SAPHIR: a Shiny application to analyze tissue section images [version 1; peer review: 2 approved with reservations]

Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. Image acquisitions performed by confocal microscopy notably allow excellent lateral resolution and more than 10 parameter measurement when using spectral or multiplex imaging. Analysis of such complex images can be very challenging and easily lead to bias and misinterpretation. Here, we have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation. It offers many features, such as filtering of spurious data points, selection of cell subsets on scatterplot, visualization of scatterplot selections back into the image, statistics of selected data and data annotation. Our application allows to quickly characterize labeled cells, from their phenotype to their number and location in the tissue, as well as their interaction with other cells. SAPHIR is available from: https://github.com/elodiegermani/SAPHIR


Introduction
The identification, localization and quantification of cell subsets in tissue is a difficult but essential task for biologists to understand spatial cellular organization in different settings (e.g. homeostasis vs inflammatory diseases or cancer). Advances in optical microscopy allow image acquisitions with more than 10 channel measurements using spectral fluorescence imaging or multiplex imaging combined with z-axis optical slices of tissue sections ranging from 10 µm to more than 200 µm when clearing methods are used [1][2][3][4] . Analysis of such complex images is very challenging due to the size and complexity of data. It requires image segmentation in 3D that can be further improved using deep learning-based segmentation 5-7 . Then, like flow cytometry, complex image analysis can benefit from scatterplot representations that allow to gate cells of interest 2,8 . Surprisingly, in existing software, this scatterplot representation is rarely interactive with the image itself, although this would allow to locate selection results back into the image but also to filter or correct results and fine-tune the gates defining cell populations to obtain in return a better visualization of them into the image.
To this end, we developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R/Shiny application for the quantitative analysis of tissue section images. Since image segmentation is a complex task in continuous development and highly dependent on image quality and information, the integration of a single type of segmentation method in an image analysis application is not necessarily recommended. Therefore, we decided to separate segmentation from SAPHIR, but we provided two Fiji macro examples with associated images, which can be used to perform this task before running SAPHIR. SAPHIR offers many features such as interactive scatterplots with the image and data filtering and correction as described below.

Data requirement
The segmentation required to use the application SAPHIR has been carried out under Fiji 9 , with custom-made macros, which are available on GitHub in the Demonstration Files. Cell nucleus detection was used for segmentation and fluorescence intensity signal of each channel within a circular ring around nuclei was determined to define cell of interest (COI) phenotype. In addition, regions of interest were defined using DBSCAN (optional) 10 . The segmentation process of the image to be analyzed should create a csv result file containing the channel intensity values (ranging from 0 to 255) and, if required, the positioning (x, y, z) linked to each COI. Another file, termed ROI.zip, should contain the image contour of each COI. Importantly, many other morphological measurements like area, roundness, or solidity as well as other information (COI belonging to a region of interest, COI interaction with other cells, COI centroid position) can be added to the segmentation result file. Finally, the minimal requirements to run the application are the image in TIFF with multiple channels and optionally slices, the segmentation result file with COI identity in csv and the roi.zip file containing contours of each COI. A legend file with all channel information can also be provided (optional).
Operation SAPHIR is built in the R programming language, version 4.0.2, and uses many packages available on CRAN (e.g. shinydashboard, ijtiff, magick, ggplot2, plotly) and one package (EBImage) available on Bioconductor. It has been tested on macOS and Windows 10. A user flowchart from image acquisition to obtention of quantitative data of COI with SAPHIR is shown in Figure 1.

