scholarly journals Novel Human Kidney Cell Subsets Identified by Mux-Seq

2020 ◽  
Author(s):  
Andrew W. Schroeder ◽  
Swastika Sur ◽  
Priyanka Rashmi ◽  
Izabella Damm ◽  
Arya Zarinsefat ◽  
...  
Keyword(s):  
Single Cell ◽  
Collecting Duct ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cost Effective ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Proximal Tubular ◽  
Highly Correlated ◽  
First Time

AbstractBackgroundThe kidney is a highly complex organ that performs multiple functions necessary to maintain systemic homeostasis, with complex interplay from different kidney sub-structures and the coordinated response of diverse cell types, few known and likely many others, as yet undiscovered. Traditional global sequencing techniques are limited in their ability to identify unique and functionally diverse cell types in complex tissues.MethodsHerein we characterize over 45,000 cells from 10 normal human kidneys using unbiased single-cell RNA sequencing. We also apply, for the first time, an approach of multiplexing kidney samples (Mux-Seq), pooled from different individuals, to save input sample amount and cost. We applied the computational tool Demuxlet to assess differential expression across multiple individuals by pooling human kidney cells for scRNA sequencing, utilizing individual genetic variability to determine the identity of each cell.ResultsMultiplexed droplet single-cell RNA sequencing results were highly correlated with the singleplexed sample run data. One hundred distinct cell cluster populations in total were identified across the major cell types of the kidney, with varied functional states. Proximal tubular and collecting duct cells were the most heterogeneous, displaying multiple clusters with unique ontologies. Novel proximal tubular cell subsets were identified with regenerative potential. Trajectory analysis demonstrated evolution of cell states between intercalated and principal cells in the collecting duct.ConclusionsHealthy kidney tissue has been successfully analyzed to detect all known renal cell types, inclusive of resident and infiltrating immune cells in the kidney. Mux-Seq is a unique method that allows for rapid and cost-effective single cell, in depth, transcriptional analysis of human kidney tissue.Significance StatementUse of renal biopsies for single cell transcriptomics is limited by small tissue availability and batch effects. In this study, we have successfully employed the use of Mux-Seq for the first time in kidney. Mux-Seq allows the use of single cell technology at a much more cost-effective manner by pooling samples from multiple individuals for a single sequencing run. This is even more relevant in the case of patient biopsies where the input of tissue is significantly limited. We show that the data from overlapping tissue samples are highly correlated between Mux-Seq and traditional Singleplexed RNA seq. Furthermore, the results from Mux-Seq of 4 pooled samples are highly correlated with singleplexed data from 10 singleplex samples despite the inherent variability among individuals.

scholarly journals Kidney pathological changes alter cell clustering features in single-cell RNA sequencing

2021 ◽  
Author(s):  
Lijun Ma ◽  
Mariana Murea ◽  
Young A Choi ◽  
Ashok K. Hemal ◽  
Alexei V. Mikhailov ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Specific Gene ◽  
Glomerular Injury ◽  
Cell Clustering ◽  
Single Cell Rna Sequencing

The kidney is composed of multiple cell types, each with specific physiological functions. Single-cell RNA sequencing (scRNA-Seq) is useful for classifying cell-specific gene expression profiles in kidney tissue. Because viable cells are critical in scRNA-Seq analyses, we report an optimized cell dissociation process and the necessity for histological screening of human kidney sections prior to performing scRNA-Seq. We show that glomerular injury can result in loss of select cell types during the cell clustering process. Subsequent fluorescence microscopy confirmed reductions in cell-specific markers among the injured cells seen on kidney sections and these changes need to be considered when interpreting results of scRNA-Seq.

scholarly journals Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney

2021 ◽  
Vol 32 (3) ◽  
pp. 614-627
Author(s):  
Amin Abedini ◽  
Yuan O. Zhu ◽  
Shatakshee Chatterjee ◽  
Gabor Halasz ◽  
Kishor Devalaraja-Narashimha ◽  
...  
Keyword(s):  
Single Cell ◽  
Collecting Duct ◽  
Microscopic Analysis ◽  
Human Kidney ◽  
Cell Types ◽  
Urine Sediment ◽  
Type Composition ◽  
Single Cell Profiling ◽  
Bladder Cells ◽  
Almost All

