scholarly journals Kidney Single-Cell Atlas Reveals Myeloid Heterogeneity in Progression and Regression of Kidney Disease

2020 ◽  
Vol 31 (12) ◽  
pp. 2833-2854
Author(s):  
Bryan R. Conway ◽  
Eoin D. O’Sullivan ◽  
Carolynn Cairns ◽  
James O’Sullivan ◽  
Daniel J. Simpson ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Kidney Disease ◽  
Single Cell ◽  
Single Cells ◽  
Kidney Injury ◽  
Myeloid Cell ◽  
Cell Types ◽  
Blood Exchange ◽  
Transcriptomic Level ◽  
Injury And Repair

BackgroundLittle is known about the roles of myeloid cell subsets in kidney injury and in the limited ability of the organ to repair itself. Characterizing these cells based only on surface markers using flow cytometry might not provide a full phenotypic picture. Defining these cells at the single-cell, transcriptomic level could reveal myeloid heterogeneity in the progression and regression of kidney disease.MethodsIntegrated droplet– and plate-based single-cell RNA sequencing were used in the murine, reversible, unilateral ureteric obstruction model to dissect the transcriptomic landscape at the single-cell level during renal injury and the resolution of fibrosis. Paired blood exchange tracked the fate of monocytes recruited to the injured kidney.ResultsA single-cell atlas of the kidney generated using transcriptomics revealed marked changes in the proportion and gene expression of renal cell types during injury and repair. Conventional flow cytometry markers would not have identified the 12 myeloid cell subsets. Monocytes recruited to the kidney early after injury rapidly adopt a proinflammatory, profibrotic phenotype that expresses Arg1, before transitioning to become Ccr2+ macrophages that accumulate in late injury. Conversely, a novel Mmp12+ macrophage subset acts during repair.ConclusionsComplementary technologies identified novel myeloid subtypes, based on transcriptomics in single cells, that represent therapeutic targets to inhibit progression or promote regression of kidney disease.

scholarly journals Kidney single-cell atlas reveals myeloid heterogeneity in progression and regression of kidney disease

2020 ◽  
Author(s):  
Bryan R Conway ◽  
Eoin D O’Sullivan ◽  
Carolynn Cairns ◽  
James O’Sullivan ◽  
Daniel J. Simpson ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Single Cell ◽  
Myeloid Cell ◽  
Limited Capacity ◽  
Cell Level ◽  
Blood Exchange ◽  
Early Injury ◽  
Macrophage Subset ◽  
Injury And Repair ◽  
Unilateral Ureteric Obstruction

AbstractThe kidney has a limited capacity to repair following injury, however, the endogenous reparative pathways are not well understood. Here we employ integrated droplet- and plate-based scRNA-seq in the murine reversible unilateral ureteric obstruction model to dissect the transcriptomic landscape at the single cell level during renal injury and resolution of fibrosis. We generate a comprehensive catalogue of the changes induced during injury and repair, revealing significant myeloid cell heterogeneity, which would not have been identifiable by conventional flow cytometry. We identify new markers for the myeloid populations within the kidney as well as identification of novel subsets including an Arg1+ monocyte population specific to early injury and a Mmp12+ macrophage subset exclusive to repair. Finally, using paired blood exchange to track circulating immune cells, we confirm that monocytes are recruited to the kidney early after injury and are the source of Ccr2+ macrophages that accumulate in late injury. Our data demonstrate the utility of complementary technologies to identify novel myeloid subtypes that may represent therapeutic targets to inhibit progression or promote regression of kidney disease.

scholarly journals Molecular Mechanisms of Kidney Injury and Repair in Arterial Hypertension

2019 ◽  
Vol 20 (9) ◽  
pp. 2138 ◽  
Author(s):  
Laura Katharina Sievers ◽  
Kai-Uwe Eckardt
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Arterial Hypertension ◽  
Signaling Pathways ◽  
Molecular Mechanisms ◽  
Kidney Injury ◽  
Cell Types ◽  
Molecular Signaling ◽  
Systemic Signaling ◽  
Injury And Repair

