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 Cellular Programming and Reprogramming: Sculpting Cell Fate for the Production of Dopamine Neurons for Cell Therapy

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Julio C. Aguila ◽  
Eva Hedlund ◽  
Rosario Sanchez-Pernaute
Keyword(s):  
Stem Cells ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Dorsal Striatum ◽  
Dopamine Neurons ◽  
Cell Fates ◽  
Intrinsic Factors ◽  
Neuronal Identity ◽  
The Right

Pluripotent stem cells are regarded as a promising cell source to obtain human dopamine neurons in sufficient amounts and purity for cell replacement therapy. Importantly, the success of clinical applications depends on our ability to steer pluripotent stem cells towards the right neuronal identity. In Parkinson disease, the loss of dopamine neurons is more pronounced in the ventrolateral population that projects to the sensorimotor striatum. Because synapses are highly specific, only neurons with this precise identity will contribute, upon transplantation, to the synaptic reconstruction of the dorsal striatum. Thus, understanding the developmental cell program of the mesostriatal dopamine neurons is critical for the identification of the extrinsic signals and cell-intrinsic factors that instruct and, ultimately, determine cell identity. Here, we review how extrinsic signals and transcription factors act together during development to shape midbrain cell fates. Further, we discuss how these same factors can be appliedin vitroto induce, select, and reprogram cells to the mesostriatal dopamine fate.

scholarly journals Using single-cell entropy to describe the dynamics of reprogramming and differentiation of induced pluripotent stem cells

2020 ◽  
Vol 34 (30) ◽  
pp. 2050288
Author(s):  
Y. Ye ◽  
Z. Yang ◽  
M. Zhu ◽  
J. Lei
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Differentiation ◽  
Single Cell ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Stem Cell Differentiation ◽  
Cell Level ◽  
Macroscopic Variable ◽  
Induced Pluripotent

Induced pluripotent stem cells (iPSCs) provide a great model to study the process of stem cell reprogramming and differentiation. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the process of cell fate changes, which show increase tendency during differentiation, and decrease upon reprogramming. Hence, scEntropy provides an intrinsic measurement of the cell state, and can be served as a pseudo-time of the stem cell differentiation process. Moreover, based on the evolutionary dynamics of scEntropy, we construct a phenomenological Fokker-Planck equation model and the corresponding stochastic differential equation for the process of cell state transitions during pluripotent stem cell differentiation. These equations provide further insights to infer the processes of cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to quantify the biological process of iPSC, and suggests that the scEntropy can provide a suitable macroscopic variable for single cells to describe cell fate transition during differentiation and reprogramming of stem cells.

scholarly journals Canonical and early lineage-specific stem cell types identified in planarian SirNeoblasts

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kaimeng Niu ◽  
Hao Xu ◽  
Yuanyi Zhou Xiong ◽  
Yun Zhao ◽  
Chong Gao ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Expression Patterns ◽  
Cell Subpopulation ◽  
Functional Study ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background The pluripotent stem cells in planarians, a model for tissue and cellular regeneration, remain further identification. We recently developed a method to enrich piwi-1+ cells in Schmidtea mediterranea, by staining cells with SiR-DNA and Cell Tracker Green, named SirNeoblasts that permits their propagation and subsequent functional study in vivo. Since traditional enrichment for planarian neoblasts by Hoechst 33342 staining generates X1 cells, blocking the cell cycle and inducing cytotoxicity, this method by SiR-DNA and Cell Tracker Green represents a complementary technological advance for functional investigation of cell fate and regeneration. However, the similarities in heterogeneity of cell subtypes between SirNeoblasts and X1 remain unknown. Results In this work, we performed single cell RNA sequencing of SirNeoblasts for comparison with differential expression patterns in a publicly available X1 single cell RNA sequencing data. We found first that all of the lineage-specific progenitor cells in X1 were present in comparable proportions in SirNeoblasts. In addition, SirNeoblasts contain an early muscle progenitor that is unreported in X1. Analysis of new markers for putative pluripotent stem cells identified here, with subsequent sub-clustering analysis, revealed earlier lineages of epidermal, muscular, intestinal, and pharyngeal progenitors than have been observed in X1. Using the gcm as a marker, we also identified a cell subpopulation resided in previously identified tgs-1+ neoblasts. Knockdown of gcm impaired the neoblast repopulation, suggesting a function of gcm in neoblasts. Conclusions In summary, the use of SirNeoblasts will enable broad experimental advances in regeneration and cell fate specification, given the possibility for propagation and transplantation of recombinant and mutagenized pluripotent stem cells that are not previously afforded to this rapid and versatile model system.

scholarly journals Using single-cell entropy to describe the dynamics of reprogramming and differentiation of induced pluripotent stem cells

