This is an open access App developed for anyone wishing to quantify the germination
of filamentous fungi regardless of coding skill. All we ask is that you cite our work
when using this App for scientific publication and communication using the following
citation:
Ortiz, S.C., Easter, T., Valero, C., Bromley, M.J., & Bertuzzi, M. (2024).
A microscopy-based image analysis pipeline for the quantification of germination of filamentous
fungi.
Fungal Genetics and Biology
, 175, 103942. https://doi.org/10.1016/j.fgb.2024.103938
Details about the Image Analysis Pipeline and this Shiny App can be found at the following publication:
Link - doi.org/10.1016/j.fgb.2024.103942
The App has been primarily designed and optimized for a handfull of
Aspergillus
species, and therefore should be optimized and validated before use with other fungi. In order to
get the most accurate results, please use the following instructions and guidance.
For further questions or help troubleshooting you may contact:
fungalgermination@gmail.com
Information & Instructions
General Description
Germination is traditionally used to define the differentiation process from a dormant and
stress resistant morphotype (such as spores or seeds) into an actively replicating morphotype
(such as hyphae). This term may also be used to describe the formation of germ-tubes.
The concept of this germination image analysis pipeline is relatively straightforward; it uses cell
morphology to quantify germination.
Aspergillus
species (along with many filamentous fungi) have asexual conidia that are small and circular,
as they germinate, they get larger transitioning into swollen conidia. Finally, they form germ tubes and become
germlings/hyphae which are large and oval. These transitions in size and shape can be assessed using microscopy,
and using this algorithm you can convert images into quantification of these morphotypes.
While this assay has its limits, it does provide quantitative measurements of fungal germination which is often
either assessed by eye or strenuous to quantify manually. Through the use of this app, you can measure the size and
shape of thousands of fungal cells at the click of a few buttons. This provides a relatively high throughput method
of evaluating germination across strains, nutrient conditions, mutants, and inhibitors.
For those interested, here are some example microscopy images of germinating spores that can be used to try out this image analysis pipeline and the associated Web App:
Before you Start
There are two methods of running this image analysis pipeline:
Method 1.
Using the fully developed Web App provided here.
Simply follow the steps in each of the tabs. This is the simplest method
of using this image analysis pipeline and only requires uploading files, and clicking a few buttons.
Method 2.
Running the analysis on your personal computer using the open-access software below.
While this method may require software installation and is a bit more complicated than using the Web App, it does
provide bulk analysis of images.
Method 2
is highly recommended for those trying to analyze a large
amount of data because it is much higher throughput than uploading and downloading data from the Web App.
Note:
These two methods perform identical analysis but
Method 1
provides simple User Interface for
those who are less experienced using FIJI or running code in RStudio
Required Software
The following Software is required for
Method 2
. If you choose to download the software
rather than using it directly off of the website interface, you will have to make sure to install FIJI along with the
adjustable watershed plugin for Step 1, and install R, RStudio and appropriate R packages for Step 2.
FIJI:
This is the key image analysis software that enables this image analysis pipeline to work. You can download FIJI at the following website:
Link - fiji.sc
FIJI Plugin - Adjustable Watershed:
This is a FIJI plugin that allows for the outlining of hyphae. You can download FIJI at the following website:
Link - imagej.github.io
You can learn more about this plugin and how to install at the following website:
Link - imagej.net/plugins
R:
This is the programming language used for the data processing. You can download R at the following website:
Link - cran.r-project.org
RStudio:
This is the integrated developmet environment we use during data processing. You can download RStudio at the following website:
Link - rstudio.com
Required Code
The following Code is required for
Method 2
and is all open access.
You can also find this code and instructions at our GitHub account:
GitHub
BatchFungus FIJI Macro This is the FIJI Macro that directs FIJI to outline images. For use instructions please read Guidance: Step 1. FIJI Cell Outline. You can download this Macro using the following Button:
FungalGermination R Script This is R script that processes and organizes data made in Step 1. For use instructions please read Guidance: Step 2. Data Analysis. You can download this Script using the following Button:
Guidance: Image Aquisition
Important Note:
There are multiple ways to make mistakes using this app;
however, the most common is acquiring images that are not compatible with the FIJI macro. If you
put in poor quality images the output will be inaccurate. Therefore, it is important to consider
quality control of your data throughout the analysis. We would recommend acquiring pilot images
and making sure that your current image acquisition set up works with the algorithm.
