Predict Cell Fate Research Articles

Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation

Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration, with the ultimate goal of accelerating recovery from diseases or injuries. To accomplish this, regulators must be identified and manipulated to control cell fate. We propose Fatecode, a computational method that predicts cell fate regulators based only on single-cell RNA sequencing (scRNA-seq) data. Fatecode learns a latent representation of the scRNA-seq data using a deep learning-based classification-supervised autoencoder and then performs in silico perturbation experiments on the latent representation to predict genes that, when perturbed, would alter the original cell type distribution to increase or decrease the population size of a cell type of interest. We assessed Fatecode's performance using simulations from a mechanistic gene-regulatory network model and scRNA-seq data mapping blood and brain development of different organisms. Our results suggest that Fatecode can detect known cell fate regulators from single-cell transcriptomics datasets.

Single-cell epigenomic reconstruction of developmental trajectories from pluripotency in human neural organoid systems

Cell fate progression of pluripotent progenitors is strictly regulated, resulting in high human cell diversity. Epigenetic modifications also orchestrate cell fate restriction. Unveiling the epigenetic mechanisms underlying human cell diversity has been difficult. In this study, we use human brain and retina organoid models and present single-cell profiling of H3K27ac, H3K27me3 and H3K4me3 histone modifications from progenitor to differentiated neural fates to reconstruct the epigenomic trajectories regulating cell identity acquisition. We capture transitions from pluripotency through neuroepithelium to retinal and brain region and cell type specification. Switching of repressive and activating epigenetic modifications can precede and predict cell fate decisions at each stage, providing a temporal census of gene regulatory elements and transcription factors. Removing H3K27me3 at the neuroectoderm stage disrupts fate restriction, resulting in aberrant cell identity acquisition. Our single-cell epigenome-wide map of human neural organoid development serves as a blueprint to explore human cell fate determination.

OneSC: A computational platform for recapitulating cell state transitions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Spatial heterogeneity analysis of seeding of human induced pluripotent stem cells for neuroectodermal differentiation

IntroductionPreparing a uniform cell population in high–density seeding of adherent human induced pluripotent stem cells (hiPSC) requires stable culture conditions and consistent culture operation. In this study, we evaluated cell distribution patterns by changing cell seeding operations and their impact on differentiation toward the neuroectodermal lineage. MethodsThe hiPSC line 201B7 was seeded at 1.23 × 105 cells/cm2 following a conventional operation, prolongated time of cell seeding suspension or vessel tilting during cell seeding operation. Fluorescent imaging of cell nuclei was performed 24 h following cell seeding and used for spatial heterogeneity analysis. Flow cytometric analysis was also performed seven days after cell differentiation induction toward neuroectodermal lineage. ResultsIndices for spatial heterogeneity following high–density cell seeding were proposed to assess cell distribution patterns. Global heterogeneity (HG) was shown to be mostly affected by vessel tilting during cell seeding operation, while local heterogeneity (HL) was affected by prolongated time of cell seeding suspension. Changes in both spatial heterogeneities in the hiPSC population resulted in a lower yield of target neuroectodermal cells compared with the control operation. ConclusionHigh–density hiPSC seeding is critical for achieving a higher yield of target cells of neuroectodermal lineage. Understanding the spatial heterogeneity in early stages detects errors in cell culture motion and predicts cell fate in later stages of cell culture.

The eighth International Forum on Stem Cells: virtual meeting, October 20-21, 2022.

The eighth International Forum on Stem Cells: virtual meeting, October 20-21, 2022.

Panning for gold: Purifying mesencephalic dopaminergic progenitors differentiated from human pluripotent stem cells.

In a recently published study, Xu et al. used two surface markers, CLSTN2 and PTPRO, to generate highly purified donor dopaminergic neurons and achieved stable and predictable therapeutic outcomes by transplantation into the brain of PD animal models (Xu et al., 2022).

Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks.

Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking are powerful in providing direct evidence of cell coordination rules, extracting and comparing these rules across many tissues with potentially different length and timescales of coordination requires a versatile framework of analysis. Here we demonstrate that graph neural network (GNN) models are suited for this purpose, by showing how they can be applied to predict cell fate in tissues and utilized to infer the cell interactions governing the multicellular dynamics. Analyzing the live mammalian epidermis data, where spatiotemporal graphs constructed from cell tracks and cell contacts are given as inputs, GNN discovers distinct neighbor cell fate coordination rules that depend on the region of the body. This approach demonstrates how the GNN framework is powerful in inferring general cell interaction rules from live data without prior knowledge of the signaling involved.

Learning biophysical determinants of cell fate with deep neural networks

Deep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning approach capable of learning an explainable model of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue, during which cell fate is thought to be determined by the local cellular neighbourhood over time. To investigate this, we developed a new approach (τ-VAE) by coupling a probabilistic encoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the τ-VAE’s latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate—a conclusion that is in agreement with our current understanding from over a decade of scientific research. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network, which using the predictions of the τ-VAE can identify conditions that deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening.

Statistically derived geometrical landscapes capture principles of decision-making dynamics during cell fate transitions.

SummaryFate decisions in developing tissues involve cells transitioning between discrete cell states, each defined by distinct gene expression profiles. The Waddington landscape, in which the development of a cell is viewed as a ball rolling through a valley filled terrain, is an appealing way to describe differentiation. To construct and validate accurate landscapes, quantitative methods based on experimental data are necessary. We combined principled statistical methods with a framework based on catastrophe theory and approximate Bayesian computation to formulate a quantitative dynamical landscape that accurately predicts cell fate outcomes of pluripotent stem cells exposed to different combinations of signaling factors. Analysis of the landscape revealed two distinct ways in which cells make a binary choice between one of two fates. We suggest that these represent archetypal designs for developmental decisions. The approach is broadly applicable for the quantitative analysis of differentiation and for determining the logic of developmental decisions.

CDKN2A Determines Mesothelioma Cell Fate to EZH2 Inhibition.

Malignant pleural mesothelioma is an aggressive cancer, heterogeneous in its presentation and behaviour. Despite an increasing knowledge about molecular markers and their diagnostic and prognostic value, they are not used as much as they might be for treatment allocation. It has been recently reported that mesothelioma cells that lack BAP1 (BRCA1 Associated Protein) are sensitive to inhibition of the EZH2 (Enhancer of Zeste Homolog 2) histone methyltransferase. Since we observed strong H3K27me3 (histone H3 lysine 27 trimetylation) immunoreactivity in BAP1 wild-type mesothelioma biopsies, we decided to characterize in vitro the response/resistance of BAP1 wild-type mesothelioma cells to the EZH2 selective inhibitor, EPZ-6438. Here we demonstrate that BAP1 wild-type mesothelioma cells were rendered sensitive to EPZ-6438 upon SIRT1 (Sirtuin 1) silencing/inhibition or when cultured as multicellular spheroids, in which SIRT1 expression was lower compared to cells grown in monolayers. Notably, treatment of spheroids with EPZ-6438 abolished H3K27me3 and induced the expression of CDKN2A (Cyclin-Dependent Kinase Inhibitor 2A), causing cell growth arrest. EPZ-6438 treatment also resulted in a rapid and sustained induction of the genes encoding HIF2α (Hypoxia Inducible Factor 2α), TG2 (Transglutaminase 2) and IL-6 (Interleukin 6). Loss of CDKN2 is a common event in mesothelioma. CDKN2A silencing in combination with EPZ-6438 treatment induced apoptotic death in mesothelioma spheroids. In a CDKN2A wild-type setting apoptosis was induced by combining EPZ-6438 with 1-155, a TG2 selective and irreversible inhibitor. In conclusion, our data suggests that the expression of CDKN2A predicts cell fate in response to EZH2 inhibition and could potentially stratify tumors likely to undergo apoptosis.

