Cell State Transitions Research Articles

Genome-wide screening identifies Polycomb repressive complex 1.3 as an essential regulator of human naïve pluripotent cell reprogramming.

Uncovering the mechanisms that establish naïve pluripotency in humans is crucial for the future applications of pluripotent stem cells including the production of human blastoids. However, the regulatory pathways that control the establishment of naïve pluripotency by reprogramming are largely unknown. Here, we use genome-wide screening to identify essential regulators as well as major impediments of human primed to naïve pluripotent stem cell reprogramming. We discover that factors essential for cell state change do not typically undergo changes at the level of gene expression but rather are repurposed with new functions. Mechanistically, we establish that the variant Polycomb complex PRC1.3 and PRDM14 jointly repress developmental and gene regulatory factors to ensure naïve cell reprogramming. In addition, small-molecule inhibitors of reprogramming impediments improve naïve cell reprogramming beyond current methods. Collectively, this work defines the principles controlling the establishment of human naïve pluripotency and also provides new insights into mechanisms that destabilize and reconfigure cell identity during cell state transitions.

PRB-Depleted Pluripotent Stem Cell Retinal Organoids Recapitulate Cell State Transitions of Retinoblastoma Development and Suggest an Important Role for pRB in Retinal Cell Differentiation.

Retinoblastoma (Rb) is a childhood cancer of the developing retina, accounting for up to 17% of all tumors in infancy. To gain insights into the transcriptional events of cell state transitions during Rb development, we established 2 disease models via retinal organoid differentiation of a pRB (retinoblastoma protein)-depleted human embryonic stem cell line (RB1-null hESCs) and a pRB patient-specific induced pluripotent (iPSC) line harboring a RB1 biallelic mutation (c.2082delC). Both models were characterized by pRB depletion and accumulation of retinal progenitor cells at the expense of amacrine, horizontal and retinal ganglion cells, which suggests an important role for pRB in differentiation of these cell lineages. Importantly, a significant increase in the fraction of proliferating cone precursors (RXRγ+Ki67+) was observed in both pRB-depleted organoid models, which were defined as Rb-like clusters by single-cell RNA-Seq analysis. The pRB-depleted retinal organoids displayed similar features to Rb tumors, including mitochondrial cristae aberrations and rosette-like structures, and were able to undergo cell growth in an anchorage-independent manner, indicative of cell transformation in vitro. In both models, the Rb cones expressed retinal ganglion and horizontal cell markers, a novel finding, which could help to better characterize these tumors with possible therapeutic implications. Application of Melphalan, Topotecan, and TW-37 led to a significant reduction in the fraction of Rb proliferating cone precursors, validating the suitability of these in vitro models for testing novel therapeutics for Rb.

UBE2T promotes β-catenin nuclear translocation in hepatocellular carcinoma through MAPK/ERK-dependent activation.

Ubiquitin‐conjugating enzyme E2T (UBE2T) has been implicated in many types of cancer including hepatocellular carcinoma (HCC). Epithelial–mesenchymal transition (EMT) process plays a fundamental role during tumor metastasis and progression. However, the molecular mechanisms underlying EMT in HCC in accordance with UBE2T still remain unknown. In this study, we showed that UBE2T overexpression augmented the oncogenic properties and specifically EMT in HCC cell lines, while its silencing attenuated them. UBE2T affected the activation of EMT‐associated signaling pathways: MAPK/ERK, AKT/mTOR, and Wnt/β‐catenin. In addition, we revealed that the epithelial protein complex of E‐cadherin/β‐catenin, a vital regulator of signal transduction in tumor initiation and progression, was totally disrupted at the cell membrane. In particular, we observed that UBE2T overexpression led to E‐cadherin loss accompanied by a simultaneous elevation of both cytoplasmic and nuclear β‐catenin, while its silencing resulted in a strong E‐cadherin turnover at the cell membrane. Interestingly, chemical inhibition of the MAPK/ERK, AKT/mTOR, and Wnt/β‐catenin signaling pathways demonstrated that the nuclear translocation of β‐catenin and subsequent EMT was enhanced mainly by MAPK/ERK. Collectively, our findings demonstrate the UBE2T/MAPK‐ERK/β‐catenin axis as a critical regulator of cell state transition and EMT in HCC.

NMD is required for timely cell fate transitions by fine-tuning gene expression and regulating translation.

