Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry

Inflammation driven by cells of the immune system and glial cells can contribute to chronic disorders of the CNS where each population can promote the activity of the other^1,2,3. Understanding how the immune system interacts with these elements of the CNS to promote CNS inflammation is currently a major topic of interest and has been greatly facilitated by high-parameter techniques such as single-cell RNA sequencing. Through single-cell RNA sequencing, we have discovered that there is extensive communication occurring between glial cells and the immune system in several CNS disorders^4,5,6. Understanding how these interactions are affecting these disorders will be crucial to elucidating the biology of these diseases.

One issue with single-cell sequencing analyses is that these techniques require that the tissue be disrupted to obtain single cells or nuclei, resulting in a complete loss of spatial information. Knowing where a cell exists in a tissue is critical for understanding the cell's role in driving inflammation. For example, immune cells such as B cells can concentrate in the CNS during neuroinflammation; however, they rarely enter the CNS parenchyma and instead concentrate in CNS barriers⁷. Given their localization, it is unlikely that these cells contribute to CNS inflammation by physically interacting with glial cells in the CNS parenchyma, suggesting any interactions they may be having with glial cells would occur through secreted factors. Additionally, the pathology occurring in CNS disorders often has structure^8,9 such that a cell's localization in the tissue could critically determine whether it is actively contributing to the disorder or is a bystander. Thus, the usage of spatial orientation to evaluate a cell's role in pathology is essential.

Studying cells in tissue has typically been accomplished by using immunohistochemistry or immune fluorescence microscopy. An issue with these techniques is that they typically can only image up to four parameters simultaneously. This is a major limitation to these techniques, as we know from flow cytometry and single-cell RNA sequencing analyses that many cell populations require two or more parameters for their identification; also, the number of parameters required typically increases when looking for specific subsets of a cell type¹⁰. Thus, it is impractical to use standard imaging techniques for studying how subsets of cells may be interacting within a tissue.

This issue has been partially overcome through newer high-parameter methods that can retain spatial information, such as spatial RNA sequencing¹¹ and imaging mass cytometry¹². While these techniques are valuable, they do have several issues, such as not being widely available, reducing three-dimensional data into two dimensions, and requiring considerable expertise to execute. Another technique known as sequential staining, wherein tissues are stained with one set of antibodies followed by inactivation of the previous set of antibodies before staining with another set of antibodies, can achieve high-parameter histology without the need for specialized equipment or expertise^13,14,15. However, sequential staining can be extremely labor intensive and requires a large amount of microscopy time, which may be impractical for laboratories that do not own a personal microscope. Thus, there is a need for techniques that can expand the number of fluorescent parameters that can be imaged at one time on microscopes that are widely available and in a timely fashion.

Once the high-parameter data has been acquired, another issue arises: conventional image analysis methods are unlikely to successfully analyze the data. Techniques such as manual counting or thresholding are only viable if the analysis consists of a single parameter or if multiple markers have the same localization where only overlapping signals are counted. This limitation makes traditional analysis inadequate to work with high-parameter datasets. Successful analysis of these datasets has been achieved by segmenting single cells from histology images and then conducting flow cytometry-like gating strategies to identify cell types^16,17. However, another issue that affects these analyses is that they only work for datasets wherein all the cells of interest are physically separated from one another, as these techniques do not employ methods that can accurately separate cells that are in physical contact. Thus, a newer method is required that can conduct single-cell analyses on histology sections even if the cells are in physical contact.

In this article, a simple protocol called histoflow cytometry is described that has previously been introduced¹⁸ that expands the number of fluorescent parameters that can be imaged simultaneously using widely available microscopes. This protocol works by staining tissues with spectrally overlapping dyes and then using spectral compensation to remove bleed-through from overlapping channels to obtain clear single stains. To facilitate the analysis of high-parameter histology images, a detailed analysis pipeline is described that extracts single cells from tissue sections for the purpose of sorting cells into distinct populations using flow cytometry-like gating strategies. This protocol works in tissues where cells are diffusely present and in tissues where cells are closely compacted together, making this technique versatile for the study of tissues like the CNS in both homeostasis and neuroinflammation. Histoflow cytometry is, therefore, a useful technique for studying interactions between complex cell types that require multiple cell markers to define cells while maintaining spatial information.

This protocol does not cover sectioning tissues for histology; please see Jain et al.¹⁸ or¹⁹ for descriptions of how to section tissues for histology. This protocol can be used with any sectioned tissues on glass slides. This article uses inguinal lymph nodes isolated from an immunized animal as described previously¹⁸. The procedure and timeline for this protocol are summarized in Figure 1. The details of the reagents and the equipment used in this study are listed in the Table of Materials.

1. Staining tissue sections

NOTE: In addition to staining tissues with all of the antibody labels the researcher is interested in, the researcher should also prepare single color controls (one for each fluorescent parameter that is intended to be used) on adjacent or otherwise identical tissue slides wherein each single color control is stained with the reagents required to produce the signal of one fluorescent parameter.

