scholarly journals Single cell profiling of Hofbauer cells and fetal brain microglia reveals shared programs and functions

2021 ◽  
Author(s):  
Alexis M Ceasrine ◽  
Rebecca Batorsky ◽  
Lydia L. Shook ◽  
Sezen Kislal ◽  
Evan A. Bordt ◽  
...  
Keyword(s):  
Single Cell ◽  
Fetal Brain ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Neurodevelopmental Outcomes ◽  
Female Fetus ◽  
Hofbauer Cells ◽  
Single Cell Profiling ◽  
Placental Macrophages ◽  
Noninvasive Biomarkers

SummaryMaternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many of which are mediated by in utero microglial programming. Microglia remain inaccessible at birth and throughout development, thus identification of noninvasive biomarkers that can reflect fetal brain microglial programming may permit screening and intervention during critical developmental windows. Here we used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells). Single-cell RNA sequencing of murine fetal brain and placental macrophages demonstrated shared transcriptional programs. Comparison with human datasets demonstrated that placental resident macrophage signatures are highly conserved between mice and humans. Single-cell RNA-seq identified sex differences in fetal microglial and Hofbauer cell programs, and robust differences between placenta-associated maternal macrophage/monocyte (PAMM) populations in the context of a male versus a female fetus. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, potentially facilitating the early identification of offspring most vulnerable to neurodevelopmental disorders.

scholarly journals Single Cell Profiling of Hofbauer Cells and Fetal Brain Microglia Reveals Shared Programs and Functions

2021 ◽  
Author(s):  
Alexis Caesarine ◽  
Rebecca Batorsky ◽  
Lydia Shook ◽  
Sezen Kislal ◽  
Evan Bordt ◽  
...  
Keyword(s):  
Single Cell ◽  
Fetal Brain ◽  
Hofbauer Cells ◽  
Single Cell Profiling

scholarly journals Single-cell RNA-seq reveals spatially restricted multicellular fibrotic niches during lung fibrosis

2019 ◽  
Author(s):  
Nikita Joshi ◽  
Satoshi Watanabe ◽  
Rohan Verma ◽  
Renea P. Jablonski ◽  
Ching-I Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Alveolar Macrophages ◽  
Lineage Tracing ◽  
Fibroblast Proliferation ◽  
Rna Seq ◽  
Autocrine Signaling ◽  
Spatial Methods ◽  
Pharmacological Blockade ◽  
Established Population ◽  
Organ Fibrosis

AbstractOntologically distinct populations of macrophages differentially contribute to organ fibrosis through unknown mechanisms. We applied lineage tracing, spatial methods and single-cell RNA-seq to a spatially-restricted model of asbestos-induced pulmonary fibrosis. We demonstrate that while tissue-resident interstitial macrophages, tissue-resident alveolar macrophages, and monocyte-derived alveolar macrophages are present in the fibrotic niche, only monocyte-derived alveolar macrophages are causally related to fibrosis. Monocyte-derived alveolar macrophages were specifically localized to fibrotic regions in the proximity of fibroblasts where they expressed molecules known to drive fibroblast proliferation, including PDGFA. Moreover, we identified autocrine M-CSF/M-CSFR signaling in monocyte-derived alveolar macrophages as a novel mechanism promoting their self-maintenance and persistence in the fibrotic niche. Pharmacological blockade of M-CSF signaling led to disappearance of the established population of monocyte-derived alveolar macrophages. Thus, our data indicate that monocyte-derived alveolar macrophages are specifically recruited to the fibrotic niche where they are maintained by autocrine signaling and drive fibrosis by stimulating fibroblast proliferation.

Single-cell Profiling Reveal Types of Interstitial Cells in Human and Rat Bladder

2020 ◽  
Author(s):  
Zhenxing Yang ◽  
Ye Gao ◽  
Peilin Liu ◽  
Weisheng Jia ◽  
Zhenqiang Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Interstitial Cell ◽  
Cell Types ◽  
Interstitial Cells ◽  
Rna Seq ◽  
Urothelial Cells ◽  
Bladder Disease ◽  
Rat Bladder ◽  
Single Cell Profiling ◽  
New Type

Abstract Backgrounds:The mechanism underlying bladder urination pathophysiologies has not been understood yet because the types of interstitial cells present in the bladder are still obscure. Results:Here, we classified the cell types in the bladder without bias using 10x genomics single-cell RNA-seq and identified 35,510 and 19,946 cells from human and rat bladder tissues, respectively. In addition to the major cell clusters including urothelial cells, smooth muscle cells, endothelial cells, neurons, and immune cells, three types of interstitial cells were identified in this study. Fibroblasts and myofibroblasts were shown to occupy large proportions of interstitial cells, located mainly between the epithelial strata and muscle strata. A new type of interstitial cell, Mki67+, which was found both in human and rat bladder tissues, may participate in bladder disease development. Conclusions:Our investigation thus lays the ground work for identifying the cell types in bladder tissues and provides potential clues to understand abnormal bladder functions.