BackgroundMicroscopic analysis of urine sediment is probably the most commonly used diagnostic procedure in nephrology. The urinary cells, however, have not yet undergone careful unbiased characterization.MethodsSingle-cell transcriptomic analysis was performed on 17 urine samples obtained from five subjects at two different occasions, using both spot and 24-hour urine collection. A pooled urine sample from multiple healthy individuals served as a reference control. In total 23,082 cells were analyzed. Urinary cells were compared with human kidney and human bladder datasets to understand similarities and differences among the observed cell types.ResultsAlmost all kidney cell types can be identified in urine, such as podocyte, proximal tubule, loop of Henle, and collecting duct, in addition to macrophages, lymphocytes, and bladder cells. The urinary cell–type composition was subject specific and reasonably stable using different collection methods and over time. Urinary cells clustered with kidney and bladder cells, such as urinary podocytes with kidney podocytes, and principal cells of the kidney and urine, indicating their similarities in gene expression.ConclusionsA reference dataset for cells in human urine was generated. Single-cell transcriptomics enables detection and quantification of almost all types of cells in the kidney and urinary tract.

scholarly journals Single-Cell Transcriptomics of a Human Kidney Allograft Biopsy Specimen Defines a Diverse Inflammatory Response

2018 ◽  
Vol 29 (8) ◽  
pp. 2069-2080 ◽  
Author(s):  
Haojia Wu ◽  
Andrew F. Malone ◽  
Erinn L. Donnelly ◽  
Yuhei Kirita ◽  
Kohei Uchimura ◽  
...  
Keyword(s):  
Endothelial Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Biopsy Specimen ◽  
Kidney Biopsy ◽  
Human Kidney ◽  
Cell Types ◽  
Biopsy Specimens ◽  
Single Cell Rna Sequencing ◽  
Transplant Biopsy

Background Single-cell genomics techniques are revolutionizing our ability to characterize complex tissues. By contrast, the techniques used to analyze renal biopsy specimens have changed little over several decades. We tested the hypothesis that single-cell RNA-sequencing can comprehensively describe cell types and states in a human kidney biopsy specimen.Methods We generated 8746 single-cell transcriptomes from a healthy adult kidney and a single kidney transplant biopsy core by single-cell RNA-sequencing. Unsupervised clustering analysis of the biopsy specimen was performed to identify 16 distinct cell types, including all of the major immune cell types and most native kidney cell types, in this biopsy specimen, for which the histologic read was mixed rejection.Results Monocytes formed two subclusters representing a nonclassical CD16+ group and a classic CD16− group expressing dendritic cell maturation markers. The presence of both monocyte cell subtypes was validated by staining of independent transplant biopsy specimens. Comparison of healthy kidney epithelial transcriptomes with biopsy specimen counterparts identified novel segment-specific proinflammatory responses in rejection. Endothelial cells formed three distinct subclusters: resting cells and two activated endothelial cell groups. One activated endothelial cell group expressed Fc receptor pathway activation and Ig internalization genes, consistent with the pathologic diagnosis of antibody-mediated rejection. We mapped previously defined genes that associate with rejection outcomes to single cell types and generated a searchable online gene expression database.Conclusions We present the first step toward incorporation of single-cell transcriptomics into kidney biopsy specimen interpretation, describe a heterogeneous immune response in mixed rejection, and provide a searchable resource for the scientific community.

scholarly journals Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity

2021 ◽  
Author(s):  
Caitriona M McEvoy ◽  
Julia M Murphy ◽  
Lin Zhang ◽  
Sergi Clotet-Freixas ◽  
Jessica A Mathews ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Significant Heterogeneity ◽  
Proximal Tubular Cells ◽  
Healthy Human ◽  
Fresh Tissue ◽  
Tubular Cells ◽  
Distinct Cell ◽  
Proximal Tubular

Maintaining organ homeostasis requires complex functional synergy between distinct cell types, a snapshot of which is glimpsed through the simultaneously broad and granular analysis provided by single-cell atlases. Knowledge of the transcriptional programs underpinning the complex and specialized functions of human kidney cell populations at homeostasis is limited by difficulty accessing healthy, fresh tissue. Here, we present a single-cell perspective of healthy human kidney from 19 living donors, with equal contribution from males and females, profiling the transcriptome of 27677 high-quality cells to map healthy kidney at high resolution. Our sex-balanced dataset revealed sex-based differences in gene expression within proximal tubular cells, specifically, increased anti-oxidant metallothionein genes in females and the predominance of aerobic metabolism-related genes in males. Functional differences in metabolism were confirmed between male and female proximal tubular cells, with male cells exhibiting higher oxidative phosphorylation and higher levels of energy precursor metabolites. Within the immune niche, we identified kidney-specific lymphocyte populations with unique transcriptional profiles indicative of kidney-adapted functions and validated findings by flow cytometry. We observed significant heterogeneity in resident myeloid populations and identified an MRC1+ LYVE1+ FOLR2+ C1QC+ population as the predominant myeloid population in healthy kidney. This study provides a detailed cellular map of healthy human kidney, revealing novel insights into the complexity of renal parenchymal cells and kidney-resident immune populations.

