a human or animal subject, or may be from a plant. More particularly, the cell may be in a cell-containing sample obtained from the human or animal subject. Alternatively, the cell sample can be from a cell line or animal model. The mitochondria in the cells can be stained to make them visible, e.g. by using a fluorescence probe that indicates the mitochondrial morphology and state of depolarisation. Tetramethylrhodamineethylester is one suitable example.
The method described herein has been termed the “mitochondrial event localiser (MEL)”, because it allows for the indication of the precise three-dimensional location at which fission, fusion and depolarisation are likely to occur next. Moreover, the method enables the determination of the individual locations of smaller structures that fuse to form a larger central structure in the two time-lapse frames that are considered. Similarly, the locations of smaller structures that will separate from a common central structure due to fission are identified. In doing so, the method provides a platform to better understand mitochondrial dynamics in the context of health and disease, with both screening and diagnostics potential. This approach is, as far as the applicant is aware, the first automated method for the detection of depolarised mitochondria in the context of fission and fusion events. The method can serve both as a standalone method or as part of a broader mitochondrial analysis pipeline to enable high-throughput analysis of time-lapse data.
The method allows the assessment of whether a cellular system is in mitochondrial fission/fusion equilibrium and whether mitochondrial depolarisation events are kept within a physiological range. Where and when mitochondrial events, specifically fission, fusion and depolarisation, take place in the cell can also be visualised. The ratio between fission and fusion events can hence be indicated and is a quantitative metric of the equilibrium between fission and fusion (
An algorithm for the method described herein can process a fluorescence microscopy time-lapse sequence of z-stack images that are stained for mitochondria and produce the 3D locations of the mitochondrial events occurring at each time step. These locations can subsequently be superimposed on the z-stacks in order to indicate the different mitochondrial events. The algorithm is organised into two consecutive steps—the image pre-processing step which normalises and prepares the time-lapse frames, and the automatic image analysis step which calculates the location of the mitochondrial events based on the normalised frames.
The image pre-processing step receives a time-lapse sequence of z-stacks of images as input and begins by selecting two z-stacks (referred to below as Frame 1 and Frame 2) for further processing. Depending on the temporal resolution that is desired, the selected z-stacks can either be consecutive time-lapse frames, or some number k of intermediate frames may be skipped. The selected Frames 1 and 2 are then each processed in the same way by the image pre-processing step to generate several new image stacks (
Since the method of the invention is not based on the analysis of a single cell, it is not necessary to select regions of interests (ROIs) before the analysis. However, since the image acquisition parameters that are used vary widely between different time-lapse sequences, the fluorescence intensity data of the z-stacks is first normalised. This is because the method relies on an accurate binarisation of the fluorescence image stack to identify voxels that contain mitochondria. These thresholding algorithms require images to be sharp, contain minimal noise, and have good contrast between the foreground and background.
The first step in normalising the raw micrographs is to apply deconvolution to the z-stacks using a point spread function (PSF) that is estimated from the microscope's acquisition parameters. Huygens Professional deconvolution software was used in the example described below (please provide the name and version of this software). Contrast stretching is then applied to the z-stack to normalise the fluorescence intensity between micrographs. The micrographs constituting the z-stack are then upscaled by a factor of two using bilinear interpolation so as to increase the resolution of the binarised image and reduce the possibility that two adjacent but unconnected structures are erroneously joined after binarisation. Finally, a three-dimensional top-hat transformation is applied to further reduce noise and enhance the mitochondria in such a way that they can be more easily isolated from the background.
In one embodiment, the normalised frames are binarised by applying Otsu thresholding to the z-stack, although other thresholding methods could also be used. Since noise remnants are also binarised, structures containing less than some appropriately small number of voxels in the upscaled images were removed (in the examples below, this number was 20, but a person skilled in the art will appreciate that a higher or lower number could be selected). The z-stack that results from this is labelled B in
After binarisation, a label is assigned to each separate 3D voxel structure. The total number of labels in Frame 1 and Frame 2, as depicted in
From the array of labelled z-stacks Lar, two similar arrays also required by the automatic image analysis step are created. The first is the result of applying a 3D Gaussian filter to each z-stack in the array. This slightly blurs the edges of the labelled stacks and results in the array of z-stacks Gar. The Gaussian blurring enhances the ability of the algorithm to match the moving mitochondrial structures between two frames by slightly inflating the structures. The second array of stacks is generated by removing all voxels that are not located on the edges of the 3D labelled structures in Lar by using Canny edge detection. The resulting array of z-stacks is referred to herein as Ear. This is performed mainly to improve the efficiency of the algorithm that later determines the location of fission and fusion events.
