scholarly journals cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors

2017 ◽  
Author(s):  
Yong Kee Tan ◽  
Xiaomeng Zhang ◽  
Jinmiao Chen
Keyword(s):  
Cell Cycle ◽  
Dendritic Cell ◽  
Single Cell ◽  
Cellular Differentiation ◽  
Developmental Trajectories ◽  
Cellular Heterogeneity ◽  
Good Representation ◽  
Continuous Processes ◽  
Inference Methods ◽  
Relationship Of

AbstractAdvances in single-cell RNA-sequencing have helped reveal the previously underappreciated level of cellular heterogeneity present during cellular differentiation. A static snapshot of single-cell transcriptomes provides a good representation of the various stages of differentiation as differentiation is rarely synchronized between cells. Data from numerous single-cell analyses has suggested that cellular differentiation and development can be conceptualized as continuous processes. Consequently, computational algorithms have been developed to infer pseudotimes and re-ordered cells along developmental trajectories. However, existing pseudotime inference methods generate one-dimensional pseudotime in an unsupervised manner, which is inadequate to elucidate the effects of individual biological processes such as cell cycle and differentiation and the links between them. Here we present a method called cycleX which infers multi-dimensional pseudotimes to reveal putative relationship between cell cycle and differentiation during dendritic cell development. cycleX can be also applied to generate multi-dimensional pseudotime for the relationship among cell cycle, differentiation, trafficking, activation, metabolism and etc.

scholarly journals scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Kaikun Xie ◽  
Yu Huang ◽  
Feng Zeng ◽  
Zehua Liu ◽  
Ting Chen
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Random Projection ◽  
Good Representation ◽  
Rna Seq ◽  
Unsupervised Deep Learning ◽  
High Level ◽  
Computational Resources

Abstract Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based k-means algorithm to accommodate the detection of rare cell types. We analyzed a 1.3 million neural cell dataset within 30 min, obtaining 64 clusters which were mapped to 19 putative cell types. In particular, we further identified three different neural stem cell developmental trajectories in these clusters. We also classified two subpopulations of malignant cells in a small glioblastoma dataset using scAIDE. We anticipate that scAIDE would provide a more in-depth understanding of cell development and diseases.

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2019 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Types ◽  
Cell Cycle Phase ◽  
Cellular Heterogeneity ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Cellular Environment

AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

scholarly journals Single-cell transcriptomics identifies divergent developmental lineage trajectories during human pituitary development

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Shu Zhang ◽  
Yueli Cui ◽  
Xinyi Ma ◽  
Jun Yong ◽  
Liying Yan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Human Pituitary ◽  
Pituitary Development ◽  
Physiological Processes ◽  
Distinct Cell ◽  
Transcriptional Landscape

Abstract The anterior pituitary gland plays a central role in regulating various physiological processes, including body growth, reproduction, metabolism and stress response. Here, we perform single-cell RNA-sequencing (scRNA-seq) of 4113 individual cells from human fetal pituitaries. We characterize divergent developmental trajectories with distinct transitional intermediate states in five hormone-producing cell lineages. Corticotropes exhibit an early intermediate state prior to full differentiation. Three cell types of the PIT-1 lineage (somatotropes, lactotropes and thyrotropes) segregate from a common progenitor coexpressing lineage-specific transcription factors of different sublineages. Gonadotropes experience two multistep developmental trajectories. Furthermore, we identify a fetal gonadotrope cell subtype expressing the primate-specific hormone chorionic gonadotropin. We also characterize the cellular heterogeneity of pituitary stem cells and identify a hybrid epithelial/mesenchymal state and an early-to-late state transition. Here, our results provide insights into the transcriptional landscape of human pituitary development, defining distinct cell substates and subtypes and illustrating transcription factor dynamics during cell fate commitment.

scholarly journals A single-cell–resolution fate map of endoderm reveals demarcation of pancreatic progenitors by cell cycle

2021 ◽  
Vol 118 (25) ◽  
pp. e2025793118
Author(s):  
Yun Yang ◽  
Hao Wang ◽  
Jia He ◽  
Wenchao Shi ◽  
Zhanmei Jiang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Progenitor Cell ◽  
Developmental Trajectories ◽  
Cell Labeling ◽  
G1 Phase ◽  
Fate Map ◽  
Pancreatic Progenitor ◽  
Foregut Endoderm

