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Van Gogh Irises Paper Cocktail Napkins - 20 Per Package
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In this Design: Van Gogh's 1889 work, Irises, from the Getty collection, features a beautiful movement and romantic color. We have introduced a fresh perspective to the work by zooming in on the blooms and focusing on the intricate brushwork.
Van Gogh Irises
The J. Paul Getty Trust is the world's largest cultural and philanthropic organization dedicated to the visual arts. Through ambitious projects in Los Angeles and around the world—including conservation work, publications, exhibitions, grant initiatives, and training programs—they work to make a lasting difference in conservation practice, art historical research, and museology and to promote knowledge and appreciation of art among audiences of all ages. J. Paul Getty viewed art as a civilizing influence in society and strongly believed in making art available to the public for its education and enjoyment. The Getty and its constituent programs—the Getty Conservation Institute, the Getty Foundation, the J. Paul Getty Museum, and the Getty Research Institute—are dedicated to the proposition that works of art are windows onto the world's diverse and intertwined histories, mirrors of humanity's innate imagination and creativity, and inspiration to envision the future.
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Julienne Melchior (Avondale, PA, US)
Went to Van Gogh show so loved the napkins
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JCI Insight
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Research ArticleNephrology Free access | 10.1172/jci.insight.129477
Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry
Nikhil Singh,1 Zachary M. Avigan,1 Judith A. Kliegel,1 Brian M. Shuch,2 Ruth R. Montgomery,3 Gilbert W. Moeckel,4 and Lloyd G. Cantley1
1Section of Nephrology, Department of Internal Medicine,
2Department of Urology,
3Section of Rheumatology, Department of Internal Medicine, and
4Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.
Address correspondence to Nikhil Singh, Section of Nephrology, Department of Internal Medicine, Yale University, 300 Cedar Street S246, New Haven, Connecticut, 06510, USA. Phone: 203.909.5772; Email: nikhil.singh@yale.edu.
Authorship note: ZMA and JAK contributed equally to this work.
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First published June 20, 2019 - More info
Published in Volume 4, Issue 12 on June 20, 2019
JCI Insight. 2019;4(12):e129477. https://doi.org/10.1172/jci.insight.129477.
© 2019 American Society for Clinical Investigation
First published June 20, 2019 - Version history
Received: April 10, 2019; Accepted: May 10, 2019
High-resolution atlas of the human kidney
Author's Take
In this episode, Singh and colleagues use Imaging mass cytometry to generate an atlas of the human kidney at a single-cell resolution.
An incomplete understanding of the biology of the human kidney, including the relative abundances of and interactions between intrinsic and immune cells, has long constrained the development of therapies for kidney disease. The small amount of tissue obtained by renal biopsy has previously limited the ability to use patient samples for discovery purposes. Imaging mass cytometry (IMC) is an ideal technology for quantitative interrogation of scarce samples, permitting concurrent analysis of more than 40 markers on a single tissue section. Using a validated panel of metal-conjugated antibodies designed to confer unique signatures on the structural and infiltrating cells comprising the human kidney, we performed simultaneous multiplexed imaging with IMC in 23 channels on 16 histopathologically normal human samples. We devised a machine-learning pipeline (Kidney-MAPPS) to perform single-cell segmentation, phenotyping, and quantification, thus creating a spatially preserved quantitative atlas of the normal human kidney. These data define selected baseline renal cell types, respective numbers, organization, and variability. We demonstrate the utility of IMC coupled to Kidney-MAPPS to qualitatively and quantitatively distinguish individual cell types and reveal expected as well as potentially novel abnormalities in diseased versus normal tissue. Our studies define a critical baseline data set for future quantitative analysis of human kidney disease.
Despite considerable research in the field of acute kidney injury (AKI), no therapies exist for the most common intrinsic renal cause of AKI, acute tubular injury (ATI). Additionally, advances in understanding based on animal models have as yet yielded few therapies for chronic kidney disease, tubulointerstitial diseases, and many glomerular diseases (1). The lack of therapeutic options for many forms of kidney disease stems in part from our incomplete understanding of the gene expression and spatial organization of the healthy and injured human kidney (2–4). Renal biopsy is a useful tool that has for decades been used for descriptive analysis of human renal pathologies at the cellular level; however, there has not yet been a concerted effort to provide quantitative information about the tubular, stromal, and nonresident cell populations in disease. In fact, the basis for the classification of most forms of kidney disease remains these pathologic descriptions, rather than clinical presentation, pathophysiology, biomarkers, or response to therapies (5, 6). Unfortunately, the limited amount of tissue that is obtained by renal biopsy has restricted the types of analyses that can be performed on patient samples, thereby hampering discovery at the molecular and even cellular level.
Several emerging technologies, including single-cell and single-nucleus transcriptome sequencing, have catalyzed efforts towards more granular interrogation of kidney tissue. Such approaches have yielded quantitative data delineating the number of cell types present in the "normal" and diseased human kidney, including tubuloepithelial, stromal, and immune cells (7, 8). These data suggest at least 16 distinct cell types that comprise the adult human kidney, including a multitude of resident and infiltrating immune cells (7–9). Many of these cell populations are distinguished by specific differentially expressed genes (DEGs), which include transporters, secreted proteins, and transcription factors. Single-cell approaches have also identified insights into disease and identified novel cell populations in the murine kidney (10, 11). Such methods, however, rely on protease digestion of tissues, resulting in underrepresentation of specific cell types (9, 12). Furthermore, technique-specific biases may be exacerbated by injury or fibrosis, limiting the ability to perform comparative analysis between uninjured and injured tissue (12). Critically, while transcriptional analysis is powerful for characterizing the cell types comprising a scarce tissue sample, it does not offer spatial context. Yet, in perhaps no other solid organ — save for the brain — is function so closely tied to the 2- and 3-dimensional relationship between cells as it is in the kidney. We sought to utilize highly multiplexed imaging to faithfully and reproducibly determine the composition of the human kidney in the normal and diseased state, and to afford us with potentially novel information regarding the identity, location, and numbers of — as well as interactions between — resident and immune cells.
Imaging mass cytometry (IMC) is a technology that supports simultaneous detection of more than 40 protein markers on a single section of formalin-fixed, paraffin-embedded tissue, with the resultant reconstructions achieving 1-μm resolution (13, 14). In the kidney, endogenous fluorescence, particularly in tubules, can reduce signal-to-noise ratio and compromise image quantitation in fluorescence-based imaging, making a mass spectrometry–based analytic technique particularly appealing (15). IMC has been used to characterize the tumor microenvironment in solid malignancies, yielding information about the identity, number, and spatial relationship between immune and stromal cells (13, 16). Additionally, the immune milieu of the human pancreas has recently been characterized using IMC (17, 18). Studies in murine models, including IMC of murine dendritic cell populations, suggest that interactions between resident renal cells and cells of immune origin may be critical in both homeostasis and disease, though there is greater uncertainty in the human kidney (19–22). We postulated that IMC could be used to address this important question and sought to develop a panel of antibodies and establish the computer-based analysis pipeline needed to faithfully characterize and quantify the 2-dimensional cellular architecture of the human kidney. Herein we describe the successful application of IMC in the human kidney, using a combined imaging and analysis approach that we term Kidney-MAPPS (multiplexed antibody-based profiling with preservation of spatial context). For our sources of normal human kidney, we have chosen biopsies from living donors as well as nephrectomy tissue from areas remote from tumors. Using Kidney-MAPPS, we have successfully generated a 2-dimensional quantitative atlas of 22 distinct cell populations that comprise the human kidney.
Antibody selection and validation for interrogation of the human kidney. In order to define the spatial localization of the cell types that comprise the human kidney using a multiplexed immunostaining approach, we first sought to optimize a panel of commercially available antibodies directed against the protein products of cell-type-specific DEGs. Importantly, only commercial antibodies that are available in BSA-free and carrier-free formulations are suitable for conjugation to rare earth metals and use with IMC. Monoclonal antibodies were preferentially tested over polyclonal antibodies in order to minimize effects of batch variability. We first performed immunofluorescent staining with individual candidate antibodies using a single, common protocol on formalin-fixed, paraffin-embedded human kidney tissue obtained by nephrectomy. Each intrinsic renal marker was evaluated by a renal pathologist blinded to the antibody used, and who identified the morphological characteristics of the stained cells or regions. Antibodies in which positive staining was detected in expected cells with a maximal signal-to-noise ratio, and in which significant ectopic or background staining was not observed, were included in the final intrinsic renal antibody panel. The antibodies were conjugated to rare-earth metals for detection by IMC and tissue specificity was validated in groups of 2–5 antibodies to confirm that binding specificity had not changed through the conjugation process (Supplemental Figure 1 and data not shown; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.129477DS1). Antibody validation data generated as a part of this work are publicly available through the NIH (Re)Building a Kidney database — https://www.rebuildingakidney.org/chaise/recordset/#2/Antibody:Antibody_Tests@sort(RID) (23).
For immune markers, commercially available metal-conjugated antibodies were validated using IMC in human lymph node and abscess tissue, as well as by immunofluorescence in human kidneys known to harbor severe interstitial nephritis (Supplemental Figure 2 and data not shown). In lymph node tissue, CD20, a B cell marker, was detectable in areas corresponding morphologically to germinal centers, with CD3-positive T cells detectable in adjacent regions (Supplemental Figure 2A). CD4 and CD8 expression was found to be mutually exclusive, and each colocalized with CD3 (Supplemental Figure 2A). All lymphocyte markers had a characteristically annular appearance, though the ability to distinguish this is somewhat limited by the 1-μm resolution and the high cell density. CD68, a marker of macrophages, was detectable in morphologically distinct cells that did not overlap with lymphocyte markers (Supplemental Figure 2, A and B). CD66b, a marker of granulocytes, was found to be present in abscess tissue and was distinct from CD68 expression (Supplemental Figure 2B). Our final panel of IMC-validated antibodies is shown in Table 1.
