scholarly journals Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level

2020 ◽  
Author(s):  
Suraj Kannan ◽  
Michael Farid ◽  
Brian L. Lin ◽  
Matthew Miyamoto ◽  
Chulan Kwon
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Pluripotent Stem Cell ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Batch Effects ◽  
Cell Level ◽  
Single Cell Rna Sequencing

The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.Significance StatementThere is significant interest in generating mature cardiomyocytes from pluripotent stem cells. However, there are currently few effective metrics to quantify the maturation status of a single cardiomyocyte. We developed a new metric for measuring cardiomyocyte maturation using single cell RNA-sequencing data. This metric, called entropy score, uses the gene distribution to estimate maturation at the single cell level. Entropy score enables comparing pluripotent stem cell-derived cardiomyocytes directly against endogenously-isolated cardiomyocytes. Thus, entropy score can better assist in development of approaches to improve the maturation of pluripotent stem cell-derived cardiomyocytes.

scholarly journals Single‐Cell Transcriptomics of Engineered Cardiac Tissues From Patient‐Specific Induced Pluripotent Stem Cell–Derived Cardiomyocytes Reveals Abnormal Developmental Trajectory and Intrinsic Contractile Defects in Hypoplastic Right Heart Syndrome

2020 ◽  
Vol 9 (20) ◽  
Author(s):  
Yin‐Yu Lam ◽  
Wendy Keung ◽  
Chun‐Ho Chan ◽  
Lin Geng ◽  
Nicodemus Wong ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Pluripotent Stem Cell ◽  
Cardiac Tissue ◽  
Induced Pluripotent Stem Cell ◽  
Developmental Trajectory ◽  
Single Cell Rna Sequencing ◽  
Human Cardiac Tissue ◽  
Induced Pluripotent

Background To understand the intrinsic cardiac developmental and functional abnormalities in pulmonary atresia with intact ventricular septum (PAIVS) free from effects secondary to anatomic defects, we performed and compared single‐cell transcriptomic and phenotypic analyses of patient‐ and healthy subject–derived human‐induced pluripotent stem cell–derived cardiomyocytes (hiPSC‐CMs) and engineered tissue models. Methods and Results We derived hiPSC lines from 3 patients with PAIVS and 3 healthy subjects and differentiated them into hiPSC‐CMs, which were then bioengineered into the human cardiac anisotropic sheet and human cardiac tissue strip custom‐designed for electrophysiological and contractile assessments, respectively. Single‐cell RNA sequencing (scRNA‐seq) of hiPSC‐CMs, human cardiac anisotropic sheet, and human cardiac tissue strip was performed to examine the transcriptomic basis for any phenotypic abnormalities using pseudotime and differential expression analyses. Through pseudotime analysis, we demonstrated that bioengineered tissue constructs provide pro‐maturational cues to hiPSC‐CMs, although the maturation and development were attenuated in PAIVS hiPSC‐CMs. Furthermore, reduced contractility and prolonged contractile kinetics were observed with PAIVS human cardiac tissue strips. Consistently, single‐cell RNA sequencing of PAIVS human cardiac tissue strips and hiPSC‐CMs exhibited diminished expression of cardiac contractile apparatus genes. By contrast, electrophysiological aberrancies were absent in PAIVS human cardiac anisotropic sheets. Conclusions Our findings were the first to reveal intrinsic abnormalities of cardiomyocyte development and function in PAIVS free from secondary effects. We conclude that hiPSC‐derived engineered tissues offer a unique method for studying primary cardiac abnormalities and uncovering pathogenic mechanisms that underlie sporadic congenital heart diseases.

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

scholarly journals VASC: dimension reduction and visualization of single cell RNA sequencing data by deep variational autoencoder

2017 ◽  
Author(s):  
Dongfang Wang ◽  
Jin Gu
Keyword(s):  
Dimension Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Original Data ◽  
Marker Genes ◽  
Single Cell Level ◽  
Sequencing Data ◽  
Cell Level ◽  
Variational Autoencoder ◽  
Single Cell Rna Sequencing

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities in single cell level. It is an important step for studying cell sub-populations and lineages based on scRNA-seq data by finding an effective low-dimensional representation and visualization of the original data. The scRNA-seq data are much noiser than traditional bulk RNA-Seq: in the single cell level, the transcriptional fluctuations are much larger than the average of a cell population and the low amount of RNA transcripts will increase the rate of technical dropout events. In this study, we proposed VASC (deep Variational Autoencoder for scRNA-seq data), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. It can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on twenty datasets, VASC shows superior performances in most cases and broader dataset compatibility compared with four state-of-the-art dimension reduction methods. Then, for a case study of pre-implantation embryos, VASC successfully re-establishes the cell dynamics and identifies several candidate marker genes associated with the early embryo development.

scholarly journals Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level

2021 ◽  
Vol 17 (9) ◽  
pp. e1009305
Author(s):  
Suraj Kannan ◽  
Michael Farid ◽  
Brian L. Lin ◽  
Matthew Miyamoto ◽  
Chulan Kwon
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Pancreatic Beta Cells ◽  
Ad Hoc ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Batch Effects ◽  
Biologically Relevant ◽  
Novel Approach

The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.

scholarly journals Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fen Ma ◽  
Siwei Zhang ◽  
Lianhao Song ◽  
Bozhi Wang ◽  
Lanlan Wei ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Communication ◽  
Cellular Communication ◽  
Specific Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Advantages And Disadvantages ◽  
Receptor Interactions ◽  
Single Cell Rna Sequencing

Abstract Background Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases. Main body This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out. Conclusion The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

scholarly journals Trajectory reconstruction identifies dysregulation of perinatal maturation programs in pluripotent stem cell-derived cardiomyocytes