scholarly journals A single cell transcriptional atlas of early synovial joint development

Development ◽  
2020 ◽  
Vol 147 (14) ◽  
pp. dev185777 ◽  
Author(s):  
Qin Bian ◽  
Yu-Hao Cheng ◽  
Jordan P. Wilson ◽  
Emily Y. Su ◽  
Dong Won Kim ◽  
...  
Keyword(s):  
Knee Joint ◽  
Single Cell ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Synovial Joint ◽  
Isolated Populations ◽  
Joint Development

ABSTRACTSynovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage-tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 7329 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization and in vitro characterization of prospectively isolated populations, we have identified the transcriptional profiles of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.

scholarly journals Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution

2021 ◽  
Vol 9 ◽  
Author(s):  
Ernesto Marín-Sedeño ◽  
Xabier Martínez de Morentin ◽  
Jose M. Pérez-Pomares ◽  
David Gómez-Cabrero ◽  
Adrián Ruiz-Villalba
Keyword(s):  
Single Cell ◽  
Single Gene ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cardiac Cell ◽  
Rna Seq ◽  
Cardiovascular Research ◽  
Cell Diversity ◽  
Unbiased Manner

During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.

scholarly journals Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

2020 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Disease Etiology ◽  
Cell Lineages ◽  
Transient Events

AbstractCancer progression is characterized by rare, transient events which are nonetheless highly consequential to disease etiology and mortality. Detailed cell phylogenies can recount the history and chronology of these critical events – including metastatic seeding. Here, we applied our Cas9-based lineage tracer to study the subclonal dynamics of metastasis in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and drivers of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to aggressive populations. These phenotypic distinctions result from pre-existing, heritable, and characteristic differences in gene expression, and we demonstrate that these differentially expressed genes can drive invasiveness. Furthermore, metastases transit via diverse, multidirectional tissue routes and seeding topologies. Our work demonstrates the power of tracing cancer progression at unprecedented resolution and scale.One Sentence SummarySingle-cell lineage tracing and RNA-seq capture diverse metastatic behaviors and drivers in lung cancer xenografts in mice.

scholarly journals Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yishu Wang ◽  
Xuehan Tian ◽  
Dongmei Ai
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Markov Random Field ◽  
Field Model ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Single Cell Profiling

Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.

scholarly journals Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations

2019 ◽  
Vol 5 (5) ◽  
pp. eaav2249 ◽  
Author(s):  
Dongju Shin ◽  
Wookjae Lee ◽  
Ji Hyun Lee ◽  
Duhee Bang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cost Effective ◽  
Specific Gene ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Cost Effective Method ◽  
Treatment Experiment ◽  
Single Cell Profiling ◽  
Multiple Samples

The development of high-throughput single-cell RNA sequencing (scRNA-seq) has enabled access to information about gene expression in individual cells and insights into new biological areas. Although the interest in scRNA-seq has rapidly grown in recent years, the existing methods are plagued by many challenges when performing scRNA-seq on multiple samples. To simultaneously analyze multiple samples with scRNA-seq, we developed a universal sample barcoding method through transient transfection with short barcode oligonucleotides. By conducting a species-mixing experiment, we have validated the accuracy of our method and confirmed the ability to identify multiplets and negatives. Samples from a 48-plex drug treatment experiment were pooled and analyzed by a single run of Drop-Seq. This revealed unique transcriptome responses for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for the single-cell profiling of multiple experimental conditions, enabling the widespread adoption of scRNA-seq for various applications.

scholarly journals Multiplexed single-cell RNA-seq via transient barcoding for drug screening

2018 ◽  
Author(s):  
Dongju Shin ◽  
Wookjae Lee ◽  
Ji Hyun Lee ◽  
Duhee Bang
Keyword(s):  
Single Cell ◽  
Cost Effective ◽  
Specific Gene ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Cost Effective Method ◽  
Treatment Experiment ◽  
Single Cell Profiling ◽  
Multiple Samples ◽  
Pooled Samples

AbstractTo simultaneously analyze multiple samples of various conditions with scRNA-seq, we developed a universal sample barcoding method through transient transfection of SBOs. A 48-plex drug treatment experiment of pooled samples analyzed by a single run of Drop-Seq revealed a unique transcriptome response for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for single-cell profiling of multiple experimental conditions.

scholarly journals A single cell transcriptional atlas of early synovial joint development

2019 ◽  
Author(s):  
Qin Bian ◽  
Yu-Hao Cheng ◽  
Jordan P Wilson ◽  
Dong Won Kim ◽  
Hong Wang ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Knee Joint ◽  
Single Cell ◽  
Lineage Tracing ◽  
Transcriptional Networks ◽  
Rna Seq ◽  
Synovial Joint ◽  
Isolated Populations ◽  
Joint Development

SUMMARYSynovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 6,202 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization, and functional analysis of prospectively isolated populations, we have inferred the underlying transcriptional networks of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.

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