Analysis Of The Proximal Tubular And Glomerulus Of The Kidney
Mar 15, 2022
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Click here for information about Part I (Introduction, materials, and methods )of this article.
Near-Single-Cell Proteomics Profiling of the Proximal Tubular and Glomerulus of the Normal Human Kidney--Part II
Tara K. Sigdel1, Paul D. Piehowski2, Sudeshna Roy1, Juliane Liberto1, Joshua R. Hansen2, Adam C. Swensen2, Rui Zhao3, Ying Zhu3, Priyanka Rashmi1, Andrew Schroeder1, Izabella Damm1, Swastika Sur1, Jinghui Luo4, Yingbao Yang4, Wei-Jun Qian2* and Minnie M. Sarwal1* for the Kidney Precision Medicine Project (KPMP) Consortium
Results
OCT Frozen Kidney Collection Is the Preferred Method for Kidney Tissue Proteomics
For the same amount of input tissue, OCT frozen kidney yielded more protein than FFPE sections from the same kidney (OCT frozen: 1871 ± 92 vs. FFPE: 296 ± 10 ng protein; p < 0.0017) suggesting possible protein degradation in FFPE, despite the tissue being embedded in paraffin blocks for <2 months. Bulk proteomics data from the two human kidneys (#1, #2) demonstrated a significant overlap of identified proteins with 84% overlap between the 2 OCT kidneys (reproducibility limit = 13.52, 97.96% values within the reproducibility limit) and 83% overlap of the same kidney proteins between the 2 FFPE kidneys (reproducibility limit = 24.43, 98.5% values within the reproducibility limit) (Supplemental Table 1 and Figure 2B). Within each kidney, irrespective of the tissue storage method, ~70% of kidney structural (mostly extracellular matrix) proteins were conserved, suggesting that some proteins were more preferentially detected by each method. To evaluate what protein enrichment is seen between different collection methods, we found that OCT tissue proteomics yielded more proteins with high spectral counts ≥ 5 (odds ratio = 1.460601; 95% confidence interval = 1.238969, 1.722593; p = 4.472e-06), which may reflect better preservation of kidney tissue in OCT (Figure 2C). Proteomic analysis of OCT tissue also sampled more unique proteins as compared to FFPE tissue (odds ratio = 3.207089; 95% confidence interval = 2.371221, 4.370600; p = 4.985e-16) (Figure 2D). Additionally, many of these unique proteins were found to be of low-abundance and are more likely to be missed, even with very short preservation times of kidney tissue in FFPE. Irrespective of the tissue preservation method, we noted that there was strong overlap in common biological pathways. The pathways included small molecule metabolic processes (p = 2.59E-121 for OCT and p = 2.15E-109 for FFPE), carboxylic acid metabolic process (p = 3.62E-80 for OCT and p = 1.93 E-76 for FFPE), organic acid metabolic processes (p = 1.26E-76 for OCT and p = 3.62E-80 for FFPE) and drug metabolism processes (p = 7.90E-83 for OCT and p = 4.01E-75 for FFPE). This data highlights how the proteomics of kidney tissue is highly reproducible, irrespective of tissue preservation method for the two methods tested.
Kidney Tissue Proteomics Is Highly Reproducible on Transported OCT Frozen Kidney Tissue
We analyzed data generated at the two sites (UCSF and Ohio State University) that followed the same protocol for performing bulk proteomics, as described in the methods section. Remarkably, ~70% proteins were commonly identified in the same kidney processed at two different sites with spectral count ≥ 5 (r = 0.95, P < 0.0001) (Raw data provided in Supplemental Table 2). More importantly, the two kidneys shipped from a central site produced highly correlated data (r = 0.89, P = 0.0001) (Supplemental Table 2), supporting that the tissue processing analytical protocols in place were quite robust.
