Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

mito_major_axis The length of the major axis of the ellipse that has the same normalized second moments as the mitochondrion.

mito_minor_axis
The length of the minor axis of the ellipse that has the same normalized second moments as the mitochondrion.
mito_orientation Angle between the horizontal line and the major axis of the ellipse that has the same second moments as the mitochondrion, ranging fromπ/2 to π/2 counter-clockwise. mito_perimeter Perimeter of the mitochondrion.  Skewness of mitochondria distribution across the cell in X direction.

Mitochondrial aggregate measurements
cell_weighted_skewness_x Skewness of mitochondria distribution across the cell in X direction, with distances normalized by the mitochondrial area.
cell_skewness_y Skewness of mitochondria distribution across the cell in Y direction cell_weighted_skewness_y Skewness of mitochondria distribution across the cell in Y direction, with distances normalized by the mitochondrial area cell_skewness_squared Skewness of mitochondria distribution across the cell cell_weighted_skewness _squared Skewness of mitochondria distribution across the cell, with distances normalized by the mitochondrial area.
cell_network_major_axis The length of the major axis of the ellipse that has the same second moments as the convex hull from the distribution of the mitochondria across the cell (as a simple estimation of cytoplasm) cell_network_minor_axis The length of the minor axis of the ellipse that has the same second moments as the convex hull from the distribution of the mitochondria across the cell (as a simple estimation of cytoplasm) cell_network_orientation Angle between the horizontal line and the major axis of the ellipse that has the same second moments as the convex hull from the distribution of the mitochondria across the cell (as a simple estimation of cytoplasm), ranging from π/2 to π/2 counter-clockwise. cell_network_eccentricity Eccentricity of the ellipse with the same second-moments as the convex hull from the distribution of the mitochondria across the cell, and it is defined the ratio of the focal distance of the ellipse over the major axis length. cell_network_effective _solidity Total mitochondria area in the cell, divided by the area of the convex hull from the distribution of the mitochondria across the cell. cell_network_fractal _dimension The fractal dimension of the mitochondrial network in the cell.

Aside I: Mito Hacker's various functions and their parameters
To ensure the best results, we highly suggest to analyze images from the same experiment in each batch or round of analysis. In this case, the images in each batch are more or less similar, and as a result, the processing on the images will result in more uniform and reliable results across the images in each batch.

Identifying nuclei in the image
As the name suggests, this function is used to identify and segment individual nuclei in the image. The nuclei play an important role in cell separation. They are used as the initial seed to detect and isolate individual cells. To deal with a large range of images with different levels of signal, noise, intensity, and nuclei sizes this function employs a set of tasks to process the images. While the default parameters are highly tuned to immediately work with a wide range of images, there is not a set of parameters that works for all. The users should find a combination of parameters that best represent the nuclei in their images, though they should maintain consistent parameters across a given experimental set. Below is the list of parameters for this function:

Nuclei Signal Threshold
• By changing the value of this parameter, you change the signal threshold for detecting nuclei in the image. Lower threshold may help you to detect dimmer nuclei in the image. However, in some cases extremely low threshold values may cause problems by capturing the background noise as nuclei.
• Select a threshold value that reasonably works for your sample images. You may check sample images by selecting them from the dropdown menu.

Nuclei Non-Uniformity
• Different nuclei in the same image may have different signal intensity and signal distribution. This option helps the user to reach a better nuclei segmentation results over a wider range of images with varying nuclei across the image.

Nuclei Size Threshold
• This option helps the user to set the minimum acceptable nucleus size in the images, and reduce noise.

Correct Nuclei Shape
• This feature tries to reconstruct the shape of the poorly illuminated nuclei in the image.
• In some cases, this correction may result in nuclei with angular shapes. § This phenomenon usually does not have an adverse effect on the analysis, since these binary nuclei are merely used as masks on the real nuclei in the image, and their goal is maximal capturing of the nuclei in the image. Ultimately the original shape of the nuclei (from the original image) would be presented in the image.

Low Nuclei Signal
• In cases where the signal level in the nuclei channel of your images is low, or the images suffer from low contrast, this option may help user to maximize nuclei capturing in the image.

Identifying mitochondria in the image
This function aims to identify mitochondria on the image and assign them to different cells across the image to separate individual cells. Cells on across set of images have a wide range of mitochondrial network distribution, signal and noise intensities. This function performs multiple processing steps on the images to ensure maximal mitochondrial capturing. We have tuned the default parameters to work with a wide range images, but again there is not set of parameters that works for all. The users should find a combination of parameters that best represents the mitochondrial distribution in their images, to ensure all mitochondria are captured and assigned to different cells, while the noise is cut. Below is the list of parameters for this function:

Mitochondrial Signal Threshold
• This parameter sets the intensity threshold for mitochondria. Lower threshold results in capturing more possible mitochondria in the image, but has the potential to capture excessive noise. This is further explained later in the text under "Global Mitochondrial Mask" section.

Mitochondrial Search Radius for Ghost Cells
• This parameter sets the radial distance around the nuclei to search for mitochondria. This tool is used to identify the ghost cells in the image. § As discussed in the main text, the ghost cells are the cells where their nucleus is stained but mitochondrial staining is missing.

Maximum Mitochondrial Content for Ghost Cells
• This parameter sets the minimum acceptable amount of mitochondrial content around a cell (within the radius set above). The cells with mitochondrial content below this threshold will be marked as ghost cells.