scholarly journals Integrated cytometry with machine learning applied to high-content imaging of human kidney tissue for in-situ cell classification and neighborhood analysis

2021 ◽  
Author(s):  
Seth Winfree ◽  
Andrew T McNutt ◽  
Suraj Khochare ◽  
Tyler J Borgard ◽  
Daria Barwinska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Large Scale ◽  
Spatial Organization ◽  
Spatial Association ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Imaging Data ◽  
High Content Imaging

The human kidney is a complex organ with various cell types that are intricately organized to perform key physiological functions and maintain homeostasis. New imaging modalities such as mesoscale and highly multiplexed fluorescence microscopy are increasingly applied to human kidney tissue to create single cell resolution datasets that are both spatially large and multi-dimensional. These single cell resolution high-content imaging datasets have a great potential to uncover the complex spatial organization and cellular make-up of the human kidney. Tissue cytometry is a novel approach used for quantitative analysis of imaging data, but the scale and complexity of such datasets pose unique challenges for processing and analysis. We have developed the Volumetric Tissue Exploration and Analysis (VTEA) software, a unique tool that integrates image processing, segmentation and interactive cytometry analysis into a single framework on desktop computers. Supported by an extensible and open-source framework, VTEA's integrated pipeline now includes enhanced analytical tools, such as machine learning, data visualization, and neighborhood analyses for hyperdimensional large-scale imaging datasets. These novel capabilities enable the analysis of mesoscale two and three-dimensional multiplexed human kidney imaging datasets (such as CODEX and 3D confocal multiplexed fluorescence imaging). We demonstrate the utility of this approach in identifying cell subtypes in the kidney based on labels, spatial association and their microenvironment or neighborhood membership. VTEA provides integrated and intuitive approach to decipher the cellular and spatial complexity of the human kidney and complement other transcriptomics and epigenetic efforts to define the landscape of kidney cell types.

scholarly journals Monocytes and Macrophages in Kidney Transplantation and Insights from Single Cell RNA-Seq Studies

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0003842021
Author(s):  
Andrew F Malone
Keyword(s):  
Kidney Transplantation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Kidney Transplant ◽  
New Technologies ◽  
Immune Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
And Function

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows for the identification of minority cell types in complex tissues, such as immune cells in the kidney. Previously, gene expression from infrequent cell types was missed using bulk RNA-sequencing methods due to an averaging effect. Additionally, single-cell RNA sequencing facilitates assignment of cell origin in a sample, a shortcoming of previous bulk sequencing technologies. Thus, scRNA-seq is ideal to study the immune cell landscape and the alloimmune response in the human kidney transplant. However, there are few studies published to date. Macrophages are known to play an important role in health and disease in the kidney. Furthermore, it is known that macrophages play key roles in rejection of the kidney transplant. The definition, ontogeny, and function of these cells is complex and nomenclature has evolved as new technologies have become available. In this review, an overview of monocyte and macrophage nomenclature, ontogeny, and function with a specific focus on kidney transplantation is provided with novel scRNA-seq findings included. Single-cell RNA sequencing offers an unbiased transcriptional approach to defining macrophages and provides insights into macrophage ontogeny and function not possible with contemporary methods.

scholarly journals Defining the variety of cell types in developing and adult human kidneys by single-cell RNA sequencing

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
A. Schumacher ◽  
M. B. Rookmaaker ◽  
J. A. Joles ◽  
R. Kramann ◽  
T. Q. Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Early Stage ◽  
Kidney Tissue ◽  
Cell Types ◽  
Intermediate Cell ◽  
Adult Human ◽  
Single Cell Rna Sequencing

AbstractThe kidney is among the most complex organs in terms of the variety of cell types. The cellular complexity of human kidneys is not fully unraveled and this challenge is further complicated by the existence of multiple progenitor pools and differentiation pathways. Researchers disagree on the variety of renal cell types due to a lack of research providing a comprehensive picture and the challenge to translate findings between species. To find an answer to the number of human renal cell types, we discuss research that used single-cell RNA sequencing on developing and adult human kidney tissue and compares these findings to the literature of the pre-single-cell RNA sequencing era. We find that these publications show major steps towards the discovery of novel cell types and intermediate cell stages as well as complex molecular signatures and lineage pathways throughout development. The variety of cell types remains variable in the single-cell literature, which is due to the limitations of the technique. Nevertheless, our analysis approaches an accumulated number of 41 identified cell populations of renal lineage and 32 of non-renal lineage in the adult kidney, and there is certainly much more to discover. There is still a need for a consensus on a variety of definitions and standards in single-cell RNA sequencing research, such as the definition of what is a cell type. Nevertheless, this early-stage research already proves to be of significant impact for both clinical and regenerative medicine, and shows potential to enhance the generation of sophisticated in vitro kidney tissue.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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