Optimization of Capturing of Kidney Cells Using Laser Capture Microdissection (LCM)
Varying thicknesses of OCT sections mounted on PET slides from the same kidney (n = 2) were tested at 5, 10, and 20 μm thickness, in triplicate, for LCM repeatability. The 10 μm section was found to be the most optimal on serial LCM testing, using the Zeiss PALM MicroBeam Laser Microdissection system (Cut Energy, 64; Cut Focus, 75). Cut cells were captured on adhesive caps. In addition to LCM process repeatability, we also found maximum capture efficiency of catapulted cells at 10 μm OCT section thickness.
Proteomics of Sub-compartment Cells in Normal Human Kidney Identifies Enriched and Unique Glomerular and Proximal Tubular Proteins
Across the 9 normal human kidneys processed by nscProteomics, with input of an average of 10–40 cells input each/Glom and PT fraction, we identified an average of ~2,560 proteins/sample. Interestingly, ~20% of the total proteins assessed by nscProteomics, from either sub-compartment overlapped across compartments/cells and bulk tissue, representing likely housekeeping or more structural kidney proteins that are not compartment specific. In a direct comparison of the two sub-compartments, 208 proteins were enriched (>2 fold) in Glom, and 67 were completely unique to Glom (absent in PT); and conversely, 247 proteins were enriched (>2 fold) in PT, and 25 were unique to PT (absent in Glom) (Supplemental Table 3). Despite the relatively small sample set, we also performed a cross-batch validation, treating each batch of runs as an independent set. Even with a small unique set of kidneys (2 in batch 1, 4 in batch 2), 210 Glom enriched proteins were highly correlated between the two independent sets, despite this data coming from different cohorts of normal kidneys (r = 0.83; Figure 3A) and analyzed at different times. 246 PT- enriched proteins could also be correlated between the two different cohorts of normal kidneys (r = 0.77; Figure 3B). The results highlight that nscProteomics is capable of identifying sub-compartment specific proteins conserved to different functional units of the kidney reproducibly.
Figure 3. (A) Correlation of a set of 210 proteins significantly enriched in glomerulus between two different batches of independent kidney samples. Batch 1 (run 1) was from 2 kidneys and Batch 2 (run 2) was from 4 kidneys. The color scale was selected for visual depiction of abundance. A strong positive correlation of 0.83 between the mean abundances of the same set proteins from two different batches The red dashed lines give a measure of the spread by depicting the 95% prediction interval for mean abundance values of these proteins in future repeat experiments. (B) Correlation of 246 proteins significantly enriched in proximal tubules vs. glomerulus between the two batches. A positive correlation of 0.77 between the mean abundances of these same proteins between the two batches.
To ascertain the specificity of the nscProtoemics approach for kidney sub-compartment analysis, we compared nscProteomics kidney sub-compartment Glom and PT data to bulk proteomics of the same set of kidney tissue. We found that ~25% (n = 656) of the proteins identified in both sub-compartments were not even detected by bulk tissue proteomics, as they were likely of very low abundance in the bulk sample; 118 Glom and 74 PT enriched proteins could not be detected in bulk kidney, highlighting the limitation of only doing bulk tissue proteomics. Only 9% of Glom-specific proteins, but a larger number of 48% PT-specific proteins, were identified by kidney bulk proteomics, reflecting the much higher abundance of PT cells over Glom cells in the normal kidney. In fact, some known markers for glomerular cells such as podocin (NPHS2), eva-1 homolog B (EVA1B), MARCKS like 1 (MARCKSL1), fibroblast growth factor 1 (FGF1), and claudin 5 (CLDN5) could not be identified by kidney bulk proteomics at standard run depths of ~2,000.