When acquiring images please keep the following things in mind:
Magnification:
We generally use a using a Nikon CFI Plan Fluor ELWD 20X objective and
Hamamatsu ORCA-Flash4.0 LT C11440 camera on a Nikon Eclipse Ti Inverted Microscope.
Anything with a similar resolution should work (0.32 pixel/µm or 0.1024 pixel²/µm²).
If your fungal spores are larger than
Aspergillus fumigatus
conidia you
should consider using lower magnification.
Too high:
If you have too high of a magnification, you may have issues with:
1. Hyphae moving out of the focal plane. If germlings go out of focus,
it will lead to inaccurate measurements of cells.
2. The number of fungal cells being quantified may be too few.
Too low:
If you have too low of a magnification, you may have issues with:
3. Insufficient resolution to discern changes in size and shape. Make note of your
pixel/µm ratio. If it is not sufficiently high, you may need a different camera or objective.
Focus:
It is essential to make sure your cells are properly in focus for the FIJI macro to work properly.
Here are a few tips:
1. Perform a Z-stack for timelapse experiment. It is likely that cells will drift
out of focus over time and by performing a Z-stack you can ensure that each location and
timepoint has an in-focus image you can analyze. This will require you to visually choose
the images in focus from the Z-stack.
2. Thermal drift is a large issue if performing assays at any other temperature than room
temperature. To minimize thermal drift, ensure that your microscope (in particular the objective),
your plate, and your media are all at the appropriate temperature before starting acquisition.
Remember that the higher your magnification, the more impact thermal drift will have on the focus
of you samples.
3. Avoid the edges of the well. Often when taking images from edges of wells, the light
scatter will change causing halos to appear that alter the focus of your cells. Additionally,
many wells tend to have a slight curve on the edges, this tends to cause cells to be out of focus
on part of your images. Both of these can have massive impacts on the analysis of the images,
therefore it is best to stick to the center of the wells.
4. Ensure that your cells have a strong level of contrast in comparison to the background.
If the FIJI macro is having difficulties outlining cells, you may need to increase the exposure
when acquiring images in order to improve contrast.
5. Avoid bubbles.
Debris:
It is important to note that the presence of any non-fungal material will be read by the FIJI macro.
If debris is present, inaccurate values may be acquired. This is of particular importance when testing
drugs and inhibitors which can often precipitate out when added to aqueous solution, therefore one
should ensure to optimize concentrations that do not lead to precipitate. Certain media may also
contain salts that precipitate out thus filtering these media may be necessary.
Spore Concentration and Clustering:
We generally use a concentration of 4x10
5
cells/ml (500 µL) / 2x10
5
cells/well / 1.05x10
5
cells/cm² in a 24-well plate; however, any well format should
work if considering concentration.
Too high:
If your spores are too concentrated, you may have issues with:
1. Clustering of spores, which may result in incorrect size/aspect ratio measurements by
the FIJI macro.
2. Inhibitory quorum sensing, which exists for many filamentous fungi. You won’t be able
to compare between concentrations without proper controls. Changing the size of your plate will
change the spores/surface area, so keep that in mind along with spores/volume.
Too low:
If your spores are not concentrated enough, you may have issues with:
3. Insufficient resolution and an increase in errors resulting from debris and lack of cell number
Tissue Culture Treated Plates:
It is important to use plates that allow for germlings and hyphae to adhere to the bottom of the
plate and minimize out-of-focus germlings/hyphae. We use Greiner #662160.
Guidance: Step 1. FIJI Cell Outline
Step 1. Running a FIJI macro outlining fungal cells to measure size and circularity
Select images for analysis and ensure that they are in focus (distinct black outline) and save them as
.tif files. If images are out of focus when analyzed, the size and shape will likely be inaccurate. You can
refer to sample images for examples of appropriate images.
Method 1 - Analysis on Web App
1.
Go to the
Step 1. FIJI Cell Outline
tab.
2.
Assign your sample a name.
3.
Upload the images (.tif) you wish to analyse using the
Browse...
button. - up to 128MB
4.