A progenitor cell could generate a certain type or multiple types of descendant cells during embryonic development. To make all the descendant cell types and developmental trajectories of every single progenitor cell clear remains an ultimate goal in developmental biology. Characterizations of descendant cells produced by each uncommitted progenitor for a full germ layer represent a big step toward the goal. Here, we focus on early foregut endoderm, which generates foregut digestive organs, including the pancreas, liver, foregut, and ductal system, through distinct lineages. Using unbiased single-cell labeling techniques, we label every individual zebrafish foregut endodermal progenitor cell out of 216 cells to visibly trace the distribution and number of their descendant cells. Hence, single-cell–resolution fate and proliferation maps of early foregut endoderm are established, in which progenitor regions of each foregut digestive organ are precisely demarcated. The maps indicate that the pancreatic endocrine progenitors are featured by a cell cycle state with a long G1 phase. Manipulating durations of the G1 phase modulates pancreatic progenitor populations. This study illustrates foregut endodermal progenitor cell fate at single-cell resolution, precisely demarcates different progenitor populations, and sheds light on mechanistic insights into pancreatic fate determination.

scholarly journals Single-cell Atlas Unveils Cellular Heterogeneity and Novel Markers in Human Neonatal and Adult Intervertebral Discs

2021 ◽  
Author(s):  
Wensen Jiang ◽  
Juliane Dagmar Glaeser ◽  
Khosrowdad Salehi ◽  
Giselle Kaneda ◽  
Pranav Mathkar ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Annulus Fibrosus ◽  
Developmental Trajectories ◽  
Intervertebral Discs ◽  
Cellular Heterogeneity ◽  
Gradual Transition ◽  
Nucleus Pulposus Cells ◽  
And Function ◽  
Ivd Degeneration

The origin, composition, distribution, and function of cells in the human intervertebral disc (IVD) has not been fully understood. Here, cell atlases of both human neonatal and adult IVDs have been generated and further assessed by gene ontology pathway enrichment, pseudo-time trajectory, histology, and immunofluorescence. Comparison of cell atlases revealed the presence of several sub-populations of notochordal cells (NC) in the neonatal IVD and a small quantity of NCs and associated markers in the adult IVD. Developmental trajectories predicted that most neonatal NCs develop into adult nucleus pulposus cells (NPCs) while some keep their identity throughout adulthood. A high heterogeneity and gradual transition of annulus fibrosus cells (AFCs) in the neonatal IVD was detected and their potential relevance in IVD development was assessed. Collectively, comparing single-cell atlases between neonatal and adult IVDs delineates the landscape of IVD cell biology and may help discover novel therapeutic targets for IVD degeneration.

scholarly journals Single cell transcriptomics and developmental trajectories of murine cranial neural crest cell fate determination and cell cycle progression

2021 ◽  
Author(s):  
Yu Ji ◽  
Shuwen Zhang ◽  
Kurt Reynolds ◽  
Ran Gu ◽  
Moira McMahon ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Neural Crest ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Cycle Progression ◽  
Developmental Trajectories ◽  
Cell Fate Determination ◽  
Cycle Progression ◽  
Fate Determination ◽  
Neural Lineage

Cranial neural crest (NC) cells migrate long distances to populate the future craniofacial regions and give rise to various tissues, including facial cartilage, bones, connective tissues, and cranial nerves. However, the mechanism that drives the fate determination of cranial NC cells remains unclear. Using single-cell RNA sequencing combined genetic fate mapping, we reconstructed developmental trajectories of cranial NC cells, and traced their differentiation in mouse embryos. We identified four major cranial NC cell lineages at different status: pre-epithelial-mesenchymal transition, early migration, NC-derived mesenchymal cells, and neural lineage cells from embryonic days 9.5 to 12.5. During migration, the first cell fate determination separates cranial sensory ganglia, the second generates mesenchymal progenitors, and the third separates other neural lineage cells. We then focused on the early facial prominences that appear to be built by undifferentiated, fast-dividing NC cells that possess similar transcriptomic landscapes, which could be the drive for the facial developmental robustness. The post-migratory cranial NC cells exit the cell cycle around embryonic day 11.5 after facial shaping is completed and initiates further fate determination and differentiation processes. Our results demonstrate the transcriptomic landscapes during dynamic cell fate determination and cell cycle progression of cranial NC lineage cells and also suggest that the transcriptomic regulation of the balance between proliferation and differentiation of the post-migratory cranial NC cells can be a key for building up unique facial structures in vertebrates.