Antibodies, clones, dilutions, metals, and suppliers
Defining the normal human kidney for analysis. To define the 2-dimensional spatial relationship between distinct cell types in the normal human kidney, we next sought to obtain histopathologically normal kidney from available sources. Living kidney donors are carefully screened for evidence of chronic kidney disease as well as comorbidities such as diabetes or hypertension; therefore, living donors represent a useful potential source for normal kidney tissue (24). We prospectively accrued living donor samples, obtained by back-bench biopsy at the time of organ donation, and scored by a renal pathologist in a blinded manner. All samples were graded as "histopathologically normal" or "less than five percent interstitial fibrosis," and were therefore included in our analysis (Supplemental Table 1).
Nephrectomy tissue taken from tumor-remote regions is another commonly used source of "normal" kidney tissue due to its ready availability (25, 26). In this study, nephrectomy tissue was obtained both prospectively and from banked samples. In total, we collected 61 nephrectomy samples prospectively. Of these, 35 underwent detailed analysis of tumor remote (>2 cm from tumor) regions by a clinical renal pathologist. The plurality of these samples was noted to feature arterio- or arteriolo-nephrosclerosis, interstitial fibrosis, tubular injury, or interstitial nephritis (data not shown). However, 7 of the 35 were reported to exhibit either "normal histology" or "minimal" histopathologic abnormalities. Of those 7, one was excluded because the remaining tissue was degraded and could not be imaged. The remainder of the normal samples were selected for imaging by IMC (Supplemental Table 1).
To image additional samples, we identified banked formalin-fixed, paraffin-embedded tissue collected from surgical nephrectomies performed at 2 campuses over a period of 9 years. Nine hundred eighty-eight pathology reports were queried for notes of "benign," "tumor-remote," or "uninvolved" tissue. This yielded 55 detailed pathology reports of tumor-remote tissue, which included only 5 samples in which pathology in tumor-remote regions was specifically noted to have "no histopathologic abnormalities." In sum, we obtained 16 total reference kidney samples meeting criteria for "no" or "minimal" histopathological abnormalities (Supplemental Table 1), obtained 1 month to 9 years prior to our IMC analysis. Chart review was performed for each patient whose sample was included. As compared with patients undergoing nephrectomy, living donors trended to be younger with lower prevalence of hypertension and were more often female, although none of these trends reached statistical significance. There were no differences between the groups in serum creatinine levels or estimated glomerular filtration rate (Supplemental Table 1).
Qualitative assessment of highly multiplexed imaging of the human kidney with IMC. To define the spatial relationship between cell types in the human kidney, 5-μm sections of each of the 16 formalin-fixed, paraffin-embedded reference kidneys were labeled in parallel using a single cocktail of 23 markers (Table 1). IMC was then performed in randomized order on sequential days over a 6-week period. We imaged regions spanning from the cortex to the outer medulla with IMC (Figure 1, A–C). In order to avoid the immune-cell-rich subcapsular area, imaging of the cortex was initiated 600–800 μm from the capsule, which was detectable on all samples (boxed area in Figure 1, A and C).
IMC can be used to recapitulate human kidney morphology. (A–C) H&E stain (A), trichrome stain (B), and light microscopic image of immunostained core biopsy from a living donor kidney (C). The inset in A and corresponding leftmost box in C show the region selected for IMC ablation (visualization of selected markers depicted in D and E). (C) Additional boxes show additional regions ablated. (D) Selected tubule and endothelial markers detected by IMC, pseudocolored as indicated, and overlaid on a pre-ablation image of the tissue. PT, proximal tubule; tALH, thick ascending loop of Henle; DCT, distal convoluted tubule; T, transitional area; MEG, megalin; THP, Tamm-Horsfall protein; CALB, calbindin; CK7, cytokeratin-7; AQP2, aquaporin-2. (E) Selected interstitial and immune markers detected by IMC, pseudocolored as indicated, and overlaid on a pre-ablation image of the tissue. aSMA, α-smooth muscle actin; COL IV, collagen IV; REN, renin; NES, nestin. (F) Inset from D. Glom, glomerulus; Ar, artery; Cy, cyst. (G) Inset from E. Arrowhead indicates renin-positive cells. Arrows indicate immune cells all designated using the same cyan color. Scale bars: 1 mm (A), 300 μm (D), and 100 μm (F).
Qualitative analysis, performed using pseudocolored images of the IMC data from human cortex, revealed faithful labeling of canonical tubule segments consistent with expected histologic findings. We used 7 tubule-specific antibodies simultaneously to identify proximal tubule (PT; aquaporin-1–positive, megalin-positive, β-catenin–positive), thick ascending limb of the loop of Henle (tALH; Tamm-Horsfall protein–positive [THP-positive], β-catenin–positive), distal convoluted tubule (DCT; calbindin-positive, β-catenin–positive), connecting tubule/collecting duct (cytokeratin-7–positive, aquaporin-2–positive, β-catenin–positive), and transition elements between tubular segments (Figure 1D and Figure 2, A–H). In the cortex, there was an obvious preponderance of PT as compared with other cell types. In contrast to the cortex, the renal medulla had few detectable calbindin-positive DCT cells (Figure 2M), but revealed a population of megalin-low, aquaporin-1–positive, cytokeratin-7–positive, aquaporin-2–negative, β-catenin–negative tubules that most likely represent the thin descending limb of the loop of Henle (Figure 2, I–K, N, and P). Similarly, the THP-positive tubule segments in the medulla were composed of smaller cells than those in the cortex and had a smaller cuboidal profile, consistent with thin ascending limb of the loop of Henle (Figure 2, D and L).
Defining tubular heterogeneity and cellular identity with IMC. (A–G) Individual tubule markers pseudocolored and depicted on a single region of cortex from living donor kidney. Arrow in E indicates distal convoluted tubule, as denoted by high calbindin staining. (H) Merged image for all 7 channels, overlaid on a pre-ablation light microscopic image of the tissue. Panels E, F, and H highlight a transitional area in which cytokeratin-7–positive, calbindin-negative cells (arrowheads) are interspersed in the same tubule. (I–O) Identical tubule markers from medulla on the same kidney, with images processed identically to those in A–G. (P) Merged image of all 7 channels, overlaid on a light image of the pre-ablation tissue. Arrows in J, N, and P denote an aquaporin-1–positive, megalin-low, cytokeratin-7–positive, β-catenin–negative tubule. Arrowheads in L, N, and P denote a THP-positive, aquaporin-1–negative, cytokeratin-7–negative tubule. β-CAT, β-catenin; AQP1, aquaporin-1; AQP2, aquaporin-2; MEG, megalin; THP, Tamm-Horsfall protein; CALB, calbindin; CK7, cytokeratin-7. Scale bar: 100 μm (A).
In addition to characterizing well-defined tubule segments, multiplexed imaging allowed us to characterize the cellular makeup of the less-well-characterized transitional tubular regions. In the superficial cortex, transition areas were identified in which calbindin-positive, cytokeratin-7–positive, and aquaporin-2–positive cells were detectable in the same tubular cross section, in tubules of qualitatively larger luminal diameter than the DCT (Figure 1, D and F, and Figure 2A). These areas most likely correspond to regions where the DCT is transitioning to connecting tubule (Supplemental Figure 3). More uniform cytokeratin-7–positive, aquaporin-2–positive tubules were found to be of higher abundance in the medulla, consistent with the presence of large numbers of collecting ducts in that region (Figure 2, N–P). Cytokeratin-7 in particular showed mosaic expression in transitional areas, as well as in connecting tubule and collecting duct.
The renal interstitium includes the vascular and stromal cell populations that surround and support the tubular epithelium. The interstitium also includes the collagenous matrix surrounding the tubules, which is believed to undergo extensive changes during acute and chronic disease (27). Interstitial markers analyzed in this experiment include collagen IV, a marker of glomerular and tubular basement membrane, as well as α-smooth muscle actin (α-SMA) and vimentin, which mark stromal cells including fibroblasts, myofibroblasts, and pericytes (Figure 1, E and G, and Figure 3). In reference kidneys, a collagen mesh of basement membrane was visualized by IMC, with greater membrane thickness qualitatively apparent in the medulla than in cortex (Figure 3, B vs. H). The lacy network of cortical peritubular capillaries was made apparent by the large number of cells positive for CD31, an endothelial marker, between the collagen IV–positive basement membranes (Figure 3C). Vessels with smooth muscle investment, including arterioles and arteriola recta in clusters, were detected identified by closely associated α-SMA and CD31 staining (Figure 3, I, K, and L).
IMC elucidates stromal, vascular, and endothelial cell populations. (A–E) Selected markers shown individually on an identical region of cortex from a living donor kidney and pseudocolored. (F) Merged image for the individual markers shown in A–E and overlaid on a pre-ablation image. (G–K) Selected markers shown individually on an identical region of medulla from the same living donor kidney, with images processed identically to those in A–E. (L) Merged image for the individual markers shown in G–K overlaid on a pre-ablation image of the tissue. Arrows in I–L indicate CD31-positive, vimentin-positive, α-SMA–positive arteriole recta. COL IV, collagen IV; VIM, vimentin; aSMA, α-smooth muscle actin. Scale bar: 100 μm (A).
