Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning : Kidney360

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Review Article: Renal Physiology

Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning

Winfree, Seth1; Al Hasan, Mohammad2; El-Achkar, Tarek M.1,3,

Author Information
Kidney360 3(5):p 968-978, May 26, 2022. | DOI: 10.34067/KID.0006802020
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Importance of the Immune System in the Kidney

The human kidney comprises at least 18 types of cells that are organized spatially in unique structures and zones within the kidney (1). This unique distribution allows the performance of specialized physiologic functions and shapes the response to disease (2). Cells of the immune system are distributed throughout the renal interstitium and are in intimate crosstalk with other cell types, such as the tubular epithelium (345–6). Immune cells are key regulators of the renal milieu in homeostatic conditions (567–8). They comprise resident phagocytes, such as macrophages and dendritic cells, but also occasional “visiting cells,” such as neutrophils and lymphocytes, that may marginate and cross over from the microcirculation in response to local signals (5,9,10). Examples of homeostatic functions include immune monitoring, tolerance of nonharmful antigens, and maintaining infection defense capability (5,6,8,11).

In disease, systemic and local events alter the composition, phenotype, and function of resident cells (6,12,13). For example, activated antigen-presenting cells travel to regional lymph nodes (14). Resident dendritic cells and macrophages activate gene expression programs, driven by cues from other cells and the environment (15). In addition, chemokines released by injured or stressed cells will attract more cells from the circulation, such as neutrophils, monocytes, natural killer cells, and lymphocytes (16,17). Upon entering the kidney milieu, many cells undergo phenotype switching (18). For instance, in response to environmental cues, monocytes transform into specific macrophage phenotypes. Many of these cells could also start proliferating within the kidney, providing additional “clones for the war” (12,19). The dynamics and fate of these infiltrating cells will depend on the course and severity of disease.

In an adaptive response, the disease is controlled and the function of immune cells changes from danger mitigation to promotion of healing (18,20). Such transformation requires curtailing cells that could become dangerous to self, if left unchecked, such as neutrophils, which are directed to die or leave the kidney (21). Furthermore, macrophages shift to a healing phenotype by removing dead cells and debris, and by secreting anti-inflammatory signals (20,22,23). Nonimmune cells can then regenerate and restore other functions (24). In a maladaptive response, which could result from a persistent or severe injury, persistently activated immune cells can cause damage to bystanders and perpetuate the injury (18,22,25). The result could be a migrating pattern of injury, leaving behind fibrosis and ongoing damage. Such a model is thought to be a key pathogenic feature of chronic disease and in progression from acute to chronic disease. Therefore, the immune system is a key regulator of kidney health and disease.

Available Methods to Assess Immune Cells within the Kidney and the Importance of Spatial Information

Technologies to quantitatively survey the types of immune cells and their changes within the kidney have experienced revolutionary growth over the last decade. The application of flow cytometry, transcriptomics, and, more recently, single-cell transcriptomics of cells from homogenized kidney tissues have provided important advancements to our understanding of the role of the immune system in the kidney (5,12,13,262728–29). Identifying the profiles of resident and infiltrating immune cells in health and disease, on the basis of specific markers, has been crucial for progress (5,6,12,15,29). Moreover, the importance of spatial distribution in determining various functions of renal immune cells within the kidney is increasingly appreciated (5,7,8,11,18,30). As we understand the effect of the microenvironment on the pathogenesis of injury and the interactions between various cell types, it becomes imperative to qualify and quantify immune cells and their interactions with the various “ecosystems” within the kidney. This fits with what we know about the physiologic function of the kidney, where specialized tasks are performed in a specific spatial context. For example, Berry et al. (11) showed that renal hypertonicity dictates the localization and function of a specific set of mononuclear phagocytes, the function of which is needed for antibacterial defense from ascending infection. Hochheiser et al. (30) showed that cortical and medullary dendritic cells play specialized roles in their renal microenvironment. On the basis of surface marker expression, we previously showed that the distribution of specific sets of mononuclear phagocytes is also specific to renal zones. For example, resident macrophages defined as CD45+, LY6G, F4/80hi, CD11Blow are more abundant in the medulla than cortex (31). The importance of the spatial distribution of immune cells is particularly relevant in disease pathogenesis and course. Ischemia reperfusion predominantly involves the outer medulla, where neutrophils and other phagocytes infiltrate shortly after the onset of injury. However, with CKD, active inflammation and fibrosis are frequently detected in the cortex (32). In experimental GN, a subset of key pathogenic dendritic cells are uniquely localized near blood vessels (33). Similarly, the renal microenvironment and epithelial immune interactions govern the site of antigen presentation in allograft rejection (7), and the association of infiltrating macrophages with specific tubules in sepsis (34). These are just a few examples of the importance of accurately identifying the spatial distribution and neighborhoods of resident and infiltrating immune cells in health and disease.