Click
Analyze Images
to start the analysis.
Please be patient, this may take a couple minutes depending on number of
images and server traffic.
5.
Once the outlining is complete, click the
Download Files
button which will download a .zip file containing Input, Output and Data folders.
The Output folder will contain images showing the outlines of cells and the Data
folder will contain .csv files to be analyzed in Step 2.
In order to make sure that the measurements from the FIJI macro are correct, check that the output files have
appropriate outlines that match the input images before moving on to Step 2.
Method 2 - Analysis on Personal Computer
Organize folders as Input, Output and Data for each sample - see the
Folder Organization
diagram below. Place images in the Input folders. It is crucial to organize and name the folders in
this way for the FIJI analysis to work.
Once you have your images added to the Input folders you are ready to start the analysis.
Ensure that you have installed
FIJI
and the
Adjustable Watershed
plugin correctly.
Open
FIJI
and go to Plugins > Macros > Run...
This will prompt you to select a macro.
Select the
BatchFungus.txt
macro, which will prompt you to select a folder for analysis.
You can select any folder which contain Input/Output/Data folders, even if these are subfolders.
Note:
Do not select a folder containing too many images, this could overburden
FIJI – stick to one sample at a time.
Folder Organization
Guidance: Step 2. Data Analysis
Step 2. Organizing .csv files from Step 1 into percent morphotype values
Method 1 - Analysis on Web App
1.
Go to the
Step 2. Data Analysis
tab.
2.
In the
INPUT
section add the following information:
--- a.
Under
Sample Name
, insert a name that will be used for all files produced
--- b.
Under
Timepoint
Insert numerical value for identification an plotting purposes
--- c.
Select the .csv files created in the Data folder in Step 1 using the
Browse...
button under
Upload Excel (.csv) Files
. All selected files will be grouped during the analysis
--- d.
Under
pixel²/µm²
Insert the appropriate conversion ratio for your images
3.
Select the
Pre-set Cut-Off
for your fungus of interest, or manually input your desired cut-offs for each morphotype.
4.
Click
Analyze
to start the analysis.
Please be patient, this may take a couple minutes depending on number of
fungal cells and server traffic.
5.
Once analysis is complete, percentage of each morphotypes will be quantified and a 2D histogram will be plotted.
You can then download the histogram, the summary of the percent morphotype, or the raw size and aspect ratio values
for each morphotype using the appropriate buttons.
6.
Finally, you can press
Add to Compiled Data
which will add the data to Step 3 for data compilation.
Method 2 - Analysis on Personal Computer
1.
Ensure that you have installed
R
and
RStudio
, open the
FungalGermination.R
script,
and follow the instructions included in the script.
2.
Fill out the
Input Parameters
with a name, pixel²/µm², and cut-offs for your fungus of interest
3.
Run the script, which will prompt you to select a .csv file (produced from Step 1).
The code will collate and analyse all .csv files within the folder containing the selected file.
4.
The resulting histogram, summary of the percent morphotype, and the raw size and aspect ratio values
for each morphotypes will be downloaded in your working directory.
Guidance: Step 3. Data Compilation
Step 3. Compiling data from Step 2
Step 3 is designed to help the user visualize the data collected in Step 2.
All further data analysis beyond Step 2 can be peformed elsewhere, and this page
should serve as an easy way to compile data for a sample across timepoints.
Data from Step 2 that have been added using the
Add to Compiled Data
in Step 2 will appear in the compiled table which can be downloaded, plotted and cleared.
Acknowledgements
For more infomation about this Image Analysis Pipeline and those involved please refer
to the
Published Work:
Ortiz, S.C., Easter, T., Valero, C., Bromley, M.J., & Bertuzzi, M. (2024).
A microscopy-based image analysis pipeline for the quantification of germination of filamentous
fungi.
Fungal Genetics and Biology
, 175, 103942.
doi.org/10.1016/j.fgb.2024.103942
We would like to profusely thank Martin Herrerias Azcue and Dr. Scott Archer-Nicholls
from the University of Manchester Research IT for their invaluable help in getting this
R Shiny App uploaded and hosted onto their server.
