scholarly journals Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior

2020 ◽  
Author(s):  
Jin-Hong Du ◽  
Ming Gao ◽  
Jingshu Wang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectories ◽  
Probabilistic Method ◽  
Synthetic Data ◽  
Developmental Trajectory ◽  
Projection Neurons ◽  
Single Cell Rna Sequencing ◽  
Mixture Prior ◽  
Inference Methods

AbstractTrajectory inference methods analyze thousands of cells from single-cell sequencing technologies and computationally infer their developmental trajectories. Though many tools have been developed for trajectory inference, most of them lack a coherent statistical model and reliable uncertainty quantification. In this paper, we present VITAE, a probabilistic method combining a latent hierarchical mixture model with variational autoencoders to infer trajectories from posterior approximations. VITAE is computationally scalable and can adjust for confounding covariates to integrate multiple datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. We also apply VITAE to jointly analyze two single-cell RNA sequencing datasets on mouse neocortex. Our results suggest that VITAE can successfully uncover a shared developmental trajectory of the projection neurons and reliably order cells from both datasets along the inferred trajectory.

scholarly journals Comprehensive single-cell sequencing reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of nasopharyngeal carcinoma

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lanqi Gong ◽  
Dora Lai-Wan Kwong ◽  
Wei Dai ◽  
Pingan Wu ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Single Cell ◽  
Rna Sequencing ◽  
Progression Free Survival ◽  
Developmental Trajectory ◽  
Free Survival ◽  
Genetic Profiling ◽  
Single Cell Rna Sequencing ◽  
Infiltrating Cells ◽  
Patient Prognosis

AbstractThe tumor microenvironment (TME) of nasopharyngeal carcinoma (NPC) harbors a heterogeneous and dynamic stromal population. A comprehensive understanding of this tumor-specific ecosystem is necessary to enhance cancer diagnosis, therapeutics, and prognosis. However, recent advances based on bulk RNA sequencing remain insufficient to construct an in-depth landscape of infiltrating stromal cells in NPC. Here we apply single-cell RNA sequencing to 66,627 cells from 14 patients, integrated with clonotype identification on T and B cells. We identify and characterize five major stromal clusters and 36 distinct subpopulations based on genetic profiling. By comparing with the infiltrating cells in the non-malignant microenvironment, we report highly representative features in the TME, including phenotypic abundance, genetic alternations, immune dynamics, clonal expansion, developmental trajectory, and molecular interactions that profoundly influence patient prognosis and therapeutic outcome. The key findings are further independently validated in two single-cell RNA sequencing cohorts and two bulk RNA-sequencing cohorts. In the present study, we reveal the correlation between NPC-specific characteristics and progression-free survival. Together, these data facilitate the understanding of the stromal landscape and immune dynamics in NPC patients and provides deeper insights into the development of prognostic biomarkers and therapeutic targets in the TME.

Developmental trajectory of oligodendrocyte progenitor cells in the human brain revealed by single cell RNA sequencing

Glia ◽  
2020 ◽  
Vol 68 (6) ◽  
pp. 1291-1303 ◽  
Author(s):  
Kelly Perlman ◽  
Charles P. Couturier ◽  
Moein Yaqubi ◽  
Arnaud Tanti ◽  
Qiao‐Ling Cui ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Progenitor Cells ◽  
Rna Sequencing ◽  
Developmental Trajectory ◽  
Oligodendrocyte Progenitor Cells ◽  
Oligodendrocyte Progenitor ◽  
Single Cell Rna Sequencing

scholarly journals Single-cell transcriptome sequencing: recent advances and remaining challenges

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 182 ◽  
Author(s):  
Serena Liu ◽  
Cole Trapnell
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectories ◽  
Population Data ◽  
Transcriptional Dynamics ◽  
Single Cell Rna Sequencing ◽  
Cell Assays ◽  
Potential Applications ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

scholarly journals Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-cell RNA Sequencing

2017 ◽  
Author(s):  
Hongjie Li ◽  
Felix Horns ◽  
Bing Wu ◽  
Qijing Xie ◽  
Jiefu Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Neuronal Cell ◽  
Projection Neuron ◽  
Projection Neurons ◽  
Open Questions ◽  
Olfactory Glomeruli ◽  
Single Cell Rna Sequencing ◽  
Genes Encoding ◽  
Circuit Assembly

AbstractHow a neuronal cell type is defined and how this relates to its transcriptome are still open questions. The Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: Different PN classes target dendrites to distinct olfactory glomeruli and PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA-sequencing, we comprehensively characterized the transcriptomes of 40 PN classes and unequivocally identified transcriptomes for 6 classes. We found a new lineage-specific transcription factor that instructs PN dendrite targeting. Transcriptomes of closely-related PN classes exhibit the largest difference during circuit assembly, but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Genes encoding transcription factors and cell-surface molecules are the most differentially expressed, indicating their central roles in specifying neuronal identity. Finally, we show that PNs use highly redundant combinatorial molecular codes to distinguish subtypes, enabling robust specification of cell identity and circuit assembly.