Context-aware synthetic biology by controller design: Engineering the mammalian cell.

The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact device performance at multiple scales, including the genetic, cellular, and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider cell fate programing as a case study to explore the potential impact of context-aware devices for regenerative medicine applications.

ScGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or “tipping point” of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene–gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the “dark genes” that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis.

SummaryLineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of development. Human induced pluripotent stem cells (hiPSCs) can recapitulate aspects of developmental processes, providing an in vitro platform to assess the dynamic collective behaviors directing tissue morphogenesis. Here, we trained an ensemble of neural networks to track individual hiPSCs in time-lapse microscopy, generating longitudinal measures of cell and cellular neighborhood properties on timescales from minutes to days. Our analysis reveals that, while individual cell parameters are not strongly affected by pluripotency maintenance conditions or morphogenic cues, regional changes in cell behavior predict cell fate and colony organization. By generating complete multicellular reconstructions of hiPSC behavior, our tracking pipeline enables fine-grained understanding of morphogenesis by elucidating the role of regional behavior in early tissue formation.

Organelle homeostasis principles: How organelle quality control and inter-organelle crosstalk promote cell survival.

To survive, cells sense their surroundings and adapt to enable homeostasis. Studies dissecting this process reveal organizational principles, including quality-control pathways, changes to organelle shape, and inter-organelle communication, that facilitate metabolic or developmental remodeling. In this issue, several reviews discuss these organelle homeostasis principles and how they are altered in disease.

Computational modeling to predict the micromechanical environment in tissue engineering scaffolds

Cell fate in tissue engineering (TE) strategies is paramount to regenerate healthy, functional organs. The mechanical loads experienced by cells play an important role in cell fate. However, in TE scaffolds with a cell-laden hydrogel matrix, it is prohibitively complex to prescribe and measure this cellular micromechanical environment (CME). Accordingly, this study aimed to develop a finite element (FE) model of a TE scaffold unit cell that can be subsequently implemented to predict the CME and cell fates under prescribed loading. The compressible hyperelastic mechanics of a fibrin hydrogel were characterized by fitting unconfined compression and confined compression experimental data. This material model was implemented in a unit cell FE model of a TE scaffold. The FE mesh and boundary conditions were evaluated with respect to the mechanical response of a region of interest (ROI). A compressible second-order reduced polynomial hyperelastic model gave the best fit to the experimental data (C10 = 1.72 × 10−4, C20 = 3.83 × 10−4, D1 = 3.41, D2 = 8.06 × 10−2). A mesh with seed sizes of 40 µm and 60 µm in the ROI and non-ROI regions, respectively, yielded a converged model in 54 min. The in-plane boundary conditions demonstrated minimal influence on ROI mechanics for a 2-by-2 unit cell. However, the out-of-plane boundary conditions did exhibit an appreciable influence on ROI mechanics for a two bilayer unit cell. Overall, the developed unit cell model facilitates the modeling of the mechanical state of a cell-laden hydrogel within a TE scaffold under prescribed loading. This model will be utilized to characterize the CME in future studies, and 3D micromechanical criteria may be applied to predict cell fate in these scaffolds.

Biomolecular phenotyping and heterogeneity assessment of mesenchymal stromal cells using label-free Raman spectroscopy

Easy, quantitative measures of biomolecular heterogeneity and high-stratified phenotyping are needed to identify and characterise complex disease processes at the single-cell level, as well as to predict cell fate. Here, we demonstrate how Raman spectroscopy can be used in the difficult-to-assess case of clonal, bone-derived mesenchymal stromal cells (MSCs) to identify MSC lines and group these according to biological function (e.g., differentiation capacity). Biomolecular stratification is achieved using high-precision measures obtained from representative statistical sampling that also enable quantified heterogeneity assessment. Application to primary MSCs and human dermal fibroblasts shows use of these measures as a label-free assay to classify cell sub-types within complex heterogeneous cell populations, thus demonstrating the potential for therapeutic translation, and broad application to the phenotypic characterisation of other cells.