Implementation
The first step of SAPHIR is either to run a segmentation program to obtain appropriate files or to load the three required files (image.tif, roi.zip, results.csv) and if needed the legend (csv file) in the "select your results" menu of the application. Here, result, region of interest (ROI) and legend files were obtained with an in house-made segmentation macro developed under Fiji.
The first tab of the "Plot to Image" menu is shown in Figure 2 and allows the users to filter their data depending on one or two parameters displayed on histogram or scatterplot, respectively. This can be used to remove cells that have been badly segmented. Indeed, such cells display an aberrant area or volume (doublets or aggregates in red in Figure 2). Here, only cells within the Gaussian curve were retained (in blue in Figure 2). This can also be used to work only on given subpopulations filtered based on their location, interaction with other cells or other criteria (see use case section below). Then, filtered cells can be separated into subsets thanks to a two-parameter (most often channel intensities) scatterplot ( Figure 3A). In addition, users can add a third parameter that is displayed on the scatterplot through a change of symbol shape for each COI beyond a threshold defined by the users for a given parameter value ( Figure 3A).
Gating can be made easily with quadrants, rectangles and lassos in single or multiple selection mode. Interactivity between scatterplot and image is optional to avoid slow interaction when high-resolution images are used. In the latter case, we recommend users to perform their gating before allowing their selection to be shown in the image by ticking the appropriate box. Users can easily change the slice and the displayed channels with buttons to monitor selected COI spatial distribution and fluorescence intensity in the image ( Figure 3B). Moreover, several options are available such as cell identity display or channel overlay ( Figure 3B). Finally, statistics of the gated COI are downloadable, and gates are saveable for further analyses.
The SAPHIR menu "image to plot" allows to select a region in the image and to display cells of this region on a two-parameter scatterplot ( Figure 4).
Finally, the menu "Annotation" allows, based on the previous analyses, correction of the data from the result csv file but also from the scatterplot-gated cells when saved in the "plot to image" menu. For each selected cell, a cropped image of this cell is displayed, and users can change its parameters if necessary ( Figure 5).

Use case
To show the usefulness of our application, we used it on a project that aimed to characterize the interaction between phagocytes and proliferative immune effector cells in murine Peyer's patches (PP), i.e. B and T cells. PP are immune inductive sites distributed along the small intestine in charge of sampling noxious antigens and mounting an immune response against them. In PP, antigens are taken up by phagocytes that, upon stimulation, migrate in the T cell zone and its periphery to interact with and prime naïve T cells. The periphery of the PP T cell zone is indeed an area of intense proliferation of immune effector cells after stimulation, suggesting that this region is a privileged site for their activation 11 . We therefore decided to examine the evolution of proliferative cell number, to determine their identity (B or T cells) and to analyze their interaction with migratory phagocytes during the course of the stimulation.
We used SAPHIR application and two ImageJ macros, which are available on GitHub in the Demonstration Files. The first macro allows the counting of proliferative cells through segmentation of Ki-67+ nuclei, provides their identity through analysis of T and B cell staining of Ki-67+ cell membrane, and analyses their interaction with phagocytes. The second macro identify the area of high density in proliferative cells as ROI, thanks to the DBSCAN algorithm and determine whether previously analysed cells belong to this ROI.
Then, SAPHIR application allowed us to integrate these data and provided scatter plots and statistical analyses. Thus, the filtering tab was used to select only proliferative cells belonging to the ROI ( Figure 6, upper left). Then, ROI-proliferative cells were split into B and T cells using the scatter plot ( Figure 6, lower left). Finally, we used the "symbol shape change parameter" option to highlight ROI-proliferative cells that interacted with phagocytes. This clearly showed that in the ROI, there were more proliferative T cells than B cells that interacted with phagocytes. Exportable statistical tables confirmed this observation. Selecting these T cells, we could localize these cells interacting with phagocytes directly into the image (Figure 6, right).

Conclusion
The SAPHIR application provides a simple and user-friendly interface to obtain quantitative data from tissue images as well as COI positioning in the tissue. It is based on the interactivity between quantitative data and image to simplify the analysis and limit bias. Further developments of SAPHIR will include use of pyramidal images to minimize loading and analysis time and unsupervised clustering methods for scatterplot generation 12 .