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Validation of Glom and PT Markers as Assessed by nscProteomics
The majority of sub-compartment proteins were reported as detected in kidney by IHC in previously published data (Human Protein Atlas) (25). The proteins that were either not detected by IHC (labeled with *) or data not available (labeled with **) are indicated in Figures 4A, B. We took the top 26 glom enriched proteins and 26 PT specific proteins and interrogated single-cell transcriptomic data (scRNA Seq data) for “cross-omics” integration and validation (Figure 4B). The result of integrative analysis of top Glom and PT markers by proteomics and transcriptomics demonstrated a strong correlation of some of the known Glom and PT markers proteins. Podocyte markers such as PODXL and CLIC5 and PT markers such as PDZK1 and ANPEP were highly enriched in both datasets. Apart from validating known markers, nscProteomics has also identified proteins that are identified in low abundance in either the transcriptomic datasets or IHC datasets. For example, PITPNB is a highly enriched glomerular marker by nscProteomics data; however, detection of this gene was low in transcriptomic data and was not detectable by IHC (25). A similar case was observed with SLC5A1, whose signal was enhanced in nscProteomics data but not in the transcriptomic or IHC datasets (Figure 4C).
Figure 4. (A) Heatmap showing the differential distribution of protein abundances in 5 normal kidneys (batch 2), of 26 proteins (out of 372) most significantly enriched in G (vs T) and 26 proteins (out of 411) most significantly enriched in T (vs G), with corresponding values in 2 Bulk samples shown for comparison. Protein abundances are measured as log 2 transformed relative intensities. Unsupervised clustering of significant proteins highlights sets of proteins related by Euclidean distance similarity measure within the larger differentiated enriched sets. It is noted that Bulk samples cluster together with PT samples; this is expected since proximal tubules are abundantly dispersed within the kidney. (B) Correlation of gene expression plot of scRNA-seq data from human kidney. Genes encoding Glom-specific and PT-specific proteins presented on the heatmap were selected. Increasing dot size corresponds to a larger percentage of cells in the cell population expressing the gene, while a darker color corresponds to a higher expression of the gene. PT, proximal tubular; Podo, podocytes; Mes, mesangial cells; LOH, Loop of Henle, Immune cells; Endo, endothelial cells; DT, distal tubules; CD, collecting duct. *Sign next to the gene symbol indicates that the protein was not detected on glomeruli as reported by Human Protein Atlas (https://www.proteinatlas.org), **Data not available on Human Protein Atlas (https://www.proteinatlas.org). (C) Representative proteins that demonstrate how nscProteomics not only identifies positive markers such as PODXL and PDZK1 but also identifies protein markers such as that are otherwise missed by transcriptomics or immunohistochemistry.
Biological Relevance of Glom and PT Enriched Proteins
We used the data generated through the developed protocol for nscProteomics to look at proteins that were significantly enriched in either the glomerular cells or the proximal tubular cells. The 208 Glom enriched proteins were enriched in vesicle-mediated transport (FDR 3.6E-15) and regulation of cellular component organization (FDR 9.75E-14) as their top biological processes and cytoskeletal protein binding (FDR 2.63E-19) and actin-binding (FDR 2.40E-17) among the top-ranked molecular function. Additionally, actin cytoskeleton (FDR 3.73 E-25) and focal adhesion (FDR 4.07E-20) were the top enriched cellular components. The 247 PT enriched proteins were enriched in small molecule metabolic processes (FDR 7.55E-50) and drug metabolic processes (FDR 5.00E-41) as top biological processes. The proteins were enriched for oxidoreductase activity (FDR 9.07E-34) and catalytic activity (FDR 1.05E-27) among the top-ranked molecular functions.
