scholarly journals Studying Kidney Diseases at the Single-Cell Level

2021 ◽  
pp. 1-8
Author(s):  
Mengmeng Jiang ◽  
Haide Chen ◽  
Guoji Guo
Keyword(s):  
Molecular Dynamics ◽  
Kidney Disease ◽  
Single Cell ◽  
Kidney Cell ◽  
Single Cell Analysis ◽  
Kidney Diseases ◽  
Treatment Strategies ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
The Impact

<b><i>Background:</i></b> The kidney is a highly complex organ that performs diverse functions that are essential for health. Kidney disease occurs when the kidneys are damaged and fail to function properly. Single-cell analysis is a powerful technology that provides unprecedented insights into normal and abnormal kidney cell types and will transform our understanding of the mechanism underlying common kidney diseases. <b><i>Summary:</i></b> Our understanding of kidney disease pathogenesis is limited by the incomplete molecular characterization of cell types responsible for kidney functions. Application of single-cell technologies for the study of the kidney has revealed cellular heterogeneity, gene expression signatures, and molecular dynamics during the onset and development of kidney diseases. Single-cell analyses of kidney organoids and allograft tissues offer new insights into kidney organogenesis, disease mechanisms, and therapeutic outcomes. Collectively, a better understanding of kidney cell heterogeneity and the molecular dynamics of kidney diseases will improve diagnostic accuracy and facilitate the identification of novel treatment strategies in nephrology. <b><i>Key Message:</i></b> In this review article, we summarize recent single-cell studies on kidney diseases and discuss the impact of single-cell technology on both basic and clinical nephrology research.

scholarly journals Single-cell analysis reveals cellular heterogeneity and molecular determinants of hypothalamic leptin-receptor cells

2020 ◽  
Author(s):  
N. Kakava-Georgiadou ◽  
J.F. Severens ◽  
A.M. Jørgensen ◽  
K.M. Garner ◽  
M.C.M Luijendijk ◽  
...  
Keyword(s):  
Single Cell ◽  
Leptin Receptor ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Molecular Signature ◽  
Neuronal Populations ◽  
Hypothalamic Nuclei ◽  
Satiety Hormone ◽  
Multiple Cell

AbstractHypothalamic nuclei which regulate homeostatic functions express leptin receptor (LepR), the primary target of the satiety hormone leptin. Single-cell RNA sequencing (scRNA-seq) has facilitated the discovery of a variety of hypothalamic cell types. However, low abundance of LepR transcripts prevented further characterization of LepR cells. Therefore, we perform scRNA-seq on isolated LepR cells and identify eight neuronal clusters, including three uncharacterized Trh-expressing populations as well as 17 non-neuronal populations including tanycytes, oligodendrocytes and endothelial cells. Food restriction had a major impact on Agrp neurons and changed the expression of obesity-associated genes. Multiple cell clusters were enriched for GWAS signals of obesity. We further explored changes in the gene regulatory landscape of LepR cell types. We thus reveal the molecular signature of distinct populations with diverse neurochemical profiles, which will aid efforts to illuminate the multi-functional nature of leptin’s action in the hypothalamus.

scholarly journals A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy

2019 ◽  
Author(s):  
Erwin M. Schoof ◽  
Nicolas Rapin ◽  
Simonas Savickas ◽  
Coline Gentil ◽  
Eric Lechman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Mass ◽  
Science Research ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Significant Shift ◽  
Cell Analysis ◽  
Computational Pipeline

AbstractIn recent years, cellular life science research has experienced a significant shift, moving away from conducting bulk cell interrogation towards single-cell analysis. It is only through single cell analysis that a complete understanding of cellular heterogeneity, and the interplay between various cell types that are fundamental to specific biological phenotypes, can be achieved. Single-cell assays at the protein level have been predominantly limited to targeted, antibody-based methods. However, here we present an experimental and computational pipeline, which establishes a comprehensive single-cell mass spectrometry-based proteomics workflow.By exploiting a leukemia culture system, containing functionally-defined leukemic stem cells, progenitors and terminally differentiated blasts, we demonstrate that our workflow is able to explore the cellular heterogeneity within this aberrant developmental hierarchy. We show our approach is capable to quantifying hundreds of proteins across hundreds of single cells using limited instrument time. Furthermore, we developed a computational pipeline (SCeptre), that effectively clusters the data and permits the extraction of cell-specific proteins and functional pathways. This proof-of-concept work lays the foundation for future global single-cell proteomics studies.

scholarly journals A multiresolution framework to characterize single-cell state landscapes

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Shahin Mohammadi ◽  
Jose Davila-Velderrain ◽  
Manolis Kellis
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Real Data ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Superior Performance ◽  
Data Sets ◽  
Structural Representation ◽  
Archetypal Analysis ◽  
Cell State

Abstract Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet’s superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.

scholarly journals AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

2020 ◽  
Author(s):  
Hananeh Aliee ◽  
Fabian Theis
Keyword(s):  
Single Cell ◽  
Prior Knowledge ◽  
Gene Selection ◽  
Ground Truth ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
The Impact

AbstractTissues are complex systems of interacting cell types. Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. When measuring such responses using RNA-seq, bulk RNA-seq masks cellular heterogeneity. Hence, several computational methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles highly depends on the set of genes undergoing deconvolution. These genes are often selected based on prior knowledge or a single-criterion test that might not be useful to dissect closely correlated cell types. In this work, we introduce AutoGeneS, a tool that automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Results from human samples of peripheral blood illustrate that AutoGeneS outperforms other methods. Our results also highlight the impact of our approach on analyzing bulk RNA samples with noisy single-cell reference profiles and closely correlated cell types. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of the predictions of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

scholarly journals A Potential Application of Triangular Microwells to Entrap Single Cancer Cells: A Canine Cutaneous Mast Cell Tumor Model