Within the glomerulus, collagen IV was found, as expected, to delineate the glomerular basement membrane, with highest staining intensity at the vascular pole and juxtaglomerular apparatus (JGA) (Figure 4, A and B). Both intraglomerular and extraglomerular endothelial cells were detectable by CD31 expression (Figure 4, C and N). Podocytes, an epithelial cell type positioned on the urinary side of glomerular capillaries, exhibit a complex 3-dimensional structure. In this study, podocytes were detected by 2 specific antibodies; anti-nestin (intermediate filament protein, Figure 4F) and anti–WT-1 (directed against the cytoplasmic form of the protein, Figure 4G). These were found to overlap to a high degree (Figure 4K), and podocytes were observed in direct apposition to the endothelium (Figure 4L).
Characterization of glomerular and juxtaglomerular cells with IMC. (A–J) IMC images for individual markers as indicated, showing a glomerulus, vasculature, and surrounding tubules. (K–O) Merged, pseudocolored images selected from A–J, illustrating the associations between glomerular and juxtaglomerular cells. (K) Overlapping signal between 2 podocyte markers, WT-1 and nestin. (L) Close association between WT-1–positive podocytes and CD31-positive endothelial cells in capillary loops. (M) Vimentin is expressed in podocytes and mesangial cells, and overlaps with WT-1 expression in podocytes. Arrowheads indicate vimentin-positive, WT-1–negative cells, which represent intraglomerular mesangium. (N) Basement membrane and vascular markers show the vascular nature of the glomerulus. (O) Channels highlighting the morphology of the juxtaglomerular apparatus. Insets in H–J and O show that the juxtaglomerular apparatus is characterized by renin-positive cells separated by basement membrane from immediately adjacent THP-negative macula densa cells. The surrounding cells of the thick ascending limb are THP positive and transition to calbindin-positive tubular cells. COL IV, collagen IV; VIM, vimentin; aSMA, α-smooth muscle actin; NES, nestin; THP, Tamm-Horsfall protein; REN, renin; CALB, calbindin. Scale bar: 100 μm (A).
Mesangial cells are specialized contractile mesenchymal cells that maintain the structure of the glomerulus. Mesangial cells, like many podocytes, express low levels of α-SMA), as well as high levels of the intermediate filament protein vimentin (Figure 4, D and E). Mesangial cells could be visually distinguished from podocytes in this study by positivity for vimentin in the absence of expression of nestin and WT-1 (Figure 4M). Our antibody against α-SMA, which detected α-SMA on smooth muscle cells surrounding arteries and arterioles, was not useful for mesangial cell identification (Figure 4E)
The JGA is a specialized region of the nephron with critical roles in tubuloglomerular feedback and systemic blood pressure regulation. Recently, this region has gained increasing interest due to the putative role of renin-positive cells in mediating podocyte regeneration (28, 29). The components of the JGA include the aforementioned renin-positive cells (Figure 4H), as well as associated tubular segments that comprise the transition point from tALH to DCT (Figure 4, I and J). Within the thick ascending limb, THP-negative cells comprising the macula densa were found to be tightly opposed to — though separated by the collagen IV–positive basement membrane from — the specialized α-SMA–positive, vimentin-positive, renin-positive cells of the JGA (Figure 4, I and O). Remaining cells in this tubular segment were THP-positive (Figure 4I). Calbindin-positive DCT was always found in the vicinity of renin-positive cells, though not directly in contact with them (Figure 4, J and O).
Immune cells detectable in normal human kidneys using IMC included macrophages (CD68-positive), T cells (CD3-positive), helper T cells (CD3-positive, CD4-positive), cytotoxic T cells (CD3-positive, CD8a-positive), B cells (CD20-positive), and granulocytes (CD66b-positive). All were abundant in lymph node or skin abscess tissue (Supplemental Figure 2) and were detectable in low abundance in the interstitium of reference kidneys (Figure 1G and Supplemental Figure 4).
Quantitative assessment of the cellular anatomy of human kidney using Kidney-MAPPS. To quantitatively assess the cellular composition of normal human kidneys, we used an approach analogous to that which Bodenmiller and colleagues applied to cancer tissue, adapted for the unique and highly varied morphology of the kidney (13, 30). IMC data from relevant channels in 4 representative living donor and nephrectomy samples (Figure 5, A and B) were used to create distinct image stacks: one stack each representing nuclei, tubules, endothelial cells, and all remaining cell types (including stroma, podocytes, and immune cells). These image stacks were used to train a machine-learning algorithm to differentiate between signal and background in each data set and assign a probability to each pixel. After training on representative samples, this algorithm was extended to the entire cohort of samples to be analyzed by IMC. The nuclear probability image (Figure 5C) was used to define the existence of cells. The remaining probability images (Figure 5, D–F) were then iteratively used to categorize each nucleus as belonging to a particular cell type (tubular, endothelial, other) and to delineate the borders of each individual cell. These 3 resulting partial cell masks were merged into one unified mask defining every individual cell in the entirety of each ablated region (Figure 5G). Once cells were individually segmented, raw data from IMC was overlaid onto these segmentation masks using HistoCAT, a program designed for IMC analysis (30). Individual phenotypes were thereby ascribed to each cell in an unsupervised manner based on combinatorial expression of each of the 23 markers, allowing for classification and quantitation of each cell type within each region of interest (Figure 5, H–J). In cortex, 13 separate clusters of cells were detected, with phenotypes manually assigned based on the pattern of antigen expression (Figure 5J). Importantly, when phenotypes were assigned to individual cells based on the Kidney-MAPPS pipeline and overlaid on the segmentation masks (Figure 5, H and I), the resultant images closely recapitulated the patterns observed from raw data (Figure 5B). This consistent detection demonstrated that our cell segmentation and phenotyping algorithm faithfully represented the morphology of the kidney.
Single-cell segmentation, characterization, and quantification of a representative region imaged with IMC. (A and B) Merged, pseudocolored images showing the entirety (A) and inset (B) of a region of a nephrectomy sample imaged by IMC, with selected markers depicted as indicated. (C–F) Probability images generated from a machine-learning algorithm applied to processed multiplexed images, in order to define pixels with the highest probability of belonging to nuclei (C), endothelial cells (D), tubular cells (E), and stromal cells, mesangium, and podocytes (F). These probability images were used as the basis to generate a single-cell segmentation mask. (G) Single-cell segmentation mask, with borders defined for each cell, and colors depicting tubular, endothelial, and all other cell types. (H and I) The segmentation mask in G was used as the basis for analysis of raw data from this IMC experiment. Based on raw data from each of the 23 input channels, each individual cell was clustered by expression by HistoCAT and manually assigned a phenotype. Resultant pseudocolored images are depicted. (H) Depicts cells assigned to tubular phenotypes, as well as vascular cells, immune cells, stromal cells, podocytes, and endothelial cells. (I) Shows specific tubular subtypes, as indicated. Colors were selected to approximate those used in the raw data in B. (J) Two-dimensional t-distributed stochastic neighbor embedding (t-SNE) projection showing the clusters and relative abundances of cells, their phenotypic relationship to each other, as well as their manually assigned phenotypic classes, with colors indicated on the associated legend. Four separate immune clusters were manually grouped together as one in this projection (cyan blue). (K) Quantification of the ratio of cells counted versus those calculated by the above machine-learning methodology for 3 selected regions. Bars indicate mean ± standard error of the mean. (L) Comparison of cells positive for the indicated markers detected either by manual counting (circles) or using the automated methodology (triangles). Mean values are shown by bars. n = 3. Statistical comparisons were made using Wilcoxon's matched-pairs signed-ranked test, with P values indicated. Tub, tubular; Endothel., endothelial; Vasc, vascular; Strom., stromal; Podo., podocyte; PT, proximal tubule; tALH, thick ascending limb of the loop of Henle; DCT, distal convoluted tubule; CT/CD, connecting tubule and collecting duct; AQP1, aquaporin-1; AQP2, aquaporin-2; CALB, calbindin; CK7, cytokeratin-7; MEG, megalin; aSMA, α-smooth muscle actin; THP, Tamm-Horsfall protein; NES, nestin. Scale bars: 600 μm (A) and 150 μm (B).
Validation of the Kidney-MAPPS pipeline. A series of experiments were performed to evaluate both the validity and reproducibility of data obtained by the Kidney-MAPPS data analysis pipeline. In representative regions from 3 kidneys, we manually counted and scored cells in a blinded manner, while simultaneously performing unsupervised quantitative analysis using the Kidney-MAPPS methodology (Figure 5K). Kidney-MAPPS was found to underestimate the total number of cells by an average of 6.6%, potentially reflecting the stringency with which we selected nuclei for inclusion in the machine-learning algorithm. To determine whether there was a bias toward dropout of specific cell types, cells positive for markers representing a wide array of cellular phenotypes were quantified both manually and with Kidney-MAPPS (Figure 5L). For 7 markers defining key structures of tubules and surrounding stroma, there were no significant differences in the proportions of positive cells detected by manual versus automated quantification, out of over 10,000 cells scored. This suggests that the slight underdetection of cells by the Kidney-MAPPS methodology is distributed proportionally among all cell types and confirms the strength of Kidney-MAPPS for assessment of cell phenotypes.