nscProteomics Identifies Unique Set of Glom and PT Proteins
Apart from enriched proteins in the Glom or the PT sections, 92 proteins were either unique to Glom (n = 67) or unique to PT (n = 25), as they were only identified in either the glomerular cells or the proximal tubular cells (Table 1). The proteins identified only in the glomerular cells were enriched in molecular functions such as actin-binding (FDR 4.92E-06), cytoskeletal protein binding (FDR 3.47E-05), integrin-binding (3.9E-03), etc. We used this list to interrogate the publicly available data that had previously reported their presence in the glomerulus. Among the 67 proteins identified only in Glom sections, 41 have been reported to be present in the kidney (25). Of these 41 proteins, a subset of 18 (43.9%) are in agreement with nscProteomics and 23 (56.1%) are reported to not be increased in Glom compared to tubules. Among the 25 proteins identified only in PT sections, 20 have been reported to be present in the kidney (25). These proteins are enriched in molecular functions such as organic anion transmembrane transporter activity (FDR 6.34E-05), carboxylic acid transmembrane transporter activity (FDR 1.4E-04), solute: sodium symporter activity (FDR 1.8 E-04), etc. Of the 25 PT specific proteins, a subset of 19 proteins (95.0%) are in agreement with nscProteomics and only 1 (5.0%) is reported to not be increased in tubules compared to Glom.
Table 1. Proteins enriched in glomerular and proximal tubular cells identified by nscProteomics.
Discussion
With the rapid advancement of single-cell genomics and transcriptomic assays, there is still an unmet need to do parallel single-cell studies on precious human samples in the field of proteomics (27). Protein profiling from single-cell faces hindrance from the virtue that we do not have protein amplification capability the way we can amplify nucleotides. In this first application of near-single-cell proteomics on the human kidney, we demonstrate that robust label-free proteomic quantification is feasible for as few as 40 kidney cells, specific to different LCM sub-compartments. The developed protocols for near-single-cell kidney proteomics will be of benefit to other researchers working in the field of kidney diseases.
A recent study by Hoehne et al. demonstrated the use of Single-Pot Solid-Phase-enhanced Sample preparation (SP3) technology for proteomic analysis of renal segments (4). Using single microdissected glomeruli with ~200 cells the authors illustrated the heterogeneity of the proteomes from single glomeruli from mouse models and human patients. Another recent study by Song et al. (28) utilized FFPE tissue from renal transplant recipients to create a molecular diagnostic platform using bulk proteomics. In this manuscript, we have optimized and analyzed in great detail the process of tissue collection to MS to establish a standard protocol for proteomic analysis of small tissue samples. First, we compared the FFPE vs. OCT method of tissue storage to show that while there is a high correlation between the two datasets, OCT tissue yield higher protein content and more importantly the ability to identify low abundance proteins. Second, MS protocols for bulk proteomics have been optimized such that highly reproducible (r = 0.95) results can be obtained at disparate sites on serially cut, adjacent kidney tissue sections. Third, where we describe an approach to couple laser capture microdissection to reduce the amount of input sample to 10–40 cells with a nano POTS technology consisting of a robotic platform to accurately handle pico-liter volumes. Our approach allows us to control the input number of cells thus enhancing the reproducibility of different glom and tubule samples. Due to the simplicity of LCM and the accuracy of nano POTS technology, we believe that our study will allow the adoption of proteomics on precious clinical specimens in a reproducible manner.
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Limitations of this study include the small sample size and the limitation of tissue which did not allow us to do tissue spatial validation by immunohistochemistry. To partially alleviate this limitation, we provide integrated data analysis with scRNA Seq data from the same tissues to confirm that proteins identified in kidney sub-compartments are also identified in the same originating tissue cell by transcription. An additional limitation is that we identify a restricted number of proteins by nscProteomics, likely due to small input material, which runs the risk of some proteins being not identified by MS as they could like below the limit of instrument detection. Improvement in instrument sensitivity and protein extraction from small sample input may help improve this current limitation.
We acknowledge that there are many variables that can contribute to the quality of the data. The storage time, FFPE fixatives (i.e., PFA vs. Glutaraldehyde), length of time in fixative, time in the freezer/on dry ice/in liquid nitrogen, approaches for protein solubilization are a few factors that could contribute to proteomics results. It is important to realize that even the biology of two kidneys is not always the same and that is true even within the same kidneys as glomeruli of the same kidney biopsies can be at a functional different state. Histologically, kidney compartments are extremely heterogeneous even within the same disease condition. By capturing the same regions of the kidney from different input samples, nscProteomics allows for the interrogation of heterogeneity within specific subcellular compartments, which is a strength of this technology.