Software Used:
FIJI, Adjustable Watershed FIJI Plugin, R, RStudio, R Shiny and additional R packages:
ggplot2, data.table, bslib, bsicons, shinyBS, shinylogs, future, promises
Foundational Work:
The QGA developed for
Cryptococcus
spore germination provided the frameword to develop this germination assay for
filamentous fungi including the original macro and matlab code developed by
Dr. Layla Barkal and modified by Hunter Gage and Dr. Sébastien Ortiz. This work can
be found at the following publications:
[1] Barkal, L. J., Walsh, N. M., Botts, M. R., Beebe, D. J., & Hull, C. M.
(2016). Leveraging a high resolution microfluidic assay reveals insights
into pathogenic fungal spore germination. Integrative biology : quantitative
biosciences from nano to macro, 8(5), 603–615.
doi.org/10.1039/c6ib00012f
[2] Ortiz, S. C., Huang, M., & Hull, C. M. (2019). Spore germination as a target
for antifungal therapeutics. Antimicrobial agents and chemotherapy, 63(12), e00994-19.
doi.org/10.1128/AAC.00994-19
[3] Ortiz, S. C., Huang, M., & Hull, C. M. (2021). Discovery of fungus-specific targets
and inhibitors using chemical phenotyping of pathogenic spore germination. mBio, 12(4),
e0167221.
doi.org/10.1128/mBio.01672-21
[4] Ortiz, S.C., McKeon, M.C., Botts, M. R., Gage, H., Frerichs, A. B & Hull, C. M.
(2023) Spores of the fungal pathogen
Cryptococcus
exhibit cell type-specific carbon
source utilization during germination. Preprint at bioRxiv,
10.1101/2023.10.01.560341
Associated Publications
2024
Original Publication: Ortiz, S.C., Easter, T., Valero, C., Bromley, M.J., & Bertuzzi, M. (2024). A microscopy-based image analysis pipeline for the quantification of germination of filamentous fungi. Fungal Genetics and Biology , 175, 103942. doi.org/10.1016/j.fgb.2024.103942
Compiled Table
Compiled Plot
App visits since version update: 666
Record
of App Versions
Version 1.0.0.0 2024.12.02 Formal Release of App
Article describing
this Image Analysis Pipeline has now been published in Journal of Fungal Genetics
and Biology
Version
0.2.1.0 2024.12.02 Change in Non-Blocking Operations
Removal of non-blocking operations for Part 2
due to long (2+ min) analysis time, but it has been kept for Part 1.
Additionally, optimization of code in Part 2 analysis to decrease analysis
time.
Version
0.2.0.0 2024.11.25 Non-Blocking Operations
A
significant update that employs ‘Extended Tasks’ to allow:
(1) Inter-session concurrency:
allows multiple users to run analysis at the same time
(2) Intra-session concurrency:
allows a single user to use other parts of the app while running analysis
Version
0.1.0.7 2024.11.11
Link Fix 39
visits
Link to R
Studio fixed
Version
0.1.0.6 2024.10.04 Use Monitoring 108 visits
Addition of
use monitoring. Use since version updated displayed in Versions Tab.
Version
0.1.0.5 2024.10.03 UI update (5)
Changing Instructions and Information, specifically Guidance of Step 2, Guidance of Step 3 and Acknowledgements. Added required input for Step 1 – “Analyze Images” and Step 2 – “Analyze” to prevent crash when no data uploaded. Additionally, fixed accordion glitch in Step 2.
Version
0.1.0.4 2024.10.02 UI update (4)
Changing Instructions and Information, specifically Guidance of Step 2. Additionally, re-organized and added instructions to Step1 tab.
Version 0.1.0.3 2024.10.01 UI update (3)
Changing Instructions and Information, specifically Guidance of Step 1. Additionally, made fungus selection buttons change color when selected.
Version 0.1.0.2 2024.09.30 UI update (1&2)
Changes to the Information & Instructions section, specifically the General Description section, with the addition of a download button for sample images. Also, Before you Start section with further information about methods of running the image analysis pipeline along with links for software download and code download buttons.
Version 0.1.0.1 2024.09.27 shinyBS buttons
Implementation of buttons that change color (using bsButton) and that lock to prevent repeated clicks. Add to step 1 analysis button, step 2 analysis button and step 2 compile button.
Version 0.1.0.0 2024.09.25 First functional Beta version