To address reproducibility, we selected a subset of tumor-remote nephrectomy samples with the most abundant tissue and for each sample performed a second distinct biopsy-sized ablation of the renal cortex (Supplemental Figure 5, A–C) (31). In comparing the originally imaged regions to the second sample from the same kidney section, we found no significant differences between the numbers of individual cortical cell types (Supplemental Figure 5D). We also performed staining with an identical antibody cocktail prepared 4 months after the initial data set was obtained. Using this validation cocktail, we stained adjacent sections from the same 4 kidneys, and selected areas corresponding closely to the original regions of interest for ablation and analysis. We found no significant differences in cell abundances when comparing the original staining and IMC ablation data to the validation data set (Supplemental Figure 5E).
Comparative analysis of nephrectomy and living donor tissue. Because of the limited size and availability of histopathologically normal human biopsy samples, nephrectomy samples are frequently employed as a surrogate for normal kidney tissue. To determine whether pathologically normal nephrectomy samples are an appropriate surrogate for normal kidney tissue, we compared these to living kidney donor samples using Kidney-MAPPS. We analyzed an area from each of the 16 reference kidney samples that approximated the cross-sectional tissue area obtained in a single section of a typical needle kidney biopsy (31). The mean total tissue area for nephrectomy samples was 3.9 mm2 for cortex (n = 11) and 2.4 mm2 for medulla (n = 7). For living donors, the mean regions of interest were 2.0 mm2 for cortex (n = 5) and 1.7 mm2 for medulla (n = 2). Medulla was not available for 5 of the nephrectomy specimens and 3 of the living donor biopsies. By conventional analysis, including H&E, trichrome, and periodic acid–Schiff staining, no differences were readily apparent between nephrectomy and living donor tissue (Figure 6, A and B, and data not shown). Similarly, images generated by IMC showed no qualitative differences for tubular, stromal, or immune markers (Figure 6, A and B). Quantification of cell proportions with the Kidney-MAPPS pipeline revealed no significant differences between nephrectomy and living donor tissue in the proportion of tubular cells, stromal cells, or endothelial cells, normalized to either total number of cells or to total tissue area (Figure 6, D and E). There was a trend toward fewer endothelial cells in nephrectomy tissue than in living donor tissue, though this did not reach statistical significance (Figure 6, D and E).
Comparative analysis of nephrectomy tissue, living donor tissue, and interstitial nephritis with IMC reveals variability in stromal and immune makeup. (A–C) Representative H&E and pseudocolored IMC images from adjacent sections of nephrectomy (A), living donor (B), and interstitial nephritis (C) tissue. The 2 IMC images for each sample represent the same region imaged at the same time, with a selection of tubular and stromal markers shown in the upper panels, and collagen IV and immune markers shown in the lower panels. Arrows mark macrophages and arrowheads mark CD8a-positive T cells in the nephrectomy and living donor tissue in A and B. Inset in C shows an area of significant interstitial nephritis, consisting of a mixed infiltrate including macrophages, CD4-positive, and CD8-positive T cells. Asterisks in C denote areas of de novo expression of cytokeratin-7 in megalin-positive tubules. (D and E) Quantification of tubular, stromal, endothelial, and immune cells, as a proportion of total cells (D) and per square millimeter (E), detected by automated analysis of IMC data. n = 11 for nephrectomy, n = 5 for living donor, and n = 3 for interstitial nephritis (I.N.). Statistical comparisons were made using the Mann-Whitney test, with P values between each group indicated. Bars show mean ± standard error of the mean. Scale bar: 800 μm (A). AQP1, aquaporin-1; AQP2, aquaporin-2; COL IV, collagen IV; VIM, vimentin; aSMA, α-smooth muscle actin; THP, Tamm-Horsfall protein; MEG, megalin; CALB, calbindin; CK7, cytokeratin-7.
To determine whether our Kidney-MAPPS algorithm could successfully identify differences in cell proportions and architecture between normal kidney and diseased kidney, we analyzed renal cortex from 3 tumor adjacent nephrectomy specimens histologically graded as having interstitial nephritis or immune infiltrate (mean area of 2.9 mm2). These kidneys had readily identifiable immune infiltrates visible by H&E staining (Figure 6C). IMC revealed persistence of tubule structures in areas of infiltrate, though with de novo expression of cytokeratin-7 in some megalin-positive PT cells (Figure 6C). The makeup of immune infiltrate could be readily determined with IMC and consisted largely of CD4-positive T cells, CD8-positive T cells, and CD68-positive macrophages (Figure 6C).
Quantitative analysis of cell numbers in the interstitial nephritis specimens using Kidney-MAPPS demonstrated significantly fewer tubule cells, fewer endothelial cells, and significantly more stromal and infiltrating immune cells as compared with both populations of reference kidneys (Figure 6, D and E). Taken as a whole, these results demonstrate that IMC coupled with Kidney-MAPPS can be used to qualitatively and quantitatively discern individual cell types and reveal expected as well as potentially novel abnormalities in injured tissue.
The normal human kidney. Given the functional equivalence between living donor and nephrectomy tissue in our analysis, these data sets were combined in order to generate an atlas of the human kidney (Figure 7). Cell types, their locations, and their relative abundances were defined for both cortex and medulla (Figure 7, A and B). In the medulla, we detected high variability across samples in terms of the proportion of PT observed (Figure 7B and Supplemental Figure 6A). When all medullary samples were averaged, the sum of the number of cells in the proximal and descending limb (PT + DL) was comparable to the ascending limb cells (ALLH), as would be expected (Figure 7B and Supplemental Figure 6A). However, within individual samples there was a skew towards either the descending or ascending segments, with the proportion of PT correlating inversely with the distance from the region analyzed to the corticomedullary junction (Supplemental Figure 6, A and B). This suggests that there is regional heterogeneity in tubule makeup that is not representative of the medulla as a whole, and that any single core biopsy can be expected to reflect this heterogeneity. We subdivided medulla into outer and inner stripe based on the depth at which the 95th percentile cutoff of PT was reached in each sample, with the outer/inner boundary found to be approximately 2.5 mm from the corticomedullary junction (Figure 7C). Indeed, we found that the proportions of all tubular segments, but not immune, vascular, or stromal cells, was highly dependent on which region of medulla — inner versus outer stripe — was imaged (Figure 7D).
The quantitative cellular makeup of the reference human renal cortex and medulla. (A and B) Quantification of cells of each indicated phenotype, as a proportion of total cells in human renal cortex (A) and medulla (B); n = 16 for cortex and n = 9 for medulla. Numbers show total proportions. (C) Distance from the corticomedullary junction that defined the inner stripe of medulla, as ascertained by the 95th percentile proximal tubule cell depth in the medulla. n = 4. (D) Comparative quantification of cell types between outer and inner medullary stripe. Outer stripe n = 8. Inner stripe n = 5. Comparisons were made using the Mann-Whitney test with P values indicated. Bars show mean ± standard error of the mean. (E) Cartoon depiction of the nephron, with protein markers and cell types depicted. PT, proximal tubule; ALLH, ascending limb of the loop of Henle (includes thick and thin); DCT, distal convoluted tubule; CM, corticomedullary; CT/CD, connecting tubule and collecting duct; UnID, unidentified; Endo (I), interstitial endothelium; Endo (G), glomerular endothelium; DL, descending limb of the loop of Henle. AQP, aquaporin; COL IV, collagen IV; VIM, vimentin; THP, Tamm-Horsfall protein; MEG, megalin; CALB, calbindin; CK-7, cytokeratin-7; β-CAT, β-catenin; REN, renin; NES, nestin.
Based on the signatures conferred by the 23 markers utilized in this study as well as the spatial information conferred by IMC, 13 distinct cell types could be distinguished via clustering algorithm, and 22 distinct cellular populations recognized by overlaying clustering data onto spatial representations of the kidney (Figure 7E), with an additional 5% of tubular cells that remain unidentified using our current antibodies. We propose that the mean values obtained by applying IMC and Kidney-MAPPS to multiple regions of these 16 kidneys provides a close approximation of the expected proportions in the cortex and medulla of normal human kidneys.
Kidney-MAPPS reveals unexpected cellular heterogeneity, potentially novel cell populations, and ectopic expression of canonical markers. The ability to achieve single-cell identification with the Kidney-MAPPS methodology has more quantitatively defined the transition area between DCT and connecting tubule (Figure 1F, Figure 2H, and Supplemental Figure 3). Our automated methodology has also revealed a population of megalin-positive, aquaporin-1–positive PT cells that is also positive for the intermediate filament vimentin. This rare population was found in low abundance in 7 out of the 16 total reference kidney samples (Figure 8A) and could be clearly identified as single cells within the tubular basement membrane (Figure 8, B–G); these cells may represent an injured, fibrotic, or even regenerative tubule cell type (32).
Kidney-MAPPS reveals potentially novel cell populations, unexpected marker expression, and potentially novel cellular relationships. (A) t-Distributed stochastic neighbor embedding (t-SNE) from a living donor cortex sample revealing 13 distinct cell populations, including a subset of proximal tubule cells that also expressed vimentin (yellow). (B–G) Pseudocolored IMC reconstructions of a selected region from this kidney showing several vimentin-positive proximal tubule cells. Arrowheads indicate positive cells, which are located within the tubular basement membrane. (H–O) IMC reconstructions showing vasa (arrow) and arteriolae (arrowhead) recta. Arteriolae are characterized by a thicker α-SMA–positive wall, which is also positive for nestin and WT-1. (P) H&E stain of a living donor kidney showing 2 sclerotic glomeruli (Glom). (Q–W) Adjacent section of the same kidney processed by IMC, showing immune infiltrate consisting of CD68-positive cells, CD4-positive cells, CD8a-positive cells, and CD20-positive cells in the region between the 2 sclerotic glomeruli. Scale bars: 100 μm. AQP1, aquaporin-1; Col IV, collagen IV; VIM, vimentin; MEG, megalin; NES, nestin; aSMA, α-smooth muscle actin.
