scholarly journals Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Grace Hui Ting Yeo ◽  
Sachit D. Saksena ◽  
David K. Gifford
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Cell Fate ◽  
Lineage Tracing ◽  
Pancreatic Β Cell ◽  
Β Cell ◽  
Modeling Framework ◽  
Generative Modeling ◽  
Cell Fate Prediction ◽  
Cell Trajectories

AbstractExisting computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from time-series scRNA-seq data. We validate PRESCIENT on an experimental lineage tracing dataset, where we show that PRESCIENT is able to predict the fate biases of progenitor cells in hematopoiesis when accounting for cell proliferation, improving upon the best-performing existing method. We demonstrate how PRESCIENT can simulate trajectories for perturbed cells, recovering the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations, and is available at https://github.com/gifford-lab/prescient.

scholarly journals Generative modeling of single-cell population time series for inferring cell differentiation landscapes

2020 ◽  
Author(s):  
Grace H.T. Yeo ◽  
Sachit D. Saksena ◽  
David K. Gifford
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Cell Differentiation ◽  
Single Cell ◽  
Cell Fate ◽  
Lineage Tracing ◽  
Model Framework ◽  
Modeling Framework ◽  
Generative Modeling ◽  
Model Complex

SummaryExisting computational methods that use single-cell RNA-sequencing for cell fate prediction either summarize observations of cell states and their couplings without modeling the underlying differentiation process, or are limited in their capacity to model complex differentiation landscapes. Thus, contemporary methods cannot predict how cells evolve stochastically and in physical time from an arbitrary starting expression state, nor can they model the cell fate consequences of gene expression perturbations. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from single-cell time-series gene expression data. Our generative model framework provides insight into the process of differentiation and can simulate differentiation trajectories for arbitrary gene expression progenitor states. We validate our method on a recently published experimental lineage tracing dataset that provides observed trajectories. We show that this model is able to predict the fate biases of progenitor cells in neutrophil/macrophage lineages when accounting for cell proliferation, improving upon the best-performing existing method. We also show how a model can predict trajectories for cells not found in the model’s training set, including cells in which genes or sets of genes have been perturbed. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations. PRESCIENT models are able to recover the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation.

scholarly journals Control of pancreatic β-cell fate by insulin signaling: The sweet spot hypothesis

Cell Cycle ◽  
2008 ◽  
Vol 7 (10) ◽  
pp. 1343-1347 ◽  
Author(s):  
James D Johnson ◽  
Emilyn U. Alejandro
Keyword(s):  
Cell Fate ◽  
Insulin Signaling ◽  
Pancreatic Β Cell ◽  
Sweet Spot ◽  
Β Cell

scholarly journals Normalization and De-noising of Single-cell Hi-C Data with BandNorm and 3DVI

2021 ◽  
Author(s):  
Ye Zheng ◽  
Siqi Shen ◽  
Sündüz Keleş
Keyword(s):  
Single Cell ◽  
Long Range ◽  
High Throughput ◽  
State Of The Art ◽  
Cell Types ◽  
Chromatin Conformation ◽  
Modeling Framework ◽  
Technical Noise ◽  
Generative Modeling ◽  
Downstream Analysis

AbstractSingle-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling long-range genomic interactions at the single-cell resolution; however, data from these technologies are prone to technical noise and bias that, when unaccounted for, hinder downstream analysis. Here we developed a fast band normalization approach, BandNorm, and a deep generative modeling framework, 3DVI, to explicitly account for scHi-C specific technical biases. We present robust performances of BandNorm and 3DVI compared to existing state-of-the-art methods. BandNorm is effective in separating cell types, identification of interaction features, and recovery of cell-cell relationship, whereas de-noising by 3DVI successfully enables 3D compartments and domains recovery, especially for rare cell types.

scholarly journals Divalent Metal Transporter 1 Regulates Iron-Mediated ROS and Pancreatic β Cell Fate in Response to Cytokines

2012 ◽  
Vol 16 (4) ◽  
pp. 449-461 ◽  
Author(s):  
Jakob Bondo Hansen ◽  
Morten Fog Tonnesen ◽  
Andreas Nygaard Madsen ◽  
Peter H. Hagedorn ◽  
Josefine Friberg ◽  
...  
Keyword(s):  
Cell Fate ◽  
Divalent Metal ◽  
Pancreatic Β Cell ◽  
Β Cell ◽  
Divalent Metal Transporter 1 ◽  
Divalent Metal Transporter ◽  
Metal Transporter

scholarly journals Single-cell transcriptomics of the mouse gonadal soma reveals the establishment of sexual dimorphism in distinct cell lineages

2018 ◽  
Author(s):  
Isabelle Stévant ◽  
Françoise Kühne ◽  
Andy Greenfield ◽  
Marie-Christine Chaboissier ◽  
Emmanouil T. Dermitzakis ◽  
...  
Keyword(s):  
Time Series ◽  
Sex Determination ◽  
Single Cell ◽  
Cell Fate ◽  
Somatic Cells ◽  
Supporting Cells ◽  
Single Population ◽  
Cell Lineages ◽  
Multipotent Progenitors ◽  
Distinct Cell

