Single Cell RNA Sequencing of Human Pulmonary Endarterectomy Specimen Reveals Distinct Cell Populations and Gene Profiles

Tissue-specific autoimmune diseases are driven by activation of diverse immune cells in the target organs. However, the molecular signatures of immune cell populations over time in an autoimmune process remain poorly defined. Using single-cell RNA sequencing, we performed an unbiased examination of diverse islet-infiltrating cells during autoimmune diabetes in the nonobese diabetic mouse. The data revealed a landscape of transcriptional heterogeneity across the lymphoid and myeloid compartments. Memory CD4 and cytotoxic CD8 T cells appeared early in islets, accompanied by regulatory cells with distinct phenotypes. Surprisingly, we observed a dramatic remodeling in the islet microenvironment, in which the resident macrophages underwent a stepwise activation program. This process resulted in polarization of the macrophage subpopulations into a terminal proinflammatory state. This study provides a single-cell atlas defining the staging of autoimmune diabetes and reveals that diabetic autoimmunity is driven by transcriptionally distinct cell populations specialized in divergent biological functions.

Download Full-text

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 ◽

Single Cell Rna Sequencing ◽

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.

Download Full-text

Modeling cellular crosstalk and organotypic vasculature development with human iPSC-derived endothelial cells and cardiomyocytes

10.1101/2020.05.04.075846 ◽

2020 ◽

Author(s):

Emmi Helle ◽

Minna Ampuja ◽

Alexandra Dainis ◽

Laura Antola ◽

Elina Temmes ◽

...

Keyword(s):

Endothelial Cells ◽

Shear Stress ◽

Endothelial Cell ◽

Single Cell ◽

Rna Sequencing ◽

Tissue Growth ◽

Cell Phenotype ◽

Cell Populations ◽

Pump System ◽

Single Cell Rna Sequencing

AbstractRationaleCell-cell interactions are crucial for the development and function of the organs. Endothelial cells act as essential regulators of tissue growth and regeneration. In the heart, endothelial cells engage in delicate bidirectional communication with cardiomyocytes. The mechanisms and mediators of this crosstalk are still poorly known. Furthermore, endothelial cells in vivo are exposed to blood flow and their phenotype is greatly affected by shear stress.ObjectiveWe aimed to elucidate how cardiomyocytes regulate the development of organotypic phenotype in endothelial cells. In addition, the effects of flow-induced shear stress on endothelial cell phenotype were studied.Methods and resultsHuman induced pluripotent stem cell (hiPSC) -derived cardiomyocytes and endothelial cells were grown either as a monoculture or as a coculture. hiPS-endothelial cells were exposed to flow using the Ibidi-pump system. Single-cell RNA sequencing was performed to define cell populations and to uncover the effects on their transcriptomic phenotypes. The hiPS-cardiomyocyte differentiation resulted in two distinct populations; atrial and ventricular. Coculture had a more pronounced effect on hiPS-endothelial cells compared to hiPS-cardiomyocytes. Coculture increased hiPS-endothelial cell expression of transcripts related to vascular development and maturation, cardiac development, and the expression of cardiac endothelial cell -specific genes. Exposure to flow significantly reprogrammed the hiPS-endothelial cell transcriptome, and surprisingly, promoted the appearance of both venous and arterial clusters.ConclusionsSingle-cell RNA sequencing revealed distinct atrial and ventricular cell populations in hiPS-cardiomyocytes, and arterial and venous-like cell populations in flow exposed hiPS-endothelial cells. hiPS-endothelial cells acquired cardiac endothelial cell identity in coculture. Our study demonstrated that hiPS-cardiomoycytes and hiPS-endothelial cells readily adapt to coculture and flow in a consistent and relevant manner, indicating that the methods used represent improved physiological cell culturing conditions that potentially are more relevant in disease modelling. In addition, novel cardiomyocyte-endothelial cell crosstalk mediators were revealed.

Download Full-text

Single-Cell RNA Sequencing in Human Retinal Degeneration Reveals Distinct Glial Cell Populations

Cells ◽

10.3390/cells9020438 ◽

2020 ◽

Vol 9 (2) ◽

pp. 438 ◽

Cited By ~ 3

Author(s):

Andrew P. Voigt ◽

Elaine Binkley ◽

Miles J. Flamme-Wiese ◽

Shemin Zeng ◽

Adam P. DeLuca ◽

...