The arteriolae and vasa recta comprise a series of vascular structures that run in parallel in the medulla. We were able to identify these structures in this study based on morphology and coexpression of the vascular markers vimentin, α-SMA, and CD31 (Figure 3L). Unexpectedly, these regions were also found to express the canonical podocyte markers WT-1 and nestin, which have previously been described as being coexpressed in putative vasculogenic niches (Figure 8, H–O) (33).
Immune cells were found in low overall numbers in the reference kidney and were found to consist largely of T cells and macrophages (Supplemental Figure 4, Figure 7, A and B, and Figure 8, P–W). In particular, T cells were associated with sclerotic glomeruli (Figure 8, P–W). In this study, granulocytes were rarely detected, and most granulocytes found in the kidney were also positive for the marker CD68, which can be coexpressed in human neutrophils (Supplemental Figure 3) (34).
It is increasingly understood that the development of viable therapies for many forms of kidney disease will require refinements to our understanding of the biology of the normal human kidney, including the interactions between immune and intrinsic renal cells (3, 35). We describe herein the development of a robust technique for multiplexed imaging as well as an associated pipeline for automated data analysis, which can be used for rapid, quantitative, 2-dimensional analysis of the cell types comprising the human kidney.
The heterogeneity and complex spatial arrangement of tubular and interstitial cell types comprising the mammalian kidney have long been appreciated. The various segments of the nephron were first distinguished from each other by painstaking morphological analysis, and more recently have been distinguished by function, consensus protein marker expression, and transcriptome (7, 9). While emerging technologies, including single-cell and single-nucleus RNA sequencing have been important in furthering efforts towards this molecular characterization of the human kidney, dissociation protocols may introduce bias towards specific populations, particularly in fibrotic or injured kidneys (12). Our data will serve as a useful reference for integration with dissociation-based interrogation of the normal kidney. Subsequent work will serve to provide complementary spatial and quantitative data of the injured state, which will further inform our knowledge of human kidney disease.
There are several emerging spatially preserved means of interrogating human kidney tissue that we view as complementary to IMC. Tissue cytometry has been shown to yield quantitative 3-dimensional information about immune cell subsets in human samples simultaneously stained for 6 markers and analyzed by fluorescence (36). Antibody removal–based fluorescent multiplexing can allow for dozens of antibodies to be analyzed on a single tissue section with subcellular resolution, with imaging occurring in an iterative manner (14, 37). Mass spectrometry imaging (MSI) has been used to yield quantitative information about metabolites in human kidney with a resolution ranging from 10 μm to 200 μm (38). An IMC-based approach can complement data obtained with these additional technologies by offering a high degree of multiplexing, the absence of background fluorescence, the ability to perform imaging in a single acquisition, reproducibility both in tissue and longitudinally, and the resultant ability to quantitatively analyze all channels in a single data set in an unsupervised fashion and without image subtraction, adjustment, or normalization. Additionally, IMC capabilities have recently been expanded to also include simultaneous localization of protein and mRNA targets (39).
Weaknesses of current IMC technology include the limitations on resolution (1 μm) owing to the width of the ablating laser pulse. Fluorescence-based imaging, by offering higher resolution and 3-dimensional capabilities, will complement findings by IMC and MSI, particularly in subcellular localization and in characterizing cells in the tightly interwoven structure of the glomerulus. Notably, the related mass cytometry platform multiplexed ion beam imaging (MIBI) supports multiplexed imaging of metal-conjugated antibodies at a resolution approaching 200 nm (40). Future studies employing the antibody reagents and analysis pipeline defined here may take advantage of that increased resolution.
Human AKI is rarely biopsied, unless glomerular disease or acute interstitial nephritis is suspected, and the ability to analyze human AKI specimens will initially rely on archival patient tissue, while larger-scale efforts are underway to prospectively bank human AKI specimens (3, 35). A critical advantage of our approach lies in the fact that Kidney-MAPPS can be used on both prospectively acquired and archived kidney biopsies. In fact, we have demonstrated the use of this technique on samples banked over 8 years prior to analysis and found no evidence of signal attenuation. Because preserved samples provide excellent labeling, imaging and analysis of control and injured samples can occur in parallel, minimizing potential technical confounders. We also believe that our strategy for cell segmentation could be readily adapted for many other solid organs, including the liver, which are composed of a complex network of epithelial, stromal, and endothelial cells.
As the field of nephrology moves toward comparative interrogation of the reference and diseased human kidney, an outstanding question remains: what source of tissue should be used as the reference standard? Absent biopsy tissue from healthy normal donors, several sources have been proposed. These include biopsies from living donors as well as nephrectomy tissue in areas remote from tumors, biopsies from patients with minimal change disease, and deceased donor tissue (24, 41). In this study, we use Kidney-MAPPS to demonstrate that careful selection of rare nephrectomy specimens that have been scored as morphologically normal can serve as appropriate surrogates for reference kidneys in future analyses.
An ongoing concern in the interpretation of human kidney biopsies is the degree to which each biopsy specimen is subject to sampling bias, and how the cellular makeup varies across randomly sampled regions of a kidney (31). Dropout of particular cell types, such as endothelium and specific tubule subsets, has been postulated to play a role in AKI and chronic kidney disease (42, 43). But a robust analysis of this requires a baseline understanding of how much those cell types vary in the normal kidney, and how that variability is influenced by kidney location and sampling bias. This study has allowed us to quantify the degree of variability in biopsy-sized regions of normal kidney samples, as well as between remote regions and serial sections of the same kidney. In the medulla, we have demonstrated that the tubular cells are organized in complex macroscopic arrays that can greatly influence the cellular makeup of a biopsy-sized region. For example, the relative proportion of PT and ALLH varies according to the distance from the corticomedullary junction that is being sampled, implying that human kidney biopsies exhibit a normal range of region-dependent sample bias for certain cell types. By providing a quantitative assessment of each cell type in each region of the kidney, these data will allow researchers, pathologists, and clinicians to directly compare cell proportions in each biopsy to the expected variability of the normal kidney, thus rapidly identifying the critical cell compartments that are impacted by specific pathologic insults. We demonstrate an application of this approach in our analysis of kidneys containing tumor-associated interstitial infiltrate, in which endothelial and tubular dropout were quantified and the stromal and immune compartments found to be vastly expanded. Indeed, we believe that the degree of endothelial dropout noted in areas of chronic tumor-associated infiltrate is a novel finding that bears further exploration in other forms of chronic kidney disease.
Additional potentially novel findings in this study included unexpected heterogeneity in tubular regions and ectopic expression of canonical markers. Cells of the mammalian DCT are characterized by expression of the intracellular calcium-binding protein calbindin (8, 44). However, marked heterogeneity of calbindin expression was observed, with 1–3 cells per tubular cross section clearly not expressing this protein (Figure 1D and Figure 2E). Although small numbers of intercalated cells are believed to populate distal regions of the DCT, the calbindin-negative cells were found throughout the DCT and appear larger and more cuboidal than a typical intercalated cell (45). Thus, these may represent a novel cell type relevant to future investigations. The transitional region between DCT and connecting tubule was characterized by cells with mosaic expression of calbindin, cytokeratin-7, and aquaporin-2, in which cytokeratin-7 and aquaporin-2 were coexpressed in cells that were mutually exclusive from those that expressed calbindin; these cytokeratin-7–positive, aquaporin-2–positive cells likely represent principal cells. While we did not seek to further explore heterogeneity in the collecting duct or to further delineate the ratios between principal and intercalated cells in this study, this could be an interesting area for future application of this technique, particularly given emerging evidence about the importance of intercalated cell dropout in human disease (10). Additional cell types relevant to human disease that could be better characterized with this methodology include CD133-positive cells, which have been proposed as tubule progenitor cells in the adult human kidney (46, 47).
Using β-catenin, aquaporin-1, and megalin, we were able to identify the vast majority of the PT, though we were not able to distinguish between the S1, S2, and S3 segments via unsupervised analyses, based on the markers selected. Furthermore, a population of unidentified tubular cells, comprising approximately 5% of total cells, was found to consist predominantly of PT cells with artifactually low levels of staining. As with the case of distinguishing between principal and intercalated cells, we predict that an expanded antibody panel will limit the number of unidentified cells and allow us to better distinguish the major cell constituents of the PT. Our unbiased analysis technique did identify a subset of PT cells, found in 7 of 16 human samples, which coexpressed the intermediate filament vimentin. Such cells have been postulated to represent regenerating tubuloepithelium (32). In several samples, these cells were found to associate closely with CD4+ T cells (data not shown). Additional work using this technique, including costaining for markers of proliferation and injury such as Kim-1, Ki-67, and MCP1 will be useful in determining whether these cells are in fact regenerating tubular cells, and whether there are important associations between immune and intrinsic renal cells that influence injury, regeneration, and repair (48, 49).
