scholarly journals Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution

2019 ◽  
Vol 5 (12) ◽  
pp. eaaw3413 ◽  
Author(s):  
Grant E. Duclos ◽  
Vitor H. Teixeira ◽  
Patrick Autissier ◽  
Yaron B. Gesthalter ◽  
Marjan A. Reinders-Luinge ◽  
...  
Keyword(s):  
Single Cell ◽  
Bronchial Epithelium ◽  
Cell Types ◽  
Premalignant Lesions ◽  
Specific Cell ◽  
Cell Hyperplasia ◽  
Bronchial Epithelial ◽  
Distinct Cell ◽  
Complex Landscape

The human bronchial epithelium is composed of multiple distinct cell types that cooperate to defend against environmental insults. While studies have shown that smoking alters bronchial epithelial function and morphology, its precise effects on specific cell types and overall tissue composition are unclear. We used single-cell RNA sequencing to profile bronchial epithelial cells from six never and six current smokers. Unsupervised analyses led to the characterization of a set of toxin metabolism genes that localized to smoker ciliated cells, tissue remodeling associated with a loss of club cells and extensive goblet cell hyperplasia, and a previously unidentified peri-goblet epithelial subpopulation in smokers who expressed a marker of bronchial premalignant lesions. Our data demonstrate that smoke exposure drives a complex landscape of cellular alterations that may prime the human bronchial epithelium for disease.

scholarly journals Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution

2018 ◽  
Author(s):  
Grant E. Duclos ◽  
Vitor H. Teixeira ◽  
Patrick Autissier ◽  
Yaron B. Gesthalter ◽  
Marjan A. Reinders-Luinge ◽  
...  
Keyword(s):  
Single Cell ◽  
Airway Remodeling ◽  
Bronchial Epithelium ◽  
Cell Types ◽  
Premalignant Lesions ◽  
Specific Cell ◽  
Cell Hyperplasia ◽  
Bronchial Epithelial ◽  
Molecular Alterations ◽  
Distinct Cell

ABSTRACTThe human bronchial epithelium is composed of multiple, distinct cell types that cooperate to perform functions, such as mucociliary clearance, that defend against environmental insults. While studies have shown that smoking alters bronchial epithelial function and morphology, the precise effects of this exposure on specific cell types are not well-understood. We used single-cell RNA sequencing to profile bronchial epithelial cells from six never- and six current smokers. Unsupervised analyses identified thirteen cell clusters defined by unique combinations of nineteen distinct gene sets. Expression of a set of toxin metabolism genes localized to ciliated cells from smokers. Smoking-induced airway remodeling was characterized by a loss of club cells and extensive goblet cell hyperplasia. Finally, we identified a novel peri-goblet epithelial subpopulation in smokers that expressed a marker of bronchial premalignant lesions. Our data demonstrates that smoke exposure drives a complex landscape of cellular and molecular alterations in the human bronchial epithelium that may contribute to the onset of smoking-associated lung diseases.

scholarly journals High Resolution Single Cell Maps Reveals Distinct Cell Organization and Function Across Different Regions of the Human Intestine

2021 ◽  
Author(s):  
John W Hickey ◽  
Winston R Becker ◽  
Stephanie A Nevins ◽  
Aaron M Horning ◽  
Almudena Espin Perez ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Open Chromatin ◽  
Specific Cell ◽  
Symbiotic Relationships ◽  
Distinct Cell ◽  
Cell Organization ◽  
Reference Map ◽  
And Function

The colon is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota, and affects overall health. To better understand its organization, functions, and its regulation at a single cell level, we performed CODEX multiplexed imaging, as well as single nuclear RNA and open chromatin assays across eight different intestinal sites of four donors. Through systematic analyses we find cell compositions differ dramatically across regions of the intestine, demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighborhoods and communities highlighting distinct immunological niches present in the intestine. We also map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization for this organ, and serve as an important reference map for understanding human biology and disease.