Keyword(s):

Single Cell ◽

Retinal Degeneration ◽

Rna Sequencing ◽

Glial Cell ◽

Glial Cells ◽

Peripheral Retina ◽

Cell Populations ◽

Specific Gene ◽

Autoimmune Retinopathy ◽

Single Cell Rna Sequencing

Degenerative diseases affecting retinal photoreceptor cells have numerous etiologies and clinical presentations. We clinically and molecularly studied the retina of a 70-year-old patient with retinal degeneration attributed to autoimmune retinopathy. The patient was followed for 19 years for progressive peripheral visual field loss and pigmentary changes. Single-cell RNA sequencing was performed on foveal and peripheral retina from this patient and four control patients, and cell-specific gene expression differences were identified between healthy and degenerating retina. Distinct populations of glial cells, including astrocytes and Müller cells, were identified in the tissue from the retinal degeneration patient. The glial cell populations demonstrated an expression profile consistent with reactive gliosis. This report provides evidence that glial cells have a distinct transcriptome in the setting of human retinal degeneration and represents a complementary clinical and molecular investigation of a case of progressive retinal disease.

Download Full-text

Single Cell RNA Sequencing of Rare Immune Cell Populations

Frontiers in Immunology ◽

10.3389/fimmu.2018.01553 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 39

Author(s):

Akira Nguyen ◽

Weng Hua Khoo ◽

Imogen Moran ◽

Peter I. Croucher ◽

Tri Giang Phan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Populations ◽

Single Cell Rna Sequencing

Download Full-text

A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset

10.1101/2020.09.20.305474 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xin Chen ◽

Zhaowei Yang ◽

Wanqiu Chen ◽

Yongmei Zhao ◽

Andrew Farmer ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Whole Genome Sequencing Data ◽

Differential Analysis ◽

Sequencing Data ◽

Reference Dataset ◽

Batch Correction ◽

Distinct Cell ◽

Single Cell Rna Sequencing ◽

Benchmark Datasets

AbstractSingle-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

Download Full-text

Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

10.1101/234872 ◽

2018 ◽

Cited By ~ 7

Author(s):

Aaron T. L. Lun ◽

Samantha Riesenfeld ◽

Tallulah Andrews ◽

Tomas Gomes ◽

John C. Marioni ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Real Data ◽

Cell Types ◽

Sequencing Data ◽

Minimum Threshold ◽

False Discovery ◽

Distinct Cell ◽

Single Cell Rna Sequencing ◽

Unique Molecular Identifier

AbstractDroplet-based single-cell RNA sequencing protocols have dramatically increased the throughput and efficiency of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Existing methods for cell calling set a minimum threshold on the total unique molecular identifier (UMI) count for each library, which indiscriminately discards cell libraries with low UMI counts. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that our method has greater power than existing approaches for detecting cell libraries with low UMI counts, while controlling the false discovery rate among detected cells. We also apply our method to real data, where we show that the use of our method results in the retention of distinct cell types that would otherwise have been discarded.

Download Full-text

COMP-16. COMPREHENSIVE TRANSCRIPTOMIC ANALYSIS OF SINGLE CELLS FROM RECURRENT AND PRIMARY GLIOBLASTOMA TO PREDICT CELL-TYPE SPECIFIC THERAPEUTICS

Neuro-Oncology ◽

10.1093/neuonc/noz175.259 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi64-vi64

Author(s):

Robert Suter ◽

Vasileios Stathias ◽

Anna Jermakowicz ◽

Alexa Semonche ◽

Michael Ivan ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Tumor Heterogeneity ◽

Drug Response ◽

Adult Brain ◽

Cell Populations ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Recurrent Gbm

Abstract Glioblastoma (GBM) remains the most common adult brain tumor, with poor survival expectations, and no new therapeutic modalities approved in the last decade. Our laboratories have recently demonstrated that the integration of a transcriptional disease signature obtained from The Cancer Genome Atlas’ GBM dataset with transcriptional cell drug-response signatures in the LINCS L1000 dataset yields possible combinatorial therapeutics. Considering the extreme intra-tumor heterogeneity associated with the disease, we hypothesize that the utilization of single-cell RNA-sequencing (scRNA-seq) of patient tumors will further strengthen our predictive model by providing insight on the unique transcriptomes of the cellular niches present within these tumors, and into the transcriptional dynamics of these same cellular niches. By sequencing single-cell transcriptomes from recurrent GBM tumors resected from patients at the University of Miami, and integrating our datasets with previously published scRNA-seq data from primary GBM tumors, we are able to gain additional insight into the differences between these clinical distinctions. We have analyzed the differential expression of kinases both across and within distinct cell populations of primary and recurrent GBM tumors. This transcriptional map of kinase expression represents the heterogeneity of potential targets within individual tumors and between recurrent and primary GBM. Additionally, by generating disease signatures unique to each cellular population, and integrating these with transcriptional drug-response signatures from LINCS, we are able to predict compounds to target specific cell populations within GMB tumors. Additional computational techniques such as RNA velocity analysis and cell cycle scoring elucidate temporal insights to further prioritize these cell-type specific therapeutics, and reveal the intra-cellular dynamics present within these tumors. Collectively, our studies suggest that we have developed a novel omics pipeline based on the single cell RNA-sequencing of individual GBM cells that addresses intra-tumor heterogeneity, and may lead to novel therapeutic combinations for the treatment of this incurable disease.

Download Full-text