In summary, we have developed an IMC-based protocol and analysis pipeline that was used to generate a spatially preserved single-cell-resolution atlas of the normal human kidney, with greater than 20 markers analyzed simultaneously. We have used this technique to identify potentially novel and unexpected cell types and to contrast the normal and abnormal kidney. We believe that expansion of our antibody panel to include specific subtypes of kidney cell injury, greater immune cell coverage, and novel cell types identified by single-cell and single-nucleus RNA sequencing holds the potential to greatly expand our understanding of the pathogenesis of human kidney disease.
Antibody selection. Antigens were selected from canonical DEGs in previously published single-cell and single-nucleus RNA sequencing from human kidney (7, 9). Commercial antibodies against intrinsic renal cell types were selected based on citations in the literature where available, validation data presented on the manufacturer's website, and validation data made publicly available through the (Re)Building a Kidney consortium (23). BSA-free, glycerol-free formulations of antibodies were used for validation steps as delineated below. Immune markers were selected from validated commercially available metal-conjugated antibodies (Fluidigm Corporation) and were subjected to the additional validation steps defined below.
Antibody validation and conjugation. Antibodies were validated by performing immunostaining using a single common protocol on human kidney and lymph node. For intrinsic renal markers, staining was analyzed morphologically by a renal pathologist blinded to the antibody used. Further validation was performed for selected antibodies by counterstaining for additional markers of the same cell type as indicated. Antibody validation data have been deposited on the (Re)Building a Kidney website https://www.rebuildingakidney.org/chaise/recordset/#2/Antibody:Antibody_Tests@sort(RID) (23). Immune markers were validated on human lymph node tissue, with staining confirmed by IMC. Intrinsic renal antibodies that were validated by immunofluorescence were conjugated using the Maxpar conjugation kit (Fluidigm Corporation) following the manufacturer's protocol, scaled for 100 μg of carrier-free antibody. Metals were selected based on expected signal strength and to minimize overlap due to isotopic impurity. After conjugation, validation was performed by IMC (Supplemental Figure 1); for approximately 70% of antibodies, conjugation did not affect the observed binding pattern, and these antibodies were therefore included in the final antibody panel (Table 1).
IMC. Immunostaining protocols are available on the (Re)Building a Kidney website (23). Except for the serial section validation cohort, immunostaining was performed on all 16 formalin-fixed, paraffin-embedded samples on a single day using the same antibody cocktail, and IMC was performed over a 6-week span, alternating between nephrectomy and living donor samples. Of injured kidneys, one was stained in parallel with all normal samples, and the other 2 were stained from a separate antibody cocktail of identical composition. For nephrectomy tissue, regions of interest were selected to encompass contiguous areas from cortex to medulla in areas free of large discontinuities in the tissue. For donor tissue, regions of interest were selected to span the entire length of the available tissue from cortex to medulla. After selection of regions of interest, IMC was performed as described using the Hyperion Imaging System (Fluidigm Corporation). Tissue of 5-μm thickness was ablated with a Nd:YAG 213 nm laser and analyzed in a Fluidigm CyTOF mass cytometer (13). Images were analyzed using MCD Viewer (Fluidigm, version 1.0). For all images shown, thresholding was performed in MCD Viewer with gamma set to 1, minimum color range set to zero, and maximum color range set to 100. Thresholding was performed to remove background signal, as assessed morphologically. For all comparative images, signal thresholds were set identically for each marker between samples to allow for unbiased comparison. Analyses of cortex and medulla were performed separately; medulla was defined morphologically.
Pixel classification. For machine learning purposes, upper and lower intensity thresholds were set manually for each channel in MCD Viewer to accentuate signal and reduce noise. Four separate tiff image stacks were created for each sample by overlaying relevant channels: nuclei (histone H3, DNA intercalator 1, DNA intercalator 2); tubular membranes (β-catenin, aquaporin-1, megalin, THP, calbindin, aquaporin-2, cytokeratin-7); endothelial membranes (CD31); and interstitial/glomerular membranes (all other membrane channels). For each sample, individual channels were excluded for segmentation purposes if poor signal or high background staining was likely to hinder cell border determination. Each of these 4 image stacks was uploaded into Ilastik version 1.3.0 (https://www.ilastik.org/), an open-source software for pixel classification. In Ilastik, pixels were identified as belonging to nuclei, 1 of the 3 membrane types above, or background in an initial training set consisting of 4 samples (2 nephrectomy and 2 living donor). This classification was then applied to all remaining images using a machine-learning approach, and probability images were generated.
Cell segmentation. Probability images for each sample were ultimately exported as tiff files into Cell Profiler version 3.1.8 for cell segmentation and mask generation (50). First, nuclei were identified by size and shape as primary objects based on the nuclear probability image. Tubular cells were defined by creating secondary objects based on the tubular membrane image and filtering for objects containing high tubular probability; nuclei assigned to tubular cells were then removed. From the remaining non-tubular nuclei, secondary objects were created using the endothelial membrane image and filtered to select objects containing a higher integrated probability intensity of endothelial membranes than other interstitial/glomerular membranes. Endothelial nuclei were then removed. Finally, all remaining nuclei were assigned to secondary objects based on the interstitial/glomerular membrane probability image. These 3 filtered cellular "sub-masks" (tubular, endothelial, and other) were subtracted from one another to remove pixel overlap and then merged to create a single segmentation mask for that entire image.
Multiplexed data analysis. Raw images for each channel were exported from MCD Viewer without any adjustments or thresholding. Samples and their corresponding masks were cropped where necessary to allow for separate analysis of cortex and medulla. Each sample and mask was imported into HistoCAT version 1.75, an open source software specifically developed for single-cell analysis of multiplexed cytometry data (30). Unbiased clustering was performed using the PhenoGraph algorithm, a nearest-neighbor–based clustering approach, and each cluster was assigned a phenotype based on its expression profile (51). Similar clusters were merged to obtain a total cell number for each phenotype.
Data validation. To validate our method for cell segmentation and quantification, results obtained by Kidney-MAPPS on selected representative sections were compared to results obtained by manual quantification of the same sections. Manual quantification was performed by a blinded observer using direct counting of cells of each phenotype on thresholded tiff images using Microsoft Paint. Results for selected markers were directly compared to results obtained through the above data processing methods. To further validate our approach, we imaged multiple areas from 4 large nephrectomy samples and used the Kidney-MAPPS data analysis pipeline to quantify intrasample variability. Areas selected for validation were greater than 1 cm from previously imaged regions, and qualitatively comprised the same ratio of cortex and medulla. In an additional validation step, a new antibody cocktail was utilized in order to stain adjacent sections of the same 4 kidneys, and regions corresponding to the original regions of interest were selected for ablation and analysis.
Medullary depth. Medulla and cortex were distinguished morphologically based on distinguishing characteristics including the exclusion of glomeruli in the medulla and were analyzed separately after cropping images that spanned both regions. After clustering and phenotyping, each medullary sample that was noted morphologically to span both the outer and inner stripes was exported from HistoCAT as a csv file, which includes both the cluster assignment and the coordinates of each cell. Of the cells contained within clusters identified as PT, the 95th percentile x or y coordinate was calculated in Microsoft Excel, depending on the tissue orientation. These values were utilized to calculate the depth at which the 95th percentile medullary PT cell was located from the corticomedullary junction.
Statistics. Wilcoxon's matched-pairs signed-rank test was used to compare total cell number, as well as the number of cells expressing specific markers, as ascertained by manual counting versus automated methodologies. This test was further used to compare total cell proportions from original versus validation regions of interest from the same kidneys. The Mann-Whitney test was used to compare proportions of total cells between nephrectomy, living donor, and interstitial nephritis tissue, as well as to compare cellular proportions in outer versus inner medullary stripe. The Student's 2-tailed t test was used to compare the ages as well as serum creatinine levels between nephrectomy and living donor patients. Fisher's exact test was used to compare the proportion of female, hypertensive, and diabetic patients, as well as the proportion of patients with estimated glomerular filtration rate ≥60 mL/min/1.73 m2. All dot plots for quantitative data are presented to show mean ± standard error of the mean.
Study approval. Human tissue was obtained in accordance with the policies of Yale University's Human Investigations Committee (HIC). Written informed consent for research use of patient samples was obtained from each patient prior to study inclusion at the time of sample collection by the performing clinician. Tumor remote samples from nephrectomies were collected prospectively from 2016 until 2018. Living donor samples were collected prospectively from 2018 onward. All prospectively accrued samples were deposited fresh into 10% formalin, fixed overnight at room temperature, and dehydrated in 70% ethanol prior to paraffin embedding and sectioning to 5-μm thickness. Additional banked nephrectomy samples obtained between 2009 and 2017 were acquired retrospectively after chart review. Patient charts were reviewed in accordance with guidelines of Yale's Joint Data Analytics Team, to ensure that informed consent had not been withdrawn in the intervening time after sample collection. For nephrectomy patients and living donors, the following information, when available, was obtained from the electronic medical record, from the time of sample collection: patient age, sex, race, serum creatinine level, estimated glomerular filtration rate, history of hypertension, history of diabetes, and tissue diagnosis (specifically for patients undergoing nephrectomy). All data were deidentified and maintained on a secure database. For nephrectomy specimens, formal pathology reports were used. For living donor samples, samples were read by a renal pathologist in a blinded manner.