Cell fate transitions depend on balanced rewiring of transcription and translation programs to mediate ordered developmental progression. Components of the nonsense-mediated mRNA decay (NMD) pathway have been implicated in regulating embryonic stem cell (ESC) differentiation, but the exact mechanism is unclear. Here we show that NMD controls expression levels of the translation initiation factor Eif4a2 and its premature termination codon-encoding isoform (Eif4a2PTC ). NMD deficiency leads to translation of the truncated eIF4A2PTC protein. eIF4A2PTC elicits increased mTORC1 activity and translation rates and causes differentiation delays. This establishes a previously unknown feedback loop between NMD and translation initiation. Furthermore, our results show a clear hierarchy in the severity of target deregulation and differentiation phenotypes between NMD effector KOs (Smg5 KO > Smg6 KO > Smg7 KO), which highlights heterodimer-independent functions for SMG5 and SMG7. Together, our findings expose an intricate link between mRNA homeostasis and mTORC1 activity that must be maintained for normal dynamics of cell state transitions.

Defining cell states and unraveling cell states transition through models of stochastic dynamics during differentiation

Cell fate decisions play pivotal roles in many fundamental biological processes such as cell differentiation, tissue reprogramming, and cancer metastasis. Computational trajectory inference using single-cell RNA sequencing data enables the discovery of cell states and state transitions. Here we present a novel method combining trajectory inference with the direction information derived from the stochastic models of the process of RNA transcription. Our approach treats cell state transition as a probabilistic process, automatically distinguishes initial starting states and the terminal states, and further delineates the underlying trajectories of cellular differentiation.

A reciprocal feedback loop between HIF-1α and HPIP controls phenotypic plasticity in breast cancer cells.

While phenotypic plasticity is a critical factor contributing to tumor heterogeneity, molecular mechanisms underlying this process are largely unknown. Here we report that breast cancer cells display phenotypic diversity in response to hypoxia or normoxia microenvironments by operating a reciprocal positive feedback regulation of HPIP and HIF-1α. We show that under hypoxia, HIF-1α induces HPIP expression that establishes cell survival, and also promotes cell migration/invasion, EMT and metastatic phenotypes in breast cancer cells. Mechanistic studies revealed that HPIP interacts with SRP14, a component of signal recognition particle, and stimulates MMP9 synthesis under hypoxic stress. Whereas, in normoxia, HPIP stabilizes HIF-1α, causing the Warburg effect to support cell growth. Concurrently, mathematical modelling corroborates this reciprocal feedback loop in enabling cell-state transitions in cancer cells. Clinical data indicate that elevated levels of HPIP and HIF-1α correlate with unfavorable prognosis and shorter survival rates in breast cancer subjects. Together, this data shows a reciprocal positive feedback loop between HPIP and HIF-1α that was unknown hitherto. It unveils how the tumor microenvironment influences phenotypic plasticity that has an impact on tumor growth and metastasis and, further signifies considering this pathway as a potential therapeutic target in breast cancer.

Mapping transcriptomic vector fields of single cells

Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Mathematical modelling of activation-induced heterogeneity in TNF, IL6, NOS2, and IL1β expression reveals cell state transitions underpinning macrophage responses to LPS

Background: Despite extensive work on macrophage heterogeneity, the mechanisms driving activation induced heterogeneity (AIH) in macrophages remain poorly understood. Here, we aimed to develop mathematical models to explore theoretical cellular states underpinning the empirically observed responses of macrophages following lipopolysaccharide (LPS) challenge. Methods: We obtained empirical data following primary and secondary responses to LPS in two in vitro cellular models (bone marrow-derived macrophages or BMDMs, and RAW 264.7 cells) and single-cell protein measurements for four key inflammatory mediators: TNF, IL-6, pro-IL-1β, and NOS2, and used mathematical modelling to understand heterogeneity. Results: For these four factors, we showed that macrophage community AIH is dependent on LPS dose and that altered AIH kinetics in macrophages responding to a second LPS challenge underpin hypo-responsiveness to LPS. These empirical data can be explained by a mathematical three-state model including negative, positive, and non-responsive states (NRS), but they are also compatible with a four-state model that includes distinct reversibly NRS and non-responsive permanently states (NRPS). Our mathematical model, termed NoRM (Non-Responsive Macrophage) model identifies similarities and differences between BMDM and RAW 264.7 cell responses. In both cell types, transition rates between states in the NoRM model are distinct for each of the tested proteins and, crucially, macrophage hypo-responsiveness is underpinned by changes in transition rates to and from NRS. Conclusions: Overall, we provide a mathematical model for studying macrophage ecology and community dynamics that can be used to elucidate the role of phenotypically negative macrophage populations in AIH and, primary and secondary responses to LPS.

Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data.

Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological processes. This calls for the development of mathematical models and methods capable of reconstructing cellular dynamic transition processes and uncovering the nonlinear cell-cell interactions. In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data. The free energy of our model explicitly takes into account of the cell-cell interactions in a nonlinear quadratic term. We then recast the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the adjoint method of optimal control. We evaluated GraphFP on the time series scRNA-seq data set of embryonic murine cerebral cortex development. We illustrated that it 1) reconstructs cell state potential energy, which is a measure of cellular differentiation potency, 2) faithfully charts the probability flows between paired cell states over the dynamic processes of cell differentiation, and 3) accurately quantifies the stochastic dynamics of cell type frequencies on probability simplex in continuous time. We also illustrated that GraphFP is robust in terms of cluster labelling with different resolutions, as well as parameter choices. Meanwhile, GraphFP provides a model-based approach to delineate the cell-cell interactions that drive cell differentiation. GraphFP software is available at https://github.com/QiJiang-QJ/GraphFP.

Using quantitative methods to understand leaf epidermal development.

As the interface between plants and the environment, the leaf epidermis provides the first layer of protection against drought, ultraviolet light, and pathogen attack. This cell layer comprises highly coordinated and specialised cells such as stomata, pavement cells and trichomes. While much has been learned from the genetic dissection of stomatal, trichome and pavement cell formation, emerging methods in quantitative measurements that monitor cellular or tissue dynamics will allow us to further investigate cell state transitions and fate determination in leaf epidermal development. In this review, we introduce the formation of epidermal cell types in Arabidopsis and provide examples of quantitative tools to describe phenotypes in leaf research. We further focus on cellular factors involved in triggering cell fates and their quantitative measurements in mechanistic studies and biological patterning. A comprehensive understanding of how a functional leaf epidermis develops will advance the breeding of crops with improved stress tolerance.

Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space

Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two cell state geometries for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; (i) by using partial differential equations on a graph representing intermediate cell states between known cell types, and (ii) by using the equations on a multi-dimensional continuous cell state-space. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell states during the pathogenesis of acute myeloid leukemia. We particularly focus on comparing the strength and weakness of the graph model and multi-dimensional model.

Genetic control of the pluripotency epigenome determines differentiation bias in mouse embryonic stem cells.

Genetically diverse pluripotent stem cells display varied, heritable responses to differentiation cues. Here, we harnessed these disparities through derivation of mouse embryonic stem cells from the BXD genetic reference panel, along with C57BL/6J (B6) and DBA/2J (D2) parental strains, to identify loci regulating cell state transitions. Upon transition to formative pluripotency, B6 stem cells quickly dissolved naïve networks adopting gene expression modules indicative of neuroectoderm lineages, whereas D2 retained aspects of naïve pluripotency. Spontaneous formation of embryoid bodies identified divergent differentiation where B6 showed a propensity toward neuroectoderm and D2 toward definitive endoderm. Genetic mapping identified major trans-acting loci co-regulating chromatin accessibility and gene expression in both naïve and formative pluripotency. These loci distally modulated occupancy of pluripotency factors at hundreds of regulatory elements. One trans-acting locus on Chr 12 primarily impacted chromatin accessibility in embryonic stem cells, while in epiblast-like cells, the same locus subsequently influenced expression of genes enriched for neurogenesis, suggesting early chromatin priming. These results demonstrate genetically determined biases in lineage commitment and identify major regulators of the pluripotency epigenome.

Flow Cytometry Analysis of Cell-Surface Markers to Identify Human Naïve Pluripotent Stem Cells.

Cell-surface proteins provide excellent biomarkers to identify specific cell types and resolve heterogeneous cell populations. The analysis of cell-surface proteins by flow cytometry produces robust and quantitative information with single-cell resolution, and allows live target cells to be purified and characterized or re-cultured. Studies using antibody screens, proteomics, and candidate analysis have identified a comprehensive set of proteins that are expressed on the surface of naïve and primed human pluripotent stem cells. These findings have led to the development of suitable protein markers and antibodies to accurately distinguish between these two cell types. Here, a detailed protocol is provided that uses multi-color flow cytometry to analyze cell-surface protein expression in naïve and primed human pluripotent stem cells. This method enables the unambiguous identification of pluripotent cell types and the opportunity to sort target cells including during cell state transitions. The protocol can be combined to additionally investigate the expression of reporter genes and other informative features, such as DNA content.

Gene Regulatory Network Dynamical Logical Models for Plant Development.

Mathematical and computational approaches that integrate and model the concerted action of multiple genetic and nongenetic components holding highly nonlinear interactions are fundamental for the study of developmental processes. Among these, gene regulatory network (GRN) dynamical models are very useful to understand how diverse types of regulatory constraints restrict the multigene expression patterns that characterize different cell fates. In this chapter we present a hands-on approach to model GRN dynamics, taking as a working example a well-curated and experimentally grounded GRN developmental module proposed by our group: the flower organ specification gene regulatory network (FOS-GRN). We demonstrate how to build and analyze a GRN model according to the following steps: (1) integration of molecular genetic data and formulation of logical rules specifying the dynamic behavior of each gene; (2) determination of steady states (attractors) corresponding to each cell type; (3) validation of the GRN model; and (4) extension of the deterministic model with the inclusion of stochasticity in order to model cell-state transitions dependent on noise due to fluctuations of the involved gen products. The methodologies explained here in detail can be applied to any other developmental module.