1. Prepare Perm and blocking buffer and Perm and staining buffer (see Table 1 for the composition).
2. Thaw tissue sections on glass slides at room temperature and wipe off the condensation using a wipe. Use a hydrophobic marker to outline the border of the tissue(s) that need to be stained and let the marks dry.
3. Rehydrate the tissues by soaking the tissue sections in phosphate-buffered saline (PBS) for 1 min. Shake off the PBS and use a wipe to remove excess PBS without directly touching the tissue.
4. Fix the tissues by adding enough 4% paraformaldehyde (PFA) to cover the tissue completely. Incubate at room temperature for 20 min.
   CAUTION: PFA can be toxic and should be handled within a fume hood.
   NOTE: This step is only required for fresh frozen tissue. If the tissue is already fixed, then ignore this step. PFA fixation is described here, but the tissue can be fixed using other methods depending on the needs of the experiment.
5. Wash the tissues in a PBS bath for 1 min with gentle rocking, and then remove the slide from the bath. Replace the PBS and repeat the wash 3 more times.
6. Shake off excess PBS and use a wipe to remove excess PBS without directly touching the tissue.
7. Determine which secondary antibodies need to be used in step 1.12 of the protocol and the species of animals from which the secondary antibodies are derived. Fortify the Perm and blocking buffer by adding serum from these animals to the Perm and blocking buffer to a final concentration of 5% for each species.
   NOTE: Usage of secondary antibodies helps amplify signal intensity. Thus, it is recommended that primary antibodies that produce weak signals should be paired with secondary antibodies to amplify their signals. If all of the antibody targets that are being stained are bright enough to not require secondary antibodies, steps 1.7-1.12 and step 1.15 can be skipped.
8. Block non-specific binding of antibodies by adding enough fortified Perm and blocking buffer to cover each tissue. Incubate the slides for 1-8 h at room temperature in a humidified chamber protected from light.
9. Add unconjugated primary antibodies to the Perm and staining buffer at experimentally determined dilutions (the species or immunoglobulin isotypes of the primary antibodies should all be different and distinguishable by the secondary antibodies the researcher intends to use in step 1.12).
10. Dump off the Perm and blocking buffer and use a wipe to remove excess buffer without directly touching the tissue. Add enough Perm and staining buffer with primary antibodies to the tissue to cover it and incubate the slide at 4 °C for 12-48 h.
    NOTE: The researcher will need to determine the optimal time for antibody incubation.
11. Prepare blocking buffer and staining buffer (Table 1).
12. Add secondary antibodies to the staining buffer prepared in step 1.11 at experimentally determined dilutions. The secondary antibodies used should target the same species or antibody isotypes that were used in step 1.9.
13. Prepare the quenching buffer (as in Table 1).
    NOTE: This step helps reduce autofluorescence from the tissue. If this is not needed, steps 1.13 and 1.14 can be skipped.
14. Wash the tissues as described in steps 1.5-1.6. Then, add enough quenching buffer to cover the tissues on the slides and incubate the slides at room temperature for 1-2 min.
15. Wash the tissue as described in steps 1.5-1.6. Then, add the secondary antibodies diluted in the staining buffer prepared in step 1.12 to the tissues such that the tissues are completely covered in the staining buffer. Incubate slides at 4 °C for 2-8 h.
16. Prepare the blocking solution by adding 50 µL of normal serum from whatever species of antibodies will be used in step 1.18 (5% of each species) to the blocking buffer prepared in step 1.11.
17. Wash the tissues as described in steps 1.5-1.6. Then, add the blocking solution with normal serum prepared in step 1.16 to the tissues so that they are completely covered in staining buffer. Incubate slides at room temperature for 1-8 h.
18. Prepare the staining solution by adding fluorophore-conjugated primary antibodies at experimentally determined dilutions to the staining buffer prepared in step 1.11. Also, add a nuclear stain, such as nuclear yellow, to the staining solution at an experimentally determined dilution.
    NOTE: Steps 1.18-1.19 are only required if the researcher needs to use additional antibody labels that will not require secondary antibodies for signal amplification. If not using primary conjugated antibodies, nuclei stains can be added at step 1.12.
19. Shake off the blocking buffer and use a wipe to remove excess buffer without directly touching the tissue. Add enough staining solution prepared in step 1.18 to the tissue to cover it and incubate at 4 °C for 12-48 h.
20. Wash the tissues as described in steps 1.5-1.6. Add 5 drops of mountant to each slide without introducing bubbles, and gently and slowly tip a glass coverslip over the tissue to cover the tissue completely in mountant and exclude all the air bubbles. Store the slides at 4 °C in the dark until they are ready to be imaged.

2. Imaging the tissue sections

NOTE: At this stage, the tissue sections are stained with all the antibodies of interest for all conditions that are intended to be imaged, and single-color controls are stained with one fluorescent parameter on each section. Imaging should be done on a microscope that has multiple detectors that can be tuned to detect specific ranges of light, and ideally, it should have access to as many laser lines for excitation as possible.