2021 ◽  
Author(s):  
Suraj Kannan ◽  
Matthew Miyamoto ◽  
Brian L. Lin ◽  
Chulan Kwon
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Transcription Factors ◽  
Single Cell ◽  
Pluripotent Stem Cell ◽  
Directed Differentiation ◽  
Trajectory Reconstruction ◽  
Single Cell Rna Sequencing

ABSTRACTA primary limitation in the clinical application of pluripotent stem cell derived cardiomyocytes (PSC-CMs) is the failure of these cells to achieve full functional maturity. In vivo, cardiomyocytes undergo numerous adaptive changes during perinatal maturation. By contrast, PSC-CMs fail to fully undergo these developmental processes, instead remaining arrested at an embryonic stage of maturation. To date, however, the precise mechanisms by which directed differentiation differs from endogenous development, leading to consequent PSC-CM maturation arrest, are unknown. The advent of single cell RNA-sequencing (scRNA-seq) has offered great opportunities for studying CM maturation at single cell resolution. However, perinatal cardiac scRNA-seq has been limited owing to technical difficulties in the isolation of single CMs. Here, we used our previously developed large particle fluorescence-activated cell sorting approach to generate an scRNA-seq reference of mouse in vivo CM maturation with extensive sampling of perinatal time periods. We subsequently generated isogenic embryonic stem cells and created an in vitro scRNA-seq reference of PSC-CM directed differentiation. Through trajectory reconstruction methods, we identified a perinatal maturation program in endogenous CMs that is poorly recapitulated in vitro. By comparison of our trajectories with previously published human datasets, we identified a network of nine transcription factors (TFs) whose targets are consistently dysregulated in PSC-CMs across species. Notably, we demonstrated that these TFs are only partially activated in common ex vivo approaches to engineer PSC-CM maturation. Our study represents the first direct comparison of CM maturation in vivo and in vitro at the single cell level, and can be leveraged towards improving the clinical viability of PSC-CMs.Significance StatementThere is a significant clinical need to generate mature cardiomyocytes from pluripotent stem cells. However, to date, most differentiation protocols yield phenotypically immature cardiomyocytes. The mechanisms underlying this poor maturation state are unknown. Here, we used single cell RNA-sequencing to compare cardiomyocyte maturation pathways in endogenous and pluripotent stem cell-derived cardiomyocytes. We found that in vitro, cardiomyocytes fail to undergo critical perinatal gene expression changes necessary for complete maturation. We found that key transcription factors regulating these changes are poorly expressed in vitro. Our study provides a better understanding of cardiomyocyte maturation both in vivo and in vitro, and may lead to improved approaches for engineering mature cardiomyocytes from stem cells.

scholarly journals TMIC-06. MYELOID POPULATIONS AND THE EFFECT OF NEOADJUVANT PD-1 INHIBITION IN THE GLIOBLASTOMA MICROENVIRONMENT: A SURFACEOMIC AND TRANSCRIPTOMIC DISSECTION AT THE SINGLE-CELL LEVEL

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi248-vi248
Author(s):  
Aaron Mochizuki ◽  
Alexander Lee ◽  
Joey Orpilla ◽  
Jenny Kienzler ◽  
Mildred Galvez ◽  
...  
Keyword(s):  
Brain Tumor ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Myeloid Cells ◽  
Single Cell Level ◽  
Mass Cytometry ◽  
Cell Level ◽  
Single Cell Rna Sequencing

Abstract INTRODUCTION Glioblastoma (GBM) is the most common malignant brain tumor in adults and is associated with a dismal prognosis. Neoadjuvant anti-PD-1 blockade has demonstrated efficacy in melanoma, non-small cell lung cancer and recurrent GBM; however, responses vary. While T cells have garnered considerable attention in the context of immunotherapy, the role of myeloid cells in the GBM microenvironment remains controversial. METHODS We isolated CD45+ immune populations from patients who underwent brain tumor resection at UCLA. We hypothesized that myeloid cells in glioblastoma contribute to T cell dysfunction; however, this immune suppression can be mitigated by neoadjuvant PD-1 inhibition. To test this, we utilized mass cytometry and single-cell RNA sequencing to characterize these immune populations. RESULTS Mass cytometry profiling of tumor infiltrating lymphocytes from patients with GBM demonstrated a preponderance of CD11b+ myeloid populations (75% versus 25% CD3+). At the transcriptomic level, myeloid cells in newly diagnosed GBMs exhibited decreased expression of CCL4 (loge fold change -1.18, Bonferroni-adjusted P = 1.62x10-254) and its ligands compared to anaplastic astrocytoma. In ranked gene set enrichment analysis, patients who received neoadjuvant pembrolizumab demonstrated enrichment in TNFα-, NFκB- and lipid metabolism-related gene sets by bootstrapped Kolmogorov-Smirnov test (Benjamini-Hochberg adjusted P = 4.74x10-3, 1.45x10-2 and 2.48x10-3, respectively) in tumor-associated myeloid populations. Additionally, single-cell trajectory analysis demonstrated increased CCL4 and decreased ISG15 with neoadjuvant checkpoint inhibition. CONCLUSIONS Here, we utilize mass cytometry and single-cell RNA sequencing to demonstrate the predominance and transcriptomic features of myeloid populations in GBM. Myeloid cells in patients who receive neoadjuvant PD-1 blockade re-express increased levels NFκB, TNFα and CCL4, a cytokine crucial for the recruitment of dendritic cells to the tumor for antigen-specific T cell activation. By delving into the GBM microenvironment at the single-cell level, we hope to better delineate the role of myeloid populations in this uniformly fatal tumor.

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