We observed that some sub-compartment proteins identified by nscProtoemics are potentially novel glomerular and proximal tubular proteins. Innovations in integrative data analysis with single-cell RNA Seq of the same kidney samples, though preliminary, would provide confirmation that these proteins are indeed localized to their sampled/predicted sub-regions.
This study provides insights into the undiscovered complexity of molecular patterns in kidney sub-compartments in health and supports the application of this technology to kidney disease samples to understand the perturbation of proteins in different kidney sub-compartments that may be preferentially affected in different kidney disorders. Differential regulation of these proteins in different renal diseases could uncover clinical and prognostic heterogeneity in complex glomerular and tubular disease phenotypes and uncover new triggers of kidney damage and recovery. In addition, given the paucity of any renal protective drugs, uncovering these new pathways in specific regions of the kidney can provide important new targets for rational drug design for addressing specific injures to different sub-compartments in a variety of renal disorders.
Thus, in conclusion, we present a nscproteomics workflow for handling 10–40 kidney cells; improvement in this technology carries the promise to reduce input material in the future to the single-cell level. Future developments such as optimized processing volumes and further refinements to the LC-MS platform are underway.
Data Availability Statement
The datasets generated for this study can be found in the PRIDE PXD015058 and 10.6019/PXD015058.
Author Contributions
TS and MS conceptualized the study. TS, JLi, JH, ACS, RZ, YZ, PR, ID, SS, JLu, and YY helped in sample processing and experimental data generation, TS, PP, SR, AS, W-JQ, and MS contributed in data analysis and manuscript preparation. All authors contributed to the article and approved the submitted version.
Funding
Funding for this work was from the Kidney Precision Medicine Project, NIDDK NIHUG3 -DK114937 (MS) and R01 DK109720-02 (MS), DP3 DK110844 (W-JQ), R01 DK122160 (W-JQ), and support from the Division of Multi-Organ Transplantation (MS).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We acknowledge the help of Zoltan Laszik and Gyula Szabo from UCSF for help with tissue processing. Samir Parekh and Brad Rovin from Ohio State University (OSU), for mass spec collaborations on bulk proteomics and the QC for the bulk proteomic tissue shared between UCSF and OSU, and Jeff Hodgin and Matthias Kretzler from the University of Michigan for kindly providing access to the reference kidney samples. Some of the experimental work described herein was performed in the Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, a national scientific user facility sponsored by the Department of Energy under Contract DEAC05-76RL0 1830.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2020.00499/full#supplementary-material
Abbreviations
nscProteomics, near-single-cell proteomics; KPMP, Kidney Precision Medicine Project; PT, proximal tubular; Glom, glomerular; LCM, laser capture microdissection; μPOTS, Microliter Processing in One pot for Trace Samples; OCT, Optimal cutting temperature; FFPE, formalin-fixed paraffin embedded; UM, University of Michigan; OSU, Ohio State University; DDM, n-dodecyl β-D-maltoside; scRNA Seq, single-cell RNA Sequencing; IHC, immunohistochemistry.
From: 'Near-Single-Cell Proteomics Profiling of the Proximal Tubular and Glomerulus of the Normal Human Kidney' by Tara K. Sigdel1, Paul D. Piehowski2, Sudeshna Roy1, Juliane Liberto1, Joshua R. Hansen2, Adam C. Swensen2, Rui Zhao3, Ying Zhu3, Priyanka Rashmi1, Andrew Schroeder1, Izabella Damm1, Swastika Sur1, Jinghui Luo4, Yingbao Yang4, Wei-Jun Qian2* and Minnie M. Sarwal1* for the Kidney Precision Medicine Project (KPMP) Consortium
---ORIGINAL RESEARCH article Front. Med., 17 September 2020 | https://doi.org/10.3389/fmed.2020.00499
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