1. Insert one of the fully stained slides into the microscope and set up the microscope to image the section using the objective that is intended to be used for collecting data.
2. Based on the fluorophores the tissue is stained with, identify the excitation laser lines needed to stimulate those fluorophores and identify the emission ranges for each fluorophore that contain the greatest amount of signal.
   NOTE: Online fluorescence spectra viewing tools (for example, see the Table of Materials) can be useful for identifying the optimal excitation wavelengths of fluorophores and identifying the optimal ranges for fluorophore detection. Fluorophores used for histoflow cytometry should be chosen such that each fluorophore differs in the optimal laser line that the fluorophore is stimulated by or differs by the optimal range of emission wavelengths. Fluorophores that have closer excitation/emission wavelengths will be more difficult to separate. The fluorophores used for this study are detailed in the results section.
3. Open Software 1 (see Table of Materials) in a confocal microscope and select the configuration tab at the top. Then select the hardware button on the left side and change the bit-depth to 16-bit.
   NOTE: It is recommended to use higher bit depths, such as 16-bit data, for data collection, as this will improve the accuracy of the spectral compensation. Additionally, higher pixel densities, greater numbers for line averages, and more steps in the z-stack will improve the quality of the spectral separation.
4. Select the Acquire tab at the top of the screen and input the format setting to include the pixel density of the image (typically, higher resolutions produce better results).
5. If available, select bidirectional X acquisition to speed up imaging time, provided this feature has been adequately calibrated.
6. Pick a pinhole value for the confocal microscope (typically between 400-600 nm) where smaller values will typically produce higher quality histoflow cytometry results, although smaller pinhole values will make it more difficult to acquire images and may require additional imaging time or higher excitation intensities to produce images.
7. Select the Show Sequential Scan Panel at the top of the left menu to open the sequential scan option. Use the '+' sign to set up additional sequences, where each sequence will contain one excitation wavelength that was decided upon in step 2.2.
8. Turn on all lasers that are needed by clicking on the ON buttons associated with lasers of interest in the center of the screen to turn them into an ON state. Go into each specific sequence and increase the laser power in the center of the screen to whatever value is desired, where each sequence will have one excitation wavelength being used.
9. Turn on as many detectors as needed at the bottom portion of the screen in each sequence to image all the fluorophores that will be imaged on that laser line. Adjust the ranges of the detectors by double-clicking on their ranges and inputting the upper and lower ranges of wavelengths that need to be collected.
   NOTE: It is recommended that high-sensitivity detectors be used where possible, and ideally, all channels should be collected on the same types of detectors, although this is not required.
10. Press the live button, then focus on the sample. Use the Auto Scale button to the left of the image in the software to evaluate whether channels are adequately saturated with signal. The user should adjust the gain values and laser intensities in each sequence until the autoscaling does not reach the maximum value (65535 for 16-bit data) but is very close to saturation for all channels.
    NOTE: As different fluorophores can differ in their brightness and different antibody targets can differ in their abundance, the sensitivity of the detectors will need to be adjusted to be of low sensitivity for bright signals and high sensitivity for dim signals if both fluorophores are stimulated by the same laser (the laser intensity cannot change in this scenario as both fluorophores would be affected). For best results, the fluorescence signal from each channel in the experiment should be balanced so that each channel has roughly equivalent amounts of signal, which will reduce spectral bleed-through into other channels and result in stronger signals. As different samples may differ in the abundance of the markers of interest being stained, it is recommended that the microscope settings be tested on multiple experimental conditions to confirm that bleaching is not occurring under any of the experimental conditions.
11. Once the microscope settings are finalized, load the microscope with the single color control slides and find representative staining for each fluorophore. Set up a Z-stack by focusing below the sample and hitting the begin button in the z stack menu, then focusing above the sample and hitting the end button.
12. Choose the number of steps and the distance between steps (and use the same number of steps and parameters such as pixel densities and bit depth that will be used to image the fully stained slides), then press the start button.
    NOTE: Ideally, these images should include the brightest signals from real staining that can be found on the slide.
13. After the single color controls have been imaged, proceed to use the same microscope settings to image all the fully stained samples.

3. Fluorescence compensation

1. Open .lif files in Software 1 and click on the process tab.
2. Choose the Dye Separation module and then choose the Automatic Dye Separation option. Select the Manual method for Dye Separation and choose no rescaling.
3. Open one of the single-color control images. Manually inspect the single color controls for spectral bleed-through from one channel to the next by observing greyscaled windows for each channel for evidence of a signal in inappropriate channels. Use the Auto Scale button if needed to see weak bleed-through.
4. Begin to remove bleed-through by manually inputting numbers (typically 0-1, where 0 represents no bleed-through and 1 represents 100% bleed-through) into the matrix on screen, then pressing apply to test whether the bleed-through is adequately removed. This should be repeated until the signals in all other channels except the channel of interest have been reduced to background levels.
   NOTE: Overcompensation will lead to inaccurate results, so the fluorescence must be reduced to background levels but not below them. Overcompensated data will appear as one channel having a signal and other channels appearing to have black holes where the signal of interest is (Figure 2).
5. Once one of the single-color controls is completed, record the values in the matrix and then reset the matrix, and move to the next single-color control. Repeat this process for each single color control.
   NOTE: The first dye will be in the Dye 1 row, and the amount of fluorescence that needs to be removed from Dye 1 will be in the adjacent columns.
6. Assemble all the values acquired from each control into one matrix and apply this matrix to a fully stained sample. If the separation looks accurate, apply the settings to all the images in the experiment, otherwise, re-evaluate the single color controls.
   NOTE: Effective compensation should remove most of the bleed-through across channels, as summarized and shown in Figure 3.
7. Save all the compensated files into a separate .lif file.

