scholarly journals Single cell derived mRNA signals across human kidney tumors

2020 ◽  
Author(s):  
Matthew D Young ◽  
Thomas J Mitchell ◽  
Lars Custers ◽  
Thanasis Margaritis ◽  
Francisco Morales ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cancer ◽  
Human Kidney ◽  
Cell Types ◽  
Renal Tumors ◽  
Tumor Type ◽  
Kidney Tumors ◽  
Cell Renal Cell Carcinoma ◽  
Cellular Origin ◽  
Normal Tissues

AbstractThe cellular transcriptome may provide clues into the differentiation state and origin of human cancer, as tumor cells may retain patterns of gene expression similar to the cell they derive from. Here, we studied the differentiation state and cellular origin of human kidney tumors, by assessing mRNA signals in 1,300 childhood and adult renal tumors, spanning seven different tumor types. Using single cell mRNA reference maps of normal tissues generated by the Human Cell Atlas project, we measured the abundance of reference “cellular signals” in each tumor. Quantifying global differentiation states, we found that, irrespective of tumor type, childhood tumors exhibited fetal cellular signals, thus replacing the long-held presumption of “fetalness” with a precise, quantitative readout of immaturity. By contrast, in adult cancers our assessment refuted the suggestion of dedifferentiation towards a fetal state in the overwhelming majority of cases, with the exception of lethal variants of clear cell renal cell carcinoma. Examining the specific cellular phenotype of each tumor type revealed an intimate connection between the different mesenchymal populations of the developing kidney and childhood renal tumors, whereas adult tumors mostly represented specific mature tubular cell types. RNA signals of each tumor type were remarkably uniform and specific, indicating a possible therapeutic and diagnostic utility. We demonstrated this utility with a case study of a cryptic renal tumor. Whilst not classifiable by clinical pathological work-up, mRNA signals revealed the diagnosis. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.

scholarly journals Single cell derived mRNA signals across human kidney tumors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Matthew D. Young ◽  
Thomas J. Mitchell ◽  
Lars Custers ◽  
Thanasis Margaritis ◽  
Francisco Morales-Rodriguez ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cancer ◽  
Quantitative Measure ◽  
Human Kidney ◽  
Renal Tumors ◽  
Cell Of Origin ◽  
Kidney Tumors ◽  
Cellular Origin ◽  
Normal Tissues ◽  
Diagnostic Potential

AbstractTumor cells may share some patterns of gene expression with their cell of origin, providing clues into the differentiation state and origin of cancer. Here, we study the differentiation state and cellular origin of 1300 childhood and adult kidney tumors. Using single cell mRNA reference maps of normal tissues, we quantify reference “cellular signals” in each tumor. Quantifying global differentiation, we find that childhood tumors exhibit fetal cellular signals, replacing the presumption of “fetalness” with a quantitative measure of immaturity. By contrast, in adult cancers our assessment refutes the suggestion of dedifferentiation towards a fetal state in most cases. We find an intimate connection between developmental mesenchymal populations and childhood renal tumors. We demonstrate the diagnostic potential of our approach with a case study of a cryptic renal tumor. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.

scholarly journals More than ancillary records: clinical implications of renal pathology examination in tumor nephrectomy specimens

2021 ◽  
Author(s):  
Johannes Philipp Kläger ◽  
Ahmad Al-Taleb ◽  
Mladen Pavlovic ◽  
Andrea Haitel ◽  
Eva Comperat ◽  
...  
Keyword(s):  
Interstitial Fibrosis ◽  
Renal Pathology ◽  
Vascular Lesions ◽  
Renal Tumors ◽  
Renal Diseases ◽  
Tumor Type ◽  
Neoplastic Tissue ◽  
Cell Renal Cell Carcinoma ◽  
Hypertensive Nephropathy ◽  
Tumor Nephrectomy