To perform such a task, tissue-preserving strategies that survey immune cells in situ are needed. This is performed by an imaging approach that is linked to an analytic pipeline that can process the imaging data through an intuitive and interactive interface, which can provide quantitative outputs. Many such approaches have been described and have advantages and limitations (353637383940414243444546474849–50). We recently reviewed such approaches and would refer the reader to these previous works (51,52). In this review, we will focus on tissue cytometry of the kidney using large-scale, three-dimensional (3D) confocal fluorescence imaging and tissue cytometry. Our group developed the Volumetric Tissue Exploration and Analysis (VTEA) software, a tool that could be used to analyze 3D image volumes from kidney tissue (51,53). We will describe the workflow and application of 3D cytometry using VTEA to analyze the immune system within the kidney. We previously demonstrated the importance of imaging in 3D, particularly for immune cells, whereby the expression of surface markers used for identification can be unevenly distributed across the cell (53). Therefore, capturing entire cells and structures in 3D will enhance the accuracy of classification.

Tissue Cytometry Description: VTEA Workflow

Tissue Preparation and Imaging

Large-scale 3D imaging of kidney tissue can be obtained from frozen sections or formalin-fixed and vibratome-sectioned tissue. For abundant tissue sources, such as experimental murine kidneys, we prefer vibratome sections (54) because the morphology is exceptionally preserved by immediate fixation and avoidance of freezing. However, for sparse tissues, such as human kidney biopsy specimens, we frequently share the tissue with clinical applications and use optimal cutting temperature compound–embedded frozen sections because they are the most compatible with our imaging process (53,55). Frozen specimens can be sectioned into 50-μm-thick sections and immediately fixed with paraformaldehyde to prepare the tissue for staining. The detection of immune and other cells within the kidney depends on the availability of reliable markers and antibodies that produce rigorous and reproducible results (56). This is a major rate-limiting step on the type of cells (i.e., subpopulations and various subtypes of cells) that can be detected by this technology. Although standard confocal microscopy can simultaneously detect up to four labels spread across the fluorescence spectrum, without any significant bleed-through between acquisition channels, it is possible to increase the number of simultaneously detected fluorophores up to eight with spectral deconvolution (52,55). To efficiently perform 3D tissue cytometry, a channel must be dedicated to stain the nuclei, which will serve as fiduciaries to survey all of the cells. Large-scale 3D imaging is performed by tile scanning and digital stitching of the acquired volume. Such an approach will assure mesoscale imaging at a subcellular resolution—a great strength of this technology (5152–53). The process for staining and imaging has been standardized and is made available on (