4. Identifying nuclei and cells using ilastik

1. Open the compensated .lif files in FIJI²⁰. Enable viewing stacks in a hyperstack and select an image.
2. (Optional) Use a selection tool such as the rectangle tool to include the portions of the image that will be analyzed and exclude areas that will not be analyzed. Crop the image using the image > crop function.
   NOTE: Reducing the size of the image is useful in subsequent steps to reduce random-access memory (RAM) consumption and increase processing speed.
3. Save the image as a .tif file. Also, save the image as an hdf5 file by using the ilastik plugin for FIJI by clicking on Plugins > ilastik > Export hdf5. Save the file as "name of choice".h5 with 0 compression.
4. Open Ilastik²¹ and make a pixel classification project. Use the Add New > Add Separate images function to add at least 3 representative images by going to add new input data.
5. Right-click on one of the images, select Edit properties, and change the Display Mode to greyscale.
6. On the left of the screen, select the feature selection tab. Click on Select features and select all features up to the highest sigma value that the program will allow you to use, then press OK.
7. Now select the Training tab. Use the paintbrush tool (while label one is selected) to highlight individual cells (Figure 4) of interest such that the interior (cytoplasm and nucleus) and the cell membrane are included in the highlight.
8. Repeat this across all the training samples where all cell types of interest are selected, and multiple cells of varying intensities, morphologies, and localizations in the tissue should be selected from each sample.
   NOTE: Depending on the markers chosen for an individual set of stains, there may or may not be intracellular stains or cell membrane stains for the cells of interest. Whatever markers are used, ensure that the highlight includes the outmost stain on the cells of interest and includes the cell interior regardless of whether an intracellular stain is used.
9. Next, use label two to highlight everything that is not the cells of interest (Figure 4). This can include blank space, unlabeled nuclei, or tissue that does not have markers of interest. Ensure to include the space immediately adjacent to cells of interest to promote the learning of exact cell boundaries.
10. Use the Live Update function to evaluate the quality of the training on images of interest. If training is inadequate, continue the training in steps 4.7 to 4.9. Otherwise, if satisfactory, click on the Prediction Export tab.
11. Set the source to be probabilities. Click on the Choose Export Image Settings button, then set the c parameter to export 0-1 (assuming label 1 is highlighting parameters of interest).
12. Convert the data type to a 16-bit Integer, and export as an hdf5 file to the directory of interest with the name of interest (we recommend using {nickname} mask.h5), then press OK.
13. Click on the Batch Processing tab. Press the Select Raw Data files button, select all .h5 files of interest that require processing, and then press Open. Click on Process all files.
14. Repeat steps 4.4 to 4.13, except instead of identifying cells, now identify the nuclei and exclude everything else (Figure 4). Export this as a separate prediction for identifying cell nuclei.
15. Open Ilastik and create a new Data Conversion project. Add a representative hdf5 file into the Input data tab using the Add New > Add Separate Image function.
16. Click on the Data Export tab and choose the Choose Export Image Settings button with the source set to input. Change the format to multipage tiff, choose a save location with the select function, and set the name of the file to {nickname}. Press OK.
17. Click on the Batch processing tab and select all your hdf5 files using the Select Input Data Files button and press the Process all files button.
18. Open Software 2 (see Table of Materials) and add all the tif/tiff files generated in steps 4.3 and 4.17 using the Add Files button. Choose a location for file outputs and press the Start All button to convert all of the files into .ims files. The files are now ready for analysis in Software 3 (see Table of Materials).

5. Analysis in Software 3

1. Open one of the images containing all the fluorescent stains. Select Edit-> Add Channels and add the .ims files corresponding to the masks made over nuclei and cell bodies. Repeat this for all samples.
2. Select the Add new Cells button. Select the detect nucleus and cell option and proceed forward.
3. Select the mask over nuclei to be the source channel for detecting nuclei and select the advanced option for splitting nuclei by seed points. Choose a value for the nucleus diameter) and proceed forward.
   NOTE: The optimal value for the nucleus diameter will need to be tested by running the simulation and manually evaluating whether the program is not splitting enough nuclei (multiple nuclei are grouped together) or whether there is over splitting (one nucleus is split into more than one).
4. Choose a value for the quality evaluation, and then proceed. Typically, it is best to include all nuclei, as the omission of nuclei can lead to multiple cells being fused into one.
5. Choose a threshold value for creating nuclear surfaces and then proceed. Ensure that all nuclei, even ones that are dimmer, are still included in the threshold. Do not make the threshold too low, as this may overestimate the size of nuclei or potentially include debris as nuclear staining.
6. Select a value for the number of minimum number Voxels per nucleus and proceed. Typically, it is best to keep everything unless debris or non-nuclei can be excluded using this parameter.
7. Choose the Cell boundary and cytoplasm option, select the mask over cells to be the source channel for detecting cell bodies, and then proceed.
8. Choose a Cell threshold that accurately captures all the cells of interest but is not so low that it includes background or any non-specific detection of cells. Select the option to split touching cells such that there is one nucleus per cell, and they are separated by the distance from nuclei. Also, choose to expand cells on nuclei and then proceed.
9. Select a value for the minimum number of voxels per cell and proceed. Typically, it is best to keep everything unless debris or non-cells can be excluded using this parameter.
10. Evaluate whether the created surfaces over the cells are accurate by looking at whether multiple cells have been fused together or if cells are missing. If the surfaces are inadequate, go back to step 5.2 and try new parameters until optimal settings are determined.
11. Once the best settings are established, click on the created cells object in the left menu, click on the creation tab, and click the Store Parameters for Batch button to save the used settings.
12. Open multiple windows of Software 3 and open separate images in each window. Press the new Cells button in each window, choose the saved settings in the Favorite Creation Parameters menu, and instruct each window to run till completion. Repeat this as many times as needed until all samples are analyzed.
    NOTE: Running multiple instances of Software 3 will make this analysis proceed faster, but the number of windows that can be run in parallel will depend on the central processing unit (CPU) processing speed and the amount of RAM that the computer that the analysis is being run on.
13. Once all samples are completed, click on the created cells object in the left menu in each image and click on the Statistics tab. Click the Configure list of visible Statistics Values button, deselect all statistics except the 'Cell Intensity Mean' value, and then press OK.
14. For every image that needs to be analyzed, click the Export all statistics to file button in the statistics tab and save the files to a location.