This concludes the image pre-processing step. Tuneable (i.e. variable) parameters in this step are the volume of the structures that are considered as noise, and the standard deviation with which the z-stacks are Gaussian blurred. The z-stack B and the arrays of z-stacks Gar and Ear are passed into the automatic image analysis step that calculates the location of the mitochondrial events.
The automatic image analysis step generates a list of locations in Frame 1 at which the mitochondrial events occur. It achieves this by receiving the z-stacks that were generated in the image pre-processing step. These are then automatically analysed to produce a list of the mitochondrial event locations. The automatic image analysis step is shown in
The three different mitochondrial events that are considered herein, namely fission, fusion and depolarisation, are defined in terms of changes in mitochondrial morphology that occur in the time interval between Frames 1 and 2. Specifically, fission is defined as the separation of a larger mitochondrial structure to two smaller structures. Similarly, fusion is defined as the joining of two mitochondrial structures to form a single larger structure. Finally, depolarisation is defined as the disappearance of a mitochondrial structure from Frame 1 to Frame 2 manifested by a complete loss of fluorescence signal. If more than two structures fuse to a single central structure, or if one structure undergoes fission to form more than two smaller structures, then separate locations are assigned to each.
In order to determine the location of mitochondrial events, candidates for fission and fusion are identified from the voxel structures labelled during the image pre-processing step. First, the voxel structures in Frame 1 that occupy the same space as a voxel structures in Frame 2, and vice versa, are identified. For the sake of conciseness, such colocation of structures between the two frames are referred to as overlap. Using this overlap information, the voxel structures which are associated with each other within the same frame can be determined. In Frame 1, these associated structures are the fusion candidates, and in Frame 2 they are the fission candidates. Structures in Frame 1 that have no associated structures in Frame 2 undergo depolarisation.
In one embodiment, to determine the associated structures between Frame 1 and Frame 2, a matrix V is calculated containing the overlapping volume (in voxels) of each structure in Frame 1 with each structure in Frame 2 using the blurred arrays of z-stacks Gar1 and Gar2. Label number 0 is reserved for the background. Blurred stacks are used in order to allow for movement of the mitochondria between frames. Using matrix V, two arrays A1 and A2 are calculated. Each entry in array A1 is a list of all the structures in Frame 2 that overlap with a particular structure in Frame 1. Similarly, A2 indicates the structures in Frame 1 that overlap with a particular structure in Frame 2.
In one embodiment, an algorithm (referred to herein as back-and-forth structure matching) can be used to determine from the information in A1 and A2 which structures in Frame 1 are fusion candidates, and which structures in Frame 2 are fission candidates. For each structure in Frame 1, A1 is used to determine all associated structures in Frame 2. For each of these associated structures in Frame 2, A2 is used to determine all the associated structures in Frame 1.
This list of Frame 1 structures comprises the fusion candidates for the Frame 1 structure being considered. An identical but opposite procedure is used to determine the fission candidates in Frame 2. The resulting two arrays of lists are denoted W1 and W2. A determination is then made as to which of these candidates actually underwent the mitochondrial event. The back-and-forth structure matching algorithm is illustrated in
Many of the mitochondrial event candidates identified in the previous step could be a result of coincidental structure overlap. These overlaps could either be due to mitochondrial movement in the time interval between Frame 1 and Frame 2, or as a result of comparing the blurred frames Gar1 and Gar2. Blurring was introduced to compensate for mitochondrial movement, but has the consequence of increasing the number of false overlaps. These coincidental overlaps can be removed as follows: Firstly, by calculating the relative percentage overlap between associated structures in Frame 1 and Frame 2. For each structure in Frame 1, the set of associated structures in Frame 2 is identified using A1. Then, using V, the overlapping volume of each of these associated structures is normalised relative to the combined overlapping volume of all the associated structures. This results in the relative percentage overlap that each structure in Frame 2 has with the structure in Frame 1. In this way, structures with a small percentage overlap can be considered coincidental and ignored as candidates when determining the type of mitochondrial event. These overlap percentages are stored in an array of lists P1, which has the same structure as A1. An analogous procedure is followed for all the structures in Frame 2, in this case using A2, resulting in P2. P1 and P2 are used to remove mitochondrial event candidates whose proportion of volume overlap is small.
The back-and-forth structure matching algorithm can also produce false matches. For example, when two small structures are on opposite sides of a larger central structure in Frame 1 and both fuse with this central structure in Frame 2, the smaller structures in Frame 1 will be identified as being associated with each other. This is, however, a false match since no mitochondrial event occurs between them. Such false matches can easily be avoided by detecting the presence of a third mitochondrial structure between the two candidates for association, or by determining that the candidate structures are too far apart for a mitochondrial event to be feasible.