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics.

Cancer reflects the dysregulation of the underlying gene network, which is strongly related to the 3D genome organization. Numerous efforts have been spent on experimental characterizations of the structural alterations in cancer genomes. However, there is still a lack of genomic structural-level understanding of the temporal dynamics for cancer initiation and progression. Here, we use a landscape-switching model to investigate the chromosome structural transition during the cancerization and reversion processes. We find that the chromosome undergoes a non-monotonic structural shape-changing pathway with initial expansion followed by compaction during both of these processes. Furthermore, our analysis reveals that the chromosome with a more expanding structure than those at both the normal and cancer cell during cancerization exhibits a sparse contact pattern, which shows significant structural similarity to the one at the embryonic stem cell in many aspects, including the trend of contact probability declining with the genomic distance, the global structural shape geometry and the spatial distribution of loci on the chromosome. In light of the intimate structure-function relationship at the chromosomal level, we further describe the cell state transition processes by the chromosome structural changes, suggesting an elevated cell stemness during the formation of the cancer cells. We show that cell cancerization and reversion are highly irreversible processes in terms of the chromosome structural transition pathways, spatial repositioning of chromosomal loci and hysteresis loop of contact evolution analysis. Our model draws a molecular-scale picture of cell cancerization from the chromosome structural perspective. The process contains initial reprogramming towards the stem cell followed by the differentiation towards the cancer cell, accompanied by an initial increase and subsequent decrease of the cell stemness.

Local chromatin fiber folding represses transcription and loop extrusion in quiescent cells.

A longstanding hypothesis is that chromatin fiber folding mediated by interactions between nearby nucleosomes represses transcription. However, it has been difficult to determine the relationship between local chromatin fiber compaction and transcription in cells. Further, global changes in fiber diameters have not been observed, even between interphase and mitotic chromosomes. We show that an increase in the range of local inter-nucleosomal contacts in quiescent yeast drives the compaction of chromatin fibers genome-wide. Unlike actively dividing cells, inter-nucleosomal interactions in quiescent cells require a basic patch in the histone H4 tail. This quiescence-specific fiber folding globally represses transcription and inhibits chromatin loop extrusion by condensin. These results reveal that global changes in chromatin fiber compaction can occur during cell state transitions, and establish physiological roles for local chromatin fiber folding in regulating transcription and chromatin domain formation.

The exploration of pluripotency space: Charting cell state transitions in peri-implantation development.

Pluripotent cells in the mammalian embryo undergo state transitions marked by changes in patterns of gene expression and developmental potential as they progress from pre-implantation through post-implantation stages of development. Recent studies of cultured mouse and human pluripotent stem cells (hPSCs) have identified cells representative of an intermediate stage (referred to as the formative state) between naive pluripotency (equivalent to pre-implantation epiblast) and primed pluripotency (equivalent to late post-implantation epiblast). We examine these recent findings in light of our knowledge of peri-implantation mouse and human development, and we consider the implications of this work for deriving human embryo models from pluripotent cells.

Dynamic regulatory networks of T cell trajectory dissect transcriptional control of T cell state transition

T cells exhibit heterogeneous functional states, which correlate with responsiveness to immune checkpoint blockade and prognosis of tumor patients. However, the molecular regulatory mechanisms underlying the dynamic process of T cell state transition remain largely unknown. Based on single-cell transcriptome data of T cells in non-small cell lung cancer, we combined cell states and pseudo-times to propose a pipeline to construct dynamic regulatory networks for dissecting the process of T cell dysfunction. Candidate regulators at different stages were revealed in the process of tumor-infiltrating T cell dysfunction. Through comparing dynamic networks across the T cell state transition, we revealed frequent regulatory interaction rewiring and further refined critical regulators mediating each state transition. Several known regulators were identified, including TCF7, EOMES, ID2, and TOX. Notably, one of the critical regulators, TSC22D3, was frequently identified in the state transitions from the intermediate state to the pre-dysfunction and dysfunction state, exerting diverse roles in each state transition by regulatory interaction rewiring. Moreover, higher expression of TSC22D3 was associated with the clinical outcome of tumor patients. Our study embedded transcription factors (TFs) within the temporal dynamic networks, providing a comprehensive view of dynamic regulatory mechanisms controlling the process of T cell state transition.