Molecular and Cellular Dynamics of Aortic Aneurysms Revealed by Single-Cell Transcriptomics

The aorta is highly heterogeneous, containing many different types of cells that perform sophisticated functions to maintain aortic homeostasis. Recently, single-cell RNA sequencing studies have provided substantial new insight into the heterogeneity of vascular cell types, the comprehensive molecular features of each cell type, and the phenotypic interrelationship between these cell populations. This new information has significantly improved our understanding of aortic biology and aneurysms at the molecular and cellular level. Here, we summarize these findings, with a focus on what single-cell RNA sequencing analysis has revealed about cellular heterogeneity, cellular transitions, communications among cell populations, and critical transcription factors in the vascular wall. We also review the information learned from single-cell RNA sequencing that has contributed to our understanding of the pathogenesis of vascular disease, such as the identification of cell types in which aneurysm-related genes and genetic variants function. Finally, we discuss the challenges and future directions of single-cell RNA sequencing applications in studies of aortic biology and diseases.

Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Cells ◽

10.3390/cells9081751 ◽

2020 ◽

Vol 9 (8) ◽

pp. 1751 ◽

Cited By ~ 1

Author(s):

Rishikesh Kumar Gupta ◽

Jacek Kuznicki

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Cellular Heterogeneity ◽

Future Directions ◽

Strong Basis ◽

Bodily Fluids ◽

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer’s disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

RNA-seq library preparation from single pancreatic acinar cells

10.1101/085696 ◽

2016 ◽

Author(s):

Damian Wollny ◽

Sheng Zhao ◽

Ana Martin-Villalba

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Acinar Cells ◽

Cell Types ◽

Cellular Heterogeneity ◽

Pancreatic Acinar Cells ◽

Library Preparation ◽

Cell Type ◽

Promising Tool ◽

Single cell RNA sequencing technology has emerged as a promising tool to uncover previously neglected cellular heterogeneity. Multiple methods and protocols have been developed to apply single cell sequencing to different cell types from various organs. However, library preparation for RNA sequencing remains challenging for cell types with high RNAse content due to rapid degradation of endogenous RNA molecules upon cell lysis. To this end, we developed a protocol based on the SMART-seq2 technology for single cell RNA sequencing of pancreatic acinar cells, the cell type with one of the highest ribonuclease concentration measured to date. This protocol reliably produces high quality libraries from single acinar cells reaching a total of 5x106 reads / cell and ∼ 80% transcript mapping rate with no detectable 3´end bias. Thus, our protocol makes single cell transcriptomics accessible to cell type with very high RNAse content.

IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis

GigaScience ◽

10.1093/gigascience/giz121 ◽

2019 ◽

Vol 8 (10) ◽

Cited By ~ 2

Author(s):

Yun-Ching Chen ◽

Abhilash Suresh ◽

Chingiz Underbayev ◽

Clare Sun ◽

Komudi Singh ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Sequencing Analysis ◽

Sequencing Data ◽

Peripheral Blood Mononuclear ◽

Biologically Relevant ◽

Cell Groups ◽

Cell Ontology

AbstractBackgroundIn single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.FindingsTo accelerate this process, we have developed IKAP—an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology.ConclusionsBy tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.

Single Cell RNA Sequencing Analysis in Postnatal Lung Discovers Effects of Hedgehog Signaling on Different Lung Cell Populations

10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a4446 ◽

2019 ◽

Author(s):

M.C. Kugler ◽

I. Sulaiman ◽

T.-A. Yie ◽

Y. Cai ◽

L.N. Segal ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Hedgehog Signaling ◽

Cell Populations ◽

Sequencing Analysis ◽

Single-cell Transcriptomics Identify Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons

10.1101/801266 ◽

2019 ◽

Cited By ~ 4

Author(s):

Andrea J De Micheli ◽

Jacob B Swanson ◽

Nathaniel P Disser ◽

Leandro M Martinez ◽

Nicholas R Walker ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Tissue Homeostasis ◽

Cellular Heterogeneity ◽

Cell Populations ◽

Cellular Composition ◽

Matrix Assembly ◽

Distinct Cell ◽

Additional Support

AbstractTendon is a connective tissue that transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, but little is known about the diversity of cells that populate tendon. Our objective was to explore the heterogeneity of cells in mouse Achilles tendons using single-cell RNA sequencing. We assembled a transcriptomic atlas and identified 11 distinct cell types in tendons, including 3 previously undescribed populations of fibroblasts. Using trajectory inference analysis, we provide additional support for the notion that pericytes are progenitor cells for the fibroblasts that compose adult tendons. We also modeled cell-interactions and identified ligand-receptor pairs involved in tendon homeostasis. Our findings highlight notable heterogeneity between and within tendon cell populations, which may contribute to our understanding of tendon extracellular matrix assembly and maintenance, and inform the design of therapies to treat tendinopathies.