The shortest vector length as well as the point in 3D space midway between all structure pairs identified in W1 and W2 are therefore calculated. This midway point represents a likely location of fission and fusion events. The shortest vector lengths can be computed from the information in Ear1 and Ear2. The resulting list of shortest distances are denoted by D1 and D2 and the midway points by M1 and M2 for fusion and fission respectively. Note that the length of each array in the lists matches those of W1 and W2 for the same subscript.
Using A1 and A2, the structures in Frame 1 that have no associated structures in Frame 2 (indicating that they will depolarise) can be found. Next, the false fission and fusion candidate structures in W1 and W2 can be filtered out by using D1, D2, M1 and M2 to determine the final sets of fission and fusion events. First, a distance threshold corresponding to the maximum distance that a mitochondrial structure could be expected to move is determined. Then, all candidates in W1 and W2 for which the respective distances in D1 and D2 exceed this threshold are removed. Next, fission and fusion candidates separated by a third structure are removed by considering the binarised stacks B1 and B2 in conjunction with the midway points in M1 and M2. Finally, by using P1, two structures within Frame 1 that are both associated with the same structure in Frame 2 with a high relative percentage overlap are considered as a fusion event. Similarly, using P2, two structures within Frame 2 that are both associated with the same structure in Frame 1 with a high relative percentage overlap are considered as a fission event. Any structure pairs with a low and high, or a low and low, combination of relative percentage overlap is ignored. Empirically, it was observed that in most cases the relative percentage overlap is either above 90% or below 10%. Therefore, any percentage above 50% is considered to be high. This step results in two more arrays of lists. The Fusion array indicates which structures in Frame 1 will fuse, depolarise, or undergo no event. The Fission array indicates which structures in Frame 2 are the result of a fission event.
The final step is to generate an output image by superimposing a label (typically a colour label), in three-dimensional space, on the z-stack to indicate the location of each mitochondrial event at the midway point between the two structures that fuse in Frame 1, or using the midway point between the two structures that are the result of fission in Frame 2 as the location at which a structure in Frame 1 will undergo fission. The structures that will depolarise are indicated by placing a label at its centre of mass, which is calculated from the z-stack for the structure in question from the Lar1 array.
One example of the automatic image analysis step, indicating which stacks and list of arrays are used for which parts of the algorithm, is shown in
Another embodiment of the invention is to perform the method of the invention without a time-lapse sequence. Neural networks can be trained to estimate the mitochondrial event locations from a single z-stack, for example by using several generated input-output pairs as ground truth data for neural network training. It is envisaged that conditional generative adversarial networks, such as the pix2pix network, could be used for this purpose.
Another embodiment of the invention is a computer-implemented method of using the method to locate and quantify mitochondrial events, to screen for drugs and exclude cell toxicity, to diagnose a disease or condition, and/or to determine the progression of a disease or condition. Other embodiments include a computer storage medium configured to store instructions to perform the computer-implemented method, and a computer system including a computer processor configured to perform the computer-implemented method.
Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. The described operations may be embodied in software, firmware, hardware, or any combinations thereof.
The software components or functions described in this application may be implemented as software code to be executed by one or more processors using any suitable computer language such as, for example, Java, C++, or Perl using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions, or commands on a non-transitory computer-readable medium, such as a random access memory (RAM), a read-only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a CD-ROM. Any such computer-readable medium may also reside on or within a single computational apparatus, and may be present on or within different computational apparatuses within a system or network.
Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a non-transient computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
The invention will now be described in more detail by way of the following non-limiting examples.
Using a mammalian cell model, the method of the invention was applied and validated by assessing the mitochondrial network under both physiological and disrupted conditions. Two control cells were analysed and these were contrasted with cells treated with hydroxychloroquine sulphate (HCQ), leading to mitochondrial dysfunction.
U-118MG cells were purchased from the American Type Culture Collection (ATCC), supplemented with Dulbecco's Modified Eagles Medium (DMEM), 1% penicillin/streptomycin (PenStrep) (Life Technologies, 41965062 and 15140122) and 10% foetal bovine serum (FBS) (Scientific Group, BC/50615-HI), and incubated in a humidified incubator (SL SHEL LAB CO2 Humidified Incubator) in the presence of 5% CO2 at 37° C.
Live cell confocal microscopy of mitochondrial fission and fusion events was conducted using a Carl Zeiss Confocal Elyra S1 microscope with LSM 780 technology. Prior to imaging, U-118MG cells were seeded in Nunc® Lab-Tek® II 8 chamber dishes and incubated with 800 nM tetramethylrhodamine-ethyl ester to allow for the visualisation of the mitochondrial network (TMRE, Sigma Aldrich, 87917) for 30 minutes in the presence of 5% CO2 at 37° C. In order to perturb the mitochondrial network, cells were exposed to 1 mM hydroxychloroquine sulphate (HCQ) (Life Technologies, T669). HCQ results in fragmentation of the mitochondrial network by disrupting electron transport chain efficiency.