Remove the Ghost Cells
• By selecting this option, the function removes the nuclei of the cells marked as ghost cells from the image.

Low Mitochondrial Signal
• You may select this option if the signal level, or the contrast (Signal to Noise Ratio (SNR)) in the mitochondrial channel is low in the set of images you analyze. § Based on the image condition this option may have additional applications. That we will discuss in more details in the "Important Notes" section.

My Cells Are Reasonably Separated
• If your cells are sparsely distributed across your images, you can use this option along with "Low Mitochondrial Signal" option to speed up your cell separation up to 10X. Please refer to the "Important Notes" section.

Separate Cells
• This box should be checked in order to separate the individual cells in the image. § Please refer to the additional notes below to find the best time to check this box.

What does the "Global Mitochondrial Mask" figure tell you?
This function generates an image titled "Global Mitochondrial Mask" which serves an important purpose, and can be extremely useful if used and interpreted properly. This figure represents the global mitochondrial content mask for the image, and is not intended to reflect the final and detailed mitochondrial mapping or network in your cells (That's Mito Catcher's job). The yellow objects on the image reflect all the objects on the image that are assessed as the possible mitochondrial content in the image, and would be assigned to different nuclei. This may also include some background noise, which is okay, since Mito Catcher will take care of it.
Lowering the "Mitochondrial Signal Threshold" would result in increase in the number and the area of the yellow objects in the image. This means that you have more mitochondrial candidate objects, which may increase the chance of capturing of true mitochondria in each image.
• This is true as long as the yellow objects across the image do not overlap and/or you are not capturing too much noise instead of mitochondria (i.e. parts of the image that you are sure that are noise and not real mitochondria) • More yellow content in the image means more assignment tasks, which in turn may increase the processing time.
• Important: check a few images (using the drop-down menu above) before you decide on your final threshold value, and make sure your set of parameters reasonably represents the mitochondrial content in all of those images, since the same threshold will be applied to the batch of images you are analyzing together.
• There is an exception which is discussed in the "My Cells Are Entangled" section.
When to use "Low Mitochondrial Signal" • The most obvious situation is when you have low signal or low contrast images. Low signal levels, and low Signal to Noise Ratio (SNR), make it harder for Cell Catcher to detect mitochondria in the image and subsequently assign them to different cells. By selecting this option Cell Catcher will perform additional pre-processing on the images in attempt to capture more mitochondria.
• If you select this option in high signal/SNR images, it will result in forming of yellow clusters in the "Global Mitochondrial Mask" figure, which is totally fine if the following two conditions are met: § As a result of selecting this option you are not capturing excessive amounts of noise as mitochondrial candidates in the image (Similar to lowering the "Mitochondrial Signal Threshold"). § The yellow blobs formed around cells, or in general the yellow objects across the image are not overlapping or excessively touching each other. § In both cases, this option may increase the processing time since Cell Catcher should assign more content to various cells. * *There is an exception which is discussed in the "My Cells Are Entangled" part.

When to use "My Cells Are Entangled"
If your cells have mitochondrial networks that are entangled, and it is very hard to decide on the boundaries of the adjacent cells, you may use this option. However, this option may increase the processing time up to 10X.
• When you have cells with entangled mitochondrial networks, if you select "Low Mitochondrial Signal" and/or you drastically lower the "Mitochondrial Signal Threshold" value, your actions may result in overlap or excessive touching of the mitochondrial networks from adjacent cells that may make the processing and separation of cells harder.
Before selecting this option, you should make sure that this approach is appropriate for all the majority of the images in your batch.

When to check the "Separate cells" box?
In short, this is the last option you may select in this function.
• While you are deciding on the best set of parameters for your sample images (which can individually be selected from the dropdown menu at the top), we suggest to keep the "Separate Cells" unchecked. Once you are happy with the combination of your parameters, then select this box to isolate individual cells in your desired images.
• Every time you select a new cell, while the segment cell is checked, it will automatically start to segment the cells in the image, which may take some time. Once segmentation is done, the segmented cells will show up.

Filter Type
There are two filters available in this function. Their underlying mechanism is very similar, yet they have been tuned differently to work with different sets of images, with different signal distributions. You may find one more suited to your images. You may try both of filters with default values as a starting point to choose a proper filter to further tune for your images.

Let It Go
• This filter is a better option for images with low background intensity, and generally works better with wider range of images.

Not One of Us
• Similar to the other filter, this filter also separates signal from noise based on the distribution of background intensity in the nucleus area. Generally, this filter may work better in case of strong background noise.

Dark Image
• This option only visually enhances the dark images and has no effect on the analysis. Selecting this option makes images 3X brighter, and it is useful for comparing images when signal level is low, and it is hard to visualize the original cells.

Filter Strength
• Determines the strength of the background removal filter.

Filter Harshness
• Determines if the background removal is harshly applied to a small group of images, or whether it is more softly applied to a wider range of images.

Adaptive Options
The settings of the adaptive option are effective only if the adaptive option is selected.

Adaptive Filter Tile Size
• The size of the tile over which the adaptive function samples the data and compares it with the reference intensity.

Adaptive Filter Power
• This option sets the strength of the adaptive function in correcting the local signal threshold.

Adaptive Filter Footprint
• Determines whether the correction factor should affect a wide, or short range of cells with different background intensities.

Remove Debris
• In certain conditions, the applied filters may result in artifacts in the image that may be interpreted as mitochondria. This option attempts to clean up and remove the noisy objects from the image.