6. Histoflow cytometry analysis and mapping populations onto tissue sections

1. Open Anaconda and JupyterLab within Anaconda. Next, open the script provided in Supplementary Coding File 1. Run the code using the Run menu and select Run All Cells, or run the cells individually in order by selecting Run Selected Cells.
2. When prompted to input the source file, input the file directory for the exported fluorescent values generated in step 5.14. The file directory can be obtained by looking at the properties of one of the exported .csv files and copying the file path into the prompt.
3. Next, the program will ask for an output location for the processed files. Input the file directory for any location that is suitable for saving the processed files. The program will automatically detect the number of fluorescent channels contained in the folder.
4. Run the next section of the code to annotate the data with the names of each channel. This code can be modified as needed to change the "numberOfChannels" parameter to match the number of channels detected in step 6.3. Additionally, the names of the channels can be modified for as many channels as needed by repeating the 'ChX':'Marker' line as many times as needed and by modifying the names of the markers.
5. Running the next line of code will create a .csv file at the specified location that is annotated and contains all the fluorescent parameters in the dataset. Repeat steps 6.1-6.5 for each set of Software 3 exports to create .csv files for all the data.
6. Convert the .csv files to .fcs files by opening Software 4 (see Table of Materials) and dragging the .csv files into the program window. Software 4 will automatically start converting the files to .fcs files.
7. Double-click on one of the samples in Software 4 to open a window on that sample. Use the gating tools in Software 4 to create a gating strategy. It is recommended to start by gating on a lineage-defining marker containing all the cells of interest, then proceeding to gates that remove rarer subsets first, and then finishing on the more abundant cell types.
8. Once a gating strategy is established, test the accuracy of the gating by right-clicking on that population in the menu and choosing export. Export all parameters as a .csv file with headers included.
9. Open JupyterLab in Anaconda. Next, open the script provided in Supplementary Coding File 2, and run the code. When prompted to input the location of the Software 4 file, input the file directory for the exported .csv file generated in the previous step. The file directory can be obtained by looking at the properties of the exported .csv file and copying the file path into the prompt.
10. When prompted to add the output location, choose any file directory of choice, and ensure that the file directory includes the file name at the end ('file directory'\filename.txt). Run the rest of the code.
11. Copy the text in the .txt file and open the corresponding .ims file in Software 3 for the same sample that the exported data is based upon. Click on the Cell object in the file, switch to the statistics tab, paste the text into the search bar, and start the search. All cells of interest will be highlighted.
12. Manually inspect whether all the cells of interest were captured using the gating strategy. If the numbers of cells are underestimated or overestimated (examples shown in Figure 5), go back to step 6.7, adjust the gate, and re-evaluate whether the cells are accurately captured with the new gate. Repeat this with all the gates in the gating strategy and evaluate the gating on multiple samples.
13. When all the gates are validated, export data from Software 4 using the table editor function and analyze the data using the preferred method of analysis.
    NOTE: Cell populations can be mapped back onto the tissue sections in Software 3 by identifying the gated population as in steps 6.8-6.11 and then creating a surface based on the selected cells. This can be done for each gated population, as shown in Figure 6 and Figure 7.

Figure 1: Histoflow cytometry workflow. Tissue sections are stained with spectrally overlapping dyes (step 1). Images are collected across individual excitation lasers paired with tunable bandpass filters to minimize spectral bleed-through between fluorophores (step 2). Spectral bleed-through between channels is corrected based on a compensation matrix generated using single color-stained tissue controls (step 3). Nuclei and cells in images are then identified using ilastik (step 4) and split into single cells in Software 3 (step 5). Additional parameters are incorporated before exporting all data from Software 3 into Software 4 to conduct flow cytometry analysis. Cell identities are then exported from Software 4 and mapped back to the cell IDs in the original tissue to resolve the spatial localization of cells in tissue sections (step 6). Money signs represent commercial software that requires purchase, and computer signs represent free software. The approximate time for each step is indicated beside the hourglass. Commercial software is listed in the Table of Materials. This figure is adapted from Jain et al.¹⁸. Please click here to view a larger version of this figure.

In Figure 3, lymph node tissues were stained with a rat anti-mouse CD138 antibody paired with a donkey anti-rat Dylight 405 secondary antibody, Nuclear Yellow, a rat anti-mouse CD4 antibody conjugated to Alexa Fluor 488, a rat anti-mouse IgD antibody conjugated to PerCP-eF710, a rabbit anti-mouse Iba1 antibody paired with a Donkey anti-rabbit Alexa Fluor 546 secondary antibody, a rat anti-mouse CD3ε antibody conjugated to PE-eF610, a rat anti-mouse B220 antibody conjugated with Alexa Fluor 647, and a rat anti-mouse CD45 antibody conjugated to APC-R700. A violet 405 nm laser was used to stimulate the Dylight 405 and Nuclear yellow dyes, and their emissions were detected in the ranges of 408-438 nm and 460-530 nm, respectively. A green 488 nm laser was used to stimulate the Alexa Fluor 488 and PerCP-eF710 dyes, and their emissions were detected in the ranges of 495-545 nm and 682-750 nm, respectively. A yellow-green 552 nm laser was used to stimulate the Alexa Fluor 546 and PE-eF610 dyes, and their emissions were detected in the ranges of 557-600 nm and 615-652 nm, respectively. A red 640 nm laser was used to stimulate the Alexa Fluor 647 and APC-R700 dyes, and their emissions were detected in the 647-682 nm and 700-750 nm ranges, respectively.

Figure 2: Examples of poor and quality compensation. Beads were stained with an antibody conjugated to PE-eFluor 610 and imaged in the channels corresponding to the PE-eFluor 610 (PE-eF610) and Alexa Fluor 546 (A546) channels. Different degrees of compensation were applied to the same image in the A546 channel (10% reduction for undercompensation, 28% reduction for correct compensation, and 60% reduction for overcompensation) to remove the bleed-through from the PE-eF610 fluorophore, resulting in different degrees of separation. To better visualize the degrees of compensation, a brightened version of the bleed-through images is provided. The yellow arrow points to the same area in each image corresponding to an area where there is real staining in the PE-eF610 channel. Scale bars: 4 µm. Please click here to view a larger version of this figure.

Figure 3: Fluorescence compensation using stained tissue sections. (A-C) Lymph node tissue sections were stained with single Abs conjugated to the listed fluorophores. Uncompensated single-stained samples were then imaged across all channels (A, top), and compensation was applied to the single-color controls (A, bottom). (B) Bleed-through between channels in (A) was quantified (mean fluorescence intensity of the current image divided by the mean fluorescence intensity of the channel of the current fluorophore) and presented in heatmaps. White squares represent values exceeding the 100% bleed-through. (C) Spectral overlap is assessed in the uncompensated and cell-based compensation conditions by comparing the spectral separation of Iba1-A546 and CD3-PEeF610 in fully stained samples. Scale bars: 30 µm. This figure is adapted from Jain et al.¹⁸. Please click here to view a larger version of this figure.

By following this protocol (summarized in Figure 1), spectrally overlapping fluorescent dyes should be effectively separated, as shown in Figure 3A,B. While complete separation of the dyes may not be possible, the separation should be sufficient to clearly differentiate different cell types (Figure 3C). If there are still shadows of spectrally overlapping cells in another channel, this may indicate that there is still bleed-through occurring, and the images may need reprocessing (Figure 2). Additionally, overcompensation will be apparent if the compensated area falls below the levels of fluorescence associated with background staining and will appear as black areas where the bleed-through once was (Figure 2). Overcompensation can lead to the loss of real signal in the channel to which compensation has been applied and must be avoided. Nonetheless, if this protocol has been followed correctly, there should be no issues with compensation, and thus, the researcher should consider whether any apparent bleed-through is artificial or a part of the biology.

Figure 4: Example of an ilastik-based nucleus and cell detection. The nuclear yellow staining (top-left) was used to identify nuclei (top-right in yellow) and what is not a nucleus (in blue). Representative CD45 staining (shown in bottom-left) is used to identify cells (bottom-right in yellow) and what is not a cell (in blue). This figure is adapted from Jain et al.¹⁸. Please click here to view a larger version of this figure.

Once dyes are accurately separated, splitting the tissue into individual cells is essential for conducting single-cell analyses. The protocol described here uses user-defined machine learning (Figure 4) to separate cells even when they are closely compacted together, as shown in the lymph nodes shown in Figure 6.

Figure 5: Evaluating whether a gate has been adjusted correctly. Representative CD138 staining (colored in cyan) in a lymph node is shown, and three separate gates (the dot plot to the left) were made to identify CD138⁺ cells in the tissue. An overly conservative estimate of CD138⁺ cells is shown as the blue gate and mapped to the tissue in blue. A balanced estimate of CD138⁺ cells is shown as the green gate and mapped to the tissue in green. An overly permissive estimate of CD138⁺ cells is shown as the red gate and mapped to the tissue in red. The yellow circle identifies an area where CD138⁺ cells are not being identified by the overly conservative gate, and the magenta circle identifies an area with no CD138+ cells, which is claimed to contain CD138⁺ cells by the overly permissive gate. Scale bars: 30 µm. Please click here to view a larger version of this figure.

Care should be taken when determining the precise positions of gates in the flow cytometry analysis (shown in Figure 5) to ensure that these gates are not overestimating or underestimating the abundance of cells. This should be determined by mapping the cells back onto the tissue, as in Figure 5, to see if the cells that do express your markers of interest are being captured in the analysis. If too many cells are being selected (for example, cells that do not express CD138 are being selected as being CD138⁺), then the gates should be shifted to be less permissive, and if too few cells are being selected, then the gates should be made more permissive. Gates should be optimized for selectivity such that the upstream gates are optimized first, then each subsequent gate in the chain is optimized next. In the end, effective optimization of a gating strategy should be capable of resolving cell subsets of interest (Figure 6 and Figure 7).

Figure 6: Flow cytometry-based identification of cells in tissue sections. (A) Fluorescent values exported from single cells from Software 3 in lymph node sections were analyzed in Software 4 to identify the stated cell populations based on the shown gating strategy. (B) Populations identified in Software 4 were mapped back onto the same tissue section, and surfaces were generated for each population. The CD45 stain is not shown. Scale bar: 30 µm. This figure is adapted from Jain et al.¹⁸. Please click here to view a larger version of this figure.

Figure 7: Breakdown of individual stains and populations in mouse lymph nodes. Individual stains for immune cell markers are shown in the top panels, and their associated gated population identified in Figure 6A is shown below each single stain. Scale bar: 30 µm, shown in the top left panel. This figure is adapted from Jain et al.¹⁸. Please click here to view a larger version of this figure.

This technique has been previously¹⁸ used to characterize immune cell infiltrates into spinal cords during neuroinflammation and validated that equivalent proportions of infiltrating immune cells are found using histoflow cytometry and flow cytometry. The technique has also been validated, demonstrating that it works in mouse spleens and brains and can identify differences between treatment conditions, which were reproduced in identical flow cytometry experiments (unpublished). This technique has also been used to analyze the spinal cords of mice receiving stereotactic injections of the CNS models.

Figure 8: Separation of 8 dyes in a human tonsil section. Human tonsil sections were stained with CD20 to identify B cells and IgM, IgA, and IgG to identify plasma cells. Blimp1 and CD38 stains were incorporated to further validate plasma cell identities, and Ki67 to identify proliferating cells. The Nuclear yellow stain is not shown in the combined image. Scale bar: 22 µm. Please click here to view a larger version of this figure.

This technique can also be applied to analyze human tissue sections. As an example of staining within human tissues, human tonsil sections were stained with a selection of 8 markers: a goat anti-human IgM antibody conjugated to Dylight 405, Nuclear Yellow, a goat anti-human IgA antibody conjugated to Alexa Fluor 488, a mouse anti-human CD20 antibody paired with a goat anti-mouse F(ab')2 fragment conjugated to PerCP-eFluor 710, a goat anti-human IgG conjugated to Cy3, a rabbit anti-human Ki67 antibody paired with a goat anti-rabbit antibody conjugated to PE-Alexa Fluor 610, a rat anti-human Blimp1 antibody paired with an donkey anti-rat antibody conjugated to Alexa Fluor 647, and a mouse anti-human CD38 antibody conjugated to APC-R700. After compensation, the separation of these markers in the tonsil section can be seen (Figure 8). This demonstrates the utility of this technique in different tissues and species.

Table 1: A list of the solutions and their compositions needed for staining tissue sections.

Supplementary Coding File 1: A Python script that collects the fluorescence values from each channel and creates one summary file. Please click here to download this File.

Supplementary Coding File 2: A Python script that creates a list of all the ID numbers of gated cells into a text file. Please click here to download this File.

Here, the use of histoflow cytometry is described, a technique that has been validated previously¹⁸. It is demonstrated that when staining tissue sections with spectrally overlapping dyes, that bleed-through across channels can be removed using spectral compensation, resulting in a greater number of fluorescent parameters being clearly resolved than would normally be possible through conventional methods. As high-parameter histology images are difficult to analyze using conventional methods, an analysis pipeline is provided to segment individual cells from these images. By exporting the fluorescent signatures from these individual cells, it is demonstrated that single-cell analyses like those conducted in flow cytometry can similarly be applied to histology images. Whereas flow cytometry does not include spatial analysis, it is demonstrated that histoflow cytometry can map populations identified by gating strategies back onto tissue sections to evaluate their spatial localization. Overall, it is demonstrated that histoflow cytometry combines the strengths of flow cytometry and histology.

While histoflow cytometry has the benefit of retaining spatial information, it also has many of the same limitations that conventional histology has. While flow cytometry is great for quantifying cells across an entire tissue, histology is typically conducted by analyzing a small sample of tissue sections that are taken to be representative of the tissue. For histology to accurately quantify the number of cells in a tissue, one would need to image every tissue slice taken from the tissue and quantify the number of cells in each, which would be impractical. Another issue is that some cells will be split in half during the sectioning of the tissue, and there is no way to reconstruct these cells. Indeed, a limitation of the protocol listed here is that it does not include any methods to exclude cells at the edges of the tissue. Despite these limitations, one advantage histoflow cytometry has over flow cytometry is that there is no need to dissociate tissues into single-cell suspensions, which can introduce biases into the populations that are isolated or are inadequately captured²². Thus, while histoflow cytometry has several advantages over conventional flow cytometry, obtaining measurements across the entire tissue will continue to be a challenge.

Here, it is demonstrated that eight spectrally overlapping fluorescent parameters can be separated; however, others have successfully separated 13 fluorescent dyes¹⁷. The number of dyes that can be separated is dependent on the properties of the microscope in which the data is collected. Fluorescent dyes differ regarding their optimal excitation wavelengths and in their most common emission wavelengths. Microscopes that make use of these properties by using a wide range of excitation wavelengths to more precisely match the optimal excitation wavelengths of fluorophores and by tuning the emission detectors to precisely match the most frequently emitted wavelengths will maximize the separation of fluorescent dyes. Minimizing spectral overlap is essential for generating high-quality data as compensating for bleed-through across channels can potentially affect the dynamic range of detection of the fluorophores interacting with one another, and thus, separation based on emission and excitation is a critical first step in separating fluorophores. Depending on the properties of your microscope and the number of fluorescent parameters that need to be imaged, it may be required to put multiple fluorophores that share the same excitation wavelengths but differ in their emission wavelengths on the same laser lines. This is typically achieved by using base fluorescent dyes in combination with conjugated fluorescent dyes, where the conjugated dyes emit at longer wavelengths and thus can be distinguished based on their emission wavelengths. Overall, dyes must be distinguished either by their excitation or emission frequencies for effective separation.