Landscape of Cell Heterogeneity and Evolutionary Trajectory in Ulcerative Colitis-associated Colon Cancer Revealed by Single-cell Rna Sequencing

2020 ◽  
Author(s):  
Quan Wang ◽  
Zhu Wang ◽  
Zhen Zhang ◽  
Wei Zhang ◽  
Mengmeng Zhang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Colon Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectory ◽  
The Cancer Genome Atlas ◽  
Cell Heterogeneity ◽  
Evolutionary Trajectory ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing

Abstract Background: Patients with colitis-associated cancer (CAC), a particular kind of colorectal cancer that develops from inflammatory bowel diseases (IBDs), have an earlier morbidity and a poorer prognosis. However, in CAC, single cell transcriptome analysis of the microenvironment composition and characteristics has yet to be performed. To understand the intra-tumor heterogeneity in CAC and to reveal a potential evolutionary trajectory from ulcerative colitis (UC) to CAC at the single cell level. Methods: Fresh samples of tumor- and adjacent tissue, from a CAC patient with pT3N1M0, were examined by single cell RNA sequencing (scRNA-seq). Data from The Cancer Genome Atlas (TCGA) and The Human Protein Atlas were used to confirm the different expression levels in normal and tumor tissues and to determine their relationships with prognosis. Results: Ultimately, 4,777 single-cell transcriptomes (1220 genes per cell) were studied, which composed of 2,250 (47%) and 2,527(53%) originated from tumor- and non-malignant tissue, respectively. And we defined the composition of cancer-associated stromal cells and identified six cell clusters included myeloid, T and B cells, fibroblasts, endothelial and epithelial cells. Likewise, the notable pathways and transcription factors (TFs) involved of these cell clusters were analyzed and described. Moreover, we graphed the precise cellular composition and developmental trajectory from UC to UC-associated colon cancer, and predicted that CD74, CLCA1 and DPEP1 had a potential role in the disease progression. Conclusions: scRNA-seq technology could reveal intratumor cell heterogeneity in ulcerative colitis-associated colon cancer, and might provide a promising direction to seek the novel potential therapeutic targets in the evolution from IBD to CAC.

scholarly journals Single-cell RNA-sequencing identifies the developmental trajectory of C-Myc-dependent NK1.1− T-bet+ intraepithelial lymphocyte precursors

2019 ◽  
Vol 13 (2) ◽  
pp. 257-270 ◽  
Author(s):  
Jonas F. Hummel ◽  
Patrice Zeis ◽  
Karolina Ebert ◽  
Jonas Fixemer ◽  
Philip Konrad ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Intraepithelial Lymphocytes ◽  
Developmental Trajectory ◽  
High Avidity ◽  
Clonal Deletion ◽  
Adaptive Immune Cells ◽  
Single Cell Rna Sequencing ◽  
Factor C

Abstract Natural intraepithelial lymphocytes (IELs) are thymus-derived adaptive immune cells, which are important contributors to intestinal immune homeostasis. Similar to other innate-like T cells, they are induced in the thymus through high-avidity interaction that would otherwise lead to clonal deletion in conventional CD4 and CD8 T cells. By applying single-cell RNA-sequencing (scRNA-seq) on a heterogeneous population of thymic CD4−CD8αβ−TCRαβ+NK1.1− IEL precursors (NK1.1− IELPs), we define a developmental trajectory that can be tracked based on the sequential expression of CD122 and T-bet. Moreover, we identify the Id proteins Id2 and Id3 as a novel regulator of IELP development and show that all NK1.1− IELPs progress through a PD-1 stage that precedes the induction of T-bet. The transition from PD-1 to T-bet is regulated by the transcription factor C-Myc, which has far reaching effects on cell cycle, energy metabolism, and the translational machinery during IELP development. In summary, our results provide a high-resolution molecular framework for thymic IEL development of NK1.1− IELPs and deepen our understanding of this still elusive cell type.

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.

scholarly journals Self-assembling Manifolds in Single-cell RNA Sequencing Data

2018 ◽  
Author(s):  
Alexander J. Tarashansky ◽  
Yuan Xue ◽  
Pengyang Li ◽  
Stephen R. Quake ◽  
Bo Wang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Selection Strategy ◽  
Sequencing Data ◽  
Biologically Relevant ◽  
Self Assembling ◽  
Single Cell Rna Sequencing ◽  
Stem Cell Populations

AbstractSingle-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is a challenging task. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

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