Micromachines ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 841
Author(s):  
Dettachai Ketpun ◽  
Alongkorn Pimpin ◽  
Tewan Tongmanee ◽  
Sudchaya Bhanpattanakul ◽  
Prapruddee Piyaviriyakul ◽  
...  
Keyword(s):  
Mast Cell ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Tumor ◽  
Cellular Heterogeneity ◽  
Cell Analysis ◽  
Mast Cell Tumor ◽  
Multiple Cell ◽  
Cutaneous Mast Cell ◽  
The Impact

Cellular heterogeneity is a major hindrance, leading to the misunderstanding of dynamic cell biology. However, single cell analysis (SCA) has been used as a practical means to overcome this drawback. Many contemporary methodologies are available for single cell analysis; among these, microfluidics is the most attractive and effective technology, due to its advantages of low-volume specimen consumption, label-free evaluation, and real-time monitoring, among others. In this paper, a conceptual application for microfluidic single cell analysis for veterinary research is presented. A microfluidic device is fabricated with an elastomer substrate, polydimethylsiloxane (PDMS), under standard soft lithography. The performance of the microdevice is high-throughput, sensitive, and user-friendly. A total of 53.1% of the triangular microwells were able to trap single canine cutaneous mast cell tumor (MCT) cells. Of these, 38.82% were single cell entrapments, while 14.34% were multiple cell entrapments. The ratio of single-to-multiple cell trapping was high, at 2.7:1. In addition, 80.5% of the trapped cells were viable, indicating that the system was non-lethal. OCT4A-immunofluorescence combined with the proposed system can assess OCT4A expression in trapped single cells more precisely than OCT4A-immunohistochemistry. Therefore, the results suggest that microfluidic single cell analysis could potentially reduce the impact of cellular heterogeneity.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Single-cell transcriptomics following ischemic injury identifies a role for B2M in cardiac repair

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bas Molenaar ◽  
Louk T. Timmer ◽  
Marjolein Droog ◽  
Ilaria Perini ◽  
Danielle Versteeg ◽  
...  
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cardiac Remodeling ◽  
Cardiac Injury ◽  
Ischemic Injury ◽  
Cell Types ◽  
Repair Process ◽  
Cardiac Repair ◽  
Cellular Heterogeneity ◽  
Intercellular Signaling

AbstractThe efficiency of the repair process following ischemic cardiac injury is a crucial determinant for the progression into heart failure and is controlled by both intra- and intercellular signaling within the heart. An enhanced understanding of this complex interplay will enable better exploitation of these mechanisms for therapeutic use. We used single-cell transcriptomics to collect gene expression data of all main cardiac cell types at different time-points after ischemic injury. These data unveiled cellular and transcriptional heterogeneity and changes in cellular function during cardiac remodeling. Furthermore, we established potential intercellular communication networks after ischemic injury. Follow up experiments confirmed that cardiomyocytes express and secrete elevated levels of beta-2 microglobulin in response to ischemic damage, which can activate fibroblasts in a paracrine manner. Collectively, our data indicate phase-specific changes in cellular heterogeneity during different stages of cardiac remodeling and allow for the identification of therapeutic targets relevant for cardiac repair.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals COVID-19 in children treated with immunosuppressive medication for kidney diseases

2020 ◽  
pp. archdischild-2020-320616
Author(s):  
Matko Marlais ◽  
Tanja Wlodkowski ◽  
Samhar Al-Akash ◽  
Petr Ananin ◽  
Varun Kumar Bandi ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Immunosuppressive Therapy ◽  
Low Income ◽  
Kidney Diseases ◽  
Low Income Countries ◽  
Reference Network ◽  
Immunosuppressive Medication ◽  
Immunosuppressive Medications ◽  
Significant Difference ◽  
The Impact

BackgroundChildren are recognised as at lower risk of severe COVID-19 compared with adults, but the impact of immunosuppression is yet to be determined. This study aims to describe the clinical course of COVID-19 in children with kidney disease taking immunosuppressive medication and to assess disease severity.MethodsCross-sectional study hosted by the European Rare Kidney Disease Reference Network and supported by the European, Asian and International paediatric nephrology societies. Anonymised data were submitted online for any child (age <20 years) with COVID-19 taking immunosuppressive medication for a kidney condition. Study recruited for 16 weeks from 15 March 2020 to 05 July 2020. The primary outcome was severity of COVID-19.Results113 children were reported in this study from 30 different countries. Median age: 13 years (49% male). Main underlying reasons for immunosuppressive therapy: kidney transplant (47%), nephrotic syndrome (27%), systemic lupus erythematosus (10%). Immunosuppressive medications used include: glucocorticoids (76%), mycophenolate mofetil (MMF) (54%), tacrolimus/ciclosporine A (58%), rituximab/ofatumumab (11%). 78% required no respiratory support during COVID-19 illness, 5% required bi-level positive airway pressure or ventilation. Four children died; all deaths reported were from low-income countries with associated comorbidities. There was no significant difference in severity of COVID-19 based on gender, dialysis status, underlying kidney condition, and type or number of immunosuppressive medications.ConclusionsThis global study shows most children with a kidney disease taking immunosuppressive medication have mild disease with SARS-CoV-2 infection. We therefore suggest that children on immunosuppressive therapy should not be more strictly isolated than children who are not on immunosuppressive therapy.

scholarly journals Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

2019 ◽  
Vol 2 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Jinchu Vijay ◽  
Marie-Frédérique Gauthier ◽  
Rebecca L. Biswell ◽  
Daniel A. Louiselle ◽  
Jeffrey J. Johnston ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Human Adipose Tissue ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Analysis ◽  
Disease Specific
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