NS and LGC conceived of and designed all experiments. NS, ZMA, and JAK performed all experiments. JAK and ZMA developed the data analysis pipeline and ZMA performed data analysis. NS, ZMA, BMS, and LGC designed human investigative committee protocols. NS, ZMA, and BMS obtained human samples. NS and ZMA performed patient chart review. GWM is the pathologist who performed antibody validation and pathological scoring. RRM provided conceptual information and reagent guidance. NS compiled all figures and tables and wrote the manuscript. All authors reviewed and contributed to the final manuscript.
We thank Shelly Ren and the Yale CyTOF facility for technical assistance and Daniel Carvajal-Hausdorf, David Rimm, Kurt Schalper, and Franz Villarroel for helpful discussion and technical guidance. We thank Marta Boeke, Lisa Gras, and Yalai Bai for assistance with collecting human specimens. We thank Madeleine Hamilton for contributions to antibody optimization. We thank Ricarda Tomlin and Sanjay Kulkarni for assistance with living donor sample acquisition. We thank our funding sources, including the National Institute of Diabetic and Digestive and Kidney Diseases via (Re)Building a Kidney and DiaComp grants (to LGC). Additional support came from the Robert E. Leet and Clara Guthrie Patterson Trust Mentored Research Award (to NS), NIH T32 (to NS and ZMA), and the TRENAL program (to JAK).
JAK's present address is: Charité Universitätsmedizin, Berlin, Germany.
BMS's present address is: Department of Urology, Ronald Reagan UCLA Medical Center, Los Angeles, California, USA.
Copyright: © 2019, American Society for Clinical Investigation.
Reference information: JCI Insight. 2019;4(12):e129477. https://doi.org/10.1172/jci.insight.129477.
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Brähler S, et al. Opposing roles of dendritic cell subsets in experimental GN. J Am Soc Nephrol. 2018;29(1):138–154.
Oxburgh L, et al. (Re)Building a Kidney. J Am Soc Nephrol. 2017;28(5):1370–1378.
Muruve DA, et al. The biobank for the molecular classification of kidney disease: research translation and precision medicine in nephrology. BMC Nephrol. 2017;18(1):252.
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Map your course, not theirs.
As we are quickly nearing the end of 2016, it's a great time to reflect on the year's successes. Perhaps it's the entrepreneur in me, but as soon as I recount my successes, I can't help but focus on what I can do to make the coming year more fulfilling than the last. Most people look at the new year as a clean slate and start talking about their new year's resolutions – what they will change, start, or stop doing in the new year.
In my book, Your Ultimate Life Vision, I talk about resolutions vs. goals. As Earnest Hemingway wisely said, "Never mistake motion for action." Some people talk about doing things differently, however those resolutions fade and nothing changes. The only way to make your plans stick, is to put together a written vision plan starting now, in December. Don't be confused, this is not a list of new year's resolutions. Your vision plan is meant to last a lifetime, but your resolutions will be distant memories by March, like your new gym membership.
In the January 2012 edition of InStyle magazine, the following readership poll results were published, listing the most popular new year's resolutions.
Webster's dictionary defines the word resolution as the act or process of resolving as: the act of analyzing a complex notion into a simpler one; the act of answering (solving); the act of determining. And it defines the word commitment as an agreement or pledge to do something in the future. Do you see the difference? Having a resolution is good in that you've identified specific things you want to do, or changes you want to make, however only when a commitment is made, will you move those plans forward and actually do them. Resolutions without a commitment are powerless and do nothing.
What are you REALLY going to change in 2017? What are you going to do to make yourself physically, mentally, and emotionally, healthier, build a career that allows you to become more financially stable so you can take care of yourself, your family, and give to those in need right next to us.
Never mistake motion for action. Proverbs 14:23 says, "All hard work brings a profit, but mere talk leads only to poverty." Don't be a talker, be a doer, and make it happen!
The Thanksgiving holiday is just two days away, and like everything else in life, it falls within the six areas of our lives. The two areas most effected, especially at this time of year, are relationships and spiritual connectedness. On Thanksgiving, we give 'thanks', but to who? For the past several years, my husband and I have hosted Thanksgiving dinner for 10 to 15 of our closest family and friends. Before the meal, as we share what is on our hearts, most often I hear thanks given to God for all the gifts bestowed – good health, a job, good career, a comfortable home, and not living in a cold snowy areal (ok, that's because most of us grew up in a place like that). But more often, the most mentioned 'thanks' is for family and friends. So, I'm offering three ways to say thanks to people who make a difference in your life.
1) Tell people in person – Tell people face to face how they have impacted your life. In this digital world, we live in, its easier to text or Facebook message people to tell them how we feel. Looking people in the eye and telling them, usually followed up by a hug, is the most powerful way to communicate.
2) Call them – If you're not physically able to meet with people, call them (Skype of Facetime even better). Hearing someone's voice is the next best thing to being with them in person.
3) Write a note – Its best to tell people in person, or over the phone, however if for some reason that is not doable, perhaps it's is a strained relationship, then write a note. A hand-written note is meaningful; however, we are in an instant messaging world, and trained to respond immediately to our incoming text tones. At the very least, sending a message of thanks and telling someone (even in a text message) how valuable they are to you, is a good thing.
With as many negative, scary, and painful things going on in this world, showing grace and love to as many people as possible is important and restorative. We need each other for good emotional and physical health.


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Big privacy hits the mainstream
Lauren Henry Scholz
"The caricature of a company with significant market power that is willfully indifferent and hostile to consumer privacy concerns is outmoded. Big privacy is coming."
Wave after wave of alarmist articles in the "Is privacy dead?" genre have desensitized the public to the prospect of an ever-decreasing sphere of private life. But such gloomy articles misrepresent the trend in privacy policy. Major industry stakeholders have determined that protecting privacy is both good for overall social wellbeing and good business. What's more, meeting consumer privacy desires and expectations is becoming technically feasible through emerging "big privacy" methods. Industry has accepted that the question is not whether privacy will be protected in the digital age, but how and to what extent it will be protected.
In 2016, the idea that privacy will be more reliably protected in the near future than it is now will step out of industry and academia and into general public discourse. The web is maturing — that is, conforming to many of the desirable characteristics of physical space. The early, unfettered web has given way to a web of walled gardens, platforms, and applications to meet consumer demand, build corporate coffers, and enable government regulation. A similarly stunning revolution in technical infrastructure and norms is coming for information privacy.
Privacy is not dying. It is hiring. Companies are in the business of addressing consumer demand, and consumers demand privacy. Surveys show that consumers want privacy and think negatively of companies that are perceived as caring little for it, but are unsure of the exact features of the privacy that they want. Privacy is an industry growth area because companies need specialized professionals to address the dual challenges of (1) figuring out the privacy consumers want in terms of specific policies, protection, and practices, and (2) understanding how to technically implement corporate privacy mission goals.
Industry groups, mostly notably the Industry Association of Privacy Professionals, have made spectacular gains in membership and certifications. The rise of privacy credentialing is a critical indicator to watch, as industry searches for individuals with the technical, legal, and policy credentials to handle the many types of privacy problems a modern company must address, and job seekers aim to specifically signal that they are prepared to do privacy work. The demand for privacy experts is not limited to entry- and mid-level positions. Chief privacy officers are becoming a standard C-level position at large companies, as they realize the inevitability of regulatory issues in the area, and seek to get ahead of such regulatory moves by showing real commitment to controlling privacy concerns. It has never been a better time to be a privacy professional.
It is far from clear that industry, acting without significant regulation or oversight, will provide a socially ideal level of information privacy. Many would argue that industry will need better incentives than are available at current law to do as much as they should to protect privacy. But the caricature of a company with significant market power that is willfully indifferent and hostile to consumer privacy concerns is outmoded. Big privacy is coming. The public will soon begin to notice, and hopefully to contribute to, the debate over how big privacy ought develop.
Lauren Henry Scholz is a Knight Law and Media Scholar at the Information Society Project at Yale Law School.


Hi, Brenton Ford here. Welcome to Feedback Friday number 25, where each week we bring you the analysis of someone's stroke. In today's video, we've only got the front shot so there's only so much we can see in the front shot. Ideally, I'd like to have a look at the side and the front. It gives me a better perspective of things, but there's still a couple of things that I'd like to point out in this video.
The first one you'll notice is that the swimmer here is coming through quite straight with his arms, so he's not getting much bend in the elbows. The high elbow position isn't quite there. The other thing you'll notice as well as the timing or the exhilaration of the pull-through. Now when we're swimming. Let's just bring it back a little bit. When we're swimming freestyle, the speed of the hand should increase as you go through the pull-through. So at the back of the stroke, it should be faster than where it moves at the start of the stroke, so it should exhilarate through. Now you can see here particularly at the back of the stroke. That right arm, notice how it's exiting the water quite slow and there doesn't seem to be much exhilaration throughout the pull-through.