Analysis of Proapoptotic Protein Trafficking to and from Mitochondria.

Mitochondria play a key role in cell death and its regulation. The permeabilization of the outer mitochondrial membrane, which is mainly controlled by proteins of the BCL-2 family, is a key event that can be directly induced by different signaling pathways, including p53-mediated, and results in the release of proapoptotic factors to the cytosol, such as cytochrome c, second mitochondria-derived activator of caspases/direct inhibitor-of-apoptosis (IAP) binding protein with low pI (SMAC/Diablo), Omi serine protease (Omi/HtrA2), apoptosis-inducing factor (AIF), or endonuclease G (Endo-G). Hence, the determination of subcellular localization of these proteins is extremely important to predict cell fate and elucidate the specific mechanism of apoptosis. Here we describe experimental protocols that can be used to study the subcellular location of different proapoptotic proteins to be used in basic cell biology and toxicology studies.

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

Cell differentiation and function are regulated across multiple layers of gene regulation, including modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of regulatory events leading to cell fate commitment. Here we developed simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq), a highly scalable approach for measurement of chromatin accessibility and gene expression in the same single cell, applicable to different tissues. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define domains of regulatory chromatin (DORCs) that significantly overlap with super-enhancers. During lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for lineage commitment. We computationally infer chromatin potential as a quantitative measure of chromatin lineage-priming and use it to predict cell fate outcomes. SHARE-seq is an extensible platform to study regulatory circuitry across diverse cells in tissues.

Dissecting the dynamics of signaling events in the BMP, WNT, and NODAL cascade during self-organized fate patterning in human gastruloids.

During gastrulation, the pluripotent epiblast self-organizes into the 3 germ layers—endoderm, mesoderm and ectoderm, which eventually form the entire embryo. Decades of research in the mouse embryo have revealed that a signaling cascade involving the Bone Morphogenic Protein (BMP), WNT, and NODAL pathways is necessary for gastrulation. In vivo, WNT and NODAL ligands are expressed near the site of gastrulation in the posterior of the embryo, and knockout of these ligands leads to a failure to gastrulate. These data have led to the prevailing view that a signaling gradient in WNT and NODAL underlies patterning during gastrulation; however, the activities of these pathways in space and time have never been directly observed. In this study, we quantify BMP, WNT, and NODAL signaling dynamics in an in vitro model of human gastrulation. Our data suggest that BMP signaling initiates waves of WNT and NODAL signaling activity that move toward the colony center at a constant rate. Using a simple mathematical model, we show that this wave-like behavior is inconsistent with a reaction-diffusion–based Turing system, indicating that there is no stable signaling gradient of WNT/NODAL. Instead, the final signaling state is homogeneous, and spatial differences arise only from boundary effects. We further show that the durations of WNT and NODAL signaling control mesoderm differentiation, while the duration of BMP signaling controls differentiation of CDX2-positive extra-embryonic cells. The identity of these extra-embryonic cells has been controversial, and we use RNA sequencing (RNA-seq) to obtain their transcriptomes and show that they closely resemble human trophoblast cells in vivo. The domain of BMP signaling is identical to the domain of differentiation of these trophoblast-like cells; however, neither WNT nor NODAL forms a spatial pattern that maps directly to the mesodermal region, suggesting that mesoderm differentiation is controlled dynamically by the combinatorial effect of multiple signals. We synthesize our data into a mathematical model that accurately recapitulates signaling dynamics and predicts cell fate patterning upon chemical and physical perturbations. Taken together, our study shows that the dynamics of signaling events in the BMP, WNT, and NODAL cascade in the absence of a stable signaling gradient control fate patterning of human gastruloids.

Single-Cell Transcriptomic Analyses of Cell Fate Transitions during Human Cardiac Reprogramming.

Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion.