Generating and Analyzing High-Parameter Histology Images with Histoflow Cytometry

Summary

Described here is a method that can be used to image five or more fluorescent parameters by immunofluorescent microscopy. An analysis pipeline for extracting single cells from these images and conducting single-cell analysis through flow cytometry-like gating strategies is outlined, which can identify cell subsets in tissue sections.

Abstract

The usage of histology to investigate immune cell diversity in tissue sections such as those derived from the central nervous system (CNS) is critically limited by the number of fluorescent parameters that can be imaged at a single time. Most immune cell subsets have been defined using flow cytometry by using complex combinations of protein markers, often requiring four or more parameters to conclusively identify, which is beyond the capabilities of most conventional microscopes. As flow cytometry dissociates tissues and loses spatial information, there is a need for techniques that can retain spatial information while interrogating the roles of complex cell types. These issues are addressed here by creating a method for expanding the number of fluorescent parameters that can be imaged by collecting the signals of spectrally overlapping fluorophores and using spectral unmixing to separate the signals of each individual fluorophore. These images are then processed using an analysis pipeline to take high-parameter histology images and extract single cells from these images so that the unique fluorescent properties of each cell can be analyzed at a single-cell level. Using flow cytometry-like gating strategies, cells can then be profiled into subsets and mapped back onto the histology sections to not only quantify their abundance, but also establish how they interact with the tissue environment. Overall, the simplicity and potential of using histoflow cytometry to study complex immune populations in histology sections is demonstrated.

Introduction

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.

Protocol

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 an…

Representative Results

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 compensatio…

Discussion

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 an…

Materials

100% Ethanol	Sigma	676829-1L
4% PFA	Electron Microscopy Sciences	157-4
Anaconda	N/A	N/A	https://www.anaconda.com/download
Bovine Serum Albumin	Sigma	A4503-50G
Cold fish stain gelatin	Sigma	G7765
Collating multichannel data from Imaris.ipynb script	N/A	N/A	https://github.com/elliottcalgary/Histoflow-Cytometry-Analysis-
Convert FlowJo output to txt file for Cell selection in Imaris.ipynb script	N/A	N/A	https://github.com/elliottcalgary/Histoflow-Cytometry-Analysis-
Donkey anti-rat Alexa Fluor 647	JacksonImmunoResearch	712-605-153	1:300 concentration
Donkey anti-rat DyLight 405	Jackson ImmunoResearch	712-475-153	1:200 concentration
Donkey Serum	JacksonImmunoResearch	017-000-001
F(ab')2-Goat anti-Mouse IgG PerCP-eFluor 710	Thermofisher	46-4010-82	1:25 concentration
FIJI	N/A	N/A	https://imagej.net/software/fiji/
FlowJo	FlowJo LLC		Software 4
Fluorescence spectraviewer			https://www.thermofisher.com/order/fluorescence-spectraviewer/#!/
Fluoromount-G	Southern Biotech	0100-01
Fresh frozen human tonsil sections	amsbio	HF-707
Glass coverslip	VWR	48393 106
Goat anti-human IgA Alexa Fluor 488	JacksonImmunoResearch	109-546-011	1:400 concentration
Goat anti-human IgG Cy3	JacksonImmunoResearch	709-166-098	1:400 concentration
Goat anti-human IgM Dylight 405	JacksonImmunoResearch	109-476-129	1:300 concentration
Goat anti-rabbit A546	Thermo Fisher Scientific	A-11035	1:250 concentration
Goat anti-rabbit IgG PE-Alexa Fluor 610	Thermofisher	A-20981	1:250 concentration
Horse Serum	Sigma	H1138
Ilastik	N/A	N/A	https://www.ilastik.org/
Ilastik FIJI plugin	N/A	N/A	https://www.ilastik.org/documentation/fiji_export/plugin
Imaris File Converter	Oxford Instruments		Software 2
Imaris with cell module	Oxford Instruments		Software 3
kimwipe	Kimtech	34155
LasX Life Science software	Leica		Software 1
Mouse anti-human CD20	VWR	CA95024-322	1:40 concentration
Mouse anti-human CD38 APC-R700	BD Biosciences	564980	1:20 concentration
Normal Goat Serum	JacksonImmunoResearch	005-000-001
Normal Mouse Serum	JacksonImmunoResearch	015-000-001
Normal Rabbit Serum	JacksonImmunoResearch	011-000-001
Normal Rat Serum	JacksonImmunoResearch	012-000-120
Nuclear Yellow	Abcam	ab138903	Dissolve in DMSO at a concentration of 2 mg/ml and store at 4°C in the dark
PAP pen	Cedarlane	MU22
PBS	Gibco	10010-023
Rabbit anti-human Ki67	Abcam	ab15580	1:500 concentration
Rabbit anti-mouse Iba1	Wako	019-19741	1:500 concentration
Rat anti-human Blimp1	Thermofisher	14-5963-82	1:40 concentration
Rat anti-mouse B220 Alexa Fluor 647	BioLegend	103226	1:250 concentration
Rat anti-mouse CD138	Biolegend	142502	1:200 concentration
Rat anti-mouse CD3 PE-eFluor 610	Thermo Fisher Scientific	61-0032-82	1:40 concentration
Rat anti-mouse CD4 Alexa Fluor 488	BioLegend	100529	1:200 concentration
Rat anti-mouse CD45 allophycocyanin-R700	BD Biosciences	565478	1:50 concentration
Rat anti-mouse IgD PerCP-eFluor 710	Thermo Fisher Scientific	46-5993-82	1:50 concentration
SP8 Confocal microscope	Leica
Triton X-100	Sigma	X100-500ml
Trueblack	Biotium	23007
Tween-20	Sigma	P7949-500ml
Ultracomp ebeads	Thermofisher	01-2222-42