SummarySex determination is a unique process that allows the study of multipotent progenitors and their acquisition of sex-specific fates during differentiation of the gonad into a testis or an ovary. Using time-series single-cell RNA sequencing (scRNA-seq) on ovarian Nr5a1-GFP+ somatic cells during sex determination, we identified a single population of early progenitors giving rise to both pre-granulosa cells and potential steroidogenic precursor cells. By comparing time-series scRNA-seq of XX and XY somatic cells, we demonstrate that the supporting cells emerge from the early progenitors with a non-sex-specific transcriptomic program, before pre-granulosa and Sertoli cells acquire their sex-specific identity. In XX and XY steroidogenic precursors similar transcriptomic profiles underlie the acquisition of cell fate, but with a delay in XX cells. Our data provide a novel framework, at single-cell resolution, for further interrogation of the molecular and cellular basis of mammalian sex determination.

scholarly journals Recent Advances in Single-Cell View of Mesenchymal Stem Cell in Osteogenesis

2022 ◽  
Vol 9 ◽  
Author(s):  
Fangyuan Shen ◽  
Yu Shi
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Progenitor Cell ◽  
Bone Development ◽  
Lineage Tracing ◽  
Research Progress ◽  
Bone Homeostasis ◽  
Differentiation Potential ◽  
Cell Technology

Osteoblasts continuously replenished by osteoblast progenitor cells form the basis of bone development, maintenance, and regeneration. Mesenchymal stem cells (MSCs) from various tissues can differentiate into the progenitor cell of osteogenic lineage and serve as the main source of osteoblasts. They also respond flexibly to regenerative and anabolic signals emitted by the surrounding microenvironment, thereby maintaining bone homeostasis and participating in bone remodeling. However, MSCs exhibit heterogeneity at multiple levels including different tissue sources and subpopulations which exhibit diversified gene expression and differentiation capacity, and surface markers used to predict cell differentiation potential remain to be further elucidated. The rapid advancement of lineage tracing methods and single-cell technology has made substantial progress in the characterization of osteogenic stem/progenitor cell populations in MSCs. Here, we reviewed the research progress of scRNA-seq technology in the identification of osteogenic markers and differentiation pathways, MSC-related new insights drawn from single-cell technology combined with experimental technology, and recent findings regarding the interaction between stem cell fate and niche in homeostasis and pathological process.

scholarly journals Quantitative lineage analysis identifies a long-term progenitor niche for the hepato-pancreato-biliary organ system

2020 ◽  
Author(s):  
David Willnow ◽  
Uwe Benary ◽  
Anca Margineanu ◽  
Maria Lillina Vignola ◽  
Igor M. Pongrac ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Computational Modelling ◽  
Lineage Tracing ◽  
Organ System ◽  
Cellular Heterogeneity ◽  
Genetic Lineage ◽  
Genetic Syndromes ◽  
Progenitor Compartment

SummarySingle cell-based studies have revealed tremendous cellular heterogeneity in stem cell and progenitor compartments, suggesting continuous differentiation trajectories with intermixing of cells at various states of lineage commitment and notable degree of plasticity during organogenesis1–5.The hepato-pancreato-biliary organ system relies on a small endoderm progenitor compartment that gives rise to a variety of different adult tissues, including liver, pancreas, gallbladder, and extra-hepatic bile ducts6, 7. Experimental manipulation of various developmental signals in the mouse embryo underscored an important cellular plasticity in this embryonic territory6, 8. This is also reflected in the existence of human genetic syndromes as well as congenital or environmentally-caused human malformations featuring multiorgan phenotypes in liver, pancreas and gallbladder6, 8. Nevertheless, the precise lineage hierarchy and succession of events leading to the segregation of an endoderm progenitor compartment into hepatic, biliary, and pancreatic structures are not yet established. Here, we combine computational modelling approaches with genetic lineage tracing to assess the tissue dynamics accompanying the ontogeny of the hepato-pancreato-biliary organ system. We show that a long-term multipotent progenitor domain persists at the border between liver and pancreas, even after pancreatic fate is specified, contributing to the formation of several organ derivatives, including the liver. Moreover, using single-cell RNA sequencing we define a specialized niche that possibly supports such long-term cell fate plasticity.

scholarly journals Reversed graph embedding resolves complex single-cell developmental trajectories

2017 ◽  
Author(s):  
Xiaojie Qiu ◽  
Qi Mao ◽  
Ying Tang ◽  
Li Wang ◽  
Raghav Chawla ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Graph Embedding ◽  
Developmental Trajectory ◽  
Loss Of Function ◽  
Cell Fate Decisions ◽  
Principal Graph ◽  
Cell Trajectories

AbstractOrganizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

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