Characterization of Alzheimer’s Disease-Associated Excitatory Neurons via Single-Cell RNA Sequencing Analysis

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.742176 ◽

2021 ◽

Vol 13 ◽

Author(s):

Fanghong Shao ◽

Meiting Wang ◽

Qi Guo ◽

Bowen Zhang ◽

Xiangting Wang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Rna Sequencing ◽

Neuronal Cell ◽

Cell Populations ◽

Sequencing Analysis ◽

Excitatory Neurons

The detailed characteristics of neuronal cell populations in Alzheimer’s disease (AD) using single-cell RNA sequencing have not been fully elucidated. To explore the characterization of neuronal cell populations in AD, this study utilized the publicly available single-nucleus RNA-sequencing datasets in the transgenic model of 5X familial Alzheimer’s disease (5XFAD) and wild-type mice to reveal an AD-associated excitatory neuron population (C3:Ex.Neuron). The relative abundance of C3:Ex.Neuron increased at 1.5 months and peaked at 4.7 months in AD mice. Functional pathways analyses showed that the pathways positively related to neurodegenerative disease progression were downregulated in the C3:Ex.Neuron at 1.5 months in AD mice. Based on the differentially expressed genes among the C3:Ex.Neuron, four subtypes (C3.1–4) were identified, which exhibited distinct abundance regulatory patterns during the development of AD. Among these subtypes, the C3.1 neurons [marked by netrin G1 (Ntng1)] exhibited a similar regulatory pattern as the C3:Ex.Neuron in abundance during the development of AD. In addition, our gene set variation analysis (GSEA) showed that the C3.1 neurons, instead of other subtypes of the C3:Ex.Neuron, possessed downregulated AD pathways at an early stage (1.5 months) of AD mice. Collectively, our results identified a previously unidentified subset of excitatory neurons and provide a potential application of these neurons to modulate the disease susceptibility.

Characterization of normal and cancer stem-like cell populations in murine lingual epithelial organoids using single-cell RNA sequencing

Scientific Reports ◽

10.1038/s41598-021-01783-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Erik Johansson ◽

Hiroo Ueno

Keyword(s):

Stem Cell ◽

Single Cell ◽

Rna Sequencing ◽

Targeted Therapies ◽

Tongue Cancer ◽

Cell Populations ◽

Specific Gene ◽

Sequencing Analysis ◽

Molecular Targeted Therapies ◽

AbstractThe advances in oral cancer research and therapies have not improved the prognosis of patients with tongue cancer. The poor treatment response of tongue cancer may be attributed to the presence of heterogeneous tumor cells exhibiting stem cell characteristics. Therefore, there is a need to develop effective molecular-targeted therapies based on the specific gene expression profiles of these cancer stem-like cell populations. In this study, the characteristics of normal and cancerous organoids, which are convenient tools for screening anti-cancer drugs, were analyzed comparatively. As organoids are generally generated by single progenitors, they enable the exclusion of normal cell contamination from the analyses. Single-cell RNA sequencing analysis revealed that p53 signaling activation and negative regulation of cell cycle were enriched characteristics in normal stem-like cells whereas hypoxia-related pathways, such as HIF-1 signaling and glycolysis, were upregulated in cancer stem-like cells. The findings of this study improved our understanding of the common features of heterogeneous cell populations with stem cell properties in tongue cancers, that are different from those of normal stem cell populations; this will enable the development of novel molecular-targeted therapies for tongue cancer.