Obtaining perfect compensation is complicated by several factors, including the spatial aspect of compensation. First, whether a fluorophore emits a photon is in part based on chance²³, where the collected photons in each image are biased toward locations of dense staining. However, less densely stained areas may, by chance, emit a photon that appears in one of the overlapping channels but not in the main channel. As compensation is applied on a per-voxel basis, the spectral overlap will not be removed during compensation because of a lack of spatial colocalization. This can be seen in Figure 2, where compensation results in the locations of dense staining being reduced (the edges of the beads) but not the center of the beads, where there is minimal staining and poor overlap with the channels with bleed-through. This staining at the center of the beads is likely because of another issue that can affect compensation, which is that photons produced outside the focal plane may appear in voxels in the focal plane that are unlikely to be reproduced in both the main channel and the channel that needs to be compensated²⁴. As a result, this 'noise' will not be effectively removed by compensation without introducing overcompensation. These issues can be partially addressed by using a line average, measuring the fluorescent values of a single voxel multiple times, and using the average to limit the impact of random chance events. If using a confocal microscope, the pinhole size can also be reduced to exclude out-of-focus photons from being incorporated into the images^25,26. Using these fixes comes at the cost of additional imaging acquisition times, which can become extensive.

While this technique was developed using images taken from a confocal microscope, it will work with other types of microscopes. Others using similar methods have applied these methodologies to images taken from two-photon microscopes¹⁷ with great success. While this technique could be applied to images taken from widefield microscopes, there are several advantages associated with confocal microscopes that improve resolving whether a photon originated within the focal plane. As an accurate resolution of the location of photons (i.e., optical sectioning) improves the quality of compensation, it is expected that the quality of the compensation would be relatively poor. This could potentially be addressed by using deconvolution to remove out-of-focus photons to improve the resolution of the widefield images²⁷. An important caveat that will determine whether deconvolution is compatible with this protocol is whether the fluorescence values in each channel remain proportional to one another across all the analyzed images. If the fluorescence values after deconvolution become arbitrary, then the same compensation matrix generated on one image is unlikely to be successfully applied to other images. Overall, the most important factor that determines whether a microscope can use this workflow is whether that microscope has detectors whose detection range can be flexibly tuned. If this is available, then multiple fluorophores stimulated through the same excitation can be resolved through the detection of their emission spectra within different detection windows.

The number of fluorescent parameters can be increased even further if combined with techniques to serially stain the same tissue multiple times¹³, such as IBEX^14,15. Using serial staining, effectively unlimited numbers of parameters can be measured; however, this can be very time-consuming. Using a combination of the compensation methods discussed here and serial staining can considerably reduce the time required to take these images, as more channels can be imaged simultaneously for each round of staining. The analysis pipeline described here will scale for as many markers as desired and be compatible with serially stained tissue sections.

A critical step in this analysis pipeline is the segmentation of single cells from histology images. Finding effective strategies to accurately segment cells from histology images is a common issue²⁸, and newer methods are continually being developed. While this method can effectively separate single cells from closely packed cells, the exact boundaries of the cells are often imperfect. It is therefore recommended that users limit the analysis to just the populations of cells that they are interested in, as the fewer the cells that need to be separated, the more accurate the cell boundaries will be. Users can also achieve better separation of cells by defining multiple populations in ilastik, allowing the program to segment out different cell populations. One issue with this approach, however, is that cells defined in this manner cannot be combined into the same 'cell' object in Software 3 and could possibly lead to double counting of cells. Nonetheless, if more accurate cell boundaries are critical to an experiment, this approach can be used. It is encouraged that users should experiment with newer segmentation algorithms as they are developed and incorporate them into this pipeline for even better results.

Several issues could arise when developing a new histoflow cytometry panel. First, it is critical that different fluorescent markers produce roughly similar amounts of fluorescence. If one fluorophore is much brighter than another, this can be problematic if this fluorophore bleeds into another fluorophore, as the bleed-through may overtake the real staining in that channel. This is seen in Figure 3, where Alexa Fluor 546 bleeds into PE-eFluor 610 severely. While compensation can still remove this bleed-through, there will often still be some lingering fluorescence due to out-of-focus light not being removed, which can make the compensation incomplete. Furthermore, most detectors used on microscopes do not have extensive dynamic ranges like those on flow cytometers. As a result, as the bleed-through in a channel increases, more of the dynamic range in that detector will be saturated with signals from other fluorophores and less from the fluorophore of interest. When compensation is removed from such a channel with extensive bleed-through, the true signal can be resolved; however, the difference between cells positive for that marker and those negative for it will become smaller and may result in an inability to resolve intermediate states or potentially even resolve positives from negatives. Thus, overlap between channels should be avoided. Second, the brightness of markers can change between different tissues. A panel of markers that works on lymph nodes, for example, may no longer work when used on the spleen or central nervous system because a marker may become too bright or dim, resulting in that channel bleeding too much into other channels or becoming overwhelmed by the bleed-through from other channels. In such a case, it may be necessary to switch the fluorophores on markers or switch the usage of primary conjugated antibodies to the usage of secondary antibodies. Lastly, it can be valuable to take advantage of differences in the localization of markers when there is excessive overlap between channels. If two channels overlap heavily with one another, but one is a nuclear marker, and the other is a cell surface marker, then the spectral overlap will minimally affect one another because compensation will not affect the same voxels. If possible, panels should be designed to ensure that overlapping channels differ in their localization or that the markers are expressed on separate cells.

Overall, it is demonstrated that histoflow cytometry is a versatile technique to analyze histology images at a single-cell level. Whereas older techniques can be limited to the analysis of cells not in direct cell-cell contact, this analysis pipeline can separate cells in contact, making it appropriate for the study of cells in tissues in homeostasis or during inflammation when immune cells cluster at sites of damage.