2020 ◽  
Author(s):  
Yusong Ye ◽  
Zhuoqin Yang ◽  
Meixia Zhu ◽  
Jinzhi Lei
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Differentiation ◽  
Single Cell ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Stem Cell Differentiation ◽  
Cell Level ◽  
Macroscopic Variable ◽  
Induced Pluripotent

AbstractInduced pluripotent stem cells (iPSCs) provide a great model to study the process of stem cell reprogramming and differentiation. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the process of cell fate changes, which show increase tendency during differentiation, and decrease upon reprogramming. Hence, scEntropy provides an intrinsic measurement of the cell state, and can be served as a pseudo-time of the stem cell differentiation process. Moreover, based on the evolutionary dynamics of scEntropy, we construct a phenomenological Fokker-Planck equation model and the corresponding stochastic differential equation for the process of cell state transitions during pluripotent stem cell differentiation. These equations provide further insights to infer the processes of cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to quantify the biological process of iPSC, and suggests that the scEntropy can provide a suitable macroscopic variable for single cells to describe cell fate transition during differentiation and reprogramming of stem cells.

scholarly journals Silencing of E-cadherin in induced human pluripotent stem cells promotes extraembryonic fates accompanying multilineage differentiation

2020 ◽  
Author(s):  
Ashley RG Libby ◽  
Ivana Vasic ◽  
David A Joy ◽  
Martina Z Krakora ◽  
Fredrico N Mendoza-Camacho ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Human Pluripotent Stem Cells ◽  
Human Embryos ◽  
List Type ◽  
Cell Fates ◽  
Cell Adhesions ◽  
Early Human ◽  
Cell Cell

Summary/AbstractIn embryonic development, symmetry breaking events and the mechanical milieus in which they occur coordinate the specification of separate cell lineages. Here, we use 3D aggregates of human pluripotent stem cells (hPSCs) encapsulated in alginate microbeads to model the early blastocyst prior to zona pellucida hatching. We demonstrate that 3D confinement combined with modulation of cell-cell adhesions is sufficient to drive differentiation and collective migration reminiscent of the pre-implantation embryo. Knockdown of the cell adhesion protein CDH1 in encapsulated hPSC aggregates resulted in protrusion morphologies and emergence of extra-embryonic lineages, whereas unencapsulated CDH1(-) aggregates displayed organized radial delamination and mesendoderm specification bias. Transcriptomic similarities between single-cell RNA-sequencing data of early human embryos and encapsulated CDH1(-) aggregates establishes this in vitro system as a competent surrogate for studying early embryonic fate decisions and highlights the relationship between cell-cell adhesions and the mechanical microenvironment in directing cell fate and behavior.HighlightsGeneration of embryonic scale 3D morphogenesis using hydrogel encapsulationManipulating adhesion triggers emergence of specific morphologies and cell fatesAcquisition of germ layer cell fates mimics early human embryonic diversity

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

scholarly journals Histone Acetyltransferases and Stem Cell Identity

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2407
Author(s):  
Ruicen He ◽  
Arthur Dantas ◽  
Karl Riabowol
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Fate ◽  
Epigenetic Modification ◽  
Cell Types ◽  
Histone Acetyltransferases ◽  
Specific Cell ◽  
Cell Fates ◽  
Cell Identity ◽  
Hematopoietic Stem

Acetylation of histones is a key epigenetic modification involved in transcriptional regulation. The addition of acetyl groups to histone tails generally reduces histone-DNA interactions in the nucleosome leading to increased accessibility for transcription factors and core transcriptional machinery to bind their target sequences. There are approximately 30 histone acetyltransferases and their corresponding complexes, each of which affect the expression of a subset of genes. Because cell identity is determined by gene expression profile, it is unsurprising that the HATs responsible for inducing expression of these genes play a crucial role in determining cell fate. Here, we explore the role of HATs in the maintenance and differentiation of various stem cell types. Several HAT complexes have been characterized to play an important role in activating genes that allow stem cells to self-renew. Knockdown or loss of their activity leads to reduced expression and or differentiation while particular HATs drive differentiation towards specific cell fates. In this study we review functions of the HAT complexes active in pluripotent stem cells, hematopoietic stem cells, muscle satellite cells, mesenchymal stem cells, neural stem cells, and cancer stem cells.

scholarly journals Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

2018 ◽  
Author(s):  
Zeinab Golgooni ◽  
Sara Mirsadeghi ◽  
Mahdieh Soleymani Baghshah ◽  
Pedram Ataee ◽  
Hossein Baharvand ◽  
...  
Keyword(s):  
Neural Network ◽  
Stem Cells ◽  
Deep Learning ◽  
Pluripotent Stem Cells ◽  
Field Potential ◽  
Input Sequence ◽  
Patient Specific ◽  
Field Potentials ◽  
Drug Induced

AbstractAimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.Methods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.ConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of “irregularity or abnormal waveforms” in an in vitro model of cardiotoxicity experiments.

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