Image Analysis and Cytometry

VTEA was designed and built as a plugin for the commonly used ImageJ/FIJI tool for image analysis (5152–53). VTEA is meant to be intuitive, where all of the required steps, from visualization, image processing, and analysis, can be done in a single space (Figure 1). The workflow of VTEA is bidirectional (between image processing and analysis), whereby the flexibility of its design allows for real-time adjustments of parameters in preprocessing and segmentation to optimize the analysis. The premise of the analysis is that every nucleus can be segmented automatically using built-in functions within VTEA and ImageJ. Each surveyed nucleus becomes a surrogate for its cell, to which the location (coordinates) and marker staining around or within the nucleus can be registered. This captured information can be classified as label based (e.g., fluorescence mean intensity, intensity distribution, texture), spatially based (e.g., x, y, z coordinates and neighborhoods), or combined (e.g., directionality of the intensity). These three classes of information become measured variables (features) associated with each nucleus/cell. The number of features associated with each cell can be expanded experimentally, by applying multiple fluorescent labels (more primary variables), or computationally, by inferring new features from existing labels and spatial distribution (more derived variables). Consequently, the feature space is potentially limitless. However, for a directed analysis, we tend to focus on specific features, such as the mean intensity fluorescence of each marker associated with each cell (53,55). The data (objects where each dot represents a cell) are plotted as a scatterplot, where the x and y axis each present a feature, and color can be used to represent a third feature (Figure 1D). Cells of interest can then be identified using these features, and a gate can be drawn to perform quantitation. An important characteristic of VTEA is that it allows back-mapping of the analysis onto the image volumes, so that the cells of interest identified by a gate are directly visualized as colored overlays on the image, with the corresponding displayed statistics (Figure 1, D and E) (31,5152–53,55). This functionality allows for the immediate interpretation and validation of the gates. In addition, direct gating on the image can be performed, which can trace all of the cells within the chosen region of interest back to the data display on the scatterplot (Figure 1, F and G). Such a back-and-forth interplay between the image volumes and the quantitative plots allows for interactive exploration and analysis of the data.

Figure 1.:
Overview of tissue cytometry with Volumetric Tissue Exploration and Analysis (VTEA). (A) Two- or three-dimensional (3D) multichannel images of fluorescently stained tissue, including 4′,6-diamidino-2-phenylindole (DAPI) and cell markers, are imaged by high-resolution microscopy. (B) These datasets are imported into VTEA and processed for noise, background correction, etc., and segmented into individual cells on the basis of DAPI-stained nuclei. (C) Segmented nuclei are processed and the intensity of the pixels surrounding each nucleus are measured and summarized statistically. (D) Per segmented nuclei, the summary statistics and other image features can be plotted, gated, and quantitated. (E) Gated cells are assessed and confirmed in tissue volume by dynamic backward mapping of selected cells to the imaged volume. (F and G) Within the imaged volume, a region of interest can be added to determine where spatially localized cells are found in the analytic space. (H) These groups of cells can be used to classify cells for use in additional unsupervised and neighborhood analyses.

Limitations of Cytometry with VTEA

Although VTEA includes a suite of state-of-the-art tools for visualization, image processing, and analysis, these tools may not be the best solution for all circumstances. We understood this limitation early in developing VTEA and have implemented an extensible framework. Therefore, VTEA can be dynamically extended with emerging and potentially more effective tools for visualization, image processing, and analysis (52). Furthermore, the workflow is adaptable because segmentations and features may be imported from other software or workflows (e.g., CellProfiler and other FIJI plugins, such as MorphoLibJ) (57,58) for use in VTEA. These capabilities will be described in further detail in an upcoming publication.

Profiling the Immune System with Tissue Cytometry in Experimental Mouse and Human Disease

We have applied tissue cytometry to survey immune cells in various experimental settings in animal models and human specimens. In mouse kidneys, we used VTEA to quantify the distribution of various subtypes of immune cells on the basis of MHCII and CD11C staining, and determined that the cortex and medulla have a differential distribution of renal mononuclear phagocytes on the basis of these two markers (53). Furthermore, we determined that deficiency of uromodulin, a protein uniquely produced by the thick ascending limbs and early distal tubules, causes a selective deficiency of phagocytes in the inner stripe of the outer medulla (53). Furthermore, we used VTEA to quantify the interaction between uromodulin and CD11C+ cells and showed that 13% of CD11C+ cells also had evidence of uromodulin staining (31), suggesting significant uptake of uromodulin within the interstitium by these phagocytes. This is of great biologic relevance because uromodulin is an important determinant of the number and function of mononuclear phagocytes within the kidney (31). Using VTEA, we also developed an in vivo quantitative phagocytosis assay by injecting fluorescent beads in live mice and quantifying the uptake of beads by MHCII+ cells in specific renal regions (31). Ongoing efforts are underway to characterize the spatial distribution of various immune cells during various types of kidney injury (ischemic, sepsis, nephrotoxic) and link the distribution of immune cells to injury signals from the subtypes of tubular epithelium (Figure 2). Finally, we have also started applying cytometry to pathway imaging, by labeling for activated proteins in the kidney (52). For example, we showed that activation of p-c-Jun can be quantified in the kidney using tissue cytometry (59). Such an approach could be very useful in profiling the activity of immune cells in the kidney during disease.

