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

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.

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Studying Kidney Diseases at the Single-Cell Level

Kidney Diseases ◽

10.1159/000517130 ◽

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

Background: 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. Summary: 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. Key Message: 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.

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Single-Cell Analysis Reveals Cellular Heterogeneity and Regulatory Networks of Hypothalamic Leptin-Receptor Cells

SSRN Electronic Journal ◽

10.2139/ssrn.3751053 ◽

2020 ◽

Author(s):

Nefeli Kakava-Georgiadou ◽

Jeppe Severens ◽

Anja Jørgensen ◽

Keith Garner ◽

Mieneke Luijendijk ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Leptin Receptor ◽

Single Cell Analysis ◽

Cellular Heterogeneity ◽

Cell Analysis ◽

Receptor Cells

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A multiresolution framework to characterize single-cell state landscapes

Nature Communications ◽

10.1038/s41467-020-18416-6 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 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.

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

10.1101/745679 ◽

2019 ◽

Cited By ~ 6

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.

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Long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 are differentially expressed in severe COVID-19 patients: An integrated single-cell analysis

PLoS ONE ◽

10.1371/journal.pone.0261242 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0261242

Author(s):

Kai Huang ◽

Catherine Wang ◽

Christen Vagts ◽

Vanitha Raguveer ◽

Patricia W. Finn ◽

...

Keyword(s):

Single Cell ◽

Mononuclear Cells ◽

Single Cell Analysis ◽

Cell Types ◽

Differentially Expressed ◽

Cellular Stress Response ◽

Peripheral Blood Mononuclear ◽

Diagnostic Measures ◽

Multiple Cell ◽

Non Coding Rnas

Hyperactive and damaging inflammation is a hallmark of severe rather than mild Coronavirus disease 2019 (COVID-19). To uncover key inflammatory differentiators between severe and mild COVID-19, we applied an unbiased single-cell transcriptomic analysis. We integrated two single-cell RNA-seq datasets with COVID-19 patient samples, one that sequenced bronchoalveolar lavage (BAL) cells and one that sequenced peripheral blood mononuclear cells (PBMCs). The combined cell population was then analyzed with a focus on genes associated with disease severity. The immunomodulatory long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 were highly differentially expressed between mild and severe patients in multiple cell types. Within those same cell types, the concurrent detection of other severity-associated genes involved in cellular stress response and apoptosis regulation suggests that the pro-inflammatory functions of these lncRNAs may foster cell stress and damage. Thus, NEAT1 and MALAT1 are potential components of immune dysregulation in COVID-19 that may provide targets for severity related diagnostic measures or therapy.

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

Micromachines ◽

10.3390/mi10120841 ◽

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.

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

Communications Biology ◽

10.1038/s42003-020-01636-3 ◽

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.

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Single-cell mapping of focused ultrasound-transfected brain

Gene Therapy ◽

10.1038/s41434-021-00226-0 ◽

2021 ◽

Author(s):

A. S. Mathew ◽

C. M. Gorick ◽

R. J. Price

Keyword(s):

Single Cell ◽

Focused Ultrasound ◽

Cell Types ◽

Brain Cell ◽

Therapeutic Modality ◽

Full Potential ◽

Stress Genes ◽

Multiple Cell ◽

Differential Gene

AbstractGene delivery via focused ultrasound (FUS) mediated blood-brain barrier (BBB) opening is a disruptive therapeutic modality. Unlocking its full potential will require an understanding of how FUS parameters (e.g., peak-negative pressure (PNP)) affect transfected cell populations. Following plasmid (mRuby) delivery across the BBB with 1 MHz FUS, we used single-cell RNA-sequencing to ascertain that distributions of transfected cell types were highly dependent on PNP. Cells of the BBB (i.e., endothelial cells, pericytes, and astrocytes) were enriched at 0.2 MPa PNP, while transfection of cells distal to the BBB (i.e., neurons, oligodendrocytes, and microglia) was augmented at 0.4 MPa PNP. PNP-dependent differential gene expression was observed for multiple cell types. Cell stress genes were upregulated proportional to PNP, independent of cell type. Our results underscore how FUS may be tuned to bias transfection toward specific brain cell types in vivo and predict how those cells will respond to transfection.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

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.

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