scholarly journals STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

2018 ◽  
Author(s):  
Huidong Chen ◽  
Luca Albergante ◽  
Jonathan Y Hsu ◽  
Caleb A Lareau ◽  
Giosue` Lo Bosco ◽  
...  
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Developmental Trajectories ◽  
Cellular Heterogeneity ◽  
State Transitions ◽  
Omics Data ◽  
Transcriptomic Data ◽  
Lineage Differentiation ◽  
Cell Trajectories

AbstractSingle-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

scholarly journals Single-cell transcriptional diversity is a hallmark of developmental potential

2019 ◽  
Author(s):  
Gunsagar S. Gulati ◽  
Shaheen S. Sikandar ◽  
Daniel J. Wesche ◽  
Anoop Manjunath ◽  
Anjan Bharadwaj ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Developmental Trajectories ◽  
Human Breast ◽  
Developmental Potential ◽  
Powerful Approach ◽  
Luminal Progenitor Cells ◽  
Resident Stem Cells ◽  
New Framework

AbstractSingle-cell RNA-sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation without prior knowledge has remained challenging. Here we describe a simple yet robust determinant of developmental potential—the number of detectably expressed genes per cell— and leverage this measure of transcriptional diversity to develop a new framework for predicting ordered differentiation states from scRNA-seq data. When evaluated on ~150,000 single-cell transcriptomes spanning 53 lineages and five species, our approach, called CytoTRACE, outperformed previous methods and ~19,000 molecular signatures for resolving experimentally-confirmed developmental trajectories. In addition, it enabled unbiased identification of tissue-resident stem cells, including cells with long-term regenerative potential. When used to analyze human breast tumors, we discovered candidate genes associated with less-differentiated luminal progenitor cells and validated GULP1 as a novel gene involved in tumorigenesis. Our study establishes a key RNA-based correlate of developmental potential and provides a new platform for robust delineation of cellular hierarchies (https://cytotrace.stanford.edu).

scholarly journals Inference of multiple trajectories in single cell RNA-seq data from RNA velocity

2020 ◽  
Author(s):  
Ziqi Zhang ◽  
Xiuwei Zhang
Keyword(s):  
Single Cell ◽  
Developmental Trajectories ◽  
Stem Cell Differentiation ◽  
Root Cell ◽  
Current State ◽  
Synthetic Datasets ◽  
Cell Trajectory ◽  
Direction Information ◽  
Inference Methods ◽  
Developmental Dynamics

AbstractTrajectory inference methods are used to infer the developmental dynamics of a continuous biological process such as stem cell differentiation and cancer cell development. Most of the current trajectory inference methods infer cell developmental trajectories based on the transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like trees or cycles, and the directions of the trajectory can only be partly inferred when the root cell is provided. We present CellPaths, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPaths is able to find multiple high-resolution trajectories instead of one single trajectory from traditional trajectory inference methods, and the trajectory structure is no longer constrained to be of any specific topology. The direction information provided by RNA-velocity also allows CellPaths to automatically detect root cell and differentiation direction. We evaluate CellPaths on both real and synthetic datasets. The result shows that CellPaths finds more accurate and detailed trajectories compared to current state-of-the-art trajectory inference methods.

scholarly journals SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development

2016 ◽  
Author(s):  
Robrecht Cannoodt ◽  
Wouter Saelens ◽  
Dorine Sichien ◽  
Simon Tavernier ◽  
Sophie Janssens ◽  
...  
Keyword(s):  
Dendritic Cell ◽  
Single Cell ◽  
Dynamic Process ◽  
Comprehensive Evaluation ◽  
Cell Development ◽  
Genome Wide ◽  
Inference Methods ◽  
Opening Up

1SummaryRecent advances in RNA sequencing enable the generation of genome-wide expression data at the single-cell level, opening up new avenues for transcriptomics and systems biology. A new application of single-cell whole-transcriptomics is the unbiased ordering of cells according to their progression along a dynamic process of interest. We introduce SCORPIUS, a method which can effectively reconstruct an ordering of individual cells without any prior information about the dynamic process. Comprehensive evaluation using ten scRNA-seq datasets shows that SCORPIUS consistently outperforms state-of-the-art techniques. We used SCORPIUS to generate novel hypotheses regarding dendritic cell development, which were subsequently validated in vivo. This work enables data-driven investigation and characterization of dynamic processes and lays the foundation for objective benchmarking of future trajectory inference methods.

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