Figure 2.:
Profiling immune cells in mouse kidneys after ischemic injury using 3D tissue cytometry. (A) Whole kidney section stained for MHCII (magenta) and with DAPI (nuclei, gray) and phalloidin (F-actin, yellow), was imaged by confocal microscopy with tile scanning, after which image volumes were stitched together. A single optical slice is shown. Inset (a) on the right is a maximum z-projection from the region indicated. Scale bar, 1 mm in (A) and 100 μm in (a). (B) Cells were segmented and quantitated with VTEA, yielding 124,548 cells. A gate was used to identify MHCII+ cells that were mapped back to the tissue (left and middle panel, respectively) and presented in the same volume given in inset (a) from panel (A) (right panel). Scale bar, 1 mm.

Large-scale 3D imaging and tissue cytometry is also being used in quantifying immune cell abundance and distribution in human kidney tissue, in health and disease (Figure 3). We previously showed this approach could be used in archived frozen human kidney biopsy specimens, whereby quantitation of immune cell densities could potentially be linked with other molecular or clinical features (51,53). Large-scale 3D imaging and tissue cytometry of human kidney biopsy tissue is currently being used as a main imaging technology in the Kidney Precision Medicine Project (KPMP) consortium (J. Hansen et al., unpublished observations) (55,56). This National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)–supported effort is one of the largest consortia studying the pathogenesis of human kidney disease by safely and ethically obtaining biopsy tissue from patients early on in the course of AKI and CKD (60). One of the goals of 3D tissue cytometry in this project is to identify the spatial distribution of immune cell subtypes and their association with specific structures and neighborhoods during disease. In addition, linking these immune signatures to comprehensive molecular, pathologic, and clinical profiling will provide important advancements in understanding the pathogenesis of disease and improve disease classification on the basis of refined cellular and molecular phenotypes (J. Hansen et al., unpublished observations) (53,55,60). Finally, by using VTEA to profile the immune system in papillary biopsy specimens of patients with kidney stones, we recently uncovered a unique signature of inflammation in patients who form brushite stones compared with those who form calcium oxalate stones (61). Brushite papillae were characterized by increased neutrophil infiltration and neutrophil extracellular trap (NET) formation. In addition, neutrophil proteins were selectively enriched in the proteome of brushite stones, supporting a possible role of inflammation and NETosis in the pathogenesis of brushite stone formation (61). Such findings have implications on the approach and management of patients with nephrolithiasis.

Figure 3.:
Quantitation of immune-cell infiltrate in reference nephrectomy tissue. Fixed human cortical nephrectomy tissue was stained with antibodies against CD45, CD31, and Nestin and with DAPI. This tissue was imaged by confocal fluorescence microscopy and analyzed with VTEA. All images are maximum z-projections. (A) Stitched confocal volumes with glomeruli (Nestin, magenta), a network of endothelial cells (CD31, yellow), and infiltrating immune cells (CD45, green) throughout the tissue. Scale bar, 100 μm. (B) Immune cells are associated with and away from CD31+ cells—see inset (a). Scale bar, 50 μm. (C) Tissue cytometry quantitatively identifies immune cells (CD45+) near CD31+ cells (red gate) or far from CD31+ cells (cyan gate). (D and E) Overlay of gated cells from (C) highlights the distributions of the two populations.