The global burden of chronic kidney disease is rising. The etiologies, heterogeneous, and arterial hypertension, are key factors contributing to the development and progression of chronic kidney disease. Arterial hypertension is induced and maintained by a complex network of systemic signaling pathways, such as the hormonal axis of the renin-angiotensin-aldosterone system, hemodynamic alterations affecting blood flow, oxygen supply, and the immune system. This review summarizes the clinical and histopathological features of hypertensive kidney injury and focusses on the interplay of distinct systemic signaling pathways, which drive hypertensive kidney injury in distinct cell types of the kidney. There are several parallels between hypertension-induced molecular signaling cascades in the renal epithelial, endothelial, interstitial, and immune cells. Angiotensin II signaling via the AT1R, hypoxia induced HIFα activation and mechanotransduction are closely interacting and further triggering the adaptions of metabolism, cytoskeletal rearrangement, and profibrotic TGF signaling. The interplay of these, and other cellular pathways, is crucial to balancing the injury and repair of the kidneys and determines the progression of hypertensive kidney disease.

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

scholarly journals Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Elliott Swanson ◽  
Cara Lord ◽  
Julian Reading ◽  
Alexander T Heubeck ◽  
Palak C Genge ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Human Peripheral Blood ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Test Case ◽  
Cell Assays ◽  
Paired Measurement

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

scholarly journals A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

2018 ◽  
Author(s):  
Douglas Abrams ◽  
Parveen Kumar ◽  
R. Krishna Murthy Karuturi ◽  
Joshy George
Keyword(s):  
Experimental Design ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Number ◽  
Fold Change ◽  
Computational Method ◽  
Marker Genes ◽  
Cell Type ◽  
Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals Single-cell RNA cap and tail sequencing (scRCAT-seq) reveals subtype-specific isoforms differing in transcript demarcation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Youjin Hu ◽  
Jiawei Zhong ◽  
Yuhua Xiao ◽  
Zheng Xing ◽  
Katherine Sheu ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Single Gene ◽  
Cell Types ◽  
Machine Learning Algorithms ◽  
Translation Efficiency ◽  
Transcription Start Sites ◽  
Long Read ◽  
Mrna Gene ◽  
Gene Isoforms

Abstract The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Gene isoforms allow a single gene diverse functions across different cell types, and isoform dynamics allow different functions over time. However, methods to efficiently identify and quantify RNA isoforms genome-wide in single cells are still lacking. Here, we introduce single cell RNA Cap And Tail sequencing (scRCAT-seq), a method to demarcate the boundaries of isoforms based on short-read sequencing, with higher efficiency and lower cost than existing long-read sequencing methods. In conjunction with machine learning algorithms, scRCAT-seq demarcates RNA transcripts with unprecedented accuracy. We identified hundreds of previously uncharacterized transcripts and thousands of alternative transcripts for known genes, revealed cell-type specific isoforms for various cell types across different species, and generated a cell atlas of isoform dynamics during the development of retinal cones.

scholarly journals CD40/CD40L Signaling as a Promising Therapeutic Target for the Treatment of Renal Disease

2020 ◽  
Vol 9 (11) ◽  
pp. 3653
Author(s):  
Shungang Zhang ◽  
Joshua D. Breidenbach ◽  
Benjamin H. Russell ◽  
Jerrin George ◽  
Steven T. Haller
Keyword(s):  
Kidney Disease ◽  
Kidney Injury ◽  
Cell Types ◽  
Cd40 Ligand ◽  
Kidney Cells ◽  
Immune Functions ◽  
Promising Therapeutic Target ◽  
Signaling Axis ◽  
Soluble Cd40l ◽  
Cluster Of Differentiation

The cluster of differentiation 40 (CD40) is activated by the CD40 ligand (CD40L) in a variety of diverse cells types and regulates important processes associated with kidney disease. The CD40/CD40L signaling cascade has been comprehensively studied for its roles in immune functions, whereas the signaling axis involved in local kidney injury has only drawn attention in recent years. Clinical studies have revealed that circulating levels of soluble CD40L (sCD40L) are associated with renal function in the setting of kidney disease. Levels of the circulating CD40 receptor (sCD40), sCD40L, and local CD40 expression are tightly related to renal injury in different types of kidney disease. Additionally, various kidney cell types have been identified as non-professional antigen-presenting cells (APCs) that express CD40 on the cell membrane, which contributes to the interactions between immune cells and local kidney cells during the development of kidney injury. Although the potential for adverse CD40 signaling in kidney cells has been reported in several studies, a summary of those studies focusing on the role of CD40 signaling in the development of kidney disease is lacking. In this review, we describe the outcomes of recent studies and summarize the potential therapeutic methods for kidney disease which target CD40.

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