Abstract Background Nephrectomy is the management of choice for the treatment of renal tumors. Surgical pathologists primarily focus on tumor diagnosis and investigations relating to prognosis or therapy. Pathological changes in non-neoplastic tissue may, however, be relevant for further management and should be thoroughly assessed. Methods Here, we examined the non-neoplastic renal parenchyma in 206 tumor nephrectomy specimens for the presence of glomerular, tubulo-interstitial, or vascular lesions, and correlated them with clinical parameters and outcome of renal function. Results We analyzed 188 malignant and 18 benign or pseudo-tumorous lesions. The most common tumor type was clear cell renal cell carcinoma (CCRCC, n = 106) followed by papillary or urothelial carcinomas (n = 25). Renal pathology examination revealed the presence of kidney disease in 39 cases (18.9%). Glomerulonephritis was found in 15 cases (7.3%), and the most frequent was IgA nephropathy (n = 6; 2.9%). Vasculitis was found in two cases (0.9%). In 15 cases we found tubulo-interstitial nephritis, and in 9 severe diabetic or hypertensive nephropathy. Partial nephrectomy was not linked to better eGFR at follow-up. Age, vascular nephropathy, glomerular scarring and interstitial fibrosis were the leading independent negative factors influencing eGFR at time of surgery, whereas proteinuria was associated with reduced eGFR at 1 year. Conclusion Our large study population indicates a high incidence of renal diseases potentially relevant for the postoperative management of patients with renal neoplasia. Consistent and systematic reporting of non-neoplastic renal pathology in tumor nephrectomy specimens should therefore be mandatory.

Lipidomic differentiation between human kidney tumors and surrounding normal tissues using HILIC-HPLC/ESI–MS and multivariate data analysis

2015 ◽  
Vol 1000 ◽  
pp. 14-21 ◽  
Author(s):  
Eva Cífková ◽  
Michal Holčapek ◽  
Miroslav Lísa ◽  
David Vrána ◽  
Bohuslav Melichar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Human Kidney ◽  
Kidney Tumors ◽  
Esi Ms ◽  
Normal Tissues

scholarly journals Kidney organoid reproducibility across multiple human iPSC lines and diminished off target cells after transplantation revealed by single cell transcriptomics

2019 ◽  
Author(s):  
Ayshwarya Subramanian ◽  
Eriene-Heidi Sidhom ◽  
Maheswarareddy Emani ◽  
Nareh Sahakian ◽  
Katherine Vernon ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Mouse Kidney ◽  
Target Cells ◽  
Rna Seq ◽  
Kidney Capsule ◽  
Human Ipsc ◽  
Kidney Organoids

AbstractHuman iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we used single cell RNA-Seq (scRNA-Seq) to profile 415,775 cells to show that organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes were largely reproducible across iPSC lines, time points, protocols, and replicates, cell proportions were variable between different iPSC lines. Off-target cell proportions were the most variable. Prolonged in vitro culture did not alter cell types, but organoid transplantation under the mouse kidney capsule diminished off-target cells. Our work shows how scRNA-seq can help score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

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 Profiling APOL1 Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics

2019 ◽  
Author(s):  
Esther Liu ◽  
Behram Radmanesh ◽  
Byungha H. Chung ◽  
Michael D. Donnan ◽  
Dan Yi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Genomic Locus ◽  
Glomerular Epithelial Cell ◽  
Risk Variants ◽  
Ifn Γ ◽  
Kidney Organoids

ABSTRACTBackgroundDNA variants in APOL1 associate with kidney disease, but the pathophysiological mechanisms remain incompletely understood. Model organisms lack the APOL1 gene, limiting the degree to which disease states can be recapitulated. Here we present single-cell RNA sequencing (scRNA-seq) of genome-edited human kidney organoids as a platform for profiling effects of APOL1 risk variants in diverse nephron cell types.MethodsWe performed footprint-free CRISPR-Cas9 genome editing of human induced pluripotent stem cells (iPSCs) to knock in APOL1 high-risk G1 variants at the native genomic locus. iPSCs were differentiated into kidney organoids, treated with vehicle, IFN-γ, or the combination of IFN-γ and tunicamycin, and analyzed with scRNA-seq to profile cell-specific changes in differential gene expression patterns, compared to isogenic G0 controls.ResultsBoth G0 and G1 iPSCs differentiated into kidney organoids containing nephron-like structures with glomerular epithelial cells, proximal tubules, distal tubules, and endothelial cells. Organoids expressed detectable APOL1 only after exposure to IFN-γ. scRNA-seq revealed cell type-specific differences in G1 organoid response to APOL1 induction. Additional stress of tunicamycin exposure led to increased glomerular epithelial cell dedifferentiation in G1 organoids.ConclusionsSingle-cell transcriptomic profiling of human genome-edited kidney organoids expressing APOL1 risk variants provides a novel platform for studying the pathophysiology of APOL1-mediated kidney disease.SIGNIFICANCE STATEMENTGaps persist in our mechanistic understanding of APOL1-mediated kidney disease. The authors apply genome-edited human kidney organoids, combined with single-cell transcriptomics, to profile APOL1 risk variants at the native genomic locus in different cell types. This approach captures interferon-mediated induction of APOL1 gene expression and reveals cellular dedifferentiation after a secondary insult of endoplasmic reticulum stress. This system provides a human cellular platform to interrogate complex mechanisms and human-specific regulators underlying APOL1-mediated kidney disease.