scholarly journals Understanding human gut diseases at single-cell resolution

2020 ◽  
Vol 29 (R1) ◽  
pp. R51-R58 ◽  
Author(s):  
Emilia Bigaeva ◽  
Werna T C Uniken Venema ◽  
Rinse K Weersma ◽  
Eleonora A M Festen
Keyword(s):  
Single Cell ◽  
Digestive System ◽  
Cell Types ◽  
Specific Cell ◽  
Human Gut ◽  
The Past ◽  
Distinct Cell ◽  
Transcriptional Variation ◽  
Single Cell Rna Sequencing ◽  
Inflammatory Bowel

Abstract Our understanding of gut functioning and pathophysiology has grown considerably in the past decades, and advancing technologies enable us to deepen this understanding. Single-cell RNA sequencing (scRNA-seq) has opened a new realm of cellular diversity and transcriptional variation in the human gut at a high, single-cell resolution. ScRNA-seq has pushed the science of the digestive system forward by characterizing the function of distinct cell types within complex intestinal cellular environments, by illuminating the heterogeneity within specific cell populations and by identifying novel cell types in the human gut that could contribute to a variety of intestinal diseases. In this review, we highlight recent discoveries made with scRNA-seq that significantly advance our understanding of the human gut both in health and across the spectrum of gut diseases, including inflammatory bowel disease, colorectal carcinoma and celiac disease.

scholarly journals Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data

2018 ◽  
Author(s):  
Florian Wagner ◽  
Itai Yanai
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Specific Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Systematic Analysis ◽  
Learning Framework ◽  
Classification Framework ◽  
Heterogeneous Tissues

AbstractSingle-cell RNA-Seq (scRNA-Seq) enables the systematic molecular characterization of heterogeneous tissues at an unprecedented resolution and scale. However, it is currently unclear how to establish formal cell type definitions, which impedes the systematic analysis of scRNA-Seq data across experiments and studies. To address this challenge, we have developed Moana, a hierarchical machine learning framework that enables the construction of robust cell type classifiers from heterogeneous scRNA-Seq datasets. To demonstrate Moana’s capabilities, we construct cell type classifiers for human immune cells that accurately distinguish between closely related cell types in the presence of experimental perturbations and systematic differences between scRNA-Seq protocols. We show that Moana is generally applicable and scales to datasets with more than ten thousand cells, thus enabling the construction of tissue-specific cell type atlases that can be directly applied to analyze new scRNASeq datasets. A Python implementation of Moana can be found at https://github.com/yanailab/moana.

scholarly journals Adult tissue-resident stem cells—fact or fiction?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Deepa Bhartiya
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Adult Stem Cells ◽  
Cell Types ◽  
Specific Cell ◽  
Tissue Specific ◽  
Cardiac Tissues ◽  
Adult Tissues ◽  
Resident Stem Cells ◽  
Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

scholarly journals A practical solution to pseudoreplication bias in single-cell studies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kip D. Zimmerman ◽  
Mark A. Espeland ◽  
Carl D. Langefeld
Keyword(s):  
Single Cell ◽  
Mixed Models ◽  
Random Effect ◽  
Cell Types ◽  
Error Rates ◽  
Specific Cell ◽  
Experimental Conditions ◽  
Type 1 Error ◽  
Sample Correlation ◽  
Inflated Type

AbstractCells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.

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

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Single Cell Atlas of Human Dura Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response

2021 ◽  
Author(s):  
Anthony Z Wang ◽  
Jay Bowman-Kirigin ◽  
Rupen Desai ◽  
Pujan Patel ◽  
Bhuvic Patel ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Copy Number Variant ◽  
Cell Types ◽  
Cell Receptor ◽  
Immune Microenvironment ◽  
Clinical Models ◽  
Insight Into

Recent investigation of the meninges, specifically the dura layer, has highlighted its importance in CNS immune surveillance beyond a purely structural role. However, most of our understanding of the meninges stems from the use of pre-clinical models rather than human samples. In this study, we use single cell RNA-sequencing to perform the first characterization of both non-tumor-associated human dura and meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, through T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. We also identify a functionally heterogeneous population of non-immune cell types and report copy-number variant heterogeneity within our meningioma samples. Our comprehensive investigation of both the immune and non-immune cell landscapes of human dura and meningioma at a single cell resolution provide new insight into previously uncharacterized roles of human dura.

Sign in / Sign up

Export Citation Format

Share Document