DDRE-36. INVESTIGATING MEDULLOBLASTOMA HETEROGENEITY AND PREDICTING COMPOUND RESPONSE

Neuro-Oncology ◽

10.1093/neuonc/noab196.320 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi82-vi82

Author(s):

Luz Ruiz ◽

Nagi Ayad

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Casein Kinase ◽

Cellular Heterogeneity ◽

Cell Populations ◽

Surgical Removal ◽

Cerebellar Tumor ◽

Distinct Cell ◽

New Treatments

Abstract Medulloblastoma is the most common malignant brain tumor found in children. It is a cerebellar tumor that affects motor and cognitive processes such as coordination and movement. The standard of care is surgical removal, radiation, and chemotherapy. These treatments can be very damaging to the developing child, in that they can impair vision and walking, among other body functions. Due to this, new treatments are necessary. Treatment plans for children with medulloblastoma need to be tailored to the specific subtype that they have. Genetic studies have revealed that there are four subtypes of pediatric medulloblastoma: Group 3, Group 4, SHH, and WNT. Beyond these bulk-resolution subtypes, we hypothesize intratumor heterogeneity as a barrier to new effective treatments. I have mined single-cell RNA sequencing data to investigate cellular heterogeneity and predict compound response. I analyzed Medulloblastoma patient tumor data along with data obtained from a 10X Genomics Chromium single-cell RNA sequencing experiment performed in the laboratory from a Tg (Neurod-Smoothened*A1) mouse. We hypothesize that distinct cell populations within medulloblastoma should show different predicted compounds that would target them. We have ranked compound predictions to investigate whether compounds may selectively target any of these populations using transcriptional response signatures derived from the LINCS L1000 perturbagen-response dataset. We also hypothesize that Medulloblastoma tumors have distinct subtypes of cells that are preferentially sensitive to BET bromodomain, casein kinase, and ATM/ATR inhibitors. Our analysis identified ten transcriptionally distinct cell types across these medulloblastoma tumors as well as compounds predicted to target them in each transcriptional subtype. Furthermore, we identified bromodomain and casein kinase inhibitors as a potential combination therapy due to their predicted synergy at targeting all cell populations within medulloblastoma. Our studies show the importance of considering cellular heterogeneity when identifying new treatments for medulloblastoma and other brain cancers.

Single-Cell RNA Sequencing Characterizes the Molecular Heterogeneity of the Larval Zebrafish Optic Tectum

10.1101/2021.11.05.467443 ◽

2021 ◽

Author(s):

Annalie Martin ◽

Anne Babbitt ◽

Allison G Pickens ◽

Brett E Pickett ◽

Jonathon T Hill ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Optic Tectum ◽

Cell Types ◽

Cell Populations ◽

Molecular Heterogeneity ◽

Specific Cell ◽

Larval Zebrafish ◽

Enhancer Trap Line ◽

The optic tectum (OT) is a multilaminated midbrain structure that acts as the primary retinorecipient in the zebrafish brain. Homologous to the mammalian superior colliculus, the OT is responsible for the reception and integration of stimuli, followed by elicitation of salient behavioral responses. While the OT has been the focus of functional experiments for decades, less is known concerning specific cell types, microcircuitry, and their individual functions within the OT. Recent efforts have contributed substantially to the knowledge of tectal cell types; however, a comprehensive cell catalog is incomplete. Here we contribute to this growing effort by applying single-cell RNA-sequencing (scRNA-seq) to characterize the transcriptomic profiles of tectal cells labeled by the transgenic enhancer trap line y304Et(cfos:Gal4;UAS:Kaede). We sequenced 13,320 cells, a 4X cellular coverage, and identified 25 putative OT cell populations. Within those cells, we identified several mature and developing neuronal populations, as well as non-neuronal cell types including oligodendrocytes, microglia, and radial glia. Although most mature neurons demonstrate GABAergic activity, several glutamatergic populations are present, as well as one glycinergic population. We also conducted Gene Ontology analysis to identify enriched biological processes, and computed RNA velocity to infer current and future transcriptional cell states. Finally, we conducted in situ hybridization to validate our bioinformatic analyses and spatially map select clusters. In conclusion, the larval zebrafish OT is a complex structure containing at least 25 transcriptionally distinct cell populations. To our knowledge, this is the first time scRNA-seq has been applied to explore the OT alone and in depth.

A Single-cell Transcriptomic Atlas of the Developing Chicken Limb

10.1101/598227 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christian Feregrino ◽

Fabio Sacher ◽

Oren Parnas ◽

Patrick Tschopp

Keyword(s):

Pattern Formation ◽

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Cell Populations ◽

Cell Type ◽

Data Set ◽

Cellular Resolution ◽

Type Specification

AbstractBackgroundThrough precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades’ worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution.ResultsUsing single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits.ConclusionsWe provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.