Data and availability
SAPHIR is provided with segmentation data from two demo pictures (https://github.com/elodiegermani/SAPHIR/tree/master/ Demonstration%20files): one with only two intensity channels to test the application in a very easy and quickly way, and a more complex one with 6 intensity channels, 12 slices and two additional parameters (belonging to a ROI and cell-cell interaction) for advanced testing of the application.
These data are parts of several projects conducted in the Center of Immunology of Marseille-Luminy (France). Acquisition of the images was made with spectral confocal microscopy and analyzed with Fiji with the provided macro.

Software availability
Source code available from: www.github.com/elodiegermani/ SAPHIR Archived source code as at time of publication: http://doi. org/10.5281/zenodo.4088899 13 License: GNU General Public License v3.0 Open Peer Review measurements", but in neither the results nor supporting material is it clear to what degree SAPHIR supports these kinds of datasets (e.g. Lelouard et al. the manuscript). Furthermore, the support for 3D datasets is unclear, as ImageJ ROIs are limited to 2D and the link referenced in the supporting material for the "Complex Image Analysis", https://zenodo.org/record/4056001#.X8HVRC2ZM_W, suggests the scripts used to generate the input files only consider a single optical slice. Please clarify these functionalities.
Tool performance: The claim of the application's quickness is hard to assess. Although, SAPHIR appears to function well with the demonstration datasets the authors also qualify their quickness claim: "Interactivity between scatterplot and image is optional to avoid slow interaction when high-resolution images are used. In the latter case, we recommend users to perform their gating before allowing their selection to be shown in the image by ticking the appropriate box." This apparent contradiction is unqualified, beyond "highresolution", the claim of quickness is poorly clarified. Please elaborate, and demonstrate if necessary, this limitation-for instance with larger or more complex datasets.

4.
Manuscript: Figures are difficult to interpret due to size and resolution, especially figure 6 in which the "orange" color could not be easily identified nor the "magenta contour". This is critical to demonstrating the behaviors of SAPHIR. Please adjust to facilitate the interpretation of the results.

Minor criticisms:
Tool performance: One of the major goals appears to be addressing "bias and misinterpretation" but this is not directly discussed in the results. It is unclear in what way(s) SAPHIR directly addresses bias. 1.
Tool performance: The title and introduction suggest that large or mesoscale datasets that can be generated by modern imaging modalities are a unique challenge due to their size and complexity. It is unclear to what degree SAPHIR is able to analyze datasets with a large number of cells. This is an important qualification that is not directly addressed.

2.
Is the rationale for developing the new software tool clearly explained? Partly

Is the description of the software tool technically sound? Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Partly
In this article, the authors describe a shiny app to load and explore the segmentation output of fluorescence images from ImageJ/Fiji. The app provides an interactive interface to filter, select, and annotate the regions of interest as defined by the ImageJ/Fiji macros. Besides, the app allows for performing statistical testing on the selected regions. The manuscript describes the motivation for developing the app, the basic functionality, and a use case. In the use case, the app was used to filter proliferative T and B cells based on cell membranes staining and determine that more T cells interacted with phagocytes in Peyer's patches images. The article is written in clear and concise language. The motivation for developing the app and the description of the operation is sound. A few issues remain:

Major Issues
The implementation section focuses on describing the interface but doesn't explain much about the inner working of the app or the development process. (related, question 3). 1.
Four types of files are needed to use the app: Images, Data files, ROIs Files, and Legend files. Each file needs to be processed and prepared to match the formats and requirements of the app. The user has to perform many steps, including using ImageJ and R, to process the files to be able to use SAPHIR. Besides, if the user wants to combine 2 images, he or she has to prepare 4 files for each image. These complicated steps could limit the replication of 2.
the analysis by others. It is better if the providers could explain the steps and the tools that they have used to prepare their files. Or if there is an option in SAPHIR that we can use to directly prepare the files.

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly