MethodsJ2: A Software Tool to Improve
Microscopy Methods Reporting
Joel Ryan
1
, Thomas Pengo
2
, Alex Rigano
3
, (ORCID: 0000-0002-5983-2296) Paula
Montero Llopis
4
, (ORCID: 0000-0001-6853-1228) Michelle S. Itano
5
, (ORCID:
0000-0002-8477-0285) Lisa Cameron
6
, (ORCID: 0000-0003-1478-1955)
Guillermo Marqués
7
, (ORCID: 0000-0002-1069-1816) Caterina Strambio-De-
Castillia
3
, (ORCID: 0000-0001-7550-5255) Mark A. Sanders
7,*
(ORCID: 0000-
0003-1622-663X) Claire M. Brown
1,*
1. Advanced BioImaging Facility (ABIF) & Department of Physiology, McGill University,
Montreal, Quebec, Canada, H4W 2R2
2. University of Minnesota Informatics Institute, University of Minnesota, 55455, United States
3. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA
01605, USA
4. MicRoN, Department of Microbiology, Harvard Medical School, Boston, Massachusetts,
02115, USA
5. Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, North Carolina,
27599, USA
6. Light Microscopy Core Facility, Duke University, Durham, North Carolina, 27708, USA
7. University Imaging Centers and Department of Neuroscience, University of Minnesota, 55455,
United States
* Corresponding Authors, equal contributions: msanders@umn.edu, claire.brown@mcgill.ca
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ABSTRACT (70 WORDS)
Proper reporting of metadata is essential to reproduce microscopy experiments, interpret results
and share images. Experimental scientists can report details about sample preparation and imaging
conditions while imaging scientists have the expertise required to collect and report the image
acquisition, hardware and software metadata information. MethodsJ2 is an ImageJ/Fiji based
software tool that gathers metadata and automatically generates text for the methods section of
publications.
ARTICLE
Optical microscopy is used in nearly all fields of research spanning from life and health sciences
to many areas of physical sciences and engineering. The lack of reproducibility in science is a
widespread problem which leads to significant challenges for researchers, slows scientific progress
and wastes valuable resources.
1-3
To improve reproducibility there needs to be detailed reporting
of both research resources
4
and experimental methods
2
. Progress has been made with tools to
promote and enable antibody validation
1,5,6
, cell line authentication
7-11
and identify reagents and
tools/services through the Research Resource ID (RRID) (https://scicrunch.org/resources). RRIDs
are used to report antibodies, model organisms, cell lines and plasmids in addition to custom
software, databases and services (e.g. core facilities such as imaging platforms). There are not
many tools for experimental methods reporting and it remains a difficult challenge to solve.
A lack of methods reporting is a widespread problem in microscopy where many articles
contain no information or lack basic details about how images were collected
12
. Analysis of 240
research articles published in 8 mainstream journals containing ~3,000 figures, of which more than
half included images, revealed that only 17% of the publications passed a test for minimal
information required to reproduce the experiment
12
. The problem is compounded by the sheer
number and variety of microscope modalities, options and associated components, such as the light
source, optics and detectors. In addition, advances in microscopy have automated the process to a
level that has distanced the researcher from the technical parameters. Finally, while researchers
are focused on scientific questions under study and have extensive expertise with their model
systems (e.g. sample preparation, imaging conditions) they typically do not have an in-depth
background in microscopy. As a result, it is difficult for experimental scientists (i.e. microscope
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users) to be aware of what information needs to be reported to enable proper evaluation and
reproduction of their work.
Essentially, to properly evaluate microscopy data and ensure it is reproducible, information
about sample preparation (e.g. tissue, cell type, dye), experimental conditions (e.g. temperature,
live, fixed), microscope hardware (e.g. objective lens, filters, camera), image acquisition settings
(e.g. exposure time, pixel size), quality control metrics (e.g. light source stability, resolution) and
image analysis parameters (e.g. segmentation, background correction) used to generate the images
and any quantitative results is required. This information is called “metadata” and is defined as “a
set of data that describes and gives information about other data”. Researchers involved in the 4D
Nucleome initiative
13
and Bioimaging North America (BINA) (https://www.bioimagingna.org/)
have developed extensive community driven Microscopy Metadata specifications
14,15
. These
specifications build on a previous Open Microscopy Environment (OME) model
16
and include an
in-depth community driven Microscopy Metadata model for light microscopy termed “4DN-
BINA-OME”
14,17
. The model scales with experimental design, instrument complexity and the
degree to which image processing and quantitative image analysis is required for interpreting the
results. This ensures that only essential information required to reproduce each type of imaging
experimental results is included to minimize the burden on experimental scientists to collect,
annotate and report metadata. The umbrella term for metadata information is Image Metadata that
is then classified into different subtypes including Experimental and Sample Metadata,
Microscopy Metadata and Analysis Metadata. Microscopy Metadata includes hardware
specifications, image acquisition settings and image structure (pixel size, number of pixels, planes,
colours and dimensions)
18
.
To help solve the complex problem of methods reporting papers have been published on
establishing minimal and accurate microscopy information guidelines
17,19,20
, information for
reporting image processing
21
, what can go wrong if detailed metadata is not reported
22
and the
importance of measuring and reporting microscope quality control
23
. Improving awareness and
education around Image Metadata and how it is essential for reproducible microscopy experiments
is important. However, to really tackle the problem and make a significant impact, it is vital to
have straightforward readily accessible tools for implementation by experimental scientists.
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This manuscript presents MethodsJ2, an extensible open source microscopy methods reporting
software tool that runs in ImageJ/Fiji and builds on our recently published work (MethodsJ)
12
.
Fundamentally, MethodsJ2 captures Image Metadata from multiple sources, consolidates it and
automatically generates a detailed methods text for publication. Integration with ImageJ/Fiji was
specifically chosen to make it broadly available and particularly straightforward to experimental
scientists to incorporate it into their imaging workflows.
Once an image is open in ImageJ/Fiji, MethodsJ2 automatically gathers metadata from the
image using OME BioFormats (e.g. camera exposure time, pixel size, magnification). It then
captures Microscopy Metadata from a Microscope.JSON file generated using Micro-Meta
App
15,24
. Micro-Meta App is a software tool that guides imaging scientists or microscope
custodians step-by-step in the collection of Microscopy Metadata associated with a specific
microscope that is consistent with community standards
14
and stores it in a Microscope.JSON file.
This file only needs to be generated once and updated if microscope hardware is upgraded or
replaced. Normally a specialist (e.g. imaging scientist) will use Micro-Meta App to set up
configurations for each microscope they manage and will provide experimental scientists with a
Microscope.JSON. Next, MethodsJ2 guides the user to manually enter specific Experimental and
Sample Metadata (e.g. cell type, dyes, live or fixed samples). The researcher is then prompted and
guided step-by-step through all collected metadata for validation and modification if needed.
Imaging scientists can automatically integrate acknowledgements text for their imaging facility
(including a RRID) into the MethodsJ2 script so it is included in the manuscript. This will
considerably improve publication tracking to monitor and demonstrate facility impact on science.
Finally, the methods text is generated and can be reviewed and finalized for publication.
Another complementary software tool to facilitate Microscopy Metadata reporting is
OMERO.mde
25
. This tool focuses on consistent handling of Image Metadata ahead of data
publication as specified by shared community Microscopy Metadata specifications
14,16
and
according to the FAIR principles
26,27
. It can be used for the early development and maturation of
image metadata extension specifications to maximize flexibility and customization while at the
same time allow for testing and validation before incorporation in community-accepted standards.
Detailed MethodsJ2 workflow (Figure 1).
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Note: An in-depth workflow with software screenshots is available as supplemental material.
Step 1: Use Micro-Meta App to create and save a Microscope.JSON file including all of the
Hardware Metadata. This is a time-consuming process but is only done once, typically by a
microscope expert. Note: When creating the Microscope.JSON file it is important to give each
component a detailed name as this will be used to populate text information in MethodsJ2. For
example, put “63x/1.4 NA Plan-Apochromatic oil immersion” not “63x”.
Step 2: Download the MethodsJ2 script, an example Microscope.JSON file and an example
image from GitHub (https://github.com/ABIF-McGill/MethodsJ2). If needed download and install
ImageJ/Fiji (https://fiji.sc/).
Step 3: Drag the MethodsJ2 script file and drop it on the ImageJ/Fiji toolbar. It will
automatically open in the Script Editor and from there press “Run”.
Step 4: MethodsJ2 will prompt the user to open an image to use to generate the microscopy
methods text. The Image Metadata is automatically extracted by MethodsJ2 using Bio-Formats
and microscopy manufacturer proprietary image formats. Then the user is prompted to select a
Microscope.JSON file for the corresponding microscope use to generate the image.
Step 5: MethodsJ2 will prompt the user for sample information, then guide the user step-by-
step to select and validate the image and hardware settings used to generate the selected image
based on metadata extracted from the image and Microscope.JSON file.
Critical Step: Have an experienced microscope user or imaging scientist from a microscopy
platform guide the researcher through the experimental, software and hardware settings
information for validation. Any missing information can be manually added based on published
community guidelines.
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted June 24, 2021. ; https://doi.org/10.1101/2021.06.23.449674doi: bioRxiv preprint