So that's another thing that we can work on. Now looking at the pull, where we mentioned that it's quite straight, the arm angle that we'd like to try and get to as your pulling through underneath the shoulder here is 100 to 120 degrees from the shoulder to the elbow, elbow to the hand. That's where you'll see most elite swimmer's being, with their arm angle in this position. So he's at about 170 degrees. That's just too straight. And the reason, or the cause of that, is he's just down too deep. So in order to get to that 100 to 120-degree angle that we want, he's going to need to not be quite as deep, he'll need to bring the hand up a little bit shallower. But more importantly, he's gonna have to get the elbow out to the side. We wanna see the arm almost looking like half a diamond, almost like a half triangle shape on the side. That's where he's going to be able to engage his lats a lot better. And that is where he's going to have a better angle with the forearm and the hand as he's pressing back. So if you look at this with another swimmer. So this is the winner of the Rottnest Channel Swim, Solomon Wright. He doesn't have a classic high elbow that you'll see with a lot of other swimmers, but he's a very quick swimmer. But he still has a very good pull-through, even though it's not a classic high elbow position. So, you can see at this arm angle right there. So he's at about 120 degrees with his arm, and you'll see it's that almost half diamond shape. So the elbow's out to the side a bit more, fingertips are pointing down. So it's not a huge change that the swimmer in the other video needs to achieve, but you can just see that position right there. So, here, much easier than you engage the lats, and the forearm and the hand is angled so much better to press back against the water. If we look at that from the side, ideally what we want to get to– I'm just gonna change the shot here. When you're pulling through underneath the shoulder in this position, the shoulder, elbow, and hand should all lineup. That should pretty much be one straight line as you're moving through underneath there.
What I've noticed with running a lot of clinics and coaching a lot of people online through our membership is that most people have their hand too far out in front, and what that does is it means the forearm and the hand presses down in the water instead of pressing back to move you forwards. So in order to really get good power from your stroke and good propulsion, all of this has to line up underneath the shoulder. So that's another thing that we'd work towards.
Now we could look at quite a few different drills to go through this. There's a number of drills that you can use. If you saw one of our most recent videos, which is called, "This is why it's so hard to get a high elbow catch," that's got two drills in it. It's got catch kick and it's got catch-catch pull drill in there that we really liked using clinics and with the online coaching, that we've had a lot of success with a lot of triathletes and open water swimmers, and pool swimmers, as well. So there's a wide range of drills that you can use, and it doesn't matter too much what drills you use, but it's more that you do the drills right. So you could have. You could do the best drill in the world, but if you don't do it right it doesn't really make a difference to your stroke. So it's about making sure you've got those key elements of the stroke with that drill right and practicing those. So that's probably what we'd look to work on with that athlete. When it comes to the exhilaration of the pull-through if we bring this back again. So you could see here with Solomon, notice how, all right, out the front in that reach phase it's not as fast as it is through the back of the stroke, so sort of slows down a little bit as he enters and extends, then he increases his speed particularly at the back of the stroke. It doesn't mean he needs to pull through really hard at the back, it just means that he exhilarates through the stroke there. So there are two things that we could work on with that swimmer in the other video.
Now in the last Feedback Friday, we ran a competition giving away an A and E pool buoy. We asked you to comment, "What are you working on in your stroke?" So thank you for adding that. Let's have a look at it. So penoir said, "I'm working on my catch, relaxing when breathing and trying to increase my swimming fitness, lung capacity. I started swimming again on March 12 this year after a 46-year absence. I was determined to overcome my fear of breathing in the freestyle stroke and it's now day 42 and I'm over the moon that can conquer this fear and I'm now swimming one kilometer four times a week." So that's excellent. That's what a lot of swimmers are working on, their catch, relaxing, really just getting to become more efficient. In swimming that's what it's all about. You know, effortless swimming is called effortless swimming because even when you're going fast you've got to stay relaxed as you do it. And it's really a key to swimming faster. On our Instagram, which is @effortlessswimming we're running a contest where I want you to submit your best photo of you or a friend swimming and we're going to feature the best picture on our Instagram over the coming week. So all you need to do is #effortlesswimming and just make sure the tag is in that photo and use the #effortlesswimming. And we're going to feature a couple of shots, the best ones, on our Instagram in the coming week.
So thanks for watching Feedback Friday. I hope you've enjoyed this video. We'll be back next week with another Feedback Friday video. And if you'd like to submit your video, one that we could choose to feature on Feedback Friday, then you can send it to us at our website. Go to effortlessswimming.com/contact, and that's where you can send your videos or just comment below. Put a link to your own video and we will pick one video every week to choose and analyze your stroke. So thanks for watching. See you next week on Feedback Friday.


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redpajama_set_name: RedPajamaGithub


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4.5 The idea of power, 1971-1989
Contemporary India
Universidad de Melbourne
This course presents some important vignettes of a complex, highly diverse India that is also witnessing unprecedented changes since its formal independence in 1947 from Great Britain. The lectures revolve around social dimensions of change, the continuing influence of ancient texts on contemporary India, political democracy, economic transition from the state to the market, gender relations, India's economic globalisation and changing world view. While one of the objectives is to capture the multifaceted process of change, the course also critically examines some of the tensions inherent in these changes. For example, how does gender inequality play itself out in a changing Indian society, how do the modernist conceptions of art entailing market valuation challenge the more socio-centric values found in South India, what are the politics linguistic identities, and how might India address its myriad development challenges such as poverty and unemployment. No specific prior knowledge is required. However, it would be helpful if students are aware of the socio political dynamics at play in contemporary India and keep themselves abreast with current affairs and debates in the country to fully appreciate the various dimensions and contours if the subject matter in the course. This course is taught in English. View the MOOC promotional video here: http://tinyurl.com/hx8mhxb
India's foreign policy
As India's influence increases in the international system, understanding its foreign policy and its engagement with the rest of the world is critical for students of international relations. This section is designed as a comprehensive review of some of the key issues related to India's word view and its behaviour towards the external world. Apart from taking stock of contemporary thinking and policies of the country, the major thrust would be on India's relations with its neighbours and great powers, its nuclear weapons policy and the ideological foundations of its foreign policy after independence.
4.1 Why study India's foreign policy?9:48
4.2 The seven phases of India foreign policy8:12
4.3 The limits of idealism7:08
4.4 The fractured years, 1962-19708:21
4.5 The idea of power, 1971-19895:54
4.6 The years of reflection, 1990-19988:32
4.7 Consolidation of U.S – India relations4:39
4.8 Back to the future, 2011-20147:31
Anthony D'Costa
Seleccionar un idiomaInglés (English)Rumano
We spoke earlier about the fractured years.
The years when India's weaknesses contributed
to a great deal of introspection and lead to policies that promised a new beginning.
The next phase that I'll talk about is the phase which I describe
as the Idea of Power, 1971 to 1989.
This was most dramatically illustrated by the rise of Indira Gandhi.
Indira Gandhi is an interesting and a very controversial figure in Indian politics.
But was perhaps the most formidable political leader of her generation.
She was also one of the world's first woman prime ministers.
And she was Nehru's daughter.
She had a slow start with having little
experience in the kind of political Machiavellian setup of the Congress party.
But soon enough, she was in command.
She was someone who centralised power, had an authoritarian streak,
but demonstrated great courage in the face of adversity.
It was in during this phase that India and
India's foreign policy began to recognise in reality the importance of power.
And it was in Indira Gandhi who exemplified that.
If you look at her Bangladesh policy, it's a textbook
case of how India was able to synergise with its strengths,
with its instruments of power from intelligence to the military,
to diplomacy in the pursuit of one goal, to ensure that Bangladesh was created.
That Bangladesh was created where East Pakistan stood.
Of course, there was an East Pakistani
freedom movement led by the Mukti Bahini and the great leader Sheikh Mujib.
But without India's aid assistance and diplomacy,
Bangladesh may have remained a Utopian goal for the Awami League.
It was Indira Gandhi and
her close advisors who were able to execute this great diplomacy.
Even though the United States had sent the seventh fleet and had
the regime of Nixon and Kissinger as his Secretary of State had,
were deeply against Indira Gandhi.
But Indira Gandhi had the support of the Soviet Union.
Not unflinching but had the support and
was able to dismember Pakistan, create a new state of Bangladesh.
Largely because of Pakistan's own hauls but it also
demonstrated to the world that India had arrived on the world's stage.
Not as an articulator of merely ideas but as,
as a country which could, through its power,
achieve clear goals, as was in the creation of Bangladesh.
India, under Indira Gandhi, again did something
against all odds and in the face of world opinion.
India tested nuclear weapons and
refused to sign the Nuclear Non-Proliferation Treaty.
Again, in writing wide spread condemnation,
including sanctions from the United States and much of the West.
And India did not of course conduct test after that, but
it demonstrated that in Western and scientific technology.
To an extent where it could even as
what was seen as a weak third world country have its own atomic bombs.
During this phase, India also
annexed the state of Sikkim which had a protectorate like relationship but
which, where there's popular opinion in favour of that annexation.
Again, Indira Gandhi demonstrated realpolitik,
Machiavellian realpolitik with tremendous finesse.
Non-alignment had been the central pillar of India's foreign policy
through all these years, through good times and bad times.
But here again, Indira Gandhi did not have, enter into a
military alliance, but entered into a special relationship.
With the Soviet Union through the signing of the Industrial Treaty.
But domestically also
the semi-socialistic policies that have prevailed from '47 to,
to the, to the '70s were consolidated even further by the nationalisation of banks.
And to other, kind of, deeply socialist majors,
which in some ways really toppled the enterprise.
And the ability of Indian entrepreneurs to do well in what became a very
closed economy.
But this was Indira Gandhi, and Indira Gandhi's view of power,
which was completely different, you might say, from her own father's view of power.
Her father was driven by idealism and
was much more democratic than even his contemporaries in the congress party.
To a point that he even wrote
a newspaper article under a pseudonym suggesting that Nehru,
Nehru himself writing an article under the pseudonym warning the people that Nehru
has an authoritarian streak and he needs to be kept in check.
Indira Gandhi, however, did not just have an authoritarian streak.
She was an authoritarian person by temperament.
And she's ruled India through a greatly centralised political machinery.
She was India's own Iron Lady.