scholarly journals Scaling single cell transcriptomics through split pool barcoding

2017 ◽  
Author(s):  
Alexander B. Rosenberg ◽  
Charles M. Roco ◽  
Richard A. Muscat ◽  
Anna Kuchina ◽  
Sumit Mukherjee ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Main Tool ◽  
Cell Types ◽  
Single Experiment ◽  
Laboratory Equipment ◽  
Cellular Origin ◽  
Stage Of Development ◽  
Collective Effort ◽  
Different Types

Constructing an atlas of cell types in complex organisms will require a collective effort to characterize billions of individual cells. Single cell RNA sequencing (scRNA-seq) has emerged as the main tool for characterizing cellular diversity, but current methods use custom microfluidics or microwells to compartmentalize single cells, limiting scalability and widespread adoption. Here we present Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq), a scRNA-seq method that labels the cellular origin of RNA through combinatorial indexing. SPLiT-seq is compatible with fixed cells, scales exponentially, uses only basic laboratory equipment, and costs one cent per cell. We used this approach to analyze 109,069 single cell transcriptomes from an entire postnatal day 5 mouse brain, providing the first global snapshot at this stage of development. We identified 13 main populations comprising different types of neurons, glia, immune cells, endothelia, as well as types in the blood-brain-barrier. Moreover, we resolve substructure within these clusters corresponding to cells at different stages of development. As sequencing capacity increases, SPLiT-seq will enable profiling of billions of cells in a single experiment.

scholarly journals Comparative analysis of kidney organoid and adult human kidney single cell and single nucleus transcriptomes

2017 ◽  
Author(s):  
Haojia Wu ◽  
Kohei Uchimura ◽  
Erinn Donnelly ◽  
Yuhei Kirita ◽  
Samantha A. Morris ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Kidney ◽  
Cell Types ◽  
Great Promise ◽  
Full Potential ◽  
Diverse Range ◽  
Adult Human ◽  
Cell Diversity ◽  
Single Nucleus ◽  
Kidney Organoids

AbstractKidney organoids differentiated from human pluripotent stem cells hold great promise for understanding organogenesis, modeling disease and ultimately as a source of replacement tissue. Realizing the full potential of this technology will require better differentiation strategies based upon knowledge of the cellular diversity and differentiation state of all cells within these organoids. Here we analyze single cell gene expression in 45,227 cells isolated from 23 organoids differentiated using two different protocols. Both generate kidney organoids that contain a diverse range of kidney cells at differing ratios as well as non-renal cell types. We quantified the differentiation state of major organoid kidney cell types by comparing them against a 4,259 single nucleus RNA-seq dataset generated from adult human kidney, revealing immaturity of all kidney organoid cell types. We reconstructed lineage relationships during organoid differentiation through pseudotemporal ordering, and identified transcription factor networks associated with fate decisions. These results define impressive kidney organoid cell diversity, identify incomplete differentiation as a major roadblock for current directed differentiation protocols and provide a human adult kidney snRNA-seq dataset against which to benchmark future progress.

scholarly journals Pan-Cancer and Single-Cell modelling of genomic alterations through gene expression

2018 ◽  
Author(s):  
Daniele Mercatelli ◽  
Forest Ray ◽  
Federico M. Giorgi
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Prediction Models ◽  
Human Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Copy Number Variations ◽  
Tumor Type ◽  
Genomic Alterations ◽  
Pan Cancer

AbstractCancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single cell sequencing.Author SummaryIn this article we show that transcript abundance can be used to predict the presence or absence of the majority of genomic alterations present in human cancer. We also show how these predictions can be improved by aggregating genes into small networks to counteract the effects of transcript measurement noise.

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.

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