For the two control samples, the time intervals between frames were 6 s and 20 s, respectively. For the HCQ treated samples, the time interval was 61.7 s.
The accuracy of the results that can be affected by image quality. Therefore, to compensate for the variable image quality of the biological samples, the position of all mitochondrial events was calculated for three different contrast stretching parameters in the pre-processing phase. This leads to three output images, each with markers indicating the location of all mitochondrial events. The output images were then divided into non-overlapping 10-by-10-by-2 voxel blocks (x, y and z-axes respectively).
Within each block, the number of times each type of mitochondrial event was detected was counted for the three different pre-processing parameters. The different types of mitochondrial events were then visualised using different colour labels, where the intensity of these labels is proportional to the relative frequency with which each type of mitochondrial event was detected within each 10-by-10-by-2 voxel block. The intensity therefore serves as an indication of the confidence that the mitochondrial event is occurring at that location.
The pre-processing parameters and percentage and distance thresholds, which are used to remove false matches, are summarised in Table 1. These parameters were selected based on empirical observation. Alternatively, an automated statistical method could also be employed to determine the parameters based on the z-stack under analysis.
The method of the invention was applied to four different time-lapse sequences of mammalian cells.
Finally, the average percentage of structures that will undergo fission, fusion and depolarisation in every fourth frame in the time-lapse sequence (shown in column A of
The steps of the method are demonstrated herein by applying the method to two synthetically generated image frames. This is intended only to clarify the MEL algorithm, and does not represent a realistic scenario as would be expected when analysing mitochondrial events. Although the method is intended for application to three-dimensional samples, two-dimensional images only are considered here for clarity.
Since the images are synthetic, the normalisation step shown in
The matrices and arrays calculated for the synthetic example by the process depicted in
In this example, there is a small overlap between Frame 1 label 4 and Frame 2 label 2 due to the blur which the filter has introduced. To compensate for such coincidental matches, all overlapping volumes that account for less than 1% of the volume of either structure in question are eliminated, and are consequently shown as 0 (
From matrix V, the arrays A1 and A2 are determined by simply reducing V to indicate which structures in the other frame presented with a non-zero overlapping volume. The relative percentage overlap, P1 and P2, of each structure in one frame with all associated structures in the other frame can then be calculated from A1 and A2. This is in effect a normalisation of the volumes in matrix V, where the volume of a certain structure combination is divided by the total volume of the given structure in either Frame 1 (producing P1) or Frame 2 (producing P2). Each row, therefore, sums to 100%.
Using the back-and-forth structure matching described in
Using W1 and W2 along with the edge array of images Ear for Frame 1 and Frame 2, the shortest distances, D1 and D2, as well as the midway points, M1 and M2, are found between each combination of structures. If the shortest distance between the candidate structures is above a set threshold (in the case of this synthetic example this was 50 pixels), the two structures are considered unrelated and is ignored in the visualisation (
Now the mitochondrial event status can be determined for each candidate structure combination in W1 and W2. Structures in Frame 1 are labelled “Fuse”, “Depolarize” or “Unrelated”, while structures in Frame 2 are labelled “Fission” or “Unrelated”.
Finally, using the midway points, as well as the centre of mass of the structures to indicate the location of the mitochondrial event, along with the status arrays, the final output image is generated (
Data extracted by applying the method of the invention, as shown in column D in
From a morphological point of view, a widely spread, highly interconnected mitochondrial network was observed for the control samples (column A in
In general, there is very little change in the overall mitochondrial network pattern of the control samples (column A in
Most of the fission and fusion events were observed in the periphery and distal regions of the mitochondrial network, with very few events detected in the strongly networked areas.
Using
To the applicant's knowledge, this method is the first approach that allows the automatic localisation of depolarisation events. This is specifically significant as depolarisation of the entire mitochondrial network has been associated with caspase-3 activation, the execution of apoptotic cell death and generally demarcates the point of no return (PONR) for apoptosis. Therefore, the method of the invention may be of particular value in the quantitative assessment of cell death onset, associated with a wide range of diseases such as neurodegenerative disease, cancer or ischaemic heart disease.
Depolarisation was observed to be most abundant in the HCQ treated samples, with the majority of the depolarisation events localised in mitochondria that become isolated from the main mitochondrial network and have a small average volume. This was usually observed to occur in the cell periphery (column A in
The above description has been presented for the purpose of illustration, and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020/00654 | Jan 2020 | ZA | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2021/050781 | 2/1/2021 | WO |