Tissue Cytometry, Big Data, and Machine Learning

Large-scale 3D imaging and tissue cytometry surveys tens to hundreds of thousands of cells. Of these surveyed cells, the proportion and distribution of immune cells vary depending on the renal area, but the number of immune cells are still likely to be in the range of thousands (55). Multiplex labeling of kidney tissue will increase the label-based features associated with a single cell and will exponentially increase the derived features on the basis of label and spatial information. In fact, with seven markers, the resulting feature space, on the basis of primary labels and spatial features alone, already exceeds 50 variables per cell. As derived variables are added, we become faced with interrogating and visualizing hyperdimensional data for thousands of cells, a true “big data” problem. Although directed approaches and various subgating strategies can still be applied for specific variables, such methods would only allow a peripheral exploration of the data, and likely not capitalize on exploring the complex spatial relationships between various cells. Furthermore, an unbiased interrogation may not be possible using these traditional approaches. Therefore, to enhance the ability to analyze and visualize hyperdimensional data, various clustering and classification strategies using machine-learning tools are being implemented in the VTEA analysis pipeline. These tools can allow the unbiased classification of cells on the basis of multiple features (these new classifications become new derived features), visualization of hyperdimensional data using dimensionality reduction, and mapping cells back to the tissue on the basis of these new derived features. On the basis of cell distributions within the tissue, cell neighborhoods can also be defined and quantitatively interrogated (Figure 4) (36,49). These approaches are particularly useful for assessing immune cell involvement in pathology, including the role of signaling in immune cell infiltration and the effect of immune cells on tissue injury or repair.

Figure 4.:
Incorporating spatial context with neighborhood analysis. (A) Cells are first classified by using either a supervised gating strategy or an unsupervised clustering approach. (B) These classified cells are grouped into nonexclusive overlapping neighborhoods by neighborhood-building algorithms, such as space-dividing (left panel) or cell-centric approaches, such as cells within a given distance or the k-nearest neighbors of a reference cell (center and right panels, respectively). (C) The composition of these neighborhoods can be interrogated on the basis of classification and connectivity with network analyses.

In Situ Cell Classification on the basis of 3D Nuclear Staining Using VTEA and Deep Learning

Classifying cells type and subtypes on the basis of imaging data typically requires validated markers. For example, subtypes of immune cells are characterized by the expression of unique cell surface markers (26,62) and, more recently, also on defining the transcriptomic profile (which still requires additional validation by RNA or protein detection) (29). Despite advances in multiplexing, a challenge remains in that only a finite number of cell-associated markers can be obtained from a single experiment. In addition, each time a new marker is discovered or needed, new experiments on additional tissue sections are required. For sparse tissues, such as a kidney biopsy specimen or unique experimental conditions, this could lead to tissue exhaustion. Furthermore, because multiplexing is performed at the experimental level, reclassification of cells using prospectively discovered markers is not feasible except with new experiments, which will lead to missed opportunities for many already imaged kidney specimens.

Recently, we investigated whether 3D nuclear staining with 4′,6-diamidino-2-phenylindole (DAPI) (63), a nuclear stain commonly used in most fluorescence imaging methods, contains enough information for reliable classification of human kidney cells in situ using a supervised learning framework (64). DAPI is commonly used in multiple platforms of research and can be easily performed as part of large-scale 3D imaging. In addition, immune cells are frequently described on the basis of their nuclear morphology. We first developed a machine-learning approach using 3D cytometry with VTEA to generate a large training dataset comprising 230,000 image volumes of nuclei (64), organized into cell types on the basis of concurrent staining with validated markers (Figure 5). The development of such ground-truth library was only feasible because of the semiautomated workflow of VTEA, which mitigated costly efforts of manual annotation and allowed us to develop this library within a few weeks. We then devised a convolutional neural network–based deep-learning approach to classify cells on the basis of their 3D nuclear staining (Figure 5). Such an approach was successful in classifying eight different cell types (various epithelial, immune, and endothelial classes) with high accuracy (up to 84% balanced accuracy with maximal training), and then mapped back for visualization in the tissue (64). In our work, we have used a standard convolutional neural network architecture for classification, but the accuracy of our proposed classification approach can likely be improved by developing custom-made deep-learning methodologies. Furthermore, including concurrent staining with additional markers could potentially improve and expand such a classification. Nevertheless, the promising results of our framework show that imaging data from DAPI-stained tissue have the potential to become a rich source of data that will suffice for comprehensive cell classification. Furthermore, we envision that this methodology will have useful applications for classifying immune cell subtypes. Combining cell classification with the VTEA cytometry interface will allow mapping of the identified cells and facilitate their biologic interpretation in the context of the tissue microenvironment. This unique approach enables the development of a ground-truth library and the continued re-evaluation of existing data, thereby lessening the need for more tissue. As new cell markers emerge, new training datasets can be generated from an abundant source of tissue. This learning can then be imputed on existing datasets from sparse tissue without the need for additional experimentation (Figure 6).

Figure 5.:
Workflow for in situ classification of cells using 3D nuclear staining. This approach combines tissue cytometry (VTEA) with deep learning using a convolutional neural network. VTEA allows ground-truth validation of cell types and subtypes on the basis of specific markers and generation of a ground-truth library. Currently, this library encompasses 270,000 image volumes of nuclei for eight different classes of cells. This ground-truth library allows the training, validation, and testing of a deep-learning approach to classify cells only on the basis of the 3D nuclear staining pattern. The classified cells can then be mapped backward in the tissue using VTEA to visualize and interpret the biologic context.
Figure 6.:
Multiplexed imaging of sparse tissue: challenges and solutions. (A) A typical workflow for large-scale imaging and analysis. As novel markers emerge, there is a need to restain or incorporate markers into a congested processing pipeline with new experiments. This process is limited by the number of markers used and will deplete the tissue as new markers become available. (B) Implementing VTEA with deep learning to classify cells on the basis of 3D nuclear staining. The nuclear signature of new cell types can be validated in abundant sources of tissue and imputed on the sparse tissue. This allows the extraction of novel data from already imaged, sparse tissue and provides a nonexhaustive source for discovery with benefits on tissue economy.

Future Outlook, Challenges, and Conclusions

In this review, we highlighted the emerging role of tissue cytometry in profiling immune cells within the kidney. New imaging and analytic tools, such as VTEA, have transformed imaging of kidney tissue into multidimensional omics data with rich information at the cell level. Such an approach will not only complement other innovative cell-based molecular technologies but will also help anchor the findings in situ and enhance the biologic interpretation.

There are outstanding challenges for tissue cytometry, particularly in the areas of image processing, analysis, and the computation and visualization of big data. For example, in the setting of disease, segmentation approaches need to be improved and refined to ensure accurate and robust data. Analytically, we need a thorough assessment of neighborhood approaches to better understand their application to the study of tissue microenvironments. Researchers also need the tools to explore and analyze the big data generated by national and international efforts, such as the KPMP consortium, through distributed cloud-based tissue cytometry platforms.

The output from tissue cytometry is expected to increase further, especially with advances in tissue-clearing methods that allow mesoscale imaging (35,49,65). Fortunately, advances in computational analysis and implementation of machine-learning tools will enhance our ability to classify cells within the tissue and detect early changes in disease. For example, using the deep-learning approach discussed above, it may become possible to detect early signatures of stress induced by disease and visualize the affected cells within the kidney using VTEA. Such advances will enhance the role of imaging and cytometry tools for discovery in nephrology.


T.M. El-Achkar reports having other interests in, or relationships with, the American College of Physicians, American Physiological Society, and American Urological Association; and serving as a scientific advisor for, or member of, Frontiers in Pharmacology–Renal Pharmacology. S. Winfree reports receiving honoraria from Marine Biological Laboratories, University of Chicago (for serving as course instructor); and having consultancy agreements with QCDx LLC. The remaining author has nothing to disclose.


This work was supported by a Veterans Affairs VA Merit award (to T.M El-Achkar), and NIDDK grants 1R01DK111651 (to T.M El-Achkar) and P30DK079312 (via the Indiana University O’Brien Center for Advanced Renal Microscopic Analysis).


The authors would like to thank Drs. Katherine J. Kelly and Daria Barwinska, for assistance in experiments, and the Indiana Center for Biological Microscopy, for imaging support in the Division of Nephrology and Hypertension at the Indiana University School of Medicine.

Author Contributions

M. Al Hasan, T.M. El-Achkar, and S. Winfree conceptualized the study and reviewed and edited the manuscript; T.M. El-Achkar wrote the original draft and was responsible for methodology and funding acquisition; T.M. El-Achkar and S. Winfree were responsible for data curation; and S. Winfree was responsible for formal analysis and software.


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renal physiology; AKI; basic science; CKD; imaging; immunology; inflammation; machine learning; pathology

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