scholarly journals CellRank for directed single-cell fate mapping

2022 ◽  
Author(s):  
Marius Lange ◽  
Volker Bergen ◽  
Michal Klein ◽  
Manu Setty ◽  
Bernhard Reuter ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Normal Development ◽  
Biological Process ◽  
Cellular Reprogramming ◽  
Intermediate Cell ◽  
Fate Mapping ◽  
Directional Information ◽  
Development Data ◽  
Lung Regeneration

AbstractComputational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

scholarly journals CellRank for directed single-cell fate mapping

2020 ◽  
Author(s):  
Marius Lange ◽  
Volker Bergen ◽  
Michal Klein ◽  
Manu Setty ◽  
Bernhard Reuter ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Normal Development ◽  
Biological Process ◽  
Single Cells ◽  
Intermediate Cell ◽  
Directional Information ◽  
Stochastic Nature ◽  
Single Cell Rna Sequencing

AbstractComputational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

scholarly journals CellRank for directed single-cell fate mapping

2020 ◽  
Author(s):  
Fabian Theis ◽  
Marius Lange ◽  
Volker Bergen ◽  
Michal Klein ◽  
Manu Setty ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Normal Development ◽  
Biological Process ◽  
Single Cells ◽  
Intermediate Cell ◽  
Directional Information ◽  
Stochastic Nature ◽  
Single Cell Rna Sequencing

Abstract Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

scholarly journals Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs

2021 ◽  
Author(s):  
Edward Ren ◽  
Sungmin Kim ◽  
Saad Mohamad ◽  
Samuel F Huguet ◽  
Yulin Shi ◽  
...  
Keyword(s):  
Stem Cells ◽  
Deep Learning ◽  
Single Cell ◽  
Real Time ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Intermediate Cell ◽  
Cell Fates ◽  
Layer Cell ◽  
Fixed Cell

Predicting how stem cells become patterned and differentiated into target tissues is key for optimising human tissue design. Here, we established DEEP-MAP - for deep learning-enhanced morphological profiling - an approach that integrates single-cell, multi-day, multi-colour microscopy phenomics with deep learning and allows to robustly map and predict cell fate dynamics in real-time without a need for cell state-specific reporters. Using human pluripotent stem cells (hPSCs) engineered to co-express the histone H2B and two-colour FUCCI cell cycle reporters, we used DEEP-MAP to capture hundreds of morphological- and proliferation-associated features for hundreds of thousands of cells and used this information to map and predict spatiotemporally single-cell fate dynamics across germ layer cell fates. We show that DEEP-MAP predicts fate changes as early or earlier than transcription factor-based fate reporters, reveals the timing and existence of intermediate cell fates invisible to fixed-cell technologies, and identifies proliferative properties predictive of cell fate transitions. DEEP-MAP provides a versatile, universal strategy to map tissue evolution and organisation across many developmental and tissue engineering contexts.

scholarly journals Determining cell fate specification and genetic contribution to cardiac disease risk in hiPSC-derived cardiomyocytes at single cell resolution

2017 ◽  
Author(s):  
Quan H. Nguyen ◽  
Samuel W. Lukowski ◽  
Han Sheng Chiu ◽  
Clayton E. Friedman ◽  
Anne Senabouth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cardiovascular Disease ◽  
Single Cell ◽  
Cell Fate ◽  
Cardiac Disease ◽  
Disease Risk ◽  
Sequence Data ◽  
Intermediate Cell ◽  
Rna Sequence

AbstractThe majority of genetic loci underlying common disease risk act through changing genome regulation, and are routinely linked to expression quantitative trait loci, where gene expression is measured using bulk populations of mature cells. A crucial step that is missing is evidence of variation in the expression of these genes as cells progress from a pluripotent to mature state. This is especially important for cardiovascular disease, as the majority of cardiac cells have limited properties for renewal postneonatal. To investigate the dynamic changes in gene expression across the cardiac lineage, we generated RNA-sequencing data captured from 43,168 single cells progressing through in vitro cardiac-directed differentiation from pluripotency. We developed a novel and generalized unsupervised cell clustering approach and a machine learning method for prediction of cell transition. Using these methods, we were able to reconstruct the cell fate choices as cells transition from a pluripotent state to mature cardiomyocytes, uncovering intermediate cell populations that do not progress to maturity, and distinct cell trajectories that terminate in cardiomyocytes that differ in their contractile forces. Second, we identify new gene markers that denote lineage specification and demonstrate a substantial increase in their utility for cell identification over current pluripotent and cardiogenic markers. By integrating results from analysis of the single cell lineage RNA-sequence data with population-based GWAS of cardiovascular disease and cardiac tissue eQTLs, we show that the pathogenicity of disease-associated genes is highly dynamic as cells transition across their developmental lineage, and exhibit variation between cell fate trajectories. Through the integration of single cell RNA-sequence data with population-scale genetic data we have identified genes significantly altered at cell specification events providing insights into a context-dependent role in cardiovascular disease risk. This study provides a valuable data resource focused on in vitro cardiomyocyte differentiation to understand cardiac disease coupled with new analytical methods with broad applications to single-cell data.

scholarly journals Single Cell Fate Mapping in Zebrafish

10.3791/3172 ◽  
2011 ◽  
Author(s):  
Vikram Kohli ◽  
Kira Rehn ◽  
Saulius Sumanas
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Fate Mapping

scholarly journals Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data

2020 ◽  
Vol 48 (17) ◽  
pp. 9505-9520 ◽  
Author(s):  
Yutong Sha ◽  
Shuxiong Wang ◽  
Peijie Zhou ◽  
Qing Nie
Keyword(s):  
Unsupervised Learning ◽  
Single Cell ◽  
Cell Fate ◽  
Epithelial To Mesenchymal Transition ◽  
Multiscale Model ◽  
Integrative Approach ◽  
Intermediate Cell ◽  
Mesenchymal Transition ◽  
Transcriptomic Data ◽  
Fate Decision

Abstract Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.

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